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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network 7

Mobility and Behavior based Trustable Routing in Mobile Wire...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Praveen Kumar, B. Umamageswaran, J. Kalpana, A.V. Dhanalakshmi, R. Department of Computer Science and Engineering KL University Andhra Pradesh Vijayawada India Department of Information Technology RMK Engineering College Tamil Nadu Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features;they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN. © 2023 IEEE.

关键词： Sensor nodes

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Incremental Learning with Pseudo Labels for Enhanced Glomeruli Segmentation Using SAM

Incremental Learning with Pseudo Labels for Enhanced Glomeru...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Liu, Xinkai Peng, Boyuan Zhu, Xin Iwasaki, Tsuyoshi Sato, Atsuya Kazama, Junichiro James Huang, Jie Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan Department of Nephrology and Hypertension Fukushima Medical University Fukushima Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan

ISBN: (纸本)9798350350548

The advent of the Segment Anything Model (SAM) marks a significant milestone in deep learning semantic segmentation tasks. In this study, an incremental learning model based on pseudo labels is proposed. The model generates bounding box prompts for SAM, enhancing the accuracy of glomeruli segmentation in renal pathology images. The model uses DenseNet-121 along with contrastive learning to enable self-supervised pre-training using mouse glomeruli images. Transfer learning is then conducted using labeled human renal pathology images to develop a binary classification model for glomeruli. Unlabeled images of human renal pathology are subsequently introduced for incremental learning. Class activation maps (CAMs) generated by the incremental learning model are post-processed to extract bounding rectangles for connected regions. These rectangles are then used as prompts for SAM segmentation. The proposed approach resulted in an average Dice similarity coefficient (DSC) of 0.825 in the glomeruli segmentation task. This result demonstrates the effectiveness of the incremental learning model and post-processing algorithms in improving SAM’s segmentation accuracy for renal pathology. © 2024 IEEE.

关键词： Contrastive Learning

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Generative AI: A Review on Models and Applications 2

Generative AI: A Review on Models and Applications

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Malik, Kiran Baliyan, Anupam School of Computing Science and Engineering Galgotias University UP Greater Noida India Koneru Lakshmaiah Education Foundation Artificial Intelligence and Data Science AP Guntur India Matu Ram Institute of Engineering and Management Computer Science and Engineering Haryana Rohtak India Chandigarh University Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350369960

Generative Artificial Intelligence (AI) stands as a transformative paradigm in machine learning, enabling the creation of complex and realistic data from latent representations. This review paper comprehensively surveys the landscape of Generative AI, encompassing its foundational concepts, diverse models, training methodologies, applications, challenges, recent advancements, evaluation metrics, and ethical dimensions. The paper begins by introducing Generative AI's significance across various domains, presenting its pivotal role in producing synthetic data with applications spanning image synthesis, text generation, music composition, drug discovery, and more. The objectives lie in elucidating the foundational concepts, delving into model intricacies, unveiling the training procedures, exploring its application landscape, addressing challenges, envisioning future directions, and discussing ethical ramifications. The foundational section elucidates the diverse array of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, Generative Reinforcement Learning (GRL), and advanced hybrid architectures. Subsequently, evaluation metrics ranging from Inception Score to perceptual similarity metrics and human evaluations are surveyed to assess generative model performance. Finally, ethical considerations underscore the necessity for addressing biases, misuse, intellectual property concerns, and the call for responsible AI development and regulation in the Generative AI landscape. © 2023 IEEE.

关键词： Advanced Hybrid Architectures Flow-Based Models Generative Adversarial Networks (GANs) Generative Reinforcement Learning (GRL) Variational Autoencoders (VAEs)

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Multi-Agent System Based data Mining Technique for Supplier Selection

Multi-Agent System Based Data Mining Technique for Supplier ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Dhatterwal, Jagjit Singh Kaswan, Kuldeep Singh Singh, Jay Naruka, Mahaveer Singh Govardhan, D. Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation AP Guntur India School of Computing Science and Engineering Galgotias University UP Greater Noida India G L Bajaj Institute of Technology and Management Department of Electrical and Electronics Engineering Greater Noida India Institute of Aeronautical Engineering Department of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798350357769

Different target knowledge representations, which correspond to different hypothesis spaces, are suitable for learning various types of target functions. A distinct learning algorithm, capitalizing on an underlying structure to guide the exploration of the hypothesis space, leverages each of these hypothesis representations. Consequently, making decisions on these matters entails navigating an expansive landscape of alternative approaches to determine the one that best aligns with the defined learning problem. To select an appropriate learning algorithm that performs optimally for the given problem and target function, it is essential to analyze the interplay between the size and completeness of the hypothesis space, the availability of training examples, the learner's prior knowledge, and the confidence in a hypothesis's ability to generalize correctly to unseen data points. This challenge often arises in the context of poorly integrated systems. The objective of this paper is to introduce a specific approach to addressing these challenges, involving the use of enhanced elicitation techniques and tools, improved development paradigms, knowledge modeling languages, ontologies, and advanced methods for system maintenance. However, in the past decade, an alternative reasoning paradigm and computational problem-solving approach have garnered significant attention, involving the use of Multi-Agent System (MAS) based data Mining (DM) techniques. This paper proposes the integration of MAS and DM for supplier selection, with a focus on partitioning-based algorithms such as K-means, Farthest First, Expectation Maximization, and Filtered Clusterer. These algorithms are compared based on factors such as dataset size, the number of clusters, and the time required for cluster formation. Finally, within this integrated model, MAS-DM is exemplified through an illustrative result-matching process. © 2023 IEEE.

关键词： Cluster data Mining JADE Multi-Agent Ontologies

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Disaster Management Redefined: Integrating SVM-AE Techniques with Remote Sensing and Meteorological data

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Remote Sensing in Earth systems sciences 2024年第4期7卷 294-308页

作者： Priyadharshini, L. Dhanke, Jyoti A. Patil, R.N. Swapna, B. Kalyani, Kapula Syamala, Maganti Deivasigamani, Shanmugavel Saveetha School of Law Saveetha Institute of Medical and Technical Science Chennai India Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of Mechanical Engineering Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of ECE Dr. M.G.R. Educational and Research Institute Chennai India Department of Electronics and Communication Engineering Aditya College of Engineering and Technology Andhra Pradesh Surampalem 533437 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram Guntur India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Anna University Tamilnadu Chennai India

This study presents a novel hybrid model, the Support Vector Autoencoder (SVAE), designed to enhance disaster management through the integration of Support Vector Machines (SVM) and Autoencoders (AE). By leveraging the strengths of both machine learning techniques, the SVAE model offers improved accuracy and reliability in predicting and managing natural disasters. The methodology involves comprehensive data collection from Sentinel-2 satellite imagery and Global Precipitation Measurement (GPM) mission data, supplemented by historical disaster records from the Emergency Events database (EM-DAT). After rigorous preprocessing, key features such as the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), soil moisture content, and various meteorological parameters are extracted. These features are then normalized and used to train the SVM for supervised learning and the AE for unsupervised learning. The outputs of these modules are integrated through a fusion layer, which combines classification scores and anomaly detection signals to generate a final risk score. Performance comparison with other models, including Random Forest, k-NN, Decision Tree, and Naive Bayes, demonstrates that the SVAE model achieves superior accuracy, precision, recall, and F1-score. The proposed model’s accuracy reaches 97%, significantly outperforming other techniques in anomaly detection and risk assessment. The results indicate that the SVAE model is a robust tool for enhancing disaster preparedness and mitigation efforts, providing timely and actionable insights to decision-makers. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Autoencoder (AE) Disaster management Emergency Events database (EM-DAT) Global Precipitation Measurement (GPM) Machine learning Normalized Difference Vegetation Index (NDVI) Sentinel-2 Satellite Imagery Support Vector Autoencoder (SVAE) Support Vector Machine (SVM)

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Successful Integration of IoT and Blockchain Technologies Using Several Machine Learning Algorithms 5

Successful Integration of IoT and Blockchain Technologies Us...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Calarany, C. Indumathy, M. Senthilraja, P. Suganya, D. Gopi Vardhan, V. Rahul, Yelisetty Shanmukh Tagore Government Arts and Science College Department of Computer Science Puducherry India Rajiv Gandhi College of Engineering & Technology Department of Information Technology Kirumampakkam India K.S.Rangasamy College of Technology Department of Computer Science Tamil Nadu India Rajiv Gandhi College of Engineering & Technology Department of Computer Science Kirumampakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

Internet of Things (IoT) produces massive amounts of data that need to be processed and saved securely. The strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT Sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. The MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. The MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. The conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. These inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. The results obtained provide a promising note for future scope in this direction. © 2024 IEEE.

关键词： IoT MML RNN

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Improved Image Classification with Manifold Neural Networks

Improved Image Classification with Manifold Neural Networks

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Caio F. Deberaldini Netto Zhiyang Wang Luana Ruiz Department of Applied Mathematics and Statistics Mathematical Institute for Data Science (MINDS) Data Science and Artificial Intelligence Institute (DSAI) Johns Hopkins University Baltimore USA Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Neural Networks (GNNs) have gained popularity in various learning tasks, with successful applications in fields like molecular biology, transportation systems, and electrical grids. These fields naturally use graph data, benefiting from GNNs’ message-passing framework. However, the potential of GNNs in more general data representations, especially in the image domain, remains underexplored. Leveraging the manifold hypothesis, which posits that high-dimensional data lies in a low-dimensional manifold, we explore GNNs’ potential in this context. We construct an image manifold using variational autoencoders, then sample the manifold to generate graphs where each node is an image. This approach reduces data dimensionality while preserving geometric information. We then train a GNN to predict node labels corresponding to the image labels in the classification task, and leverage convergence of GNNs to manifold neural networks to analyze GNN generalization. Experiments on MNIST and CIFAR10 datasets demonstrate that GNNs generalize effectively to unseen graphs, achieving competitive accuracy in classification tasks.

关键词： Manifolds Electric potential High dimensional data Autoencoders Transportation Signal processing Graph neural networks Molecular biology Speech processing Image classification

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Socioeconomic Changes Based Climate Training for Agricultural Application Using Deep Learning Model

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Remote Sensing in Earth systems sciences 2024年第4期7卷 399-410页

作者： Sunitha, M. Durairaj, M. Rajalingam, A. Yusoff, Siti Khalidah Mohd Prasad, S. Hari Chandra Malluvalasa, S. N. Lakshmi Kiran, Ajmeera Department of CSE Vasavi College of Engineering Hyderabad India Department of ECE Saveetha School of Engineering SIMATS Chennai India Department of Engineering University of Technology and Applied Sciences-Shinas and Saveetha Institute of Medical and Technical Sciences Chennai India Management and Science University Selangor Shah Alam Malaysia Department of Electronics & amp Communication Engineering Aditya University Surampalem India Department of Artificial Intelligence & amp Data Science Koneru Lakshmaiah Education Foundation Guntur Dist. Andhra PradeshVaddeswaram 522302 India Department of Computer Science and Engineering MLR Institute of Technology Dundigal Telangana Hyderabad 500043 India

In socioeconomic factor studies, the use of the logit and probit models has the disadvantage of representing random variation in preference for unobserved components and time-linked errors. The agricultural sector in the coastal nations of South-East Asia is facing mounting strain due to the adverse effects of climate change. Anticipating and controlling climate variables is challenging and necessitates expensive fixes. Based on social and economic shifts and the use of a deep learning model to detect climate change, the two main facets of agricultural application have been examined in this study. The suggested model focusses on these two factors because the economics and climate are crucial components of agriculture. Initially, financial flow–based data analysis employing recurrent LSTM spatial neural networks for text data analysis was used to assess the social economic analysis for those countries with minimal agricultural development. The study of crop growth data based on climate change is then done using Boltzmann Markov quantile regression neural networks. In terms of training accuracy, sensitivity specificity, and ROC, a performance study is conducted for crop growth data based on climatic change and agricultural development based on economic data. The proposed approach in this instance produced training accuracy of 79%, specificity of 83%, ROC of 84%, and sensitivity of 80% for economic data–based agricultural development;the suggested method achieved 75% training accuracy, 73% specificity, 80% ROC, and 84% SENSITIVITY for climate change–based crop growth data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Agricultural application Climate change detection Deep learning model Quantile regression Social economic changes

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Fog-Sec: Secure end-to-end communication in fog-enabled IoT network using permissioned blockchain system

Fog-Sec: Secure end-to-end communication in fog-enabled IoT ...

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作者： Babu, Erukala Suresh Rao, Mekala Srinivasa Swain, Gandharba Nikhath, A. Kousar Kaluri, Rajesh Department of Computer Science and Engineering National Institute of Technology Warangal India Department of CSE Lakireddy Bali Reddy College of Engineering Mylavaram India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram India VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Hyderabad India School of Information Technology and Engineering VIT Vellore India

The technological integration of the Internet of Things (IoT)-Cloud paradigm has enabled intelligent linkages of things, data, processes, and people for efficient decision making without human intervention. However, it poses various challenges for IoT networks that cannot handle large amounts of operation technology (OT) data due to physical storage shortages, excessive latency, higher transfer costs, a lack of context awareness, impractical resiliency, and so on. As a result, the fog network emerged as a new computing model for providing computing capacity closer to IoT edge devices. The IoT-Fog-Cloud network, on the other hand, is more vulnerable to multiple security flaws, such as missing key management problems, inappropriate access control, inadequate software update mechanism, insecure configuration files and default passwords, missing communication security, and secure key exchange algorithms over unsecured channels. Therefore, these networks cannot make good security decisions, which are significantly easier to hack than to defend the fog-enabled IoT environment. This paper proposes the cooperative flow for securing edge devices in fog-enabled IoT networks using a permissioned blockchain system (pBCS). The proposed fog-enabled IoT network provides efficient security solutions for key management issues, communication security, and secure key exchange mechanism using a blockchain system. To secure the fog-based IoT network, we proposed a mechanism for identification and authentication among fog, gateway, and edge nodes that should register with the blockchain network. The fog nodes maintain the blockchain system and hold a shared smart contract for validating edge devices. The participating fog nodes serve as validators and maintain a distributed ledger/blockchain to authenticate and validate the request of the edge nodes. The network services can only be accessed by nodes that have been authenticated against the blockchain system. We implemented the proposed

关键词： Blockchain

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