Journal of Machine and Computing最新文献_第4页

6G Traffic Prediction with a Novel Parallel Convolutional Neural Networks Architecture and Matrix Format Method Integration 利用新型并行卷积神经网络架构和矩阵格式整合方法进行 6G 流量预测

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404006

R. P. M. Bolivar, Senthil Kumar N K, Vishnu Priya V, Amarendra K, Rajendiran M, Edith Giovanna Cano Mamani

{"title":"6G Traffic Prediction with a Novel Parallel Convolutional Neural Networks Architecture and Matrix Format Method Integration","authors":"R. P. M. Bolivar, Senthil Kumar N K, Vishnu Priya V, Amarendra K, Rajendiran M, Edith Giovanna Cano Mamani","doi":"10.53759/7669/jmc202404006","DOIUrl":"https://doi.org/10.53759/7669/jmc202404006","url":null,"abstract":"In the evolving world of wireless communication, sixth generation (6G) networks represent a significant leap forward. Beyond its high-speed and reliable communication, 6G integrates Artificial Intelligence (AI), making networks intelligent entities. This elevates the infrastructure of smart cities and other ecosystems. A critical factor in 6G's success is real-time traffic analysis. As 6G aims to interconnect billions of devices, it faces unprecedented traffic patterns. Practical traffic analysis ensures optimal performance, resource distribution, and energy efficiency. It also supports the network in handling vital sectors like healthcare and transportation by anticipating congestion and prioritizing crucial data. However, traditional traffic analysis techniques designed for earlier generations cannot accommodate 6G's demands. With 6G's integration of diverse technologies, understanding traffic becomes more challenging. Recent advancements have incorporated deep learning architectures, notably Convolutional Neural Networks (CNNs), for traffic analysis. While these models show potential, adapting them to 6G's specifics remains challenging. This research presents a unique parallel CNN architecture for 6G traffic prediction. It converts network data into an image using the Matrix Format Method (MFM), making it suitable for CNN processing. This innovation addresses the limitations of traditional methods and meets 6G's requirements. Compared to other models, our parallel CNN architecture highlights enhanced performance, promising increased traffic prediction accuracy. It also paves the way for improved resource allocation, energy management, and quality of service in 6G environments.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449474","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

Multilayer Seasonal Autoregressive Integrated Moving Average Models for Complex Network Traffic Analysis 用于复杂网络流量分析的多层季节自回归综合移动平均模型

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404023

Prathipa Ravanappan, Maragatharajan M, Rashika Tiwari, Srihari T, Lavanya K

{"title":"Multilayer Seasonal Autoregressive Integrated Moving Average Models for Complex Network Traffic Analysis","authors":"Prathipa Ravanappan, Maragatharajan M, Rashika Tiwari, Srihari T, Lavanya K","doi":"10.53759/7669/jmc202404023","DOIUrl":"https://doi.org/10.53759/7669/jmc202404023","url":null,"abstract":"The ever-increasing amount of network traffic generated by various devices and applications has made it crucial to have efficient methods for analyzing and managing network traffic. Traditional approaches, such as statistical modeling, have yet to be proven enough due to network traffic's complex nature and dynamic characteristics. Recent research has shown the effectiveness of complex network analysis techniques for understanding network traffic patterns. This paper proposes multilayer seasonal autoregressive integrated moving average models for analyzing and predicting network traffic. This approach considers the seasonal patterns and interdependencies between different layers of network traffic, allowing for a more accurate and comprehensive representation of the data. The Multilayer Seasonal Autoregressive Integrated Moving Average (MSARIMA) model consists of multiple layers, each representing a different aspect of network traffic, such as time of day, day of week, or type of traffic. Each layer is modeled separately using SARIMA, a popular time series forecasting technique. The models for different layers are combined to capture the overall behavior of network traffic. The proposed approach has several benefits over traditional statistical approaches. It can capture network traffic's complex and dynamic nature, including short-term and long-term seasonal patterns. It also allows for the detection of anomalies and the prediction of future traffic patterns with high accuracy.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"69 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450195","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

Intrusion Detection in Internet of Things Systems: A Feature Extraction with Naive Bayes Classifier Approach 物联网系统中的入侵检测：使用 Naive Bayes 分类器的特征提取方法

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404003

Juan Carlos Juarez Vargas, Hayder M. A. Ghanimi, Sivaprakash S, Amarendra K, Rajendiran M, Sheylla L Cotrado Lupo

{"title":"Intrusion Detection in Internet of Things Systems: A Feature Extraction with Naive Bayes Classifier Approach","authors":"Juan Carlos Juarez Vargas, Hayder M. A. Ghanimi, Sivaprakash S, Amarendra K, Rajendiran M, Sheylla L Cotrado Lupo","doi":"10.53759/7669/jmc202404003","DOIUrl":"https://doi.org/10.53759/7669/jmc202404003","url":null,"abstract":"The Internet of Things (IoT) has proliferated, transitioning from modest home automation to encompass sectors like healthcare, agriculture, transportation, and manufacturing. This evolution is characterized by devices' ability to autonomously gather, disseminate, and analyze data, leading to improved real-time decision-making, predictive insights, and customized user experiences. The ubiquity of IoT, while promising, introduces significant data security concerns. The vast number of interlinked devices and diverse and often insufficient security features make them vulnerable to cyber threats, emphasizing the need for robust security mechanisms. Intrusion Detection Systems (IDS) have traditionally acted as vital guards against such threats; however, with the ever-increasing data in the IoT, traditional IDS models, such as Naive Bayes, face processing speed and accuracy challenges. This paper introduces a novel model, \"FE+NB,\" which merges advanced Feature Extraction (FE) with the Naive Bayes (NB) classifier. Central to this model is the \"Temporal-Structural Synthesis\" technique tailored for IoT traffic data, focusing on data compression, temporal and structural analyses, and Feature Selection (FS) using mutual information. Consequently, the model enhances efficiency and accuracy in Intrusion Detection (ID) in complex IoT networks.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"39 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449794","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

Analyzing the Effectiveness of Ensemble Based Analysis in Wireless Sensor Networks 分析无线传感器网络中基于集合的分析的有效性

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404019

Seng-Phil Hong

{"title":"Analyzing the Effectiveness of Ensemble Based Analysis in Wireless Sensor Networks","authors":"Seng-Phil Hong","doi":"10.53759/7669/jmc202404019","DOIUrl":"https://doi.org/10.53759/7669/jmc202404019","url":null,"abstract":"The usefulness of ensemble-based total time series analysis in Wi-Fi sensor networks is examined in this paper. A device to uses an ensemble approach combines multiple strategies to enhance overall predictive performance. This research assesses various tactics using unique metrics, such as robustness and accuracy. It contrasts the effectiveness of traditional time series methods with ensemble-based total fashions. An experimental approach focusing mostly on exceptional Wi-Fi sensor network scenarios is employed to evaluate the overall effectiveness of the suggested methods. Additionally, this study looks into how changes to community features like energy delivery, conversation range, and node density affect how effective the suggested methods are. The study's findings maintain the capacity to create effective Wi-Fi sensor networks with improved predicted overall performance. The usefulness of ensemble-based time collecting and analysis techniques for wireless sensor networks is investigated in this research. This study primarily looks upon function extraction and seasonality discounting of time series records in WSNs. In this analysis, seasonality is discounted using an ensemble median filter, and feature extraction is accomplished by primary component assessment. To assess the performance of the suggested ensemble technique on every simulated and real-world international WSN fact, multiple experiments are carried out. The findings suggest that the ensemble approach can improve the exceptional time-gathering records within WSNs and reduce seasonality. Furthermore, when compared to single-sensor strategies, the ensemble technique further improves the accuracy of the function extraction system. This work demonstrates the applicability of the ensemble approach for the investigation of time collection data in WSNs","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449919","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

Artificial Intelligence Powered Congestion Free Transportation System Through Extensive Simulations 通过大量模拟实现人工智能驱动的无拥堵交通系统

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404024

Cuddapah Anitha, Shweta Sharma, V. K. Nassa, Sachine Kumar Agrawal, Rajasekaran A, M. R.

{"title":"Artificial Intelligence Powered Congestion Free Transportation System Through Extensive Simulations","authors":"Cuddapah Anitha, Shweta Sharma, V. K. Nassa, Sachine Kumar Agrawal, Rajasekaran A, M. R.","doi":"10.53759/7669/jmc202404024","DOIUrl":"https://doi.org/10.53759/7669/jmc202404024","url":null,"abstract":"Intelligent traffic monitoring is a prominent topic of investigation due the emergence of advancements like the Internet interconnected Things and intelligent computers. Combining these technologies will make it easier to methods to aid in making better choices and accelerating urban growth. Intelligent sensing has come to the forefront in recent years due to its capacity to make calculated decisions on its own to address difficult issues. Automatic vehicles and smart gadgets are equipped with sensors that are part of an IoT-based system in order to recognize, gather, and transmit data. Artificial intelligence (AI)-based techniques allow machines to acquire knowledge and keep tabs on their surroundings through continuous sensing. Improvements in variable traffic control strategies for overcrowded cities have numerous positive outcomes, one of which is increased road safety. Since the sensors on which conventional dynamic controllers relied had their own shortcomings, we might use vision sensors (like cameras) to avoid these issues. Image and video-based computing has a lot of potential for measuring traffic volumes. A new traffic management system named Enhanced Transportation Technologies (ETT) is implemented to relieve congestion at the busy intersection after the old one was deemed to be inadequate. The term \"intelligent transportation system\" (ITS) refers to a group of transportation systems to keep drivers and passengers safe on the road and to facilitate autonomous mobility by optimizing control systems. To further improve urban planning, crowd behavior, and traffic forecasting, dependable AI models have been developed to work in tandem with ITS. Compared to controllers using conventional sensors, the proposed model has been shown through extensive simulations to reduce waiting time and increase movement speed on average.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450078","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

DDOS Attack Packet Detection and Prevention On a Large-Scale Network Utilising the Bi-Directional Long Short Term Memory Network 利用双向长短期记忆网络的大规模网络 DDOS 攻击数据包检测与防范

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404011

Jeevan Pradeep K, Prashanthkumar Shukla

{"title":"DDOS Attack Packet Detection and Prevention On a Large-Scale Network Utilising the Bi-Directional Long Short Term Memory Network","authors":"Jeevan Pradeep K, Prashanthkumar Shukla","doi":"10.53759/7669/jmc202404011","DOIUrl":"https://doi.org/10.53759/7669/jmc202404011","url":null,"abstract":"Security is one of the most challenging conditions for dispersed networks because exclusive threats can damage output overall and can be classified in several ways. At this time, distributed denial-of-service (DDoS) assaults pose the greatest threat to internet security. Rapid identification of communication records for messages referencing DDoS occurrences enables organizations to take preventative action by instantly identifying both positive and negative attitudes in cyberspace. This research suggests a method for locating such assaults. The method includes the use of deep learning models that had been trained on the present dataset using Bi Long Short-Term Memory (Bi LSTM). Our model beats more established machine learning techniques, according to the experimental data.The method includes the use of deep learning models that had been trained on the present dataset using Bi Long Short-Term Memory (Bi LSTM). Our model beats more established machine learning techniques, according to the experimental data. Experimental results showed that the proposed technique could achieve an accuracy of 96.7%, making it the best option for use in the detection of breaches applications.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"81 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450283","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 Performance Comparison of Neural Networks and Fuzzy Systems for Time Series Forecasting 神经网络和模糊系统在时间序列预测中的性能比较

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404010

Jeong Hee Woong

{"title":"A Performance Comparison of Neural Networks and Fuzzy Systems for Time Series Forecasting","authors":"Jeong Hee Woong","doi":"10.53759/7669/jmc202404010","DOIUrl":"https://doi.org/10.53759/7669/jmc202404010","url":null,"abstract":"Artificial neural networks and fuzzy structures have gained significant popularity in the last decade for time series forecasting. The objective is to conduct a performance comparison of various strategies to determine which ones are more effective for time series forecasting. The dataset provides instruction and evaluates forecasting models, utilizing artificial neural networks and fuzzy architectures. The observation evaluates the overall effectiveness of the forecasting models and the use of the root mean square error and means absolute error measures. This comparison analysis provides initial insights into the efficacy of artificial neural networks and fuzzy structures for predicting time series data. In predicting time series data, this study examines the precision of two renowned artificial intelligence systems, Neural Networks and Fuzzy structures. To evaluate the two algorithms, two distinct types of time series were utilized: a synthetic dataset consisting of 150 variables and a real-world dataset including 129 data points about monetary forecasts. The models' forecasting accuracy, training duration, and generalization abilities were compared. The findings validated that neural network surpassed fuzzy structures in all performance metrics when handling synthetic data. This research emphasizes the capabilities of artificial neural networks and fuzzy structures in addressing complicated forecasting problems. It demonstrates that both techniques may be utilized for predicting future time series values.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"5 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449873","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

Designing a Smart Agri-Crop Framework on Cotton Production using ABO Optimized Vision Transformer Model 利用 ABO 优化视觉变压器模型设计棉花生产智能农作物框架

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404022

Bhavani R, B. R, Sona K, Rajakumar B, Saraswathi S, Arunkumar P M

{"title":"Designing a Smart Agri-Crop Framework on Cotton Production using ABO Optimized Vision Transformer Model","authors":"Bhavani R, B. R, Sona K, Rajakumar B, Saraswathi S, Arunkumar P M","doi":"10.53759/7669/jmc202404022","DOIUrl":"https://doi.org/10.53759/7669/jmc202404022","url":null,"abstract":"Due to its widespread cultivation and large yields by most farmers, cotton is another vital cash crop. However, a number of illnesses lower the quantity and quality of cotton harvests, which causes a large loss in output. Early diagnosis detection of these illnesses is essential. This study employs a thorough methodology to solve the crucial job of cotton leaf disease identification by utilising the \"Cotton-Leaf-Infection\" dataset. Preprocessing is the first step, in which noise is removed from the dataset using a Prewitt filter, which improves the signal-to-noise ratio. Next, a state-of-the-art process for image classification errands called Vision Transformer (ViT) model is used to carry out the disease categorization. Additionally, the study presents the African Buffalo Optimisation (ABO) method, which optimises weight during the classification procedure. The African buffalo's cooperative behaviour served as the model's inspiration for the ABO algorithm, which is remarkably effective at optimising the model's parameters. By integrating ABO, the problems caused by the dynamic character of real-world agricultural datasets are addressed and improved model resilience and generalisation are facilitated. The suggested ViT-based categorization model shows remarkable effectiveness, with a remarkable 99.3% accuracy rate. This performance is higher than current models.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"29 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449898","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

Leveraging the Application of IoT based Deep Learning Prediction Model in Smart Healthcare 基于物联网的深度学习预测模型在智能医疗中的应用

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404009

Tai hoon Kim

{"title":"Leveraging the Application of IoT based Deep Learning Prediction Model in Smart Healthcare","authors":"Tai hoon Kim","doi":"10.53759/7669/jmc202404009","DOIUrl":"https://doi.org/10.53759/7669/jmc202404009","url":null,"abstract":"The standard IoT sensors and tools are to learn data construction techniques for creating a predictive model.The use of time series evaluation tools to identify thyroid tumors in their early stages is examined in this research. The records of thyroid ultrasound scans from 475 individuals are examined. The analysis is utilized to evaluate the predictor model's accuracy and the Time Series evaluation methodologies' suitability for correctly identifying thyroid cancer in its early stages. The results demonstrate the effectiveness of time-collection analytic techniques in the early detection of thyroid cancer. The results also highlight the potential for utilizing time series analytic techniques in various cancer-related early detection initiatives. The majority of thyroid tumors were found at an early stage using time series analysis, a finding that is the focus of this technical report. The program developed the ability to distinguish between benign and malignant tumors. The results of the observation demonstrated that the set of guidelines was effective in increasing the precision degree measurement using various wearable IoT Sensors. Additionally, the set of guidelines can identify the presence of a tumor before any scientific symptoms are apparent. The observer concluded that time-collecting analysis might be utilized to identify early cancer symptoms, which would undoubtedly lead to improved outcomes and more advanced treatments.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"56 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449926","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

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging 利用优化的长短期记忆对电动汽车电池充电进行时序预测

Journal of Machine and Computing Pub Date : 2024-01-05 DOI: 10.53759/7669/jmc202404002

Alfredo Tumi Figueroa Figueroa, Hayder M. A. Ghanimi, Senthil Raja M, Shamia D, Samrat Ray, Jorge Ramos Surco

{"title":"Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging","authors":"Alfredo Tumi Figueroa Figueroa, Hayder M. A. Ghanimi, Senthil Raja M, Shamia D, Samrat Ray, Jorge Ramos Surco","doi":"10.53759/7669/jmc202404002","DOIUrl":"https://doi.org/10.53759/7669/jmc202404002","url":null,"abstract":"The last decade has seen a significant rise in the adoption and development of Electric Vehicles (EVs), driven by environmental concerns, technological advancements, and governmental support. Batteries, central to EVs, have witnessed groundbreaking innovations in terms of energy density, charging speeds, and longevity. Expanding charging infrastructure and the automotive industry's investment in EV research have made them more mainstream. Effective Battery Management (BM), which includes monitoring essential parameters and thermal management, is critical for the longevity and reliability of EVs. Accurate charge prediction, in particular, aids in trip planning, reduces range anxiety and facilitates cost-effective charging coordinated with dynamic electricity pricing. Traditional models like linear regression and Autor-Rgressive Integrated Moving Average (ARIMA) have been standard for EV battery charge prediction. However, these often struggle with the dynamic nature of EV charging data. Even models like the vanilla Long Short-Term Memory (LSTM), which are adept at recognizing long-term patterns, require meticulous hyperparameter tuning. This work introduces the DWT-DE-LSTM model, which utilizes the Discrete Wavelet Transform (DWT) to dissect battery charging data at different resolutions and a Differential Evolution (DE) strategy for model optimization. Tests using the Panasonic 18650PF Li-ion Battery Dataset revealed the superior efficacy of the DWT-DE-LSTM model, emphasizing its suitability for real-world battery charge prediction.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449853","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