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Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

computers, Materials & Continua 2022年第4期71卷 255-268页

作者： R.Krishnaswamy B.Sivakumar B.Viswanathan Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India School of Computing SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Factually of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia

Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.

关键词： Biomedical signals ecg disease diagnosis artificial intelligence parameter tuning gru model

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Defending Against Digital Threats: Machine Learning Techniques for Cyber Persecution Detection 5

Defending Against Digital Threats: Machine Learning Techniqu...

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5th International Conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Krishnan, Kesavan Kumar, Vijay Jalil, Norazira Binti A Kiran, Ajmeera School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Graphic Era Hill University Department of Physics Dehardun India Graphic Era Deemed to be University Dehradun India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331528553

Cyber persecution has become a widespread problem on the social media. It has resulted in omissions such as suicide and sadness. Content regulation on social media sites is becoming increasingly important. The following work employs natural language processing and Machine Learning techniques to construct a cyber harassment detection model utilizing data from cyberbullying, hate speech tweets from Twitter, and personal assaults from Wikipedia. The optimal approach is investigated by two feature extraction methods and six classifiers (SVM, Random Forest, XGBoost, MLP, and Logistic Regression). This study summarizes the general procedure for detecting cyberbullying and, more crucially, the technique. During experimentation, SVM, Random Forest, and Logistic Regression performed well with an average of 90%. Our model has been created to detect offensive comments on social media websites. © 2024 IEEE.

关键词： Adversarial machine learning

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Real-time Glass Recycling Quality Assurance and Contamination Reduction with IoT and Random Forest algorithm 7

Real-time Glass Recycling Quality Assurance and Contaminatio...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Balasubadra, K. Shadaksharappa, B. Seeni, Senthil Kumar Sridevi, V. Thamizhamuthu, R. Srinivasan, C. R.M.D. Engineering College Department of Information Technology Chennai Tamil Nadu Kavaraipettai India Sri Sairam College of Engineering Department of Computer Science And Engineering Bengaluru Karnataka Anekal India Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Academy of Maritime Education And Training Deemed To Be University Electrical And Electronics Engineering Tamil Nadu Chennai India Srm Institute of Science And Technology Department of Computing Technologies Kattankulathur Campus Chennai Tamil Nadu Chengalpattu India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

The recycling industry is crucial to environmental protection and resource conservation. Glass recycling has great sustainability potential, but quality assurance and contaminant reduction are major challenges. Integrating the Internet of Things (IoT) with the Random Forest algorithm, this work offers a new solution to these difficulties. IoT devices are used in glass recycling collection, transportation, sorting, and processing. These devices measure glass quality, contaminants, temperature, humidity, and other parameters in real-time. Data is sent to a central system for analysis and decision-making. Random Forest, a machine learning method that handles complicated, multidimensional datasets, powers the system. This algorithm learns from past data and updates with real-time data. It detects contaminants, foreign elements, and faults in recycled glass. This permits fast remedial action, enhancing recycled glass quality and decreasing contamination. Application of this mechanism yields two outputs. First, it improves glass recycling efficiency and produces high-quality glass for remanufacturing. Second, it significantly minimizes the environmental and economic implications of contamination and low-quality glass, which may limit recycling yields and increase waste disposal. In comparison to traditional approaches, the proposed approach that utilizes the IoT and the Random Forest algorithm reduces contamination and ensures the quality of glass recycling by 30%. © 2024 IEEE.

关键词： Internet of things

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An IoT-based Deep Belief Network in Intrusion Detection System using Butterfly Optimization 10

An IoT-based Deep Belief Network in Intrusion Detection Syst...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Cherian, Aswathy K. Vaidhehi, M. Arshey, M. Simpson, Serin V. Briskilal, J. Singh, Astitva Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Scms School of Engineering and Technology Department of Ai & Ds Kerala Ernakulam India Madanapalle Institute of Technology & Science Department of Computer Science and Technology Chittoor Andhra Pradesh Madanapalle India

ISBN: (纸本)9798350353068

Rapid technological advancements in the communication and internet industries have led to a huge increase in network scale and data volume. Network security is having a hard time identifying attacks as new threats are being created. Additionally, you can't overlook the possibility of unauthorized users trying to attack the network in various methods. Numerous technologies, such as an intrusion detection system, can be utilized to protect the network from potential threats (IDS). The collecting and dissemination of data by Internet-connected devices is referred to as the 'Internet of Things' (IoT). There are now more privacy and security problems due to the proliferation of Internet connections and the introduction of new technologies like the Internet of Things (IoT). IoT incursions are becoming increasingly complex and numerous as a result of the proliferation of connected devices. Companies are boosting their investment in research and development in order to better detect these threats. In this research, Deep Belief Network (DBN) and Butterfly Optimization Algorithm (BOA) Intrusion Detection Systems (IDS) are proposed for detecting Io T network assaults. In order to minimize information leakage on the test data, the UNSW-NB15 dataset was normalized using the Absolute Maximum Scaling (AMS) approach in the first stage of this study process. This dataset contains a mixture of current assaults and typical network traffic, categorized into nine different attack types. The next step was to use Principal Component Analysis to reduce the dimensionality (PCA). Last but not least, results showed that DBN-BOA was superior to other IoT intrusion detection systems in terms of accuracy and speed. © 2024 IEEE.

关键词： Principal component analysis

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Big Data Infrastructures Using Apache Storm for Real-Time Data Processing

Big Data Infrastructures Using Apache Storm for Real-Time Da...

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2025 International Conference on Intelligent Control, computing and Communications, IC3 2025

作者： Ranganathan, Chitra Sabapathy Sampathrajan, Rajeshkumar Reddy, P. L. Kishan Kumar Parkavi, S. Meenakshi, B. Sujatha, S. Mphasis Corporation Tamil Nadu Chennai India McKinsey & Company Fort WorthTX United States Department of Computer Science and Engineering Andhra Pradesh Guntur India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527495

Apache Storm-based Big Data infrastructures strive to provide a scalable and fault-tolerant platform that will transform real-time data processing. The goal is to use Apache Storm's features to handle and analyze massive amounts of data in real-time, guaranteeing correct insights that are delivered promptly. The objective is to provide a solid foundation that can manage data moving at high speeds from many sources, allowing for analytics in real-time and continuous computing. The goal is to maximize throughput while reducing delay via architectural optimization and efficient data flow methods. The goal is to improve Apache Storm's capabilities and make sure data processes smoothly across dispersed systems by combining it with other big data technologies. The end goal is a robust system that can provide analytics on data in real-time, which will aid in decision-making across several industries, including banking, telecoms, and social media. Efficiency, scalability, and dependability in managing massive data streams are key considerations. The data flow topology dataset for 5 distinct components and 5 streams shows throughput values ranging from 2500 to 4100 tuples per second. Similarly, the fault tolerance and reliability dataset for 5 distinct nodes and 5 different intervals shows throughput values ranging from 700 to 1050 tuples per second. All these results are derived from real-time sensor data used for traffic monitoring. © 2025 IEEE.

关键词： Large datasets

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Tomato Leaf Disease Classification with Lightweight Federated Learning using Identically Distributed Images 9

Tomato Leaf Disease Classification with Lightweight Federate...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Trivedi, Naresh Kumar Maheshwari, Himani Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Gautam, Vinay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9798350383201

Tomatoes grown and consumed extensively in Asia are an essential source of protein (22% per Tomato) and energy (18% per Tomato). Bacterial, fungal, and other microbial diseases severely impact plant health and productivity for tomato farmers. Manual diagnosis of many diseases is challenging, especially in places that need access to crop protection experts. Automated disease identification is crucial for Tomato leaf protection and reduced crop loss. Unfortunately, no one method of categorizing leaf diseases in tomatoes has proven to be consistently accurate enough for widespread usage. The present study offers a streamlined federated deep learning framework to classify tomato leaf diseases while ensuring the preservation of sensitive data. This framework safeguards client information with a decentralized client-server architecture and verifies federated deep learning models with IID. The researchers piloted the framework with a single and several clients in both conventional and federated learning environments. After extracting features from numerous pre-trained models, AlexNet is selected as baseline because of its superior 99.53% accuracy. Next, IID datasets were put through their paces using federal learning (FL). The extra capabilities of the framework make it the ideal option for early Tomato leaf disease categorization. © 2023 IEEE.

关键词： Image classification

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Enhancing Security in Physical Layer Communications Based on Continuous Wavelet Transform with Convolutional Vision Transformer 4

Enhancing Security in Physical Layer Communications Based on...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Obaid, Saif Siri, Dharmapuri Sathish Kumar, G. Kumar, S. Senthil Rajasekaran, Arun Sekar College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Gojan School of Business and Technology Department of Information Technology Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Sr University Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798350352931

Nowadays, the Enhancing Security in Physical Layer Communications underway on implementing the 6G radio because the attacks are increasing more on wireless edges such as jamming and false based stations and it placed a low-cost software radio. However, in Cyber-Physical System (CPS) the unauthorized access affects system process, such information lacks on the system and there is no adverse effect about such data. The quick environment changes and unpredicted events are such various factor that affect CPS, and it gives false alarm on the detection of cyber-physical attack. To overcome these drawbacks an ensemble method combining Continuous Wavelet Transform (CWT) with Convolutional Vision Transformer (CVT) to enhance physical layer communication security. The process involves collecting Real-World Photoplethysmography (RW-PPG) dataset and then the preprocessing is done with the Principal Component Analysis (PCA) and applying CWT with CVT signal transformations. Classification is then performed using Automatic Modulation Classification (AMC) and Specific Emitter Identification (SEI). The proposed CWT with CVT model obtained high results in the context of precision (PPV), achieving a score of 0.692, Sensitivity (TPR) of 0.841, Specificity (TNR) of 0.985, and Area Under the Curve (AUC) of 0.827 when compared to existing methods like Recurrent Neural Network (RNN) respectively. © 2024 IEEE.

关键词： Convolution

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A Classification of Inflammatory Bowel Disease using Ensemble Learning Model 1

A Classification of Inflammatory Bowel Disease using Ensembl...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Sitaraman, Surendar Rama Adnan, Myasar Mundher Maharajan, K. Krishna Prakash, R. Dhilipkumar, R. Intel Corporation CA United States The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Academy of Research and Education School of Computing Kalasalingam Department of Computer Science and Engineering Krishnankoil India Sona College of Technology Department of Information Technology Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

Inflammatory Bowel Disease (IBD) is an enduring, intermittent bowel disorder with unidentified mechanism and etiology. It typically contains Ulcerative colitis (UC) and Crohn's disease (CD) that are categorized by several bowel ulcers. An impedance phase displaces dynamic development, accumulating susceptibility to exterior pathogens and theoretically leading to neurotic symptoms which encompasses bowel blockade, intestinal tear and liver lesions, with high disability ratio. The existing models have more overfitting, and class imbalance problems. To overcome that, an Ensemble Learning (EL) model such as Logistic Regression, Random Forest and Gaussian Naive Bayes is proposed in this research. Initially, the collected Gene Expression Series 106817 (GSE106817) dataset is preprocessed with Recurrent MultiArray Average (RMA) Normalization and Log2 Data Transformation to eliminate the noises. Next, Recursive Feature Elimination (RFE) technique is used to reduce high-dimensional data for selecting optimal features;and finally, the proposed EL model gives a precise rate in detecting and classifying IBD as CD, UC and Healthy controls. The proposed EL model gives better results than the other compared existing methods Random Forest-Support Vector Machine (RF-SVM) in the context of recall, accuracy, f1 score, and precision as 99.38%, 99.75%, 94.72%, and 97.94% respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Minimizing Age of Result in Multi-Task Networked Control Systems

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IEEE Journal on Selected Areas in Communications 2025年

作者： Qiu, Xiaoxing Fu, Chenchen Sun, Sujunjie Du, Yuhan Chau, Vincent Wu, Weiwei Luo, Junzhou Han, Song Southeast University Department of Computer Science and Engineering Nanjing China University of Connecticut School of Computing United States

This work studies the challenge of scheduling real-time control commands in Networked Control Systems (NCS), where control actions rely on the freshness of data collected from multiple sources. In dynamic environments, ensuring that control commands in an NCS are accurate and frequent is essential for maintaining the system responsiveness. For this aim, we introduce a new metric, Age of Result (AoR), which quantifies the time elapsed since the last control command was generated and executed. This metric reflects the system's capability to adapt to real-time changes in the operational environment by considering both data freshness and control command frequency. We conduct a detailed analysis of AoR in NCS, paying special attention to the dependencies between sensing and computing phases. We first address computation-intensive and network-intensive scenarios, proposing random sampling (RS)-based approximate algorithms for each case. Subsequently, we develop another RS-based algorithm and a heuristic approach for the general model. Simulation results demonstrate that our approach can effectively minimize AoR and significantly enhance the system performance and real-time adaptability compared to existing strategies. © 1983-2012 IEEE.

关键词： Networked control systems

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Implementing Energy-Efficient IoT Connectivity with Federated Learning 15

Implementing Energy-Efficient IoT Connectivity with Federate...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Suresh, Vaibhav Goutham Kumar, Shyam Doda, Davendra Kumar School of Mangement - Pg Department of Management Karnataka Bangalore India Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350370249

By using the use of federated mastering (FL), internet of factors (IoT) devices can talk over the net, at the same time as still retaining the privateers of users' statistics. This approach is energy-efficient, because it allows local device education that reduces the amount of information that must be exchanged with the primary server. FL may be used to reduce strength fees related to the use of IoT devices in IoT systems. FL enables devices to speak the usage of nearby models, which are educated by way of the relevant server. This manner can lessen strength consumption related to statistics change, because the fashions most effective want to gain knowledge of once as opposed to retransmit to each device. Moreover, FL can be used alongside power-conscious scheduling algorithms to further enhance electricity efficiency in IoT structures. With the assist of FL, IoT gadgets can communicate securely, at the same time as still preserving the privacy and strength performance of the gadget. © 2024 IEEE.

关键词： Algorithms Consumption Educated Education Effective Exchanged Scheduling

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