Journal of Machine and Computing最新文献_第5页

Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation 探索用于低能耗数据聚合的长短期记忆算法

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404008

Gi Hwan Oh

{"title":"Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation","authors":"Gi Hwan Oh","doi":"10.53759/7669/jmc202404008","DOIUrl":"https://doi.org/10.53759/7669/jmc202404008","url":null,"abstract":"Long short-term memory methods are employed for data consolidation in intricate low-energy devices. It has enabled accurate and efficient aggregation of statistics in limited electricity settings, facilitating the review and retrieval of data while minimizing electricity wastage. The LSTM rules analyze, organize, and consolidate vast datasets inside weakly connected structures. It has employed a recurrent neural network to handle data processing, particularly nonlinear interactions. The machine's capabilities are subsequently examined and stored utilizing memory blocks. Memory blocks retain extended temporal connections within the data, facilitating adaptive and precise information aggregation. These blocks facilitate the system's ability to shop and utilize relevant capabilities for quick retrieval. The proposed algorithm offers realistic tuning capabilities such as learning rate scheduling and total regularization based on dropout like green information aggregation. These enable systems to reduce over fitting while permitting precise adjustment of the settings. It allows for optimizing the algorithm to provide highly dependable performance within weak structures, enhancing data aggregation techniques' energy efficiency. Standard algorithms provide an efficient, accurate solution for aggregating information in low-power systems. It facilitates evaluating, retrieving, and aggregating accurate and reliable information using memory blocks, adaptive tuning, and efficient learning rate scheduling.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"53 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research On the Innovation of Host Ability Integrating Natural Language Processing Technology 融合自然语言处理技术的主持人能力创新研究

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404017

He Sun, Xi You

引用次数: 0

Applying Machine Learning models to Diagnosing Migraines with EEG Diverse Algorithms 将机器学习模型应用于利用脑电图多样化算法诊断偏头痛

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404016

Hye Kyeong Ko

{"title":"Applying Machine Learning models to Diagnosing Migraines with EEG Diverse Algorithms","authors":"Hye Kyeong Ko","doi":"10.53759/7669/jmc202404016","DOIUrl":"https://doi.org/10.53759/7669/jmc202404016","url":null,"abstract":"This study investigates how well time collection analysis may be used by system-studying algorithms to diagnose migraines. Through the use of various algorithms and current statistical resources, such as EEG activity and affected person histories, the mission will develop a predictive model to identify the start of migraine signs and symptoms, allowing for prompt and early management for sufferers. The results will help to compare how the algorithms affect migraine accuracy predictions and how well they forecast migraine presence early enough for preventative interventions. Furthermore, studies may be conducted to examine the model's ability to be employed in real-time patient monitoring and to identify actionable inputs from the algorithms. This work presents novel machine learning algorithms software for time series analysis of functions such as temperature, heart rate, and EEG indications, which can be used to identify migraines. The paper delves into the idea of utilizing machine learning algorithms to identify migraine styles, examines the pre-processing procedures to accurately arrange the indications, and provides the results of a study conducted to evaluate the efficacy of the solution. The observation's results show that the suggested diagnostic framework is capable of accurately identifying and categorizing migraines, enabling medical professionals to recognize the warning indications of migraine and predict when an attack would begin. The examination demonstrates the possibility of devices learning algorithms to correctly and accurately diagnose migraines, but more research is necessary to obtain more detailed information about this situation.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"49 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Analysis of Hierarchical Clustering Techniques for Recommendation Systems using Integrated Deep Learning 利用集成深度学习对用于推荐系统的分层聚类技术进行增强分析

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404007

Young Jun Park

引用次数: 0

A Comparative Analysis of IoT based Network Anomaly Detection and Prediction Using Vector Autoregressive Models 使用矢量自回归模型对基于物联网的网络异常现象检测和预测进行比较分析

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404013

Ok-Hue Cho

{"title":"A Comparative Analysis of IoT based Network Anomaly Detection and Prediction Using Vector Autoregressive Models","authors":"Ok-Hue Cho","doi":"10.53759/7669/jmc202404013","DOIUrl":"https://doi.org/10.53759/7669/jmc202404013","url":null,"abstract":"This research provides a comparative analysis of the use of Vector Autoregressive models for network anomaly detection and prediction. It starts by giving a brief overview of the models and going over the two versions that are available for network anomaly detection. Ultimately, the study offers an empirical assessment of the two types of models, just considering how well they detect and forecast anomalies overall. The results show that the unmarried-node anomaly detection performance of the model is superior. Simultaneously, the Adaptive Learning version is particularly effective in identifying anomalies among a few nodes. The fundamental reasons for the differences in the two fashions' overall performance are also examined in this research. This work provides a comparative analysis of two widely utilized algorithmic approaches: vector autoregressive models and community anomaly detection and prediction. Each method's effectiveness is assessed using two different network datasets: one based on real-world global measurements of latency and mobility ranges, and the other focused on a fictional community. The study also examines the trade-offs between employing the versus other modern and classic techniques, Markov Chain Monte Carlo, and Artificial Neural Networks for network anomaly detection. Finally, it provides an overview of the advantages and disadvantages of each technique as well as suggestions for improving performance.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"78 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Pattern Recognition and Machine Learning 模式识别与机器学习回顾

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404020

T. Adugna, A. Ramu, A. Haldorai

引用次数: 0

Enhancing Renewable Energy Storage Conversion Efficiency using ERFE with FFNN 利用 ERFE 和 FFNN 提高可再生能源存储转换效率

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404005

Elqui Yeye Pari Condori, Ganga Rama Koteswara Rao, Rasheed Abdulkader, Kiran Kumar V, Josephine Pon Gloria Jeyaraj, Estela Quispe Ramos

{"title":"Enhancing Renewable Energy Storage Conversion Efficiency using ERFE with FFNN","authors":"Elqui Yeye Pari Condori, Ganga Rama Koteswara Rao, Rasheed Abdulkader, Kiran Kumar V, Josephine Pon Gloria Jeyaraj, Estela Quispe Ramos","doi":"10.53759/7669/jmc202404005","DOIUrl":"https://doi.org/10.53759/7669/jmc202404005","url":null,"abstract":"The 21st century witnesses a pivotal global shift towards Renewable Energy Sources (RES) to combat climate change. Nations are adopting wind, solar, hydro, and other sustainable energy forms. However, a primary concern is the inconsistent nature of these sources. Daily fluctuations, seasonal changes, and weather conditions sometimes make renewables like the sun and wind unreliable. The key to managing this unpredictability is efficient Energy Storage Systems (ESS), ensuring energy is saved during peak periods and used during low production times. However, existing ESSs are not flawless. Energy conversion and storage inefficiencies emerge due to temperature changes, inconsistent charge rates, and voltage fluctuations. These challenges diminish the quality of stored energy, resulting in potential waste. There is a unique chance to address these inefficiencies using the vast data from renewable systems. This research explores Machine Learning (ML), particularly Neural Networks (NN), to improve REES efficiencies. Analyzing data from Palm Springs wind farms, the study employs an Entropy-Based Recursive Feature Elimination (ERFE) coupled with Feed-Forward Neural Networks (FFNN). ERFE utilizes entropy to prioritize essential features, reducing redundant data and computational demands. The tailored FFNN then predicts energy conversion rates, aiming to enhance energy storage conversion and maximize the usability of generated Renewable Energy (RE).","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"33 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trust Aware Nero Fuzzy Based Agglomerative Hierarchical Clustering with Secure Whale Optimization Routing for Enhancing Energy Efficiency in WSN 基于信任意识的 Nero Fuzzy 聚合分层聚类与安全鲸优化路由，提高 WSN 的能效

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404014

Sasikumar M S S, N. A. E

{"title":"Trust Aware Nero Fuzzy Based Agglomerative Hierarchical Clustering with Secure Whale Optimization Routing for Enhancing Energy Efficiency in WSN","authors":"Sasikumar M S S, N. A. E","doi":"10.53759/7669/jmc202404014","DOIUrl":"https://doi.org/10.53759/7669/jmc202404014","url":null,"abstract":"Wireless sensor networks (WSNs) comprise a network of dispersed, carefully positioned sensor nodes in their deployment environment to monitor and collect data on natural phenomena. These sensor nodes collaborate to transmit data via multi-hop communication, ultimately reaching a central base station for processing. However, WSNs face significant challenges due to the resource-constrained nature of these devices and the harsh, open environments in which they operate. Addressing energy optimization and ensuring secure communication are primary concerns in the successful operation of WSNs. This paper introduces anovelTrust aware Neuro Fuzzy Clustering head selection (TNFCH) and agglomerative hierarchical clustering approach (AHC) with Secure Whale Optimization (SWO) Algorithm Routing to enhance energy-efficient transmission in WSNs. Our proposed protocol (TNFCH-AHWO) efficiently organizes nodes by utilizing neural network and Fuzzy logic then securely transfers the data into the communication network. We employ a Trust calculation algorithm in our system to ensure Trust and data integrity, facilitating efficient lightweight operations such as key generation, encryption, decryption, and verification. This ensures hop-to-hop authentication among the nodes in WSNs. To assess the performance of our proposed protocol, we conducted simulations using the NS3 simulator. The findings of the simulation show that the suggested protocol greatly enhances various performance metrics, including energy consumption analysis, throughput, network delay, network lifetime, and packet delivery ratio when compared to existing protocols.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"42 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Approach to Anomaly Detection in Internet of Things Using Autoencoders and Dynamic Thresholds 使用自动编码器和动态阈值的物联网异常检测自适应方法

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404001

Nayer Tumi Figueroa E, Vishnu Priya A, S. Shanmugam, Kiran Kumar V, Sudhakar Sengan, Alexandra Melgarejo Bolivar C

{"title":"Adaptive Approach to Anomaly Detection in Internet of Things Using Autoencoders and Dynamic Thresholds","authors":"Nayer Tumi Figueroa E, Vishnu Priya A, S. Shanmugam, Kiran Kumar V, Sudhakar Sengan, Alexandra Melgarejo Bolivar C","doi":"10.53759/7669/jmc202404001","DOIUrl":"https://doi.org/10.53759/7669/jmc202404001","url":null,"abstract":"The Internet of Things (IoT) represents a vast network of interconnected devices, from simple sensors to intricate machines, which collect and share data across sectors like healthcare, agriculture, and home automation. This interconnectivity has brought convenience and efficiency but also introduced significant security concerns. Many IoT devices, built for specific functions, may lack robust security, making them vulnerable to cyberattacks, especially during device-to-device communications. Traditional security approaches often fall short in the vast and varied IoT landscape, underscoring the need for advanced Anomaly Detection (AD), which identifies unusual data patterns to warn against potential threats. Recently, a range of methods, from statistical to Deep Learning (DL), have been employed for AD. However, they face challenges in the unique IoT environment due to the massive volume of data, its evolving nature, and the limitations of some IoT devices. Addressing these challenges, the proposed research recommends using autoencoders with a dynamic threshold mechanism. This adaptive method continuously recalibrates, ensuring relevant and precise AD. Through extensive testing and comparisons, the study seeks to demonstrate the efficiency and adaptability of this approach in ensuring secure IoT communications.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"7 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harnessing K-means Clustering to Decode Communication Patterns in Modern Electronic Devices 利用 K 均值聚类解码现代电子设备中的通信模式

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404004

Leonid Alemán Gonzales, Kalaivani S, Saranya S S, Anto Bennet M, Srinivasarao B, Alhi Jordan Herrera Osorio

{"title":"Harnessing K-means Clustering to Decode Communication Patterns in Modern Electronic Devices","authors":"Leonid Alemán Gonzales, Kalaivani S, Saranya S S, Anto Bennet M, Srinivasarao B, Alhi Jordan Herrera Osorio","doi":"10.53759/7669/jmc202404004","DOIUrl":"https://doi.org/10.53759/7669/jmc202404004","url":null,"abstract":"From smart home devices to wearable devices, electronics have become an indispensable part of modern life. Vast volumes of data have been collected by these electronic devices, revealing precise information about device communications, user behaviours, and more. Improvements to device features, insights into the user experience, and the detection of security risks are just some of the many uses for this information. However, advanced analytical methods are required to make sense of this plethora of data successfully. The K-means clustering algorithm is used in the present research to analyse the data sent and received by different types of electronics. The first step of the research is collecting data, intending to create a representative sample of people using various devices and communication methods. After collecting data, preprocessing is necessary to ensure it can be analysed successfully. In the next step, the K-means algorithm classifies the information into subsets that stand for distinct modes of interaction. The primary objective of the research is to gain an improved understanding of these groups by demonstrating how users communicate, device communication, and possibilities for enhancing functionality and security.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"1 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0