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

作者： Prabakaran, T. Babu, A. Ramesh Hemalatha, S. Ramasamy, Jayabharathi Kovarasan, Rajesh Kambattan Elango, B. Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study combines the potential of wearable health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may be achieved by seamlessly integrating real-time health data collected from IoT-enabled wearables with individual genetic profiles. Using algorithms for machine learning, connections between genetic differences and instantaneous health responses may be uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can be used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has been done sheds light on the revolutionary potential of merging genetics with the Internet of Things to establish a new paradigm of preventative and individualized medical treatment. © 2024 IEEE.

关键词： Genome

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Power Conscious Clustering Algorithm Using Fuzzy Logic in Wireless Sensor Networks 1st

Power Conscious Clustering Algorithm Using Fuzzy Logic in Wi...

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1st International Conference on Innovations in Intelligent Computing and Communication, ICIICC 2021

作者： Sarangi, Sanjaya Kumar Nanda, Arabinda Chowdhury, Manas Ranjan Mishra, Subhadra Department of Computer Science and Applications Utkal University Odisha Bhubaneswar India Department of Computer Science and Engineering Krupajal Engineering College Odisha Bhubaneswar India Department of Computer Science and Engineering Trident Academy of Technology Odisha Bhubaneswar India Department of Computer Science and Applications Odisha University of Agriculture and Technology Odisha Bhubaneswar India

ISBN: (纸本)9783031232329

The most challenging difficulties in Wireless Sensor Networks (WSNs) is energy conservation. Data fusion is the process of combining data from several sources to produce a single scenario. It has the ability to save a large amount of sensor energy while simultaneously enhancing sensing data accuracy. Reduced energy usage is mainly essential and difficult examination issue in WSNs. Computational intelligence (CI) principles like localization, grouping, power-sensitive routing, job arrangement, and protection are now widely employed WSNs & other applications. WSNs are organized into clusters to optimize data-collecting efficiency while minimizing energy dissipation. Clustering allows you to organize a deployed network into a connected hierarchy while simultaneously balancing network load and increasing the system's lifespan. Each sensor node in a cluster-based WSN sends the data it has acquired to the cluster coordinator for the cluster in which it is located. The expanse among cluster heads & events, with the energy of clusters, is fuzzified in order to choose clusters for data uploading and fusing using fuzzy logic. The cluster heads apply the fugitive logic technique in confined resolution building as well, and the outcomes of the local decision-making are then relayed to the base station. As a result of this research, a fuzzy logic-based power-conscious active clustering method is developed, that amplifies system duration assessed by means of Last Node Dies (LND). Results of this simulations carried out illustrate the usefulness of this approach. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy logic

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Object Recognition Through Content Based Feature Extraction and Classification of Sounds in IoT Environment 2nd

Object Recognition Through Content Based Feature Extraction ...

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2nd International Conference on Computing, Communication, and Learning, CoCoLe 2023

作者： Sethi, Srinivas Rath, Manisa Kuanar, Sanjaya Kumar Sahoo, Ramesh Kumar Computer Science Engineering and Applications IGIT Sarang Dhenkanal India Computer Science Engineering GIET Gunupur India

ISBN: (纸本)9783031569975

The content-based order of urban sound classes is an important aspect of several emerging techniques and applications as a result, the research issue has gotten a lot of attention recently. The goal of this work is to develop an effective Machine Learning(ML) based approach for urban sound categorization in cities for intelligent object detection and recognition through sound for environmental study. Audio samples from urban space has been classified through various ML algorithms based on Mel spectrogram technique. Where, mel spectrogram techniques is a frequency based approach for extraction of features from audio samples for classification. The Urban Sound dataset, which contains a total of 8732 number od sound samples belongs to ten different classes, has been used in this work. This may be used for smart object detection through sound coming from objects in different applications, such as Traffic Management, Agriculture, Smart city, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Object detection

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Revolutionizing Healthcare by Accessing the Opportunities for Virtual and Augmented Reality 7

Revolutionizing Healthcare by Accessing the Opportunities fo...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Mehta, Kapil Aayushi Singh, Chamkaur Chugh, Himani Kumar, Manibhushan Chandigarh Group of Colleges Department of Computer Science and Engineering Mohali Punjab Landran India Chandigarh Group of Colleges Department of Computer Applications Mohali Punjab Landran India

ISBN: (纸本)9798350397253

In recent years, the use of new technologies in the healthcare industry has substantially increased. In particular, this research study explores the potential benefits of virtual reality (VR) and augmented reality (AR). With the use of these technologies, difficult judgments can be made by using the information and specialized expertise. Given that maintaining the health is among the most fundamental human aspirations, the healthcare industry affects every one of us. This paper discusses how advances in artificial intelligence, ARvR and blockchain technology will spur innovation and transform the healthcare sector. This research study witnesses the unparalleled growth in tailored, intelligent healthcare that is accessible everywhere at a reasonable cost when these technologies merge. The fusion of mobile healthcare and support, AR/VR for medical training, and customized pharmaceuticals based on health data from sensors implanted in the human body can give therapies that are specifically tailored to a person's genetic makeup. The acceptance and integration of VR in the healthcare industry are also discussed in this paper. It examines the development, the benefits, and the drawbacks, and offers viewpoints on the potential expansion and effects of these technologies in the future. The use of AR and VR in healthcare services may lead to problems with huge data availability and trustworthy sharing. Some systems that use software-type algorithms to simulate human cognitive processes in healthcare decision-making handle the privacy of a patient's data. The dentist and the support staff oversee managing data, which carries a risk of data privacy and security violations. This study examines how the use of VR and AR is allowing decentralized care, personalized treatment, and a new generation of intelligent detection and prevention. © 2023 IEEE.

关键词： Blockchain

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1st International Conference on Computational Intelligence for engineering and Management applications, CIEMA 2022

作者： Anand Kumar, M. Purohit, Kamlesh Chandra Singh, Anuj Department of Computer Applications Graphic Era Deemed to be University Dehradun India Department of Computer Science Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9789811984921

The concept of bringing fundamental theories and analytical procedures to medicine and biology is known as biomedical engineering. Bio-artificial intelligence medical engineering is one of the newest and fastest-growing branches of research. From the adoption of medical devices to diagnostic expert systems, it can be profitable in the field of healthcare. These gadgets and expert systems generate data that are multidimensional and erratic. The use of deep learning (DL) algorithms in those devices will be useful for signal analysis and disease detection. Multiple levels of neural networks are used in DL, which are a subset of machine learning. It can learn features on its own. Strokes are one of the deadly diseases worldwide, impacting a huge percentage of the population. Stroke-related diseases require immediate medical attention, which requires emergency treatments. If it was treated in the initial stages, then brain injury and other consequences can be avoided. The main objective of this work was to evaluate different machine learning algorithms to find a suitable approach for stroke-related diseases prediction applications. This research work evaluates different machine learning algorithms, namely logistic regression, random forest, support vector machine (SVM), KNN, and the decision tree for predicting and identifying stroke and its related diseases. Finally, deep belief networks are applied for the symptom dataset to get results that are more accurate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Efficient Skin Lesion based Classification System for Monkeypox Detection using VGG16 and Ensemble Learning 10

Efficient Skin Lesion based Classification System for Monkey...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Pabbi, Varinder Khullar, Vikas Angurala, Mohit Ramgarhia Institute of Engineering and Technology Department of Computer Applications Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789380544472

Monkeypox is a recently emerged disease outbreak that has impacted the health of many individuals. This study focuses on developing a skin lesion-based classification system for monkeypox disease. Many recent works suggest that the direct implementation of machine learning (ML) and deep learning (DL) approaches for this task has not resulted in high accuracy. Therefore, this paper proposes an efficient technique that employs a hybrid of VGG16-based feature engineering, class balancing, and ensemble learning for the detection of monkeypox skin lesions. Specifically, DL-based feature extraction is applied, but the classification is carried out through ensemble learning techniques. The results demonstrate that the random forest, extra tree, and histogram gradient descent methods achieved better performance in terms of different performance metrics. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Feature extraction

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An Improved Blockchain Based Security Framework for IoT Enabled Wireless Network

An Improved Blockchain Based Security Framework for IoT Enab...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Shrivastava, Kapil Singh, Sandeep Chaudhary, Preeti Singh, Ruchi Saraswat, Birendra Kumar Garg, Ashish Gla University Department Of Computer Engineering And Applications UP Mathura India Gl Bajaj Institute Of Technology And Management Department Of Information Technology UP Greater Noida India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Srm Institute Of Science And Technology Department Of Management UP Modinagar India Raj Kumar Goel Institute Of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

The net of factors (Iota) has opened new opportunities for connecting a diverse range of devices and making them handy over the net. However, it has additionally accelerated the vulnerability of these gadgets to cyberassaults. One capability technique to cope with It trouble is implementing Block chain generation, which offers allotted and relaxed facts storage and transaction validation. It paper advocates a progressed Block chain-based safety framework for Iota-enabled wireless networks. Our framework uses clever contracts, which might be self-executing virtual contracts that implement the agreed-upon policies and conditions among parties. Those clever contracts ease communication among Iota gadgets and the Wi-Fi community, ensuring that the most effective legal and demonstrated devices can access the network. Our framework incorporates a consensus mechanism for validating transactions on the Block chain, ensuring the integrity and immutability of statistics. Our proposed framework contains a dynamic key control gadget, which often updates and rotates keys to prevent unauthorized admission to the community. It selection also permits relaxed communication among devices, even in the case of a compromised key. In conclusion, our improved Block chainbased total protection framework presents a strong answer for securing Iota-enabled Wi-Fi networks. © 2024 IEEE.

关键词： Wi-Fi

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Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

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Journal of Harbin Institute of Technology(New Series) 2024年第1期31卷 90-98页

作者： K.Rama Devi V.Srinivasan G.Clara Barathi Priyadharshini J.Gokulapriya Department of Information Technology Panimalar Engineering CollegeChennaiTamil Nadu 600029India Department of Computer Applications Dayananda Sagar College of EngineeringBengaluru 560078KarnatakaIndia Department of Computer Appliations Karpagam Academy of Higher EducationCoimbatoreTamil Nadu 641021India Department of Computer Science Rathinam College of Arts and ScienceCoimbatoreTamil Nadu 641021India

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

关键词： electricity consumption predictive model data analytics smart metering machine learning

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Big Data Graph Node Importance Using Page Rank 1

Big Data Graph Node Importance Using Page Rank

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Roseline, R. Vasudevan, B. Clementking, A. Rani, S. Sivakumar, T. Srinivasan, C. PG Department of Computer Applications Cuddalore India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur India Karnataka Bengaluru India Department of BCA/IT Tamil Nadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Improving the efficiency and accuracy of finding important nodes in large networks is the goal of enhancing Big Data graph node significance analysis using PageRank. To deal with the massive and complicated data sets seen in big data settings, it is necessary to modify and enhance the PageRank algorithm. The objective is to provide a more efficient framework that can speed up computation and improve the accuracy of significance ranking in large-scale networks. To control the algorithm's scalability, it is necessary to use state-of-the-art computational methods, such as parallel processing and effective data structures. The expected result is a scalable and reliable system that can quickly and correctly analyze large networks, yielding useful insights for applications like recommendation systems, online search, and social network analysis. The focus is on making PageRank more feasible for use in large data settings while also increasing its computing efficiency and scalability. The Adjacency Matrix of the Graph in a sample of 5 nodes has a value ranging from 15 to 55 in numbers, according to results from synthetic graph data. In another instance of the same dataset, the Initial PageRank Scores range from 0.2-0.15 in numbers, and in yet another instance, the final PageRank Scores for nodes range from 0.177 to 0.242 in numbers. © 2024 IEEE.

关键词： Data accuracy

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Robust Sensor Networks: Applying AI for Fault Identification in Challenging Operational Contexts 5

Robust Sensor Networks: Applying AI for Fault Identification...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Manjunatha, A.S. Venkatramana Bhat, P. Ramakrishna, M. Prabhu, Matti Nidhi Dept. of Computer and Communication Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Dept. of Computer Science and Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal576104 India

ISBN: (纸本)9798331518097

Wireless Sensor Networks (WSNs) are highly implemented in harsh and dynamic environments. In such configurations, faults and anomalies easily occur, causing unwanted disruptions in data accuracy and network performance. In the context of reliability and stability in WSN operations, effective fault detection techniques are vital. This research work discusses how several machine learning models may be applied, including Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors in order to detect fault in WSNs. The performance evaluation was performed based on major criteria, which are accuracy, precision, recall, and Fl score. This, therefore, makes these approaches to machine learning effective in identifying faults efficiently, hence offering a well-balanced solution that is scalable and robust for integrity and operational effectiveness of WSN s. © 2024 IEEE.

关键词： Random forests

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