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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Navya, R. Bv, Santhosh Krishna Rawat, Anurag Satheeshkumar, S. New Horizon College of Engineering Department of CSE India Himalayan Institute of Medical Science Department of Cardiology India Bannari Amman Institute of Technology Artificial Intelligence and Data Science India

ISBN: (纸本)9798350315127

Untreated, arrhythmia is a potentially life-threatening condition that can lead to severe illness in patients. Early detection of heart arrhythmias can save lives. Based on ECG recordings. New advances in various types of equipment and machine learning models provide an opportunity to use computer-aided diagnosis (CAD) to make ECG classification procedures applicable in a real clinical setting. This allows classification of electrocardiographic arrhythmias between patients. The objective of this paper is to design and evaluate an innovative cross-patient approach for analyzing extensive electrocardiogram datasets, with the goal of enhancing the identification of rare arrhythmia We study four supervised machine learning models: support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and a suite of three methods. We will test the effectiveness of these strategies in classifying: normal beaters (NOR), left leg blockers (LBBB), right leg blockers (RBBB), failure of atrial complexes (PAC) and premature ventricular complexes (PVC), next. segmenting and normalizing the data, obtaining patients' actual ECG data from MIT-DB, and measuring four parameters: precision, accuracy, recall, and f1 score. The results of the experiment show that, without the use of complex data preprocessing or engineering methods, the SVM classifier outperforms other methods using our patient interviews in all cases. An achieved Calculation value accuracy is of 0.84;is still very important. ECG is important in using the arrhythmia classification model. This approach is more accurate in clinical settings. where multiple ECG signals are collected from different patients. © 2023 IEEE.

关键词： Electrocardiograms

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An Enhanced Power Management and Prediction for Smart Grid Using Machine Learning

An Enhanced Power Management and Prediction for Smart Grid U...

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International Conference on data science, Computation and Security, IDSCS 2023

作者： Kumar, Shilpa Mohan Nagaraj, Sharmila Veerabhadraswamy, Pushpalatha Nanjundaswamy, Mahendra Hanumanapura Srikantaswamy, Mallikarjunaswamy Chandratta, Kiran Yarehalli Department of Information Science and Engineering Bangalore Institute of Technology Karnataka Bengaluru India Electrical and Electronics Engineering JSS Science and Technology University Karnataka Mysore India Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bengaluru India Electronics and Communication Engineering JSS Academy of Technical Education Karnataka Bengaluru India Information Science Engineering Global Academy of Technology Karnataka Bengaluru India

ISBN: (纸本)9789819709748

In recent years, managing power generation and utilisation to stay on track has played a major role. Apart from this, analysis of energy bill generation and smoothing the peak demand curve to an optimal level are very difficult with the existing system. More power loss has occurred due to the fault detection in the smart grid;it has created manipulation data and obstacles for the training of the data. Hence, to overcome this, the proposed machine learning-based power management and prediction system (ML-PMPS) for the smart grid has been operated, which is essential for providing an uninterrupted power supply to consumers. As per simulation analysis, the proposed method performs better as compared to the conventional methods such as Multilayer Perceptron (MLP) and Naive Bayesian (NB) with respect to accuracy, prediction rate, and power management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Yolo5-Based UAV Surveillance for Tiny Object Detection on Airport Runways

Yolo5-Based UAV Surveillance for Tiny Object Detection on Ai...

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2023 International Conference on data science, Agents and artificial intelligence, ICDSAAI 2023

作者： Sri Hari, R.V. Ambalam, Rishywanth Ruban Kumar, B. Ibrahim, M. Ponnusamy, R. Chennai Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Chennai Institute of Technology Department of Mechanical Engineering Tamilnadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350348910

Foreign Object Debris (FOD) present on runways poses a threat to various aircraft components, including the frame, windshield, tires, and engine, while also posing a risk of injury to personnel in the surrounding area. The spectrum of foreign objects includes small rocks, pebbles, nuts, bolts, metal pieces, papers, and objects of millimetric sizes. Currently, a substantial number of air personnel engage in manual runway inspections to identify FOD, a process that demands significant manpower. Conducting visual inspections becomes challenging. An automated object detection framework has been developed to enhance the efficiency of the detection process and minimize dependence on human resources. Utilizing YOLOv5, this study explores Unmanned Aerial Vehicle (UAV) surveillance for precise detection of small objects on airport runways. The focus is on enhancing efficiency and accuracy in monitoring runway spaces prone to Foreign Object Debris (FOD). The integration of YOLOv5 technology offers a robust solution for proactive detection and maintenance of runway safety. © 2023 IEEE.

关键词： Object detection

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Emergency vs Non-Emergency Vehicle Classification: Enhancing Intelligent Traffic Management Systems

Emergency vs Non-Emergency Vehicle Classification: Enhancing...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Madan, S. Shetty, Kishan Kodipalli, Ashwini Rao, Trupthi Kumaraswamy, S. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept of Artificial Intelligence and Data Science Karnataka Bengaluru India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350300826

Due to its dense population, India frequently experiences traffic congestion, which puts lives in danger by trapping vehicles like ambulances, police cars, and fire trucks which run on road for emergency purpose. It becomes crucial to give these vehicles priority and allow for their easy passage. However, it becomes challenging or even impossible for traffic police to effectively handle such circumstances. Therefore, there is a requirement for an automated system that can locate emergency vehicles in high-traffic areas, alert the controller, or drive itself to direct other vehicles to make room. This study suggests an automated method for identifying emergency vehicles from CCTV footage that makes use of deep convolutional neural networks (CNN). The objective is to effectively identify and classify emergency vehicles in real-time, leveraging the power of advanced object detection techniques. With an accuracy of 91.73% and a loss of 0.2120, the suggested technique outperformed existing optimizers in accurately recognizing and classifying emergency vehicles. © 2023 IEEE.

关键词： Object detection

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Detection of Behavioral Patterns of Viral Hepatitis Using data Mining 4

Detection of Behavioral Patterns of Viral Hepatitis Using Da...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Shende, Vaishali Gajendra Jency, A. Pavithra, K. Jayasudha, T. Venkatesan, G. Sharma, Manish East Point College of Engineering and Technology Department of Computer Science Engineering Bangalore560049 India Rmk College of Engineering and Technology Department of Artificial Intelligence and Data Science Thiruvallur601206 India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai600123 India Kpr Institute of Engineering and Technology Department of English Coimbatore641407 India Saveetha Engineering College Department of Civil Engineering Chennai602105 India Graphic Era Deemed to Be University Department of Computer Science Engineering Dehradun248002 India

ISBN: (纸本)9798350300888

The data collected by the Information Systems related to Public Health are underutilized, mainly due to their large volume and their complexity. This work was applied to the Knowledge Discovery in databases technique based on the National Centre for Disease Control (NCDC), with the objective of tracing the epidemiological profile of viral hepatitis. Foram used the data referring to the corporation of Chennai in the year 2022, from two quais it was possible to compare the information obtained with the knowledge available in the specialized literature. Obtain a clear, simple and objective representation of the epidemiological profile of viral hepatitis through a decision tree and classification rules. © 2023 IEEE.

关键词： Decision trees

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Optimizing Wireless Ad-Hoc Networks: A Holistic Approach for Sustainable Energy Efficiency 5

Optimizing Wireless Ad-Hoc Networks: A Holistic Approach for...

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5th International Conference on Information Management and Machine intelligence, ICIMMI 2023

作者： Vashishtha, Laxmikant Singh, Uday Pratap Saxena, Vivek Patel, Shubham Department of Artificial Intelligence & Data Science Poornima Institute of Engineering & Technology Rajasthan Jaipur India Department of CSE Poornima Institute of Engineering & Technology Rajasthan Jaipur India Department of Computer Science Poornima College of Engineering Rajasthan Jaipur India

ISBN: (纸本)9798400709418

This research paper explores the challenges and opportunities in designing energy-efficient architectures for wireless ad-hoc networks. As the demand for wireless communication continues to grow, the need for energy-efficient solutions becomes imperative to extend the lifespan of battery-powered devices and reduce the environmental impact. The paper investigates various techniques, protocols, and algorithms aimed at optimizing energy consumption in wireless ad-hoc networks, with a focus on enhancing overall network performance. Ad Hoc Wireless Network Wireless Ad Hoc Networks are made up of wirelessly connected groups of mobile terminals that allow unrestricted mobility within a defined region. Given mobile terminals' short battery life, emphasizing energy conservation is critical for the long-term viability of mobile Ad Hoc wireless networks. Identifying energy-saving measures benefits network designers in constructing more efficient networks with longer service life cycles. The purpose of this research is to conduct a simulation analysis of the sleep/wake-up, energy-saving protocol MM3, which is based on the IEEE 802.11b standard. The MM3 protocol is intended to improve power savings and is compared to IEEE 802.11 power save mode. Furthermore, two levels of multilayer power-saving protocols are employed to build the Enhanced Ad-Hoc On-Demand Distance Vector (EAODV), a better ad-hoc routing method. © 2023 ACM.

关键词： Mobile ad hoc networks

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Review on application progress of federated learning model and security hazard protection

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Digital Communications and Networks 2023年第1期9卷 146-158页

作者： Aimin Yang Zezhong Ma Chunying Zhang Yang Han Zhibin Hu Wei Zhang Xiangdong Huang Yafeng Wu Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina College of Science North China University of Science and TechnologyTangshanHebeiChina College of Artificial Intelligence North China University of Science and TechnologyTangshanHebeiChina

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data *** data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data *** is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our *** the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied *** paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

关键词： data silos Machine learning Federated learning Privacy protection Learning framework

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Masking of Colposcopy Images- A New Approach

Masking of Colposcopy Images- A New Approach

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Chatterjee, Priyadarshini Das, Surajit Siddiqui, Shadab B V Raju Institute Of Technology Department Of Computer Science And Engineering Medak Telengana Narsapur India B V Raju Institute Of Technology Department Of Artificial Intelligence And Data Science Medak Telengana Narsapur India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering Hyderabad India

ISBN: (纸本)9798350371567

Masking, in the context of image processing and computer vision, refers to the process of isolating specific regions or objects of interest within an image while suppressing or removing irrelevant information. It involves the creation of a mask, which is essentially a binary image that defines which pixels in the original image belong to the region of interest and which do not. The mask typically consists of black and white pixels, where white pixels represent the areas to be preserved or highlighted, and black pixels represent the areas to be ignored or masked out. This paper presents a novel approach aimed at enhancing the accuracy of masking algorithms for colposcopy images, a crucial component in the diagnosis of cervical cancer among women. While previous research has demonstrated promising outcomes in this domain, our study introduces a novel masking algorithm tailored specifically to improve segmentation accuracy. The colposcopy dataset utilized in this research is sourced from the International Agency for Research on Cancer in France. © 2024 IEEE.

关键词： Diseases

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Modeling of metaheuristic-based dual cluster head selection with routing protocol for energy-efficient wireless sensor networks

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Soft Computing 2025年第6期29卷 2999-3020页

作者： Reddy, Maddikera Krishna Veluswamy, Anusha Sowbarnika Selvanayaki, S. Harini, C. Kumar, Pavan Shameem, Syed Department of Electronics and Communication Engineering Rajeev Gandhi Memorial College of Engineering and Technology Andhra Pradesh Nandyal India Department of Mathematics and Computer Science Susquehanna University Pennsylvania SelinsgrovePA United States Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India Maisamma Guda Secunderabad India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Dakamarri Vizayanagaram India Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Wireless Sensor Networks (WSN) are decentralized networks of spatially distributed sensors that gather and monitor environmental information for various applications, including industrial automation and environmental monitoring. This enables real-time information gathering without relying upon wired connections. In WSN, clustering and routing are essential techniques for improving data transmission. Clustering comprises grouping sensor nodes (SN) into clusters to optimize energy efficacy and extend the network's lifetime. However, selecting suitable and efficient cluster head (CH) components is crucial to harness their benefits. Selecting appropriate CHs and finding optimum coefficients for all the parameters of applicable fitness function (FF) in CH selection is a non-deterministic polynomial-time (NP-hard) problem that needs additional processing. In contrast, routing focuses on defining the most effective path for data to traverse through the network, ensuring timely and reliable data delivery and reducing energy consumption. Therefore, this study proposes a new Metaheuristic-Based Dual Cluster Head Selection with Routing Protocol (MBDCHS-RP) method for energy-efficient WSNs. The MBDCHS-RP method aims to cluster the nodes and select optimum routes for transmitting information in the WSN. To achieve this, the MBDCHS-RP method follows a two-stage clustering process, including tentative CH (TCH) and final CH (FCH) selection. The MBDCHS-RP technique utilizes a cheetah optimization algorithm (COA) with FF comprising residual energy (RE) and average node distance as input parameters for the TCH selection process. In addition, the flower pollination algorithm (FPA) is used to select final CHs with multiple input parameters, namely average intra-cluster distance, RE, and average sink distance. A carnivorous plant algorithm (CPA) is employed for the optimal route selection process with two parameters, RE and distance to the base station, to enable an effective data transm

关键词： Sensor nodes

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Addressing data Imbalance in Machine Learning: Challenges and Approaches

Addressing Data Imbalance in Machine Learning: Challenges an...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Meher Langote Nikita Zade Swapnil Gundewar Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

A key difficulty of dealing with imbalanced datasets is that machine learning models are trained to favor the majority class and therefore perform poorly when it comes to a minority class classification. In this paper, we consider the following methods of tackling the issue of the overrepresentation of a class; random oversampling, under-sampling, SMOTE, cost-sensitive learning, and ensemble methods. Comparing them, we assess the performance of each of these techniques within different classifiers. Techniques including SMOTE together with combined methods including Random Forests also show higher capacity in minority class predictions. However, problems such as overfitting and the increase in the computational load are not escapable yet. We briefly recap the strengths and weaknesses of each approach and propose potential refinements including a combination of both methods and dynamic resampling methods to serve as the next steps toward improving the model. Thereby, our results draw attention to the necessity of accurate methods for handling the class imbalance, which will further yield more accurate and fair machine learning solutions across a variety of applications.

关键词： Adaptation models Accuracy Machine learning algorithms Computational modeling Ensemble learning Informatics Random forests Mobile computing Overfitting Load modeling

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