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Improved fuzzy C means with deep learning-based MANET routing model for multimedia communication

International Journal of Networking and Virtual Organisations 2024年第4期31卷 306-338页

作者： Biradar, Manjula A. Mallapur, Sujata Department of Computer Science and Engineering Sharnbasava University Sharan Nagar Karnataka Kalaburagi585105 India Department of Artificial Intelligence and Machine Learning Sharnbasava University Sharan Nagar Karnataka Kalaburagi585105 India

The rapid growth of wireless communication amazed the researchers to done multimedia communication via wireless networks. The significant rise of mobile ad-hoc networks (MANET) in wireless communication is widely researched by researchers for an efficient transmission of multimedia data. MANET works on non-infrastructure topology with high quality of service (QoS) for better user experience. Though, it suffers from limitations such as routing, limited bandwidth, dynamic mobile node nature, etc. That’s why, this research paper is dedicated to introduce a MANET routing model for multimedia communication by providing reliable retransmission for better communication. An optimal cluster-based routing via improved fuzzy C-means (FCM) clustering takes place before it gets started nodal energy prediction is done by modified deep CNN model. And optimal cluster head selection is done via self-improved coati optimisation (SICO) under certain constraints. Finally, it is finished off with reliable retransmission to enhance the reliability of communication. Further, the efficiency of the proposed model is proved by various analysis. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Mobile ad hoc networks

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Multi-Criteria-based Graph Neural Networks for a Medical Emergency Response System 7

Multi-Criteria-based Graph Neural Networks for a Medical Eme...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prakash, J. Princess, P. Joyce Beryl Karunya Institute of Technology And Science Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Science Division of Artificial Intelligence And Machine Learning Coimbatore India

ISBN: (纸本)9798350359299

The ability to get an immediate response from medical emergencies quickly and more effectively is essential for saving both lives and ensuring people's well-being. In the Traditional medical emergency response systems often struggle to correctly assess the seriousness and urgency of emergency response, leading to ineffective resource allocation and prolonged response times. This research work suggests a unique method for medical emergency response systems based on a multi-criteria-based graph neural network to resolve such problems in recent times. The process of choosing the best hospital based on a variety of conditions is optimized by the graph neural network (GNN), which makes use of the graph structure with hospital nodes and accident location nodes. The GNN will examine several conditions to choose the best hospital after identifying the nearby hospitals. It evaluates whether each nearby hospital has the medical resources required to meet the needs of the patient, including available beds, emergency gear, and doctor availability to get a quick medical treatment. To promote quick transportation, the GNN also confirms the availability of ambulances connected to each nearby hospital and alerts the hospital management to arrange needs for patients. The above approach improves the effectiveness and efficiency of medical emergency response systems, guaranteeing that injured persons receive immediate and appropriate medical treatment. © 2024 IEEE.

关键词： Hospitals

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Aspect Sentiment Analysis with Triplet Extraction for E-Commerce 7

Aspect Sentiment Analysis with Triplet Extraction for E-Comm...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Meenakshi Sundaram, A. Joyce Beryl Princess, P. Karunya Institute of Technology And Science Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Science Division of Artificial Intelligence And Machine Learning Coimbatore India

ISBN: (纸本)9798350359299

In the age of e-commerce, it is critical for companies to comprehend the thoughts and feelings of their customers in order to enhance their goods and services. A powerful method for deriving insights from consumer feedback is the combination of aspect sentiment analysis and triplet extraction. The objective of this method is to recognize the characteristics or facets of a good or service under discussion, gather feedback on each component, and create connections between the components, feedback, and viewpoints. Businesses can gain important insights into customer feelings towards certain features like design, usability, price, and customer service by examining customer reviews, comments, and ratings. Opinion sources (experts, customers, etc.), sentiment expressions (positive, negative, or neutral), and opinion targets (aspects) are all identified from the text as part of the triplet extraction process. This gives companies a comprehensive grasp of the thoughts and preferences of their customers, which helps them design products, set prices, and provide customer service with more focus and eventually increase customer happiness. © 2024 IEEE.

关键词： Sentiment analysis

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2023 International Conference on Artificial Intelligence science and Applications in Industry and Society, CAISAIS 2023

作者： Ghanem, Sahar I. Computer Science and Artificial Intelligence Faculty Artificial Intelligence and Machine Learning Department Egypt

ISBN: (纸本)9798350314786

In an attempt to facilitate the way students, succeed academically, academic advising is an integral part of the educational system. However, it has frequently been dependent on time-consuming manual processes and personalized interaction, which are not scalable. machine learning and deep learning have the potential to completely transform advising by automating parts of process and giving students distinctive guidance. This paper explores the relationship between student academic performance and correlated features using deep learning methodology in academic advising systems. The model was trained and tested with the Kaggle dataset, and the results show high accuracy, specificity, and true positive rate values compared to naive bayes and random forest methodologies. The paper also addresses potential challenges and limitations of deep learning in academic advising, such as data privacy concerns, algorithmic bias, and the need for human intervention. It concludes by emphasizing the importance of incorporating ethical considerations and human expertise in the development and deployment of deep learning models in academic advising. Overall, the paper highlights the potential of deep learning algorithms to transform academic advising and improve student success. © 2023 IEEE.

关键词： Students

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Deep learning based RAGAE-SVM for Chronic kidney disease diagnosis on internet of health things platform

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Multimedia Tools and Applications 2024年 1-39页

作者： Kandukuri, Prabhakar Abdul, Ashu Kumar, Kuchipudi Prasanth Sreenivas, Velagapudi Ramesh, G. Gundu, Venkateswarlu Department of Artificial Intelligence and Machine Learning Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering SRM University Andhra PradeshNeerukonda Mangalagiri 522503 India Department of Computer Science & amp Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India Department of Computer Science and Engineering SRK Institute of Technology Andhra Pradesh Vijayawada521108 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology TelanganaBachupally Hyderabad500090 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India

Chronic kidney disease (CKD) is a prominent disease that causes loss of functionality in the kidney. Doctors can now more easily gather patient health status data due to the growth of the Internet of Health Things (IoHT). The IoHT data contains a huge number of redundant data, making it challenging to predict CKD disease quickly and accurately. In healthcare applications like feature-based classification, a variety of disease diagnosis systems were used to address this problem. Current disease detection algorithms suffer from imbalanced dataset processing, low-accuracy feature learning, and high computational power requirements. Thus, deep learning-based clinical decision support systems have been developed to solve these complexities. To remove outliers from medical data, data collected with IoHT devices is first pre-processed using an enhanced K-means clustering technique. The Synthetic Minority over Sampling Technique is used to balance data because the IoHT dataset is highly imbalanced. The classifier detects anomalies in less time because of the implemented processing step. The balanced CKD dataset is then presented for use with a novel classifier called Residual Attention-Gated Autoencoder with Support Vector machine. In order to improve accurate detection, the adopted classifier can learn and extract features. The proposed method results in an increased accuracy of 99.43% within 70.1 s of computation time. The mean intersection of the union and kappa coefficient of the proposed method is 98.15% and 98.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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SecureFedPROM: A Zero-Trust Federated learning Approach with Multi-Criteria Client Selection

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2025-2041页

作者： Tahir, Mehreen Mawla, Tanjila Awaysheh, Feras Alawadi, Sadi Gupta, Maanak Ali, Muhammad Intizar Dublin City University SFI Centre for Research Training in Machine Learning Dublin Ireland Tennessee Tech University Department of Computer Science CookevilleTN United States Umeå University Department of Computer Science Umeå Sweden Blekinge Tekniska Högskola School of Computer Science Karlskrona Sweden Dublin City University School of Electronic and Computer Engineering Dublin Ireland

Federated learning (FL) enables decentralized learning while preserving data privacy. However, ensuring security and optimizing resource utilization in FL remains challenging, particularly in untrusted environments. To address this, we propose SecureFedPROM, a novel zero-trust FL framework that integrates Attribute-Based Access Control (ABAC) for secure client authorization and Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) for dynamic, multi-criteria client selection. Unlike traditional FL client selection methods that prioritize security or efficiency, SecureFedPROM optimizes trustworthiness, computational efficiency, and performance, ensuring robust participation in each training round. We evaluate SecureFedPROM across multiple real-world datasets, demonstrating its superiority over state-of-the-art client selection protocols. Our results show that SecureFedPROM achieves a 7.19% improvement in model accuracy, accelerates convergence, and reduces the number of training rounds. Additionally, it minimizes wall-clock time and computational overhead, making it highly scalable for edge AI environments. These findings highlight the importance of integrating zero-trust security principles with multi-criteria decision-making to enhance security and efficiency in FL. © 1983-2012 IEEE.

关键词： Federated learning

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Graph Convolutional Networks for SEO: A Comprehensive Framework for Healthcare Information Ranking

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Journal of The Institution of Engineers (India): Series B 2025年 1-16页

作者： RR, Prianka M, Vedaraj R, Anto Arockia Rosaline Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Kavaraipettai Thiruvallur India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India

Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO) has become paramount in healthcare industries, which helps patients enhance and understand their health status based on their records. In the context of healthcare, it is more significant to improve search results from specific keywords related to clinical conditions, treatments, and healthcare services. So, this research work proposes a Graph Convolutional Network (GCN)-based Search Engine Optimization (SEO) algorithm for healthcare applications. The algorithm utilizes two distinct datasets: MIMIC-III Clinical Database and Consumer Health Search Queries to optimize search engine rankings for health related queries. Following data acquisition, data pre-processing is performed for better enrichment of analysis. The preprocessing steps involve data cleaning, data integration, feature engineering, and knowledge graph construction procedures to remove noisy data, integrate medical data with user search behavior, compute significant features, and construct knowledge graphs, correspondingly. The relation between the data entities is examined within constructed graph through link analysis. The pre-processed data including medical knowledge weights, content relevance scores, and user interaction signals are processed further on GCN model with Adam-tuned weights and bias for ranking healthcare data based on relevance score in response to user query using cosine similarity. The search relevance estimation indicators namely recall, precision, f1-score, and normalized discounted cumulative gain (NDCG) are computed to measure search optimization performance. The proposed GCN-SEO approach benchmarked its effectiveness over existing methods in optimizing web searches related to healthcare with a high performance rate of 95.75% accuracy and 48.25 s dwell time. This sho

关键词： Clinical research

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NOVASCORE: A New Automated Metric for Evaluating Document Level Novelty 31

NOVASCORE: A New Automated Metric for Evaluating Document Le...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Ai, Lin Gong, Ziwei Deshpande, Harshsaiprasad Johnson, Alexander Phung, Emmy Emami, Ahmad Hirschberg, Julia Machine Learning Center of Excellence JPMorgan Chase & Co. Japan Department of Computer Science Columbia University United States

ISBN: (纸本)9798891761964

The rapid expansion of online content has intensified the issue of information redundancy, underscoring the need for solutions that can identify genuinely new information. Despite this challenge, the research community has seen a decline in focus on novelty detection, particularly with the rise of large language models (LLMs). Additionally, previous approaches have relied heavily on human annotation, which is time-consuming, costly, and particularly challenging when annotators must compare a target document against a vast number of historical documents. In this work, we introduce NOVASCORE (Novelty Evaluation in Atomicity Score), an automated metric for evaluating document-level novelty. NOVASCORE aggregates the novelty and salience scores of atomic information, providing high interpretability and a detailed analysis of a document's novelty. With its dynamic weight adjustment scheme, NOVASCORE offers enhanced flexibility and an additional dimension to assess both the novelty level and the importance of information within a document. Our experiments show that NOVASCORE strongly correlates with human judgments of novelty, achieving a 0.626 Point-Biserial correlation on the TAP-DLND 1.0 dataset and a 0.920 Pearson correlation on an internal human-annotated dataset. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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An autonomous robot for removing Underwater pollution : OCTACLEANER 3

An autonomous robot for removing Underwater pollution : OCTA...

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3rd IEEE International Conference on Technology, Engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Reddy, A. Nymisha Nandini Pavani, Dhanni Rakshitha, B. Shiva Saw, Neha Computer Science with Artificial Intelligence and Machine Learning Hyderabad Institute of Technology and Management Hyderabad India

ISBN: (纸本)9798350397819

In the contemporary world, in which all life forms, counting with aquatic life, are stricken by global warming, there are various answers for numerous kinds of problems, however there's very moderate interest paid to the waste that is amassing underwater. The waste that we dump into bodies of water does not simply float, but settles under water after a period of time. To resolve this, we proposed a solution in the form of an octopus-like robot named OCTACLEANER. This robot would help in the removal of trash accumulated beneath, with the added benefit of removing muck. © 2023 IEEE.

关键词： Global warming

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

作者： Mallikarjuna, Shruthi Bennur Raju, Channakrishna Sjce Jss Science and Technological University Department of Computer Science and Engineering Mysuru India Sri Siddhartha Institute of Technology Ssahe Department of Artificial Intelligence and Machine Learning Tumakuru India

ISBN: (纸本)9798350352931

Wireless Sensor Networks (WSN) are designed to monitor sensor nodes systems, with a focus on data processing, collection and transmission. Developments in wireless communication have extended the scope of WSNs to various domains such as underwater communication and Internet of Things (IoT). However, secure clustering and routing remain challenging due to increased vulnerability to attacks in IoT-WSN, which negatively impact communication and data integrity. This research proposes Similarity Preference Weight-Chimp Optimization Algorithm (SPW-ChOA) for secure clustering and routing in WSN-IoT. Trust, node degree, distance between Cluster Head (CH) and Base Station (BS), energy and distance are employed as fitness functions for Secure CH (SCH) selection. The factors considered for routing in SPW-ChOA include energy and distance. The performance of SPW-ChOA is evaluated in terms of residual energy, Packet Delivery Ratio (PDR), throughput and energy consumption, with the number of nodes varied at 500,1000,1500 and 2000. The proposed SPW-ChOA consumes lesser energy at 0.28 which compared to the existing Butterfly Optimization Algorithm (BOA) and Antlion Optimization Algorithm (AOA). © 2024 IEEE.

关键词： Energy utilization

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