Indonesian Journal of Computer Science

Enhancing AdaBoost Performance: Comparative Analysis of CPU Parallel Processing on Breast Cancer Classification

2024-03-09T15:14:31+00:00

The implementation of time-sharing across processes in a real-time way has the potential to increase the execution efficiency of multiprocessor systems like the one described above. The system is able to carry out tasks that make use of a large number of processors in an effective way as a result of this. The aim of this research is to design a system with two primary goals: to enhance accuracy and to minimise the amount of time necessary with processing. This will be accomplished by integrating the ADABoost model with the decision tree algorithm. Furthermore, the statistics unambiguously demonstrate that the accuracy remains the same regardless of whether or not the central processing unit (CPU) makes use of parallel processing, which suggests that there is no variation in parallelization. As a consequence of this, there is a direct connection between the amount of time that is spent and an increase in the amount of parallel processing that is carried out by the central processing unit pertaining to the breast cancer dataset that is being investigated. This research was carried out using Python, which was the programming language that was used for the coding technique that was carried out during the course of its execution.

DeepX-Ray: A Comparative Study of Deep Learning-Based Classification and Segmentation Techniques for Automated Detection and Diagnosis of COVID-19 from Chest X-ray Images

2024-03-17T20:47:02+00:00

The relentless spread of the SARS-CoV-2 virus, causing COVID-19, has underscored the urgent need for efficient early detection and diagnosis methods to mitigate its impact. While traditional techniques like RT-PCR are valuable, they often suffer from time-consuming processes. In this study, DeepX-Ray is presented, a comprehensive investigation into deep learning-based classification and segmentation approaches for the automated detection of COVID-19 from chest X-ray images. Specifically, the authors focus on constructing a custom convolutional neural network (CNN) model to distinguish between COVID-19 and standard X-ray images and benchmark its performance against established models. For segmentation tasks, the effectiveness of various backbone architectures, including ResNet34, ResNet101, DenseNet201, and ResNet50 within the UNet model framework, is explored, with ResNet50 exhibiting superior performance. Furthermore, a novel dataset comprising 7657 images sourced from three publicly available authentic datasets is introduced, and the labelMe tool is employed by the authors to generate ground truth mask datasets for 4137 images to facilitate segmentation of infected lung areas. The authors' custom CNN model achieves an outstanding classification accuracy of 100%, while the segmentation approach attains a mean Intersection over Union (IoU) score of 96.19%. These results underscore the efficacy of the proposed model in enabling early automatic detection and diagnosis of COVID-19, particularly in resource-constrained and remote settings where establishing traditional laboratories may be impractical. This research significantly advances medical imaging techniques for combating the COVID-19 pandemic.

Distributed Graph Processing in Cloud Computing: A Review of Large-Scale Graph Analytics

2024-03-13T00:45:50+00:00

The rapid growth of graph data in various domains has propelled the need for efficient distributed graph processing techniques in cloud computing environments. This paper presents a comprehensive review of distributed graph processing for graph analytics of massive size in the context of cloud computing. The paper begins by highlighting the challenges associated with distributed graph processing, including load balancing, communication overhead, scalability, and partitioning strategies. It provides an overview of existing frameworks and tools specifically designed for distributed graph processing in cloud environments. Furthermore, the review encompasses various techniques and algorithms employed in distributed graph processing. The paper also reviews recent research advancements in optimizing distributed graph processing in cloud computing. To provide practical insights, the paper presents a comparative analysis of representative large-scale graph analytics applications implemented on different cloud computing platforms. Performance, scalability, and efficiency metrics are evaluated under varying workload sizes and graph characteristics. Overall, this comprehensive review paper serves as a highly prized asset for researchers and large-scale graph analytics professionals who are practitioners in the field. It provides a holistic understanding of the state-of-the-art distributed graph processing techniques in cloud computing and guides future research efforts towards more efficient and scalable graph processing in cloud environments.

Crosslingual Transfer Learning for Arabic Story Ending Generation

2024-03-21T09:28:25+00:00

In the field of natural language processing, the task of generating story endings (SEG) requires not only a deep understanding of the narrative context but also the ability to formulate coherent conclusions. This study delves into the use of crosslingual transfer learning to address the challenges posed by the scarcity of Arabic data in SEG, proposing the utilization of extensive English story corpora as a solution. We evaluated the efficacy of multilingual models, such as mBART, mT5, and mT0, in generating Arabic story endings, assessing their performance in both zero-shot and few-shot scenarios. Despite the linguistic complexities of Arabic and the inherent challenges of crosslingual transfer, our findings demonstrate the potential of these multilingual models to transcend linguistic barriers, significantly contributing to the domain of natural language processing across different languages. This research has significant implications for generating creative text and improving multilingual natural language processing in resource-limited language contexts

Blockchain for Distributed Systems Security in Cloud Computing: A Review of Applications and Challenges

2024-03-09T15:15:39+00:00

The blockchain is a technology that utilizes a decentralized and distributed ledger system to enhance security in cloud computing for distributed systems. It has gained significant attention in various applications, including the Internet of Things (IoT) and cloud computing. However, the blockchain has scalability limitations that restrict its ability to handle different types of transactions effectively. On the other hand, cloud computing provides the availability of shared computer system resources on demand, but it faces challenges related to automation, process management, policy, and others. By combining blockchain technology with cloud computing in a unified system, it is possible to improve data integrity, resource management, pricing, fair compensation, and resource allocation. This article examines the applications and challenges of blockchain, emphasizing how it ensures data integrity, transparency, and resistance to tampering. It also explores various use cases to address obstacles like scalability issues and interoperability concerns, providing a comprehensive overview of the intersection between blockchain, distributed systems, and cloud computing security. The integration of cloud computing and blockchain is important for business applications because it offers advantages in terms of privacy, security, and service support. This review provides an extensive and up-to-date summary of the integration of cloud computing and blockchain, highlighting its significance in business contexts.

A Review of Blockchain-Rooted Energy Administration in Networking

2024-03-14T09:25:36+00:00

Energy Administration (EA) in networking involves improving energy efficiency by managing energy. The blockchain framework involves a chain of associated blocks that obviously protects the genuineness, upholds accountability, and upholds disguised-anonymity of its transactions/entries with the help of peer-to-peer consensus techniques and cryptographic mechanisms. Driven by the fact that existing surveys do not focus on the EA in the broad scope of networking, we review diverse blockchain-rooted EA solutions, where we recognize 7 roles of blockchain in EA and explore them in detail with regard to EA techniques, EA approaches, blockchain-linked factors, network-linked factors, and such. We assembled a first-stage sample of 80 document citations by appraising the articles for qualification criteria hunted from E-libraries operating a detailed and prolonged process. Considering the review, in blockchain-rooted EA, blockchain can facilitate storing and exchanging data in a trustworthy manner, operate energy-efficient consensus approaches, act as an energy manager, provide authentication and access control for EA, facilitate secure offloading for EA, and provide automated EA tasks operating smart contracts. Detailed exploration shows that from blockchain-rooted EA, 32.5% operate blockchain to store and exchange data for an EA task, 95% operate uniform blockchain, 30% operate PoW consensus, 82.5% operate fully decentralized EA, 57.5% operate cross-layer EA, and 10% operate in IoT networks. Finally, we debate the possibilities and barriers to the conception of blockchain-rooted EA and then present guidance to vanquish them.

The Cloud Architectures for Distributed Multi-Cloud Computing: A Review of Hybrid and Federated Cloud Environment

2024-03-13T00:46:41+00:00

The concept of several clouds has greatly extended the use of cloud computing and gained popularity in academic and business circles. The use of multi-cloud techniques has increased as businesses use cloud computing more and more to meet their computational demands. A thorough analysis of cloud architectures intended for distributed multi-cloud computing is presented in this study, with an emphasis on federated and hybrid cloud systems. The study looks at the opportunities and difficulties of adopting and overseeing a variety of cloud resources from several providers. The review starts out by going over the basic ideas and reasons for using multi-cloud strategies, emphasizing how important flexibility, scalability, and resilience are in contemporary computing settings. The study then explores the nuances of hybrid cloud architectures, with a focus on how private and public cloud resources can be seamlessly combined. In the context of hybrid cloud installations, important factors including data sovereignty, security, and workload orchestration are covered. In addition, the research delves into federated cloud architectures, clarifying how enterprises can coordinate and oversee workloads across several cloud providers. An examination of resource identification, policy enforcement, and interoperability procedures sheds light on the intricacies of federated cloud computing. The review delves into new developments in standards, best practices, and technology that help multi-cloud ecosystems mature. The study analyses the state of research and industry practices now, pointing out gaps and possible directions for future development. The intention is to provide decision-makers, researchers, and practitioners with a comprehensive grasp of the changing cloud architectural scene so they can plan and execute distributed multi-cloud solutions with knowledge. In conclusion, this article provides a thorough overview of hybrid and federated cloud architectures by combining information from many sources. Through a comprehensive analysis of the difficulties and possibilities associated with multi-cloud computing, the study hopes to add to the current conversation on cloud environment design and optimization in the rapidly changing technological landscape.

Data science for energy applications: A Bibliometric Analysis

2024-02-27T10:24:50+00:00

Global digitalization is altering the energy sector, demanding the adoption of data science applications to improve efficiency and innovation, despite the industry's existing data analytics. Data science is revolutionizing the energy and utilities industries, enabling efficient, sustainable, and innovative decision-making through data analysis and smart grid optimization. In the energy industry, organizations are turning to data science to reduce waste, optimize energy usage, and provide alternative energy sources. With the different parts of Africa facing energy crises, different applications are needed to provide a solution. Data science has the potential to provide good information and knowledge that could be used to contribute to energy solutions. To address these concerns, data science models enable utilities to accurately forecast energy demand, enabling efficient generation, distribution, waste reduction, and informed investment decisions by leveraging historical consumption data, weather patterns, and economic indicators. This article aims to explore data science for energy applications. The findings show tools and techniques that can be utilized to provide energy efficiency and energy sustainability through data science applications.

Distributed Systems for Machine Learning in Cloud Computing: A Review of Scalable and Efficient Training and Inference

2024-03-13T22:44:39+00:00

Traditional computer systems have been pushed to their limits as a result of the exponential rise of data and the rising complexity of machine learning (ML) models. As a result of its on-demand scalability and resource agility, cloud computing has emerged as the platform of choice for training and deploying large-scale machine learning models. However, in order to make good use of cloud resources for machine learning, it is necessary to make use of distributed systems. These systems are responsible for coordinating computations over several nodes in order to manage the demanding workloads. The purpose of this paper is to investigate the realm of distributed systems for machine learning in cloud computing, with a particular emphasis on training and inference that is both scalable and efficient. During the discussion on the need of distributed systems in machine learning, it was made clear why conventional single-machine techniques are not enough for the requirements of current machine learning and how distributed systems might help solve these difficulties. Scalability and Efficiency Considerations were reviewed in relation to the primary elements that contribute to the effectiveness of a distributed system for machine learning. These elements include task partitioning, communication overhead, fault tolerance, and resource optimization that were discussed. In the context of cloud computing, the purpose of this review research is to provide a complete overview of the fascinating topic of distributed systems for machine learning. In order to successfully traverse the intricate and ever-changing world of cloud-based machine learning, it provides vital insights and information.

Improvement of Data Operations Management using CMMI and DMBOK in Soekarno-Hatta Meteorology Station

2024-02-29T15:17:19+00:00

Soekarno-Hatta Meteorology Station is a Technical Implementation Unit within Badan Meteorologi, Klimatologi, dan Geofisika (BMKG). It has the duties to manage, and process data for safety, regulation, and aviation navigation efficiency by distributing the processed data to their stakeholder, such as Air Traffic Service (ATS), flight operators, and pilots. Furthermore, research was also conducted in the station regarding the creation of Aerodrome Climatological Summary (ACS) annually. Loss of flight document data occurred in 2019 due to a ransomware attack, causing the cessation of the data operation process on the flight document. Unfortunately, there is no data recovery resulting in the unavailability of flight document data. This accident is very crucial because flight document data should not be lost within a 30-day timeframe as it is needed in case of any flight accident mechanism. It shows that Data Operations Management needs to be improved to support the business process. This study aims to evaluate the maturity level of data operations management in Soekarno-Hatta Meteorology Station by a conceptual model based on DAMA-DMBOK and the level of maturity is assessed using the Capability Maturity Model Integration (CMMI). The result showed that the average activity reaches level 2. Six activities are at level 1, namely obtaining externally sourced data, plan for data recovery, set database performance service levels, archive, retain, and purge data, support specialized database inventory and track data technology licenses, support data technology usage and issues. The rest of the activities will require action to improve their maturity level as given in the recommendation.

Proactive Fault Tolerance in Distributed Cloud Systems: A Review of Predictive and Preventive Techniques

2024-03-12T04:28:48+00:00

In a cloud computing environment, various hardware and software services are provided to the users across multiple servers and data centers. These servers are communicated to each other to allow greater scalability, flexibility, and reliability. Reliability is a vital factor in cloud computing that ensures that the requested services will be delivered to the users whenever they request them. However, different hardware or software faults may occur in cloud servers or data centers that prevent the users from receiving the service. Fault tolerance is defined as the ability of the system to provide services to the users even with the presence of faults or failures. In this review, we focused on some of the emerging fault tolerance techniques researchers have proposed to tackle the fault issues in cloud computing. We divided these techniques into three main categories: proactive and reactive techniques. Proactive techniques involve protecting the system defects by proposing certain procedures to prevent reaching the defective condition. Reactive techniques refer to the ability of the cloud system to recover the defective server or framework to continue working and providing the service.

Harnessing the Power of Distributed Systems for Scalable Cloud Computing A Review of Advances and Challenges

2024-03-12T04:11:36+00:00

In the realm of cloud computing, the literature defines scalability as the inherent ability of a system, application, or infrastructure to adapt and accommodate varying workloads or demands efficiently. It encompasses the system's capability to handle increased or decreased usage with compromising performance, responsiveness, or stability. In this paper, a comprehensive review is presented regarding the scalability in the cloud computing network. In addition, the research community define the scalability as a dynamic attribute, emphasizing its ability to facilitate both horizontal and vertical scaling. Horizontal scalability involves adding or removing instances or nodes to distribute workloads across multiple resources, while vertical scalability focuses on enhancing the capacity of existing resources within a single entity. They established a global frameworks to evaluate scalability, often emphasizing response time, throughput, resource utilization, and cost-efficiency as critical metrics. These metrics serve as benchmarks to assess the system's ability to scale effectively without compromising performance or incurring unnecessary costs [1]. The literature underscores scalability's interconnectedness with elasticity, highlighting the need for on-demand resource provisioning and de-provisioning to maintain an agile and adaptable infrastructure. Overall, in academic papers, cloud scalability is portrayed as a fundamental attribute crucial for modern computing infrastructures, enabling systems to flexibly and efficiently adapt to dynamic computing needs.

Implementation of Predicting the Availability of Chicken Eggs on Christmas Day Using Artificial Neural Network Backpropagation

2024-03-17T03:53:42+00:00

Prediction can be called a science that is used to predict events that are likely to occur in the future based on past events. One of the other prediction methods in circulation is Backpropagation Neural Network. Backpropagation Neural Network (BPNN) is a Neural Network (NN) that is forward in nature and does not have a loop through which signals flow from input neurons to output neurons. This research aims to determine a prediction of egg supply in 2023, especially during Christmas in Manokwari district to meet market and customer needs. By analyzing the availability of egg supplies in the city of Manokwari from January 2018 to December 2022. From the methods used in this research, starting from data collection methods as well as variables and research stages which include the data collection process, data sharing, then training and data testing and validation crosswise, the prediction pattern for the number of egg stocks is 12-16-1, where there are 12 variables in the input layer, then 16 variables in the hidden layer, 1 variable in the output layer, the learning rate value is 0.9 and the value the momentum is 0.1, resulting in a prediction of egg stock in 2023, especially in December, of 131053 eggs. With a MAPE value of 27.4767%. with the results of a feasible prediction model value. With the predicted results, the number of egg stocks in 2023, especially in December (during Christmas celebrations) in Manokwari Regency is 131,053 eggs during December 2023.

Comparative Analysis of XGBoost Performance for Text Classification with CPU Parallel and Non-Parallel Processing

2024-03-10T16:19:10+00:00

This paper shows the findings of a study that looks at how CPU parallel processing changes the way Extreme Gradient Boosting (XGBoost) classifies text. XGBoost models can sort news stories into set groups faster and more accurately, with or without CPU parallelism. This is the main goal of the study. The Keras dataset is used to prepare the text so that the TF-IDF (Term Frequency-Inverse Document Frequency) features can be found. These features will then be used to train the XGBoost model. This is used to check out two different kinds of the XGBoost classifier. There is parallelism between one of them and not it in the other. How well the model works can be observed by how accurate it is. This includes both how long it takes to learn and estimate and how well predictions work. The models take very different amounts of time to compute, but they are all pretty close in terms of how accurate they are. Parallel processing on the CPU has made tasks proceed more rapidly, and XGBoost is now better at making the most of that speed to do its task. The purpose of the study is to show that parallel processing can speed up XGBoost models without affecting their accuracy. This is helpful for putting text into categories.

Power Sharing at Rooftop Solar PV System Based Community Microgrid Using Helioscope Software

2024-03-17T08:56:20+00:00

Due to increase in use of rooftop solar PV system and increase of electricity price, the power sharing among the prosumers of community microgrid become an interesting research area. This study proposes a power sharing mechanism that captures the interaction within a community microgrid. The efficient management of energy sharing is crucial for the efficient operation of community microgrids. To design rooftop solar PV systems, Helioscope software is employed. One of the key features of Helioscope is its ability to efficiently arrange arrays and blocks of solar panels based on the designated location within the software. The power sharing is obligated to coordinate the sharing of PV energy with maximization of the own profit, while the prosumers are autonomous to maximize their utilities with demand response availability. Finally, a load demand and PV generation mechanism is designed to deal with the uncertainty of PV energy and load consumption.

Feature Selection using Extra Trees for Breast Cancer Prediction

2024-03-31T04:20:37+00:00

Breast cancer is a disease that seriously threatens women's health. It is considering a common death cause in women. Machine learning has made significant progress in recent years to improve the effectiveness of early diagnosis of various diseases. Accurate predication and detection help decrease the death rate of breast cancer. This paper aims to predict breast cancer using several machine-learning techniques. The idea is to lower the number of features in the Wisconsin Breast Cancer Dataset (WCDB) and use it for prediction. The study used the extra trees method for feature selection and Random forest, Logistic regression, and Support Vector Machine (SVM) for testing the dataset. According to the results, SVM achieved the best performance among the other models with 98% accuracy. The proposed method in this study proved its effectiveness in breast cancer prediction.

Parallel Processing Impact on Random Forest Classifier Performance: A CIFAR-10 Dataset Study

2024-03-12T04:02:47+00:00

Using the CIFAR-10 dataset, this research investigates how parallel processing affects the Random Forest method's machine learning performance. Accuracy and training time are highlighted in the study as critical performance indicators. Two cases were studied, one with and one without parallel processing. The results show the strong prediction powers of the Random Forest algorithm, which continues to analyze data in parallel while retaining a high accuracy of 97.50%. In addition, training times are notably shortened by parallelization, going from 0.6187 to 0.4753 seconds. The noted increase in time efficiency highlights the importance of parallelization in carrying out activities simultaneously, which enhances the training process's computational efficiency. These results provide important new information about how to optimize machine learning algorithms using parallel processing approaches.

Deep Learning in Medical Image Analysis Article Review

2024-03-23T02:20:03+00:00

Transfer learning, in evaluation to common deep studying strategies which include convolutional neural networks (CNNs), stands proud due to its simplicity, efficiency, and coffee education value, efficaciously addressing the venture of restricted datasets. The importance of scientific picture analysis in both scientific research and medical prognosis can't be overstated, with image techniques like Computer Tomography (CT), Magnetic Resonance Image (MRI), Ultrasound (US), and X-Ray playing a crucial function. Despite their utility in non-invasive analysis, the scarcity of categorized medical images poses a completely unique challenge in comparison to datasets in other pc imaginative and prescient domains, like facial reputation.

Given this shortage, switch getting to know has won reputation amongst researchers for medical photo processing. This complete evaluation draws on one hundred amazing papers from IEEE, Elsevier, Google Scholar, Web of Science, and diverse sources spanning 2000 to 2023 It covers vital components, which includes the (i) shape of CNNs, (ii) foundational know-how of switch learning, (iii) numerous techniques for enforcing transfer mastering, (iv) the utility of switch gaining knowledge of throughout numerous sub-fields of medical photo analysis, and (v) a dialogue at the future potentialities of transfer studying within the realm of medical image analysis. This evaluate no longer handiest equips beginners with a scientific understanding of transfer mastering applications in medical image analysis but additionally serves policymakers by means of summarizing the evolving trends in transfer learning within the scientific image domain. This insight might also encourage policymakers to formulate advantageous rules that support the continued development of Transfer learning knowledge of in medical image analysis.

Skin Cancer Segmentation On Dermoscopy Images Using Fuzzy C-Means Algorithm

2024-03-13T00:28:53+00:00

Millions of people around the world suffer from skin cancer, a common and sometimes fatal disease. Dermoscopy has become an effective diagnostic technique for skin cancer. Precise segmentation is essential for skin cancer diagnosis. Segmentation allows more precise analysis of dermoscopic images by defining the boundaries of the lesion and separating it from surrounding healthy tissue. Dermoscopy images served as a source of research data, and Fuzzy C-Means (FCM) segmentation techniques were used. FCM is a promising method and has received a lot of attention lately. FCM is able to distinguish the various components within the lesion and effectively separate the lesion from the surrounding area. As a result, the distribution of membership degree values of each pixel in the image for each cluster represents the segmentation results obtained through FCM. The FCM technique for segmenting dermoscopic images is expected to significantly improve the precision and effectiveness of skin cancer diagnosis.

Performance Evaluation of Extra Trees Classifier by using CPU Parallel and Non-Parallel Processing

2024-03-12T03:36:13+00:00

This research delves into assessing the performance of the Extra Trees Classifier, specifically examining the influence of CPU parallel processing on classification accuracy and computational efficiency. Fashion MNIST, a collection of grayscale images representing clothing items, serves as the foundational dataset for this study. Two variations of the Extra Trees Classifier are implemented: one configured without CPU parallel processing and another utilizing maximum CPU cores for parallel execution. The primary evaluation metrics include accuracy measurement and computational time taken for both training and prediction tasks. The findings reveal notable insights, showcasing that while the Extra Trees Classifier demonstrates commendable accuracy in classifying Fashion MNIST images, the implementation of CPU parallel processing significantly reduces computational time without compromising accuracy levels. This observation underscores the pivotal role of optimizing computational resources for efficient model training and deployment in machine learning applications. The results of this study are very helpful for understanding how to use parallel processing to make machine learning tasks more accurate and more efficient. It also shows how important it is to optimize resources for scalable and effective model development.

Impact of Parallel Processing on LightGBM Implementation a Comparative Analysis CPU on Iris Plants Dataset

2024-03-13T22:45:43+00:00

Parallel processing has emerged as base for machine learning to address the computational requirements of complex models and expanded datasets. In additions, Parallel functions give the ability to algorithms exploiting the full potential of available accounting resources. This mechanism enhances parallel processing capabilities, as calculations are distributed through a multiple processor. This research explores the impact of the parallel processing of the central processing unit on the performance of LightGBM. The gradient-based learning in LightGBM enables efficient feature split decisions during tree construction. framework for scaling up, using the IRIS plant dataset. The study aims at comparing accuracy measures and training time for trained models with or without parallel processing units. The methodology includes advance data processing steps and the formation of environmentally sound management models with or without parallel processing units. The results reveal marked differences in accuracy and training time between the model and parallel processing of the central processing unit and its counterpart without it. Research contributes to understanding the role of parallel processing in the optimal functioning of the automated learning model.

Challenges and Outcomes of Combining Machine Learning with Software-Defined Networking for Network Security and management Purpose: A Review

2024-03-25T02:02:13+00:00

Current research in data dissemination in Vehicular Ad Hoc Networks (VANETs) has examined different approaches and frameworks to enhance the effectiveness and dependability of information sharing between vehicles on the road. The integration of Machine Learning (ML) with Software-Defined Networking (SDN) has fundamentally transformed the field of network administration and security. This paper specifically addresses the challenges faced by traditional network architectures in effectively handling the increasing amount of data and complex applications. Software-Defined Networking (SDN), a cutting-edge framework, separates the control of network operations from the actual forwarding of data, offering a versatile and cost-effective solution. The combination of Software-Defined Networking (SDN) and Machine Learning (ML) allows for the extraction of valuable information from network data, leading to enhanced network management and the facilitation of predictive analytics. The aim of this study is to examine the feasibility and challenges of incorporating machine learning into software-defined networking (SDN), focusing particularly on practical applications. Integrating Machine Learning (ML) into Software-Defined Networking (SDN) presents challenges, including the requirement for robust algorithms to detect patterns and ensure security. It is crucial to carry out the tasks of developing and implementing machine learning models for real-time predictions and ensuring the robustness of the system. Research is essential to strike a balance between the transformative abilities of ML-SDN and the practical network environments. This helps to improve the resilience, security, and adaptability of network infrastructures in the digital age.

GEQ and PENS Applications for Analyzing Levels of Player Experience and Satisfaction in Mobile Video Games

2024-03-18T01:10:26+00:00

Nowadays, video games are getting popular due to the rapid growth of technology, especially mobile technology. Video games are now able to be played on mobile devices whether in single or multiplayer mode by using the internet and it's free and available everywhere. Understanding player experience in mobile video games is essential for developers and researchers. This research evaluates player experience in a mobile video game using the Game Experience Questionnaire (GEQ) and Player Experience Questionnaire (PENS). The evaluation was performed using an online questionnaire form to the participants (N = 110) who had played the specific mobile game. Using the Core Questionnaire from the Game Experience Questionnaire and Player Experience of Need Satisfaction module, this research found that the Game Experience Questionnaire shows high scores values in the aspects of Immersion (4.22), Competence (3.25), and Positive Aspects (4.38) which indicates a good player experience, and the Player Experience of Need Satisfaction shows a rather balanced yet high score for each value that indicates a satisfying player experience.

Cyber Security Challenges in Industry 4.0: A Review

2024-03-23T02:22:04+00:00

In the era of Industry 4.0, when smart factories and networked systems are reshaping the landscape of industrial production, the protection of important data and information security is a top priority. Cyber-physical systems and the technology that supports it are the keys to Industry 4.0. It is founded on four essential design principles: interoperability, availability of information, technological assistance, and decentralized decision-making. These design principles, however, provide new weaknesses that could be exploited by bad people. To protect these systems from emerging dangers, great consideration should be given to the proactive and adaptive security measures, which will consequently enable the continuing growth and success of Industry 4.0 technologies. This paper will delve into the multifaceted challenges that Industry 4.0 presents in terms of data security and the emerging solutions and strategies required protecting vital information in this brave new world of manufacturing. The exploration of these challenges and the proposed solutions are essential for businesses and policymakers alike to navigate the complexities of data security and ensure the resilience of critical information in the digital age of Industry 4.0.

Redesign of Green Coffee Processing Machine using Value Engineering with Ergonomic approach

2024-03-14T09:29:45+00:00

Green Coffee (Coffea Canephore Var. Robusta) is coffee made from unroasted coffee beans (Coffea spa). Green coffee extract contains higher levels of antioxidants compared to roasted coffee. The current issue is the variability in the quality of green coffee produced, mainly due to manual processing methods. To enhance the economic value of coffee, its quality must be maintained, especially during the production process. Based on this issue, this research is conducted to design a coffee processing machine to improve the quality of the produced coffee. Although coffee machines already exist, their design process often overlooks user comfort. Hence, this research employs anthropometric approaches to produce ergonomic product designs. Additionally, utility engineering is utilized to identify costs. This research results in innovative product designs considering ergonomic aspects and offering economically viable prices compared to similar products currently available in the market.

Embracing Distributed Systems for Efficient Cloud Resource Management: A Review of Techniques and Methodologies

2024-03-12T07:06:50+00:00

The development of parallel computing, distributed computing, and grid computing has introduced a new computing model, combining elements of grid, public computing, and SaaS. Cloud computing, a key component of this model, assigns computing to distributed computers rather than local computers or remote servers. Research papers from 2017 to 2023 provide an overview of the advancements and challenges in cloud computing and distributed systems, focusing on resource management and the integration of advanced technologies like machine learning, AI-centric strategies, and fuzzy meta-heuristics. These studies aim to improve operational efficiency, scalability, and adaptability in cloud environments, focusing on energy efficiency and cost reduction. However, these advancements also present challenges, such as implementation complexity, adaptability in diverse environments, and the rapid pace of technological advancements. These issues necessitate practical, efficient, and forward-thinking solutions in real-world settings. The research conducted between 2017 and 2023 highlights the dynamic and rapidly evolving field of cloud computing and distributed systems, providing valuable guidance for ongoing and future research. This body of work serves as a crucial reference point for advancing the field and emphasizing the need for practical, efficient, and forward-thinking solutions in the ever-evolving landscape of cloud computing and distributed systems.

Deep Learning Based Security Schemes for IoT Applications: A Review

2024-03-24T13:41:28+00:00

Due to its widespread perception as a crucial element of the Internet of the future, the Internet of Things (IoT) has garnered a lot of attention in recent years. The Internet of Things (IoT) is made up of billions of sentients, communicative "things" that expand the boundaries of the physical and virtual worlds. Every day, such widely used smart gadgets generate enormous amounts of data, creating an urgent need for rapid data analysis across a range of smart mobile devices. Thankfully, current developments in deep learning have made it possible for us to solve the issue tastefully. Deep models may be built to handle large amounts of sensor data and rapidly and effectively learn underlying properties for a variety of Internet of Things applications on smart devices. We review the research on applying deep learning to several Internet of Things applications in this post. Our goal is to provide insights into the many ways in which deep learning techniques may be used to support Internet of Things applications in four typical domains: smart industrial, smart home, smart healthcare, and smart transportation. One of the main goals is to seamlessly integrate deep learning and IoT, leading to a variety of novel ideas in IoT applications, including autonomous driving, manufacture inspection, intelligent control, indoor localization, health monitoring, disease analysis, and home robotics. We also go over a number of problems, difficulties, and potential avenues for future study that make use of deep learning (DL), which is turning out to be one of the most effective and appropriate methods for dealing with various IoT security concerns. The goal of recent research has been to enhance deep learning algorithms for better Internet of Things security. This study examines deep learning-based intrusion detection techniques, evaluates the effectiveness of several deep learning techniques, and determines the most effective approach for deploying intrusion detection in the Internet of Things. This study uses Deep Learning (DL) approaches to better expand intelligence and application skills by using the large quantity of data generated or acquired. The many IoT domains have drawn the attention of several academics, and both DL and IoT approaches have been explored. Because DL was designed to handle a variety of data in huge volumes and required processing in virtually real-time, it was indicated by several studies as a workable method for handling data generated by IoT.

Distributed Resource Management in Cloud Computing: A Review of Allocation, Scheduling, and Provisioning Techniques

2024-03-12T04:12:40+00:00

This review paper provides an in-depth examination of distributed resource management in cloud computing, focusing on the critical elements of allocation, scheduling, and provisioning. Cloud computing, characterized by its dynamic and scalable nature, necessitates efficient resource management techniques to optimize performance, cost, and service. The study encompasses a comprehensive analysis of various strategies in resource allocation, scheduling methodologies, and provisioning techniques within the cloud computing paradigm. Through comparative analysis, this paper aims to highlight the synergies and trade-offs inherent in these methods, offering a holistic view of distributed resource management. It contributes to the field by bridging the gap in existing literature, presenting a critical, comparative analysis of current strategies and their interplay in distributed cloud environments.

Enhancing Problem-Solving Learning Models: A Review from the Lens of Independent Learning in the Post-Pandemic Era

2024-03-30T03:49:51+00:00

This research aims to explore the optimization of the problem-solving learning model within the context of independent learning in the post-pandemic era. Utilizing a systematic literature review method and the PRISMA model, the study identifies 25 pertinent articles concerning the implementation of the problem-solving learning model in independent learning. The analysis indicates that applying this model positively impacts students' critical thinking abilities, enhances creativity, and reinforces communication and collaboration skills. From an independent learning standpoint, the problem-solving learning model grants students the autonomy to cultivate creative thinking patterns and fosters heightened engagement in the learning process. The study also highlights adapting the model to online learning, with teachers as facilitators. In conclusion, these findings underscore the effectiveness of the problem-solving learning model in independent learning, especially in the post-pandemic era. They also offer valuable insights for educators and policymakers to develop adaptive learning strategies suited to the current educational environment.

Deep Learning Algorithms for IoT Based Crop Yield Optimization

2024-03-25T09:18:08+00:00

Precision agriculture, with its objectives of optimizing crop yields, decreasing resource waste, and enhancing overall farm management, has emerged as a revolutionary technology in modern agricultural practices. The advent of deep learning techniques and the Internet of Things (IoT) has brought about a paradigm shift in monitoring, decision-making, and predictive analysis within the agriculture industry. This review paper investigates the relationship between deep learning, the (IoT), and agriculture, with an emphasis on how these three domains might work together to optimize crop yields through intelligent decision-making. The integration of deep learning techniques with (IoT) technology for precision agriculture is thoroughly analyzed in this study, covering recent developments, obstacles, and possible solutions. The paper investigates the role of deep learning algorithms in analyzing the vast amounts of data generated by IoT devices in agriculture. It scrutinizes various deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants applied for crop disease detection, yield prediction, weed identification, and other crucial tasks. Furthermore, this review critically examines the integration of IoT-generated data with deep learning models, highlighting the synergistic benefits in enhancing agricultural decision-making, resource allocation, and predictive analytics. This review underscores the pivotal role of IoT and deep learning techniques in revolutionizing precision agriculture. It emphasizes the need for interdisciplinary collaboration among agronomists, data scientists, and engineers to harness the full potential of these technologies for sustainable and efficient farming practices.

Fog Computing in Next Generation Networks: A Review

2024-03-25T12:20:25+00:00

Cloud, Edge, and Fog computing has recently attracted significant attention in both industry and academia. However, finding their definition in computing paradigms and the correlation between them is difficult. In order to support modern computing systems, the cloud, edge devices, and fog computing offer high-quality services, lower latency, multi-tenancy, mobility support, and many other features. Fog/edge computing is an emerging computing paradigm that uses decentralized resources at the edge of a network to process data closer to user devices, like smartphones and tablets, as an alternative to using remote and centralized cloud data center resources. Fog networking or fogging is one of the best used models recently. By addressing this issue, this work serves as a valuable resource for those who will come after. Initially, we present an overview modern computing models and associated areas of interest research. After that, we discuss each paradigm. After that, we go into great detail about fog computing, highlighting its exceptional function as the link between edge, cloud, and IoT computing. Finally, we briefly outline open research questions and future directions in Edge, Fog, Cloud, and IoT computing.

Unveiling the Synergistic Relationship between Distributed Systems and Cloud Computing: A Review of Architectural Trends

2024-03-06T22:28:36+00:00

Cloud providers use distributed systems for scalability, availability, performance, automation, multi-tenancy, and innovation. Distributed cloud computing distributes workload across multiple locations, improving application performance and responsiveness. Significantly potential computational resources are developed in cloud, where large-scale, intricate tasks are performed with the backbone of distribute infrastructure in cloud systems, similar to supercomputing. Cloud computing development has significantly impacted software development and testing, necessitating applications compatible with the cloud, large data users, and high security. Distributed applications hoist on to cloud platforms where increased efficiency, reliability and low costs are favored and further be stored in the cloud for flexibility and scalability. Cloud service models include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), each offering different application services, programming languages, and hosting environments. The synergistic aspects of Distributed Systems and Cloud Systems with respect to their basic capabilities are discussed and systematically reviewed.

Comparative Evaluation of VXLAN with Traditional Overlay Network Protocols

2024-03-23T02:22:51+00:00

This article examines various network virtualization technologies, including Virtual Extensible LAN (VXLAN), as well as overlay network protocols such as VXLAN-EVPN (Ethernet VPN) and VXLAN-LISP (Locator/Identifier Separation Protocol). These protocols play a crucial role in improving the scalability and flexibility of big cloud computing infrastructures. While each of these technologies can be employed to expand a Layer 2 connection across an already established network, they possess unique qualities and applications. The objective is to offer a comprehensive comprehension of these technologies and their suitability in diverse network contexts.VXLAN-EVPN has higher performance in terms of encapsulation speed and reduced packet overhead, rendering it highly suitable for high-speed and large-scale deployments. Conversely, VXLAN-LISP demonstrates superior network latency and interoperability, offering benefits in multi-tenant and geographically distributed networks. VXLAN can be combined with other widely used overlay network protocols, including Generic Network Virtualization Encapsulation (GENEVE), Stateless Transport Tunneling (STT), and Network Virtualization using Generic Routing Encapsulation (NVGRE). The objective is to offer a comprehensive comprehension of these technologies and their suitability in diverse network contexts.

Developing a Gantry Robot with Pre-calculated pure S-curve Motion Profiles for Delicate Egg Handling: Utilizing ESP32, FreeRTOS, and AS5600 Encoders

2024-03-30T03:43:16+00:00

The poultry industry faces challenges due to egg breakage during transfer. This paper describes a gantry robot specifically designed for delicate egg handling. The robot utilizes pre-calculated pure S-curve motion profiles to achieve smooth and precise movements, minimizing stress on the eggs. This approach leverages the computational efficiency of pre-calculation, making it suitable for low-power microcontrollers like the ESP32. FreeRTOS(Free Real-Time Operating System) ensures real-time task management for profile execution and data collection every 4 milliseconds from the AS5600 encoders. These encoders provide high-resolution angular position feedback, allowing for comparison with the planned S-curve profile after each movement step. This system offers advantages such as reduced egg breakage, improved transfer efficiency, and a simpler design compared to real-time control. However, limitations include limited adaptability to significant environmental changes and disturbances. Future work may investigate incorporating real-time feedback control for enhanced robustness.

A Monte Carlo Simulation study on the Gamma Radiation Shielding Properties of Concrete with PET Plastic Composite using the PHITS Code

2024-03-27T02:33:26+00:00

The gamma radiation shielding properties of four different types of PET concretes, containing 0 %, 5 %, 10 %, and 15 % PET additives, were simulated using the PHITS code. The simulation covered photon energy levels ranging from 0.01 to 1.5 MeV and employed a NaI (Tl) scintillation detector. Parameters such as the linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), half-value layer (HVL), and mean free path (MFP) were calculated to evaluate the gamma-ray attenuation for each photon energy level. The effectiveness of PET plastics as a radiation shield depends on factors like material thickness, the type of radiation, and specific application requirements. However, this research provides valuable insights into repurposing waste PET plastics to enhance the radiation-shielding properties of concrete, contributing to improved waste management practices and the development of radiation-shielding materials. The results obtained from the PHITS code align satisfactorily with both the simulation results and the theoretical XCOM data.

Unveiling the Benefits and Challenges of Test-Driven Development in Agile: A Systematic Literature Review

2024-03-30T03:47:34+00:00

The adoption of Test-Driven Development (TDD) in Agile software development prompts extensive discussion. Advocates highlight its benefits, while skeptics question empirical evidence. This study investigates TDD in Agile settings, examining its merits and challenges. Conducting a systematic literature review, it synthesizes insights from scholarly and industry sources. Results indicate TDD aids development, aligns with Agile practices, and enhances product delivery. Yet, challenges include procedural complexity and skill requirements. Proficiency in Agile practices like refactoring and unit testing is essential. TDD's impact on productivity is moderate and can be counterproductive. This research contributes new perspectives on TDD and Agile development, benefiting academia and informing practitioners for informed decision-making.

Enhancing Educational Paradigms: A Comprehensive Review of Virtual Desktop Infrastructure (VDI) Applications in Learning Environments

2024-03-25T02:01:58+00:00

This article comprehensively evaluates Virtual Desktop Infrastructure (VDI) in academic environments. It explores the role of VDI in transforming and gaining knowledge via offering more advantageous accessibility and flexibility, addressing the digital divide, and adapting to various learning patterns. The paper examines case studies throughout one-of-a-kind educational settings, discusses the technical components, and evaluates VDI's effect on mastering and teaching. It additionally highlights the challenges and potential risks related to VDI implementation. Synthesizing the outcomes from various case studies and study papers lays a stable foundation for understanding the multifaceted nature of VDI's implementation and its effect on instructional paradigms. The technical limitations of reviewed cases play a significant function in determining the fulfillment of VDI implementations in instructional environments. Well-structured planning and evaluation of these elements are vital to ensure that the selected VDI efficiently meets the goals of instructional concerns and their participants. Future research instructions are cautioned to deal with diagnosed gaps, including their application in various educational contexts and lengthy-term impacts. The article is valuable for educators, policymakers, and era providers.

Distributed Systems for Real-Time Computing in Cloud Environment: A Review of Low-Latency and Time Sensitive Applications

2024-03-13T22:43:32+00:00

As a result of its many benefits, including cost-efficiency, speed, effectiveness, greater performance, and increased security, cloud computing has seen a boom in popularity in recent years. This trend has attracted both consumers and businesses. Being able to process and provide data or services in a quick and effective manner while adhering to low latency and time limits is the hallmark of an efficient distributed system that is designed particularly for real-time computing in cloud environments. It is essential to place a high priority on low latency and time sensitivity while developing and putting into action a distributed system for real-time computing in a cloud environment. In order to fulfil the particular requirements of the application or service, consideration must be given to a number of different aspects. In particular, the topic of load balancing will be discussed in this paper. It is possible to ensure a more effective distribution of workload and reduce latency by using load balancers, which distribute incoming traffic over many servers or instances. The throttled algorithm is believed to be the most efficient load balancing strategy for reducing service delivery delay in cloud computing. This research investigates a hybrid method known as Equally Spread Current Execution (ESCE), which is known for its combination with the throttled algorithm.

The Industrial Internet of Things (IIoT) and its roles in the Fourth Industrial Revolution: A review

2024-03-25T00:32:05+00:00

The Industrial Internet of Things and Industry 4.0 are now two highly sought-after areas of research and development, attracting significant interest from both academic and industrial sectors. The two ideas, Industry 4.0 and IIoT, share significant similarities, with Industry 4.0 being seen as the use of IIoT specifically in the automation and manufacturing sectors. Within the framework of the present Industry 4.0 paradigm, many growth pathways have emerged, collectively leading to notable enhancements in terms of efficiency, flexibility, communication, adaptability, customization, and modularity in the industrial sector. The Industry 4.0 is rapidly evolving within the framework of the Industrial Internet of Things (IIoT), and the authors are recognizing the necessity for a comprehensive and in-depth overview of the many research areas that are currently expanding. The area will remain intriguing in the foreseeable future due to its significant potential for enhancing the existing industrial technologies. An exhaustive evaluation of the current systems in the automotive sector, emergency response, and chain management on IIoT has been conducted, revealing that IIoT has been widely adopted across several technological domains. Industry 4.0 is the term used to describe the present automation and data sharing trend in businesses. Presently, there is a dearth of agreement about the assessment of an organization's readiness for Industry 4.0. Industry 4.0 encompasses a diverse array of digital technologies that profoundly influence industrial enterprises. The literature on Industry 4.0 has had significant exponential growth during the previous decade. The results of our research confirm the idea of Industry 4.0 as a concept that goes beyond the Smart Manufacturing sector, hence opening up possibilities for collaboration with other interconnected disciplines.

Distributed Architectures for Big Data Analytics in Cloud Computing: A Review of Data-Intensive Computing Paradigm

2024-03-13T00:47:18+00:00

Big Data challenges are prevalent in various fields, including economics, business, public administration, national security, and scientific research. While it offers opportunities for productivity and scientific breakthroughs, it also presents challenges in data capture, storage, analysis, and visualization. This paper provides a comprehensive overview of Big Data applications, opportunities, challenges, and current techniques and technologies to address these issues. This study presents a system for managing big data resources using cloud for the development of data-intensive applications. It addresses even the challenges related to technologies that combine cloud computing with other allied technologies and devices. In addition, the increasing volume, velocity, and variety of data in the era of Big Data necessitate advanced methods for data processing and management. This study delves into the intricacies of data scalability, real-time processing, and the integration of diverse data types. Furthermore, it explores the role of machine learning algorithms and artificial intelligence in extracting meaningful insights from massive datasets.

Optimizing Performance in Distributed Cloud Architectures: A Review of Optimization Techniques and Tools

2024-03-12T04:15:49+00:00

This research paper presents a groundbreaking hybrid transactional/analytical processing (HTAP) architecture designed to revolutionize real-time point cloud data processing, particularly in autonomous driving environments. Integrating elements from both columnar and row-based tables within a spatial database, the proposed architecture offers unparalleled efficiency in managing and updating point cloud data in real-time. The architecture's distributed nature operates through a seamless synergy of Edge and Cloud components. The Edge segment operates within the Robot Operating System (ROS) environment of the vehicle, while the Cloud counterpart functions within the PostgreSQL environment of cloud services. The communication between these components is facilitated by Kafka, ensuring rapid and reliable data transmission. A pivotal aspect of the proposed system lies in its ability to autonomously detect changes in point cloud data over time. This is achieved through a sophisticated algorithm that analyzes dissimilarities in the data, triggering real-time updates in areas where high dissimilarity is detected. The system ensures the maintenance of the latest state of point cloud data, contributing significantly to the generation of safe and optimized routes for autonomous vehicles. In terms of optimization, the paper demonstrates how the HTAP architecture achieves real-time online analytical processing through query parallelization in a distributed database cluster. The system's efficacy is evaluated through simulations conducted in the CloudSim framework, showcasing its scalability, adaptability, and robustness in handling point cloud data processing for a single vehicle. While acknowledging the achievement of the proposed architecture, certain limitations are recognized. The study highlights the need for further investigation into the system's performance under simultaneous analysis and updates from multiple vehicles. Additionally, ensuring seamless scalability and robustness for uninterrupted operation and expansion during runtime is identified as an area requiring further development.

Signal Propagation and Path- Loss in 6G Mobile Telecommunication System

2024-03-23T02:21:27+00:00

As previous we know about many types of generation like (2G,3G and 4G network), and also it has been developed to the fifth – generation mobile communication system. Compare with 5G, 6G will be faster and lower latency, in 6G era, the theoretical network speed will reach to 1Tbps. That is 100 times the speed of the 5G, which is 10,000 times the speed of the current 4G. 5G is officially commercialized in 2019, but 5G has no yet covered a large area and have problems with high tariffs and incomplete network coverage ,6G network makes up for these problems. The 6G is through the integration of ground base stations and satellite communications, thus covering the whole world, it is also the real coverage. But also, there’s some issues with 6G networks, like here in this article I will explain the propagation signal for 6G and I will talk about path loss signals because it uses sub-millimeter waves or tera-hertz waves to make faster connection but it will cause so much issues in propagation filed. And explained how to address path-loss challenge.

The Effect of Blended Learning on Students' Creative Thinking Ability: A Meta-Analysis

2024-03-30T03:48:30+00:00

This study aims to investigate the influence of technology-based learning, particularly blended learning, on students' creative thinking abilities. Through quantitative analysis, data comprising sample size, mean, and standard deviation extracted from Google Scholar indexed journals were examined. Employing a group contrast analysis design with a random effects model, effect sizes were corrected using JASP software. The analysis revealed a summary effect of 6.74 with a confidence interval ranging from 4.68 to 8.80, indicating a significant difference between groups utilizing blended learning and those employing conventional methods. Notably, students engaged in blended learning exhibited superior creative thinking skills compared to their counterparts. These findings underscore the pressing need to integrate blended learning methodologies to enhance the learning process effectively.

Distributed Systems for Data-Intensive Computing in Cloud Environments: A Review of Big Data Analytics and Data Management

2024-03-13T22:43:42+00:00

Because of the increasing increase of data, which is frequently referred to as "big data," many different businesses have been severely impacted in recent years, necessitating the implementation of sophisticated data management and analytics solutions. By virtue of the fact that it provides scalable resources for applications that are data-intensive, cloud computing has emerged as an indispensable platform for the management of these enormous databases. The evolving landscape of distributed systems in cloud settings is the primary emphasis of this study, which is situated within the framework of big data analytics and data management. With the purpose of providing a comprehensive overview of distributed systems that are used in cloud settings for data-intensive computing, the review article seeks to offer. Furthermore, it evaluates the many ideas, techniques, and technical improvements that have been established in order to properly manage, store, and analyse large amounts of data. A comprehensive literature evaluation of recently published scientific references was successfully completed by our team. The analysis takes into account the theoretical foundations, as well as the research that has already been conducted on distributed computing systems, cloud-based data management, and enormous data analytics. The study places an emphasis on the significant role that distributed computing plays in ensuring the success of big data analytics. The interplay between distributed systems and cloud computing paradigms has resulted in the development of solutions that are robust, scalable, and economical for activities that need a significant amount of data. It is still a huge problem to ensure that data security, privacy, and interoperability are maintained across the many cloud services.

Exploring the Landscape of Smart Cities: A Comprehensive Review of IoT and Cyber-Physical Systems

2024-03-25T02:01:44+00:00

They focus on housing, well-being, equality, clean energy and fair conditions. The cyber-physical approach involves the development of IoT and Cyber-Things. Smart cities have a variety of use cases, including electricity and transportation. Automating is used for efficiency in industrial manufacturing. An integrated supply and demand side management system is required for the reliability, security and ability to manage the power grid. This paper introduces an integrated energy approach, enhances existing standards, and establishes a shared basis for multidisciplinary planning. It also introduces new semantic network ontologies to provide a comprehensive framework for solving resource-related challenges. This new approach aims to fill the gaps in current standards and create an integrated environment for multi-stakeholder collaboration, using a semantic web ontology for communication and improved decision making in energy systems Provides information integrated, including various forms of smart cities With flexibility for flexibility and inclusion in the energy industry, can accommodate the specific characteristics and needs of various smart city applications In this study, computing -physical system (CPS), software-defined network (SDN), internet (IoT). ), and analyze how smart cities are connected. CPS combines physical channels with electronic systems to provide increased network management efficiency and flexibility. SDN improves dynamic capacity and flexibility, while IoT is more connected for real-time data exchange and decision-making.

Optimal Sizing and Comparative Analysis of Renewable Energy Integration for the Existing Microgrid System in Kadan Island

2024-04-03T07:23:18+00:00

The rapid depletion of fossil fuels and the necessity for reduced carbon emissions have led to an increased focus on renewable energy resources. The existing microgrid system comprises solely a diesel generator and a small portion of hydropower. Currently, Kadan Island relies mainly on diesel generators to supply power, resulting in a significantly higher cost of energy in comparison to other areas. Furthermore, there is still not enough electricity available on the entire island of Kadan. However, research has shown that integrating renewable-based systems with storage technologies into existing systems can help mitigate these issues. Therefore, the main objective of this paper is to investigate the optimal size and operation of a hybrid renewable system on the Myanmar Islands. The optimization process will focus on minimizing the net present cost (NPC) and cost of energy (COE) of the selected location. Additionally, the island's network will be analyzed under normal operating conditions with different scenarios, and the best scenario for the existing microgrid on Kadan Island will be recommended.

Deep Learning Algorithms for Detecting and Mitigating DDoS Attacks

2024-03-25T09:23:42+00:00

Raising the threat of Distributed Denial of Service (DDoS) attacks means that high and adapted detection tools are required now more than ever. This research focuses on exploring the latest solutions in preventing DDoS attacks and emphasizes how Artificial Intelligence (AI) is involved in enhancing end-to-end detection techniques. Through the analysis of several key approaches, this work notes that AI-guided models quickly identify and counteract any unusual traffic patterns that may indicate an oncoming DDoS attack. Essential aspects towards creating more resilient networks against such attacks include machine learning algorithms, sophisticated data analytics together with AI based detection systems for traffic pattern recognition. Importantly, AI does well in behavioral analysis because it can distinguish and adapt to changing attack vectors. Additionally, it puts AI into perspective as making positive mitigation strategies possible that contain quick interferences such as temporary halt of traffic, rerouting and targeted block listing with real time control panel operations. On the contrary, current DDoS detection prevention techniques remain critically addressed of persistent challenges and limitations fundamental to them. From what emerges, they should always be ready for innovation and improvement because of how attacks might evolve over time. This paper aligns itself with the position that AI-driven detection mechanisms are natural to network security against DDoS attacks. It underlines the importance of integrating AI-based solutions with conventional practices in order to enhance network resilience and efficiently counteract cyber threats that are evolving all the time.

Image Copyright Protection Based on Blockchain Technology Review

2024-03-24T13:57:47+00:00

On a daily basis, a significant number of individuals distribute several photos and videos that have been marginally modified from the original material produced by copyright owners, such as photographers, graphic designers, and video producers. Individuals that infringe upon the rights of others, lacking the legal authority to access multimedia content, employ various digital image and picture manipulation techniques, it involves converting to gray scale, trimming, rotating, contracting the frame, and adjusting the background speed, to modify said content. Blockchain technology obviates the necessity of an intermediary, hence circumventing the possibility of a singular point of failure. Infractions to copyright poses a significant barrier to protecting commercial image and video information. The IPFS blockchain technology offers on-chain preservation for copyright information and off-chain storing for distinct multimedia files. The enhanced perceptual hashing algorithm significantly enhances the precision of identifying connections to identify digital image piracy. The photographers and designers that submit their photographs on websites are experiencing significant dissatisfaction due to a prevalent practice in which others attempt to claim credit and profit from the initial creator's effort.

Design Optimization Of PV And WTE Integration In Electricity Supply System For Mandalay Urban Area

2024-04-05T14:28:41+00:00

Hybrid energy systems, which combine two or more energy systems, are
becoming increasingly popular due to the rise in petroleum product
prices, growing CO2 emission awareness, and advancements in renewable
energy technologies. These systems are being adopted to meet the energy
and electricity demands of decentralized networks. In Myanmar, some
areas still face significant challenges in accessing grid-based electricity
supply. In such cases, the hybrid renewable microgrid systems offer a
better alternative by reducing dependency on diesel fuel and meeting
energy demands in an environmentally friendly manner. To enhance the
reliability of these hybrid systems, renewable sources can be integrated
with diesel generators and utility grid. This integration allows hybrid
systems to compensate for the intermittent nature of renewable energy
sources and achieve higher overall energy efficiency. One of the main
advantages of hybrid systems is their potential for energy autonomy, as
they are not reliant on a single energy source. This paper introduces a
novel approach for designing a grid connected hybrid system that
incorporates photovoltaic, and biomass especially the municipal solid
waste. The Hybrid optimization model for electrical renewable (HOMER)
is a powerful optimization model that simplifies the evaluation of off-grid
and grid-connected power system designs for various applications. In this
paper, the grid connected hybrid renewable energy system is designed
and analyze for Mandalay region.

Investigating of Signal Propagation Characteristics in a Realistic Rural Scenario using Verified Tools: A Case of South Chunya-Tanzania

2024-03-30T04:45:14+00:00

Effective radio network planning requires understanding an area's radio propagation characteristics. This investigation uses dependable techniques to examine the movement of wireless signals in Tanzania's rural locations. Propagation characteristics in selected rural Tanzanian communities (South Chunya) are examined as a case study, and recommendations are made based on the findings, particularly for wireless service providers or researchers looking to improve wireless connectivity in rural communities. Important radio parameters such as Pathloss, Signal to Noise ratio, Bandwidth throughput, Spectral efficiency and Spectral band were examined. This was done after simulating signal transmission from the Base station transmitter to the Base station receiver and observing power and attenuation. Their corresponding results are discussed and evaluated. The general observation of the pass loss results for both operating frequencies 800MHz and 2000MHz have shown that only few areas are experiencing strong signal after initial signal power transmission, large areas served by these macro sites are experiencing huge path loss. This is evidently affected by the terrain of an area and the carrier frequency of operators.

The Effect of the Problem-Based Learning Model on 21st Century Student Skills: A Meta-Analysis

2024-03-25T06:23:45+00:00

This study aims to investigate the impact of Problem Based Learning (PBL) on the development of 4C skills (Critical Thinking, Creativity, Collaboration, Communication) among students. Through meta-analysis, data from 20 national and international journal articles published between 2018 and 2023 were analyzed. Findings indicate that the consistent use of PBL yields a highly significant effect, with an average effect size of 1.72. These results affirm the effectiveness of PBL in enhancing students' 4C skills, underscoring the importance of this instructional approach in the context of 21st-century education, which emphasizes critical thinking, creativity, collaboration, and communication skills. The practical implications of this study highlight the necessity of integrating PBL models in instructional design to facilitate the development of 21st-century skills among students. Further research could delve into specific aspects of PBL usage and its impacts on student learning outcomes and academic achievement.

A Review of Bitcoin Price Prediction Based on Deep Learning Algorithms

2024-03-28T09:30:18+00:00

This study provides a comprehensive analysis of the existing body of work on predicting the price of Bitcoin using deep learning techniques. It discusses the fundamental concepts behind deep learning and Bitcoin, including recurrent neural networks, convolutional neural networks, and long short-term memory networks. The study also examines the data sources used in training these models, including historical Blockchain transaction data, social media sentiments, and Bitcoin prices. The report also highlights the importance of metrics like mean absolute error, mean squared error, and root mean squared error for evaluating the effectiveness of various models. It also discusses future research topics, such as incorporating external factors into prediction models. The article offers valuable insights for academics, practitioners, and policymakers interested in cryptocurrency prediction.

A Review on Diabetes Classification Based on Machine Learning Algorithms

2024-04-05T14:30:18+00:00

Diabetes, a chronic metabolic disorder, is a significant global health concern affecting millions of individuals worldwide. Early and accurate diagnosis of diabetes is crucial for effective management and prevention of complications. Machine learning (ML) techniques have emerged as powerful tools for analyzing diabetes-related data, aiding in the classification and prediction of diabetes types. This review provides a comprehensive overview of recent advancements in diabetes classification using ML algorithms, highlighting their strengths, limitations, and future directions. Various ML algorithms, including but not limited to support vector machines, decision trees, random forests, artificial neural networks, and ensemble methods, are discussed in details. Furthermore, data preprocessing techniques, feature selection methods, and evaluation metrics employed in diabetes classification studies are examined. Additionally, challenges such as data imbalance, interpretability, and generalization across diverse populations are addressed. Finally, potential avenues for future research to enhance the accuracy and applicability of ML-based diabetes classification systems are proposed.

Mapping the Spectrum of Expertise of Graduates from the Electrical Engineering Education Study Programme at Universitas Negeri Padang: A Study

2024-04-03T08:08:10+00:00

Upon graduation, the aim of education graduates is to become qualified educators capable of meeting the needs of education, society, and industry. Graduates of the Electrical Engineering Education Study Programme are expected to possess superior competence and provide solutions to societal problems. The research conducted is descriptive in nature, utilising a qualitative approach. The study was carried out at the Faculty of Engineering, State University of Padang. The research instrument comprised documents such as course outlines, course synopses, and syllabi in Electrical Engineering Education at Padang State University. Data was collected through interviews and documentation. The study aimed to create a mapping of Electrical Engineering Education courses at Padang State University. The UNP Electrical Engineering Education program requires students to complete a set number of courses each semester. In semester 1, students must complete 22 SKS; in semester 2, 22 SKS; in semester 3, 22 SKS; in semester 4, 19 SKS; in semester 5, 22 SKS; in semester 6, 20 SKS; in semester 7, 8 SKS; and in semester 8, 10 SKS of final courses. The requirement for Semester Credit Units for Electrical Engineering Education students at Padang State University is 145 credits.

A Meta-Analysis of the Problem-Based Learning Model to Enhance Students' Creative Thinking Skills

2024-03-25T06:40:33+00:00

This study aims to investigate the influence of the Problem Based Learning (PBL) model on enhancing students' creative thinking skills. Meta-analysis method was employed to collect and analyze data from 17 relevant articles published in online journals between 2018 and 2023. The analysis results indicate that the implementation of the PBL model has a significant impact on enhancing students' creative thinking skills, with an effect size value of 0.40 categorized as a moderate effect. The implications of these findings underscore the importance of integrating the PBL model into educational approaches to facilitate the development of students' creative thinking skills in various educational contexts.

A Review on Heart Disease Detection Classification Based on Deep Learning Algorithm

2024-04-07T13:54:07+00:00

Heart disease it is one of the main causes of death in the globe. Heart illness encompasses a spectrum of disorders that impact the heart, its blood arteries, and its overall functionality. Also referred to as cardiovascular disease. This paper investigates the potential benefits of deep learning (DL) architectures for improving diagnostic accuracy addressing the critical need for improved diagnosis of cardiac disease, and the difficulties associated with applying DL methods for heart disease identification. This survey study highlights the important role that DL plays in cardiovascular diagnostics from a number of tasks like as diagnosing, predicting, and classifying heart diseases. Convolutional Neural Networks (CNNs), a type of deep learning, are being used in the context of heart illness with the primary goal of creating accurate and dependable models for the identification, diagnosis, and prognosis of various heart-related disorders.

Development of Corrosion Segmentation Using Deep Learning Double Architecture Method to Assist the Analysis and Evaluation Process of Corrosion Inspection

2024-04-16T10:29:56+00:00

Corrosion of pump unit components often occurs in coal mines and can lead to frequent failures of some components. As a result, a corrosion inspection needs to be performed on each component to minimize the possibility of damage. Currently, manual inspection methods are used for corrosion testing but there are still metal defects in the form of corrosion that are uninspected. Therefore, this study aimed to develop corrosion segmentation using computer vision with deep learning double architecture method for detection and evaluation of metal corrosion in order to reduce the loss due to manual inspections. To produce a faster and more accurate analysis method, deep learning double architecture algorithm, namely VGG16-UNET, can be applied with the help of computer vision technology. Consequently, the use of VGG16-UNET method achieved an accuracy of 98.42%. This is in contrast with the single UNET architecture, which produced an accuracy of 92.6%. Based on these findings, it was concluded that the development of this recommended inspection made the analysis and evaluation of corrosion inspection to be quick and easy.

State-of-Charge Evaluation for Real-Time Monitoring and Evaluation of Lithium-Ion Battery Performance

2024-04-07T04:01:58+00:00

Nowadays, lithium-ion batteries have been garnered significant attention as the primary energy source for energy storage devices within the renewable energy sector. Key concerns surrounding the utilization of lithium-ion batteries include ensuring satisfactory design lifespan and safe operation. Consequently, there's been a practical need for battery management. Responding to this demand, various battery state indicators have seen widespread implementation. Among the battery state indicators, accurate state-of-charge (SOC) estimation is an essential requirement for many situations where Li-Ion batteries (LiBs) are used. The effectiveness of a Battery Management System (BMS) safeguards the battery against deep discharging and over-charging to maximize its lifespan. This paper conducts state of charge (SOC) evaluation of a Li-ion battery module (12V, 13 Ah lithium titanate oxide (LTO) battery) for battery management systems (BMS) in energy storage systems (ESSs).

A Review of Heart Disease Classification Base on Machine Learning Algorithms

2024-04-07T18:52:17+00:00

Heart disease is currently the leading cause of death. This problem is acute in developing countries. Predicting heart disease helps patients avoid it in its early stages and can also help medical practitioners find out the main causes. Machine learning has proven over time to play an important role in decision making and forecasting through massive data sets created by the healthcare sector. This review provides an overview of heart disease prediction using applied machine learning algorithms such as Naïve Bayes, Random Forest, Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression, and K-Nearest Neighbour (KNN). And these differences in the techniques are a reflection of many strategies for predicting heart disease. We present a synopsis of classification techniques that are primarily used in the predicted of heart disease. Additionally, we review several previous studies that conducted over the past four years, that used machine learning algorithms to predict cardiovascular.

Analysis of The Quality of Plastic Sack Products using the Statistical Quality Control (SQC) Method at PT. XYZ

2024-04-07T10:57:51+00:00

PT. XYZ is a PT work unit manufacturing company. Perkebunan Nusantara I Regional 4 is engaged in the plastic sack industry which produces 2 products, namely outer bags and inner bags. The research carried out aims to determine and measure the level of product quality and provide appropriate product improvement suggestions for quality control problems in plastic sack products in PT. XYZ. The method used is Statistical Quality Control (SQC). Based on the results of research on Statistical Quality Control (SQC), there are 5 production stations and there are each type of defect at each production station. The defects that affect product quality are 14,541 kg of holey film defects, 13,849 kg of uneven threads, 22,706 kg of outgoing threads, 17,876 kg of jagged webbing, 10,186 kg of misprinting, 8,846 kg of faded colors, 11,323 kg of inner holes. , and opened seal of 3,206 kg. There are suggestions for improvements that can be proposed for each defect by adjusting the factors in the fishbone diagram.

Economic Analysis of PV-Utility Grid Hybrid Electric Vehicle Charging Station in Mandalay City

2024-04-05T03:50:07+00:00

Electric vehicles (EVs) are catching on everywhere worldwide. Building more clean energy infrastructure for EVs could help lessen greenhouse gas emissions and make city air cleaner. EVs charged with electricity from solar panels emit fewer pollutants compared to those charged with grid electricity. Therefore, combining solar power with EV charging stations could be a good way to promote sustainable development in the EV market. Despite rapid EV adoption in Mandalay, the charging infrastructure remains limited, mostly stations reliant on grid electricity. In this paper, the proposed system integrates photovoltaic technology with the existing utility grid infrastructure of EV charging station at the corner of 78th road & 101st road in Mandalay city, Myanmar. HOMER Grid is utilized to analyze the economic feasibility. The results of the proposed system describe that the cost of energy (COE) is reduced by $0.05/kWh. Additionally, the integrated system incurs fewer costs and generates more profits.

Optimization of Furniture Raw Material Inventory Using The Lagrange Multiplier Method

2024-04-07T13:57:08+00:00

PT XYZ is a furniture company specializing in products made from wood and rattan. PT XYZ has suboptimal problems controlling raw material inventories, resulting in inventory cost overruns and warehouse overcapacity. This research aims to determine the optimization of the amount of furniture raw material in the inventory according to the available warehouse capacity using the Lagrange Multiplier method. From the research results, it was found that the total warehouse using the Lagrange Multiplier method was 107,922 m3, which shows that this value is optimal because it does not exceed the capacity of the warehouse owned by PT XYZ, which is 108 m3. By applying the Lagrange Multiplier method, it is possible to save total inventory costs of IDR 17.219.173 or 6.36% of the company's total inventory costs, with an order size of 37 m3 for mindi wood and 26 m3 for mahogany wood.

Regulation of Network Condensation Based on Fitness Ordered Access Strategy

2024-04-17T23:07:44+00:00

The condensation in complex networks disclose the underlying mechanism of the monopoly in socioeconomic system, which can help us to design and access the anti-monopoly policy based on the study on network condensation. Inspired by the consideration, We introduce a set of rearrangement mechanism into the fitness model to regulate the order of nodes with different fitness to enter the network, and study the influence of this regulation strategy on network condensation. By extensive Monte Carlo simulations and finite size scaling analysis, we obtain the critical rearrangement index under a typical fitness distribution, establish the relationship between the index and the condensation intensity, and finally construct the condensation phase diagram. These results show that there exists an interval of the critical rearrangement index, outside which the condensation of the fitness model will be effectively suppressed. We carry out a theoretical analysis on some key results to understand their underlying origin, and discuss their instructive significance on the anti-monopoly market management.

Expert System Detects Damage to Corn Grinding Machines Using the Certainty Factor Method on CV. Central Corn Persada

2024-04-06T02:32:07+00:00

A corn grinding machine is a combustion engine that is used to drive a propeller which functions to knock out the corn kernels from the cob, so that the release of the corn kernels from the cob is very fast and time efficient. This really helps corn farmers and also corn mill owners to speed up the process to the processing plant. However, in the process of threshing corn from the cobs, the machine often experiences damage, so it is necessary to detect damage to the corn grinding machine with an expert system using the Certainty Factor method in order to help farmers quickly understand what damage is occurring to the machine without having to bring the machine or call someone. machine repair experts, where the system created can also provide solutions related to damage to corn grinding machines.

Evaluasi Manajemen Data Warehouse & Business Intelligence Menggunakan CMMI Pada E-Commerce XYZ

2024-03-30T03:46:35+00:00

PT. XYZ, perusahaan e-commerce Indonesia, mengalami tantangan dalam mengintegrasikan data dari berbagai sumber ke dalam data warehouse, termasuk masalah duplikasi data yang menghabiskan banyak waktu dan usaha untuk mencapai data berkualitas. Hal ini berdampak pada lama waktu penyajian laporan ke Dewan Direksi yang bisa mencapai satu bulan tanpa hambatan berarti. Penelitian ini bertujuan untuk meningkatkan manajemen data warehouse dan business intelligence (DW-BI) dengan harapan menciptakan single source of truth yang efektif untuk kebutuhan manajemen pada tahun 2022. Menggunakan metode Capability Maturity Model Integration (CMMI) dan rekomendasi berbasis DAMA-DMBOK, penelitian ini berambisi mengangkat maturity level DW-BI e-commerce XYZ ke level 3. Hasilnya, tiga sub-aktivitas berada pada level 2 sementara empat lainnya telah mencapai level 3. Dengan rekomendasi yang ditargetkan pada sub-aktivitas level 2, penelitian ini berusaha mengatasi permasalahan integrasi data, mengoptimalkan manajemen DW-BI di PT. XYZ.

Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa Teknik Informatika dengan Orange Data Mining

2024-03-13T01:49:01+00:00

Penyelesaian studi tepat waktu menjadi parameter penting untuk menilai kompetensi lulusan. Meskipun demikian, muncul tantangan karena tidak semua mahasiswa dapat menyelesaikan studi mereka sesuai jadwal yang telah ditentukan. Pada penelitian ini dilakukan pengembangan prediksi status kelulusan mahasiswa dengan empat model klasifikasi, yakni Decision Tree, Naïve Bayes, K-NN dan SVM. Dataset yang digunakan adalah data mahasiswa angkatan 2018-2020 di Universitas Muhammadiyah Prof. Dr. Hamka, terdiri dari 500 data mahasiswa (60% data latihan dan 40% data uji). Analisis dilaksanakan dengan memanfaatkan perangkat lunak Orange Data Mining, dengan evaluasi model melibatkan K-Fold Cross Validation dengan nilai (K=5), Confusion Matrix, dan ROC. Hasil penelitian menunjukkan bahwa algoritma K-NN menjadi algoritma paling efektif dalam memprediksi status kelulusan mahasiswa, dengan tingkat akurasi dan presisi mencapai 92%, tingkat recall sebesar 89%.

Analisis Pola Musik Karawitan di Tengah Era Digital

2024-02-29T15:36:42+00:00

Traditional arts, especially musical music, are facing challenges in the digital era and the influence of foreign cultures. Efforts continue to be made to combine technology with traditional arts. The method adopted in this research is library research, which involves collecting data and information in depth through various references such as books, magazines, other references, and research results. This research explains the adaptation of Karawitan music patterns to modern technology and its impact on the interests of the younger generation. Use of digital musical instruments to create innovative compositions that combine traditional elements with contemporary elements. The role of the community in supporting the preservation of Karawitan art is also emphasized, with active participation in traditional music events and efforts to promote the culture to others. In conclusion, this article emphasizes the complexity of the relationship between tradition and innovation in the context of Karawitan music in the era of modern technology.

Pendekatan Transfer Learning Untuk Klasifikasi Tangisan Bayi Dengan Imbalance Dataset

2024-03-23T12:19:59+00:00

Klasifikasi tangisan bayi dapat dimanfaatkan untuk mengidentifikasi masalah kesehatan bayi dan memenuhi kebutuhan bayi dengan cepat. Dalam studi ini, teknik transfer learning, dengan model terlatih YAMNet, diterapkan untuk klasifikasi bayi dengan dataset terbatas dan tidak seimbang. YAMNet, sebuah model Convolutional Neural Network khusus untuk analisis audio, mengatasi keterbatasan metode tradisional yang bergantung pada interpretasi manusia. Dengan mempelajari fitur-fitur audio secara otomatis, memungkinkan kinerja klasifikasi yang lebih akurat. Dalam studi ini, dilakukan eksplorasi dan analisis manfaat penggunaan YAMNet, melalui perbandingan dengan model baseline tanpa teknik transfer learning. Hasilnya menunjukkan bahwa model YAMNet tidak hanya nilai akurasinya yang tinggi 0.8106, namun juga nilai skor-F1nya tinggi yaitu mencapai 0.9831. Terbukti bahwa penggunaan transfer learning dapat meningkatkan kinerja dalam klasifikasi tangisan bayi, terutama dalam mengatasi ketidakseimbangan data dan meningkatkan prediksi untuk kelas minoritas.

Pre- Estimasi Daya Aktif pada Gedung Bertingkat dengan menggunakan Time Series Neural Network

2024-03-23T05:29:18+00:00

Penggunaan energi listrik untuk kehidupan sehari – hari semakin meningkat tanpa adanya pengawasan dan pembatasan yang mengakibatkan penggunaan energi semakin semena – mena , penggunaan energi berlebihan juga disebabkan perkembangan teknologi yang semakin memudahkan pekerjaan manusia. Namun kebutuhan energi listrik yang besar tidak disertai dengan kapasitas energi listrik yang memadai. Oleh sebab itu diperlukan sebuah metode estimasi beban listrik jangka menengah dengan menggunakan Time Series Neural Network. Penelitian ini diharapkan dapat mengurangi jumlah energi listrik yang tidak terpakai dan digunakan se efisien mungkin. Pada penelitian ini menghasilkan nilai MAPE sebesar 5.36% dan nilai RMSE sebesar 9.2

Eksplorasi Praktis Pembelajaran Musik di Android dengan Aplikasi Flowkey

2024-02-22T10:59:49+00:00

Artikel ini mengeksplorasi peran aplikasi pembelajaran musik Flowkey dalam era digital pasca-pandemi Covid-19 di Indonesia. Flowkey menawarkan interaktivitas visual, fleksibilitas waktu dan tempat, serta adaptasi tingkat kesulitan, memastikan pengalaman belajar yang disesuaikan dengan kebutuhan individu. Metode penelitian menggunakan pendekatan "Library Research" dengan sumber referensi dari jurnal-jurnal terverifikasi. Hasil penelitian menunjukkan bahwa Flowkey tidak hanya alat pembelajaran musik, tetapi juga pengalaman eksplorasi musik yang menyenangkan dan mendidik. Keunggulan aplikasi ini menciptakan lingkungan pembelajaran yang mendukung pengembangan keterampilan musik individu. Artikel ini menyimpulkan bahwa Flowkey dapat menjadi solusi inovatif dalam membentuk masa depan pembelajaran musik yang holistik di era digital, dengan potensi untuk terus mengembangkan fitur-fitur inovatif dan mendukung perkembangan keterampilan musik secara menyeluruh.

Dampak Pengambilan Sampel Data untuk Optimalisasi Data tidak seimbang pada Klasifikasi Penipuan Transaksi E-Commerce

2024-02-26T13:02:14+00:00

Tujuan dari penelitian ini adalah untuk mengatasi masalah pengklasifikasian dan prediksi data yang tidak seimbang terkait dengan kondisi transaksi E-Commerce. Menjamurnya transaksi e-commerce menimbulkan potensi permasalahan: penipuan dalam pembelian e-commerce. Kasus penipuan e-niaga terus meningkat setiap tahun sejak tahun 1993. Menurut survei tahun 2013, untuk setiap $100 transaksi e-niaga, terdapat kerugian sebesar 5,65 sen akibat penipuan. Mendeteksi penipuan merupakan pendekatan yang efektif untuk meminimalkan terjadinya aktivitas penipuan dalam transaksi e-commerce. Pembelajaran menjadi metode yang semakin dapat diandalkan untuk memprediksi keadaan. Tidak adanya keseimbangan antara data yang curang dan tidak curang mengakibatkan klasifikasi menjadi bias. Algoritma SMOTE diperlukan untuk mencapai keseimbangan data. Selanjutnya peristiwa transaksi akan diklasifikasikan menggunakan algoritma Support Vector Machine, K-Nearest Neighbor, Naive Bayes, dan C45, dengan mempertimbangkan hasil penyeimbangan data. Di antara algoritma SVM, KNN, dan C45, metode Naive Bayes menunjukkan nilai akurasi tertinggi. Oleh karena itu, disarankan untuk menggunakan teknik ini untuk tujuan mengidentifikasi kondisi e-commerce

Seni Tari di Era Virtual dan Augmented Reality

2024-02-26T05:42:45+00:00

The art of dance has been revolutionized in the era of virtual and augmented reality (VR and AR), bringing about significant transformations in presentation, interaction and audience participation. This article explores the impact of these technologies on the art of dance, investigating how VR and AR are changing artists' creativity, enriching the audience experience, and enabling the development of new dance styles. In this context, dance is not only a witness to change, but also the main actor in combining traditional beauty with modern innovation. The method applied is "Library Research" which is sourced from national and international journals through platforms such as scholar.google.com, www.elsevier.com, and www.crossref.org. As a result, the art of dance in the era of virtual and augmented reality does not maintain its traditional richness, but also becomes an agent of innovation that combines the beauty of art with technological progress.