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Text Filtering Insignificant Word (TFIW) Readability Framework Model to Explain NlP in Sentiment Analysis Using Reinforcement Machine learning 2

Text Filtering Insignificant Word (TFIW) Readability Framewo...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kandakatla, Rajesh Kumar, V dhilip Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Dept. Computer Science and Engineering Chennai India BVRIT HYDERABAD College of Engineering for Women Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350366570

Reading of a text in a given sentence have lot of parameters to consider for expressing an opinion on a given text to conclude nature of the text. The data which need to be formed in the form of data sets based on specific constrains. The Natural language Processing (NlP) will always presume that Text readability made as the considerable measure for better round spread of any newspaper. The data related to these documents must be collected and store them in data sets. during this process of measuring text readability at the granular level of the text reading is a cumbersome task. This will always implicate a problem of machine readability of the text and explainability of the text taken from the documents mentioned above. In this context human readability will act an important role in predicting the text reading at granular level where the text-based sentiment analysis have been predicted through a machine learning model. The text reading has been evaluated in order to improve the readability through explainable system. The explainable systems will capture and explain the predictions of text reading. In this technical work an explainable system Text Filtering Insignificant Word (TFIW) is introduced where it improves the textual reading at the granule level based on sentiments by prediction the words which need to be filtered. This will improve the explainability of text and text reading and improve the fast text and explainability of the document. © 2024 IEEE.

关键词： Reinforcement learning

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A Hybrid Approach for Enhanced detection of Fake Profiles in Online Social Networks 1

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Bhosale, Rohini Mane, Vanita Department of Computer Engineering Ramrao Adik Institute of Technology D. Y. Patil Deemed to be University Nerul Navi Mumbai India Department of Computer Engineering Pillai HOC College of Engineering and Technology University of Mumbai Mumbai India

ISBN: (数字)9789819761036

ISBN: (纸本)9789819761029

The proliferation of information sharing on social media platforms has attracted attackers who often use fake profiles to commit cybercrimes. This work presents a hybrid methodology that addresses the limitations of current techniques by integrating the advantages of deep learning and machine learning algorithms. The suggested technique employs an improved feature extraction layer and a hybrid classifier to optimize the performance of false profile detection. The methodology synergistically integrates the advantages of content-based and behavioral-based analysis methodologies, utilizing a wide range of features that improve accuracy. Content-based analysis involves the examination of textual and multimedia content linked to user profiles. It utilizes natural language processing to identify abnormalities and conflicts. The behavioral-based study centers on examining user interactions, utilizing machine learning algorithms to identify trends in user activity, relationships, and posting behavior. Experimental studies carried out on real data sets obtained from Twitter demonstrated a notable enhancement in the precision of detection when compared to conventional approaches. A combination of these methods shows potential for improving the security and reliability of online social networks, offering an essential tool for platforms and users to reduce the dangers linked to fake profiles. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2024.

关键词： Adversarial machine learning

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Intelligent IoT-driven Advanced Predictive Maintenance System for Industrial Applications

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SN computer Science 2025年第2期6卷 1-24页

作者： dhinakaran, d. Edwin Raja, S. Velselvi, R. Purushotham, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of CSE School of Computing Mohan Babu University Tirupati India

Predictive Maintenance (PdM) aims to ensure the continuous operation of high-risk industrial systems. This challenge is especially critical in environments where equipment failure can cause major financial losses and disrupt operations. Traditional PdM approaches, while fairly effective, often fall short in accurately predicting failures due to their reliance on simple statistical methods and predefined rules. The proposed work introduces an Intelligent IoT-driven Advanced Predictive Maintenance System (APdM) that addresses these limitations. By integrating advanced machine learning techniques, including supervised and reinforcement learning, with IoT technologies to enhance predictive accuracy as well as the operational efficiency. The system leverages Explainable AI (XAI) to ensure transparency in decision-making and employs federated learning for distributed model training, preserving data privacy across multiple edge devices. The proposed APdM system was evaluated using the Kaggle "Air Compressor Predictive Maintenance" dataset, which provided extensive sensor data from heavy vehicle operations. Comparative analysis against four prevailing PdM approaches—Traditional PdM, decision Support System-PdM, Genetic Algorithm-based PdM, and AI-based PdM—demonstrated the superior performance of our system. The results show that the APdM system, powered by intelligent IoT integration, diminishes the Mean Error Percentage (MEP) by up to 38.4% compared to traditional methods and decreases the Mean Absolute Error (MAE) by 36.7% relative to dSS-PdM. Additionally, the system achieves a 78% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) over GA-PdM, underscoring its robust performance in real-world industrial scenarios. These outcomes validate the effectiveness of the proposed APdM system, marking a significant improvement in the field of predictive maintenance through the intelligent integration of IoT technologies. © The Author(s), under exclusive licence to S

关键词： Explainable AI IoT Machine learning Predictive maintenance Supervised and reinforcement learning

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Machine learning based development and Evaluation of a Hand Exoskeleton Rehabilitation Robot for Patients with Partial Paralysis 2

Machine learning based Development and Evaluation of a Hand ...

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2nd International Conference on disruptive Technologies, ICdT 2024

作者： Singh, durgesh Kumar Srivastava, Anand Prakash Singh, Shivanshu Paliwal, Sanjay Singh, Anupam Singh, Jagendra Hi-Tech Institute of Engineering & Technology Mechanical Engineering Department Ghaziabad India Nitra Technical Campus Department of Computer Science Ghaziabad India R D Engineering College Mechanical Engineering Department Ghaziabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350371055

This work focuses on the estimation of elbow joint angles from electromyography (EMG) signals and investigates the use of machine learning models in rehabilitation robotics. Four distinct machine learning algorithms were tested for their capacity to precisely translate EMG inputs into actuator commands: Artificial Neural Network (ANN), Naive Bayes (NB), linear Regression (lR), and decision Trees (dT). The models were trained and tested using a dataset generated by a wireless EMG sensor and motion capture system that recorded the dynamic relationship between elbow movements and related muscle activations. The results show the remarkable 98.7% accuracy of the ANN model, indicating that it can be a valuable tool for real-time rehabilitation robotics applications. Additionally, NB, lR, and dT displayed competitive results that demonstrated their unique capacities to manage a range of sensor inputs. The discussion highlights how important these models' interpretability, computational efficiency, and continuous adaptation are to their successful integration into real-world rehabilitation settings. This work contributes to the field of rehabilitation robotics by applying machine learning models for accurate actuation control based on EMG signals. The findings demonstrate how these models could enhance the accuracy and responsiveness of assistive technology, offering individuals with partial paralysis customized and adaptable rehabilitation regimens. © 2024 IEEE.

关键词： assistive devices electromyography joint angle prediction machine learning Rehabilitation robotics

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Research progress on bearing fault diagnosis with localized defects and distributed defects for rolling element bearings

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Noise and Vibration Worldwide 2022年第7-8期53卷 352-365页

作者： Patel, Samruddhi Shah, Utpal Khatri, Bharat Patel, Utkarsh Mechanical Department Government Engineering College Modasa affiliated to Gujarat Technological University Gujarat India Mechanical Department L.D. College of Engineering Ahmedabad India

In rotating machinery, rolling elements are the key members of the rolling element bearing (REB), which has tremendous application in automobiles, aerospace, ships, machinery apparatuses, and many more industrial sectors. Bearings are a significant source of various non-linear parameters which strongly affect the whole system. In the last two decades, tremendous research and approaches have been developed, considering the different parameters and faultiness for the proper understanding of the system’s non-linear behavior. This research paper summarizes the literature review on bearing fault diagnosis with localized and distributed defects for rolling element bearings with various non-linear parameters. Artificially-generated defects on the bearing races provides the vibration analysis of the system. These factors like radial clearance, unbalanced force, preload, misalignment, etc. are also considered defects in bearings. Moreover, the literature highlights some significant gaps that should be examined for future research in the condition monitoring of the mechanism. © The Author(s) 2022.

关键词： Condition monitoring

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A Comprehensive Exploration of Convolutional Neural Network Architectures in deep learning 6th

A Comprehensive Exploration of Convolutional Neural Network ...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Bhairnallykar, Sujata Tukaram Narawade, Vaibhav Department of Computer Engineering Ramrao Adik Institute of Technology D Y Patil Deemed to be University Nerul Maharashtra Navi Mumbai400706 India Department of Computer Engineering Saraswati College of Engineering Kharghar Maharashtra Navi Mumbai410210 India

ISBN: (纸本)9789819734412

The fields of computer vision, natural language processing, and medical image analysis have all seen significant transformation because of deep learning’s convolutional neural networks (CNNs). This comprehensive review explores the extensive landscape of CNN-based techniques, providing a detailed analysis of their architecture, training strategies, and applications. The review starts by introducing the fundamental concepts of CNNs, emphasizing their ability to automatically learn hierarchical features from raw data, analysis of the different components, design choices that influence the performance of CNN architectures, including the choice of activation functions, pooling strategies, and network depth. It then delves into various CNN architectures, including leNet, AlexNet, ZFNet, VGG, GoogleNet, ResNet, denseNet, MobileNet, and UNet, outlining their unique structures and applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2024.

关键词： Convolutional neural networks

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Estimation of Tropical Cyclone Intensity from Satellite data Using Self Attentive - TCNN-BiGRU Approach 5

Estimation of Tropical Cyclone Intensity from Satellite Data...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Suthar, Trilok Shah, Tejas Pokhariya, Hemant Singh Ranichandra, S. Kalpana, V. Yuvaraj, K.P. Gujarat Technological University Ahmedabad India L D College of Engineering Ahmedabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Department of Computer Science Tamilnadu Perambalur India Rmk College of Engineering and Technology Department of Science and Humanities Tamilnadu Thiruvallur Puduvoyal India Sri Krishna College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350321425

Predicting a tropical cyclone's intensity is difficult since it need for human involvement at every stage of the process, from feature extraction through pre-processing to the integration of data from many satellites. There are many reasons why intensity estimation could be difficult, including inconsistent results, a huge quantity of data preprocessing, a difficult problem area, and concerns regarding generalizability. For the purpose of developing a hurricane classification system, this approach presents a GPU-based TCNN-BiGRU architecture. The proposed model achieves better accuracy and root-mean-square error compared to state-of-the-art methods when only satellite photos are available. To further elucidate the learning procedure, they also provide visual representations of learned features and related deconvolutions at many depths. The suggested method utilizes normalization for preprocessing, allowing for faster computations and higher quality results. PCA is used to lessen the number of dimensions and prevent model overfitting. The SA-TCNN-BiGRU Model is used to analyze the results. When compared to the CNN and GRU models, it fares exceptionally well. © 2023 IEEE.

关键词： Hurricanes

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Cyber-Syndrome:Concept,Theoretical Characterization,and Control Mechanism

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Tsinghua Science and Technology 2024年第3期29卷 721-735页

作者： Feifei Shi Huansheng Ning liming Chen Sahraoui dhelim School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Computing Ulster UniversityNewtownabbeyBT370QBUK School of Computer Science University College DublinDublinD04 V1W8Ireland

The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking(CPST)***,the easy access,the lack of governance,and excessive use has generated a raft of new behaviors within CPST,which affects users’physical,social,and mental *** this paper,we conceive the Cyber-Syndrome concept to denote the collection of cyber disorders due to excessive or problematic Cyberspace interactions based on CPST *** we characterize the Cyber-Syndrome concept in terms of Maslow’s theory of Needs,from which we establish an in-depth theoretical understanding of Cyber-Syndrome from its etiology,formation,symptoms,and ***,we propose an entropy-based Cyber-Syndrome control mechanism for its computation and *** goal of this study is to give new insights into this rising phenomenon and offer guidance for further research and development.

关键词： Cyber-Syndrome cyber-physical systems Cyber-Physical-Social-Thinking(CPST)space Maslow’sHierarchy of Needs entropy

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deploying Faster R-CNN for Real-Time Tiger Monitoring in Wildlife Conservation 3

Deploying Faster R-CNN for Real-Time Tiger Monitoring in Wil...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Saravanan, S. Raj, J. Relin Francis Rajkumar, G. Vinoth Kumar, C. Antony Vasantha Soundiraraj, N. Bharathi, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Rohini College of Engineering and Technology Kanyakumari India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (纸本)9798331532420

This study introduces a real-time tiger monitoring system built on the Faster R-CNN deep learning framework, designed to support wildlife conservation through accurate tiger detection and tracking. The primary aim is to effectively identify and monitor tigers across various environments, using an extensive dataset drawn from Google’s Open Images dataset and further enriched with synthetic images created by Generative Adversarial Networks (GANs). These synthetic images simulate challenging conditions, such as low lighting and uncommon camera angles, to improve the model’s robustness and adaptability in field settings. The model is initialized with a pre-trained ResNet50 backbone, utilizing transfer learning to accelerate training while ensuring precise detection. Advanced techniques such as dynamic data augmentation and hard negative mining have been integrated to minimize false positives and increase model resilience. Hyperparameter tuning, including cyclical learning rate scheduling and anchor box optimization, is applied to maximize both accuracy and recall, refining the model's ability to capture tiger-specific features under diverse conditions. To facilitate real-time monitoring, the model is deployed on NVIdIA Jetson devices, supporting edge processing without relying on cloud-based infrastructure. This deployment strategy ensures efficient data processing in remote areas, where stable network connectivity may be limited. Non-Maximum Suppression is used to reduce duplicate detections, while a Kalman Filter maintains tracking stability across video frames, ensuring seamless observation of tigers in motion. This optimized Faster R-CNN-based approach enables high-accuracy, low-latency tiger detection, equipping conservationists with reliable data on tiger presence and movement patterns. By enhancing detection and tracking capabilities, this system aims to support informed decision-making in wildlife conservation and habitat management. © 2024 IEEE.

关键词： decision making

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Your Interest, Your Summaries: Query-Focused long Video Summarization

Your Interest, Your Summaries: Query-Focused Long Video Summ...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Nirav Patel Payal Prajapati Maitrik Shah Department of Computer Engineering L. D. College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Generating a concise and informative video summary from a long video is important, yet subjective due to varying scene importance. Users' ability to specify scene importance through text queries enhances the relevance of such summaries. This paper introduces an approach for query-focused video summarization, aiming to align video summaries closely with user queries. To this end, we propose the Fully Convolutional Sequence Network with Attention (FCSNA-QFVS), a novel approach designed for this task. leveraging temporal convolutional and attention mechanisms, our model effectively extracts and highlights relevant content based on user-specified queries. Experimental validation on a benchmark dataset for query-focused video summarization demonstrates the effectiveness of our approach.

关键词： Attention mechanisms Automation Benchmark testing Robots

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