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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Bhuvaneshwari, V. Sadaieswaran, R. Malathi, P. Legapriyadharshini, N. Thirumalaikumari, T. Raja, S.R. Acharya College of Engineering Department of Computer Science and Engineering Achariyapuram Puducherry India Tagore College of arts and science Department of Computer Applications Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Data Analytics Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350384369

The growing number of autoimmune diseases necessitates the needs novel drug delivery system alternatives to improve targeted therapy. The study deals with the immediate requirement for improvements by investigating the limitations of existing systems, highlighting their limitations in precision, patient adherence, and real-time monitoring. The proposed system combines Internet of Things (IoT) technology into medicine distribution, providing an extensive structure that makes use of smart devices, sensors, and networking solutions. The integration attempts to improve patient participation and treatment efficacy by ensuring tailored regimens. The study demonstrates promising discoveries in optimizing therapeutic interventions, cost-effectiveness, and data-driven insights through an in-depth evaluation of case studies and successful implementations. The proposed IoT-enhanced drug delivery system not only addresses the limitations of existing systems but also represents an evolution for healthcare professionals and pharmaceutical businesses. Patient privacy and data security are essential for ethical and regulatory reasons. The study's results and analysis highlight the proposed system's potential for overcoming limitations and enhancing targeted therapy for autoimmune disorders, setting the groundwork for a future in which IoT seamlessly interacts with drug delivery systems to transform patient care. © 2024 IEEE.

关键词： Targeted drug delivery

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Modeling the homophily effect between links and communities for overlapping community detection 25

Modeling the homophily effect between links and communities ...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Zhang, Hongyi Zhao, Tong King, Irwin Lyu, Michael R. Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

Overlapping community detection has drawn much attention recently since it allows nodes in a network to have multiple community memberships. A standard framework to deal with overlapping community detection is Matrix Factorization (MF). Although all existing MF-based approaches use links as input to identify communities, the relationship between links and communities is still under-investigated. Most of the approaches only view links as consequences of communities (community-to-link) but fail to explore how nodes' community memberships can be represented by their linked neighbors (link-to-community). In this paper, we propose a Homophily-based Nonnegative Matrix Factorization (HNMF) to model both-sided relationships between links and communities. From the community-to-link perspective, we apply a preference-based pairwise function by assuming that nodes with common communities have a higher probability to build links than those without common communities. From the link-to-community perspective, we propose a new community representation learning with network embedding by assuming that linked nodes have similar community representations. We conduct experiments on several real-world networks and the results show that our HNMF model is able to find communities with better quality compared with state-of-the-art baselines.

关键词： Matrix factorization

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IoT-Genomics Analysis for Personal Health with Cybergenomics and Cloud Technology 10

IoT-Genomics Analysis for Personal Health with Cybergenomics...

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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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DCNN and LSTM based Plant Leaf Disease Detection Model For Edge IoT Device

DCNN and LSTM based Plant Leaf Disease Detection Model For E...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： L. Selvam S. Roseline Mary A. Manimuthu M. Pandian Department of Computer Applications Faculty of Science and Humanities SRMIST Kattankulathur chengalpattu Department of Mathematics Loyola College Chennai Department of Data Science Loyola College Chennai

Edge Computing is an incredibly practical and affordable alternative IoT device for cloud computing that uses a revolutionary methodology. Not only does edge processing reduce the cost of systems administration frameworks, but it also improves edge-cloud latency, which is essential for all critical applications. Plant diseases have a catastrophic impact on food production security and can lead to a sharp decline in the quantity and quality of agricultural products. In severe cases, plant diseases may continue to be the cause of partial grain failure. As a result, an automated method for diagnosing and detecting plant diseases is vitally needed for all agricultural data. In this paper, we have proposed a plant disease detection method based on transfer learning and tested the model’s performance on IoT edge device. Long-Short Term Memory (LSTM) is used in the proposed system to classify plant leaf diseases based on data extracted by CNN. An internal memory within the LSTM network allows it to learn from critical experiences with long-term conditions. Therefore, the combination of CNN and LSTM layout features significantly enhances classification. It is suggested to use a Deep Convolutional Neural Network based on a pre-trained MobileNetV1 feature extractor and an LSTM classifier. The proposed model’s performance is contrasted with that of other pre-trained models, including NASNetMobile and MobileNetV2. The proposed model outperformed other models, achieving a validation accuracy of 0.9892.

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Development of Advanced Noise Filtering Techniques for Medical Image Enhancement 5

Development of Advanced Noise Filtering Techniques for Medic...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Kushwaha, Sumit Amuthachenthiru, K. Geetha, K. Narasimharao, Jonnadula Dileep Kumar, M. Gadde, Sai Sudha University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603 203 India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad501401 India Hensard University Faculty of Management Sciences Bayelsa 561101 Nigeria Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Vaddeswaram500075 India

ISBN: (纸本)9798350385649

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality. © 2024 IEEE.

关键词： Medical imaging

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Empowering Breast Cancer Detection with AI: A Modified Support Vector Machine Approach for Improved Classification Accuracy 4

Empowering Breast Cancer Detection with AI: A Modified Suppo...

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4th International Conference on Expert Clouds and applications, ICOECA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Shiny, K.V. Sujatha, T. N.r, Wilfred Blessing College of Computing and Information Sciences University of Technology and Applied Information Technology Department Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India College of Computing and Information Sciences Univesity of Technology and Applied Sciences Information Technology Department Ibri Oman

ISBN: (纸本)9798350385793

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient survivable mapping algorithm for logical topology in IP-over-WDM optical networks against node failure

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Journal of Supercomputing 2023年第5期79卷 5037-5063页

作者： Cheng, Dun-Wei Chang, Jo-Yi Lin, Chen-Yen Lin, Limei Huang, Yanze Thulasiraman, Krishnaiyan Hsieh, Sun-Yuan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Yunlin64002 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University No. 1 University Road Tainan701 Taiwan College of Mathematics and Informatics and the Key Laboratory of Network Security and Cryptology Fujian Normal University Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Mathematics and Physics Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science University of Oklahoma 660 Parrington Oval NormanOK73019 United States Department of Computer Science and Information Engineering National Chi Nan University No. 1 Daxue Rd Nantou County Puli Township54561 Taiwan

The survivable mapping problem in an IP-over-WDM network with a logical graph and physical graph is the problem of finding a mapping of a logical network in a physical layer so that any failure in physical topology does not break the logical topology’s connection. Determining whether a survivable mapping against failure exists is an NP-complete problem, and therefore many heuristic algorithms have been proposed in the literature. In this paper, a heuristic mapping design strategy is proposed to enable the lightpaths to more efficiently endure a node failure. We individually consider the logical topology by building a Hamiltonian cycle and then deal with the mapping problem for disjoint physical paths. Experimental results demonstrate that the proposed algorithm can provide efficient survivable mapping in IP-over-WDM networks. The simulation shows that the survivability of previous methods (SNF and SMART-Node) is around 60–80%, but our proposed algorithm can build survivable mapping in all kinds of situations. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Heuristic algorithms

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Detection of Number Plate in Vehicles using Deep Learning based Image Labeler Model

Detection of Number Plate in Vehicles using Deep Learning ba...

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2023 IEEE International Conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Gupta, Shashi Kant Saxena, Surabhi Khang, Alex Hazela, Bramah Dixit, Chandra Kumar Haralayya, Bhadrappa Eudoxia Research University Department of Cse United States Pes University Department of Computer Applications Bengaluru560085 India University of Science and Technology Ai and Data Scientist Department of Information Technology Viet Nam University of Science and Technology Ai and Data Scientist Department of Information Technology United States Amity School of Engineering and Technology Lucknow Amity University Computer Science and Engineering Uttar Pradesh India Dr. Shakuntala Misra National Rehabilitation University Department of Physics UP Lucknow226017 India Lingaraj Appa Engineering College Bidar Department of Mba Karanataka Bidar585403 India

ISBN: (纸本)9798350396195

Deep learning is playing a vital role in most of the applications being used in today's technological era. There seems to be no application where the use of deep learning concepts has not been used yet. One such application is the detection of number plates of vehicles on roads. The incidence of automobile crashes has greatly expanded along with the population growth. Their needs to be system that could automatically detect the vehicle's number plate whenever necessary. The situation arises when any of the rider does not obey to the traffic rules, or skips the traffic signals or indulges in any other sort of activities such as accidents. In these situations, the rider tries to run away. In order to detect the person, these kind of deep learning models helps us to detect the vehicle. Image labelers on the roadsides. Need of sensors are required to store the data and they need to be deployed on the roadside. These labelers are used to track the vehicles that are not obeying the traffic rules. With the use of sensors, it could be stated that the use of IoT also plays in the deployment of the current work when it comes to real-time. The model initially is trained with numerous number plates. The number plates have various patterns of texts and numbers and it is essential for the deep learning model to get trained. With successful training, the model is able to detect the new number plates. The model proposed in the current paper is compared with the other existing systems and proves to be efficient and cost-efficient than them. © 2023 IEEE.

关键词： Vehicles

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Research and Innovation in Next Generation Security and Privacy in Industry 5.0 Iot 6

Research and Innovation in Next Generation Security and Priv...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Navale, Geeta S. Madala, Ravikiran Managuli, Manjunath Jayalakshmi, N. Kadiravan, G. Rawat, Ruchira Sinhgad Institute of Technology and Science Department of Computer Engineering Maharashtra Pune India Ph.D Information Technology Network and Data Center Engineer University of Cumberlands DurhamNC United States KLS Gogte Institute of Technology Department of Electronics and Communication Engineering Karnataka Belagavi India PSNA College of Engineering and Technology Master of Computer Applications Tamilnadu Dindigul India KL Deemed to Be University Department of Computer Science and Information Technology Andhra Pradsh India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350304480

The objective of the newest manufacturing paradigm, known as 'Industry 5.0,'is to combine human intelligence with cutting-edge technology like the Internet of Things (IoT), artificial intelligence (AI), and robots to develop production systems that are incredibly effective and adaptable. But as we rely more on these technologies, we face additional privacy and security issues that might affect how dependable and trustworthy these systems are. In this report, we examine the most recent advancements in security and privacy for Industry 5.0 IoT and pinpoint the newest trends, obstacles, and possibilities. We also cover how these changes may affect manufacturing in the future and provide advice to scholars, practitioners, and policymakers. © 2023 IEEE.

关键词： artificial intelligence cyber-physical systems human-machine collaboration Industry 5.0 Internet of Things IoT manufacturing privacy robotics security

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Estimating causal effects under network interference with Bayesian generalized propensity scores

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 13101-13161页

作者： Laura Forastiere Fabrizia Mealli Albert Wu Edoardo M. Airoldi Yale Institute for Network Science Yale University New Haven CT Department of Statistics Computer Science Applications University of Florence Florence Italy Department of Statistics Harvard University Cambridge MA Department of Statistics Operations and Data Science Fox School of Business Data Science Institute Temple University Philadelphia PA

Real-world systems are often comprised of interconnected units, and can be represented as networks, with nodes and edges. In a social system, for instance, individuals may have social ties and financial relationships. In these settings, when a node (the unit analysis) is exposed to a treatment, its effects may spill over to connected units; then estimating both the direct effect of the treatment and its spillover effects presents several challenges. First, assumptions about the mechanism through which spillover effects occur along the observed network are required. Second, in observational studies, where the treatment assignment has not been randomized, confounding and homophily are further potential threats to the identification and to the estimation of causal effects, on networks. Here, we make two structural assumptions: (i) neighborhood interference, which assumes interference operates only through a function of the immediate neighbors' treatments, and (ii) unconfoundedness of the individual and neighborhood treatment, which rules out the presence of unmeasured confounding variables, including those driving homophily. Under these assumptions we develop a new covariate-adjustment estimator for direct treatment and spillover effects in observational studies on networks. We proposed an estimation strategy based on a generalized propensity score that balances individual and neighborhood covariates across units under different levels of individual treatment and of exposure to neighbors' treatment. Adjustment for propensity score is performed using a penalized spline regression. Our inference strategy capitalizes on a three-step Bayesian procedure, which allows to take account for the uncertainty in the propensity score estimation, and avoids model feedback. The correlation among connected units is taken into account using a community detection algorithm, and incorporating random effects in the outcome model. All these sources of variability, including variability of

关键词： causal inference interference spillover effect Bayesian inference network data

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