Journal of Machine and Computing最新文献

Improving Autonomous Underwater Vehicle Navigation: Hybrid Swarm Intelligence for Dynamic Marine Environment Path-finding 改进自主潜水器导航：动态海洋环境路径搜索的混合群集智能

Journal of Machine and Computing Pub Date : 2024-07-05 DOI: 10.53759/7669/jmc202404061

Husam Alowaidi, Hemalatha P, Poongothai K, Sundoss ALmahadeen, Prasath R, Amarendra K

{"title":"Improving Autonomous Underwater Vehicle Navigation: Hybrid Swarm Intelligence for Dynamic Marine Environment Path-finding","authors":"Husam Alowaidi, Hemalatha P, Poongothai K, Sundoss ALmahadeen, Prasath R, Amarendra K","doi":"10.53759/7669/jmc202404061","DOIUrl":"https://doi.org/10.53759/7669/jmc202404061","url":null,"abstract":"Underwater research and monitoring operations rely significantly on Autonomous Underwater Vehicles (AUVs) for scientific investigations, resource management, and monitoring, and underwater infrastructure is provided maintenance levels amid other applications. Efficient navigation and preventative methods are only a couple of the numerous challenges that Path-Finding (PF) in rapidly changing and sophisticated Underwater Environments (UE) requires overcoming. Dynamic environments and real-time improvements are problems for traditional models. In order to provide superior solutions for navigating uncertain UE, this work suggests a hybrid optimization technique that combines Ant Colony Optimization (ACO) for local path selection with Particle Swarm Optimization (PSO) for global path scheduling. Runtime efficiency, accuracy, and distance focused on decrease are three metrics that demonstrate how the PSO-ACO hybrid method outperforms conventional algorithms, proving its significance for improving AUV navigation. The improvement of AUV functions in fields such as underwater research, along with others, is supported by the current research, which further assists with the invention of Autonomous Underwater Navigation Systems (AUNS). The PSO+ACO hybrid method is superior to the PSO, ACO, and GA algorithms in pathfinding with a 6.43-second execution time and 93.5% accuracy—the ACO model completed in 12.53 seconds, superior to the proposed system.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675433","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

Automated Manufacturing Robot Fault Diagnosis in Real Time Using Convolutional Neural Networks 使用卷积神经网络实时自动诊断制造机器人故障

Journal of Machine and Computing Pub Date : 2024-07-05 DOI: 10.53759/7669/jmc202404053

Hussein Ali Mahdi, Akilandeswari K, Mayura Shelke, Sureshkumar Chandrasekaran, Vijaya Bhaskar Sadu, Sudha Rani U

{"title":"Automated Manufacturing Robot Fault Diagnosis in Real Time Using Convolutional Neural Networks","authors":"Hussein Ali Mahdi, Akilandeswari K, Mayura Shelke, Sureshkumar Chandrasekaran, Vijaya Bhaskar Sadu, Sudha Rani U","doi":"10.53759/7669/jmc202404053","DOIUrl":"https://doi.org/10.53759/7669/jmc202404053","url":null,"abstract":"This study introduced a novel real-time Fault Diagnosis Model (FDM) in manufacturing robots by integrating Depthwise Convolutional Neural Networks (CNNs) with Bidirectional Long Short-Term Memory (BiLSTM) networks. The objective is to design a model that can handle the complex high-dimensional sensor data that arrives out of complex, non-linear systems for effective FDM. The work introduced a Feature Extraction (FE) model based on Monte Carlo Filtering (MCF). The work integrates a Depthwise CNN with BiLSTM (DC-BiLSTM) for diagnosis. The integration helps to reduce the computational need and, at the same time, preserve the feature representation. The model was experimented against other models, such as CNN, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Feed-Forward Neural Networks (FFNN), using a fault dataset sourced from a simulated environment. The results have shown that the proposed model fared well in terms of accuracy, precision, recall, and F1 score against all compared models. The results have judged the proposed model’s applicability in the field of fault diagnosis, which could effectively predict mishaps in advance, thereby helping with efficient maintenance and ensuring continuous productivity.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675514","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

Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes 在可持续制造工艺中优化选择可生物降解聚合物的遗传算法

Journal of Machine and Computing Pub Date : 2024-07-05 DOI: 10.53759/7669/jmc202404054

Shaymaa Hussein Nowfal, Vijaya Bhaskar Sadu, Sudhakar Sengab, Rajeshkumar G, Anjaneyulu Naik R, Sreekanth K

{"title":"Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes","authors":"Shaymaa Hussein Nowfal, Vijaya Bhaskar Sadu, Sudhakar Sengab, Rajeshkumar G, Anjaneyulu Naik R, Sreekanth K","doi":"10.53759/7669/jmc202404054","DOIUrl":"https://doi.org/10.53759/7669/jmc202404054","url":null,"abstract":"Sustainable Manufacturing Practices (SMP), particularly in the selection of materials, have become essential due to environmental issues caused by the expansion of industry. Compared to conventional polymers, biodegradable Polymer Materials (BPM) are growing more commonly as an approach to reducing trash pollution. Suitable materials can be challenging due to numerous considerations, like ecological impact, expenditure, and material properties. When addressing sophisticated trade-offs, standard approaches drop. To compete with such challenges, employing Genetic Algorithms (GA) may be more successful, as they have their foundation in the basic concepts of biological development and the natural selection process. With a focus on BPM, this study provides a GA model for optimal packaging substance selection. Out of the four algorithms for computation used for practical testing—PSO, ACO, and SA—the GA model is the most effective. The findings demonstrate that GA can be used to enhance SMP and performs well in enormous search spaces that contain numerous different combinations of materials.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677049","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

Clustering Uniformity Methods for Energy Efficiency in Wireless Sensor Networks 提高无线传感器网络能效的聚类均匀性方法

Journal of Machine and Computing Pub Date : 2024-07-05 DOI: 10.53759/7669/jmc202404070

Joong-Ho Lee

{"title":"Clustering Uniformity Methods for Energy Efficiency in Wireless Sensor Networks","authors":"Joong-Ho Lee","doi":"10.53759/7669/jmc202404070","DOIUrl":"https://doi.org/10.53759/7669/jmc202404070","url":null,"abstract":"The wireless sensors that make up a wireless sensor network (WSN) are randomly deployed in nature and cannot be artificially replaced when their batteries are depleted. Failure of communication connection between wireless sensors causes continuous connection attempts, which results in a lot of power dissipation and shortens the lifetime of the WSN. In this paper, we propose to extend the lifetime of WSNs by limiting the appropriate distance between the cluster head (CH) node and the communicating sensor nodes (SNs) so that a group of clusters of appropriate size can be formed on a two-dimensional plane. To equalize cluster size, sensor nodes with the shortest distance communicate with each other to form member nodes, and nodes with closer distances are bring together to form clusters. The simulation results show the improvement rate of cluster uniformity over the shortest distance-based clustering method for clustering based on the proposed cluster uniformity algorithm. The proposed method can improve the cluster uniformity of the network by about 20%. In addition, the power consumption of the proposed method is analyzed according to the difference in the density of sensor nodes in the cluster groups to examine the improvement in power consumption.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675498","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

An Improved Image Enhancement Technique for Low Light Images Using Deep Learning Approach 利用深度学习方法改进弱光图像增强技术

Journal of Machine and Computing Pub Date : 2024-07-05 DOI: 10.53759/7669/jmc202404060

Rajesh Gopakumar, Karunakar A. Kotegar

{"title":"An Improved Image Enhancement Technique for Low Light Images Using Deep Learning Approach","authors":"Rajesh Gopakumar, Karunakar A. Kotegar","doi":"10.53759/7669/jmc202404060","DOIUrl":"https://doi.org/10.53759/7669/jmc202404060","url":null,"abstract":"Image enhancement in facial detection is a critical component of facial recognition systems. Face identification in an uncontrolled environment is affected by a multitude of difficulties such as poor light levels, low-resolution cameras, occlusions from surrounding objects, and tiny faces in distant photographs. Low signal-to-noise ratio, low brightness, and noise in low-light photographs lead to issues such as color distortion and poor visibility, which makes it challenging to identify faces. Many techniques to enhance low-light images have been developed, improving the face detection system’s accuracy. This will improve the picture at the expense of higher running costs and lower model robustness. The proposed technique, DCE-Net, uses performance-intensive deep learning and light-enhanced image properties. A non-referential deep learning technique was employed to acquire and modify the image attributes. A set of loss functions designed to perform without ground-truth images is the foundation of the deep network learning employed. Compared to the current referential methods, straightforward non-referential light estimation curve mapping minimizes the computational demand for low-light image improvement. Several experiments conducted on standard datasets demonstrated the efficacy and reduced computational requirements of the approach. The effectiveness of this method is supported by both the qualitative and quantitative outcomes. The PSNR and SSIM computation for paired images shows promising results using the proposed image enhancement technique.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673425","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

Design of a Model Using Machine Learning and Deep Dyna Q Learning Integration for Improved Disease Prediction in Remote Healthcare 利用机器学习和 Deep Dyna Q 学习集成设计模型，改进远程医疗中的疾病预测

Journal of Machine and Computing Pub Date : 2024-07-05 DOI: 10.53759/7669/jmc202404051

Gaikwad Rama Bhagwatrao, Ramanathan Lakshmanan

{"title":"Design of a Model Using Machine Learning and Deep Dyna Q Learning Integration for Improved Disease Prediction in Remote Healthcare","authors":"Gaikwad Rama Bhagwatrao, Ramanathan Lakshmanan","doi":"10.53759/7669/jmc202404051","DOIUrl":"https://doi.org/10.53759/7669/jmc202404051","url":null,"abstract":"In the domain of proactive healthcare management, the imperative for remote health monitoring has escalated, the remote health care in this scenario specially means, the patient is seating at the remote location that is not in the hospital setting, and doctor or healthcare worker is monitoring the health parameters gathered using biomedical sensors and passed through the network. Conventional methodologies, while partially effective, encounter challenges in predictive precision, responsiveness to evolving health dynamics, and managing the vast array of patient data. These limitations underscore the demand for a sophisticated, holistic solution catering to diverse use cases. This work introduces a pioneering framework amalgamating traditional machine learning (ML) models with the advanced capabilities of Deep Dyna Q Learning process to overcome existing constraints. This framework strategically utilizes ensemble of traditional algorithms which amalgamates the strengths of these diverse models. Central to this model is the integration of Deep Dyna Q Learning, empowering the system with real-time adaptability and dynamic decision-making process through reinforcement learning principles, thereby deriving insights from historical and simulated datasets to foster more nuanced, patient-centric decisions. The impact of this comprehensive approach is profound, evidenced by preliminary results showcasing significant enhancements in the efficiency of remote health monitoring systems. Notably, the model achieves increase in precision, accuracy and recall for disease prediction. These improvements signify a paradigm shift towards proactive and efficient healthcare interventions, especially in remote settings. The fusion of traditional ML techniques with Deep Dyna Q Learning emerges as a potent solution, heralding a revolution in remote health monitoring and establishing a new benchmark for proactive healthcare delivery scenarios.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675111","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

Advanced Explainable AI: Self Attention Deep Neural Network of Text Classification 高级可解释人工智能：文本分类的自关注深度神经网络

Journal of Machine and Computing Pub Date : 2024-07-05 DOI: 10.53759/7669/jmc202404056

Prasanna V, Umarani S, Suganthi B, Ranjani V, Manigandan Thangaraju, Uma Maheswari P

{"title":"Advanced Explainable AI: Self Attention Deep Neural Network of Text Classification","authors":"Prasanna V, Umarani S, Suganthi B, Ranjani V, Manigandan Thangaraju, Uma Maheswari P","doi":"10.53759/7669/jmc202404056","DOIUrl":"https://doi.org/10.53759/7669/jmc202404056","url":null,"abstract":"The classification of texts is a crucial component of the data retrieval mechanism. By utilizing semantic details representation, and the text vector sequence is condensed, resulting in a reduction in the temporal and spatial order of the memory pattern. This process helps to clarify the context of the text, extract crucial feature information, and fuse these features to determine the classification outcome. This approach represents the preprocessed text data using character-level vectors. The self-attention mechanism is used to understand the interdependence of words in a text, allowing for the extraction of internal structure-related data. Furthermore, the semantic characteristics of text data have been extracted independently using Deep Convolutional Neural Network (DCNN) and Bi-directional Gated Recurrent Unit (BiGRU) using a Soft-Attention mechanism. These two distinct feature extraction outcomes are then merged. The Softmax layer is employed to categorize the deep-extracted attributes, hence enhancing the accuracy of the classification model. This improvement is achieved by including a uniform distribution component into the cross-entropy loss function. Our results demonstrate that our suggested method for explainability outperforms the model that was suggested in terms of accuracy and computing efficiency. For the purpose of assessing the effectiveness of our suggested approach, we developed many baseline models and performed an evaluation their studies.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676728","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

Predicting Factory Equipment Lifespan Through Manufacturing Data Analysis using AI 利用人工智能通过制造数据分析预测工厂设备寿命

Journal of Machine and Computing Pub Date : 2024-07-05 DOI: 10.53759/7669/jmc202404066

Jae-Hak Lee, Young-Han Jeong, Jung Kyu Park

引用次数: 0

Energy Efficient AODV routing protocols for FANETs 用于 FANET 的高能效 AODV 路由协议

Journal of Machine and Computing Pub Date : 2024-07-05 DOI: 10.53759/7669/jmc202404065

M. K., Satish Kumar R, Rajesh Sharma R, Archana Sasi

{"title":"Energy Efficient AODV routing protocols for FANETs","authors":"M. K., Satish Kumar R, Rajesh Sharma R, Archana Sasi","doi":"10.53759/7669/jmc202404065","DOIUrl":"https://doi.org/10.53759/7669/jmc202404065","url":null,"abstract":"All system units in flying UAV like transmitter, receiver equipment, control unit, information processing unit and payloads are powered by in build power sources. UAVs equipped with the limited on-board energy capability restrict the flying time, which will significantly affect the performance of a FANETs. Optimizing the energy consumption among nodes is one of the important research challenges. Optimization techniques for energy usage can be implemented at different OSI layer level. In our study we focused on OSI networks layers solution for performance improvements based on energy efficient routing techniques in flying ad hoc network environments. Routing algorithm that is used in MANETs applications can be optimized and used in FANETS as in both networks majority of the operational characteristics similar. But Few characteristics in FANETs like distance coverage, node mobility velocity, capacity and types of power supply used differentiate from other mobiles networks (VANETs,MANETs). During first phase of our work we evaluated the performance of classical routing protocols AODV, DSR, DSDV in FANETs using energy metrics. Result in first phase revel that AODV performance is superior to other protocols in FANETs. In second phase we designed EEAODV protocols which use energy metrics in addition to hop count for path optimization. Finally details performance comparison study between EEAODV and AODV performed, the result shows that EEAODV performance is much better that AODV. For our performance evaluation we used NS-3 simulator and random waypoint mobility models.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674872","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

Experimental Investigation of Modified Level Shift PWM with Capacitor Voltage Balancing on Single Phase Modular Multilevel Converter 单相模块化多电平转换器上带电容器电压平衡的修正电平移动 PWM 的实验研究

Journal of Machine and Computing Pub Date : 2024-07-05 DOI: 10.53759/7669/jmc202404072

Balamurugan S, Bhavani M, Sudha R, Padmanaban K

{"title":"Experimental Investigation of Modified Level Shift PWM with Capacitor Voltage Balancing on Single Phase Modular Multilevel Converter","authors":"Balamurugan S, Bhavani M, Sudha R, Padmanaban K","doi":"10.53759/7669/jmc202404072","DOIUrl":"https://doi.org/10.53759/7669/jmc202404072","url":null,"abstract":"Modular Multilevel Converters (MMCs) are a prominent voltage source converter topology that is rapidly gaining popularity in medium/high power/voltage applications, including high-voltage DC transmission systems and electric vehicle systems. However, MMC has the critical issue of unbalanced submodule capacitor voltages and circulating current. The MMC distributes DC-link energy evenly among its submodule capacitors, rather than storing it in a large capacitor like conventional voltage source converters. In MMC, the submodule floating capacitors interface the DC input voltage and AC output voltage. Therefore, capacitor voltages must be balanced. The existing capacitor voltage control struggles to handle many gate pulses at medium and high voltages. An effective control scheme is needed to solve the issues mentioned earlier. This paper presents a performance analysis of a 1-Φ MMC using a modified level shift PWM technique based on Universal Control Modulation Scheme (UCMS) and a sorting-based capacitor voltage balancing algorithm. MATLAB/Simulink software implements the proposed control method for a 1-Φ , seven-level MMC. The outcomes of the simulation demonstration reveal that phase disposition PWM offers less harmonic distortion of output phase voltage than the other level shift PWM techniques, such as phase disposition PWM and alternate phase disposition PWM. The simulation results also demonstrate the effective use of the sorting-based capacitor balancing algorithm to regulate the voltages of the submodule capacitors. Finally, the real-time GUI tool validates the simulation results using an experimental prototype of 1-Φ MMC with the dSPACE MicroLabBox 1202.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674685","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