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A Collaborative Approach for Secured Routing in Mobile Ad-Hoc Network

Intelligent Automation & Soft Computing 2023年第2期35卷 1337-1351页

作者： W.Gracy Theresa A.Gayathri P.Rama Department of Computer Science and Engineering Panimalar Institute of TechnologyChennai600123Tamil NaduIndia Department of Information Technology Vellore Institute of TechnologyVellore632014Tamil NaduIndia Department of Computer Science and Engineering Loyola Institute of TechnologyChennai600123Tamil NaduIndia

Mobile computing is the most powerful application for network com-munication and connectivity,given recent breakthroughs in theﬁeld of wireless networks or Mobile Ad-hoc networks(MANETs).There are several obstacles that effective networks confront and the networks must be able to transport data from one system to another with adequate *** most applications,a frame-work must ensure that the retrieved data reﬂects the transmitted *** driv-ing to other nodes,if the frame between the two nodes is deformed in the data-link layer,it must be *** link-layer protocols immediately disregard the frame and enable the high-layer protocols to transmit it *** other words,because of asset information must be secured from threats,information is a valu-able *** MANETs,some applications necessitate the use of a network method for detecting and blocking these *** a secure intrusion detection system in the network,which provides security to the nodes and route paths in the network,is a major difﬁculty in *** on the network can jeopardize security issues discovered by the intrusion detection system engine,which are then blocked by the network’s intrusion prevention *** bringing the Secure Intrusion Detection System(S-IDS)into the network,a new technique for implementing security goals and preventing attacks will be *** Secure Energy Routing(SER)protocol for MANETs is introduced in this *** protocol addresses the issue of network security by detecting and preventing attacks in the *** data transmission in the MANET is forwarded using Elliptical Curve Cryptography(ECC)with an objective to improve the level of *** Simulator–2 is used to simulate the network and experiments are compared with existing methods.

关键词： Mobile ad-hoc network(MANET) intrusion detection system secure energy routing(SER) elliptical curve cryptography(ECC) security

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A Self-Labeling Method for Adaptive Machine Learning by Interactive Causality

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2093-2102页

作者： Ren, Yutian Yen, Aaron Haohua Li, Guann-Pyng University of California Irvine Department of Electrical Engineering and Computer Science The California Institute for Telecommunications and Information Technology IrvineCA92697 United States

Learning from unlabeled data or self-learning, can substantially reduce the complexity of machine learning (ML) utilization in real-time deployment. While the development of un/semisupervised algorithms shows promising results in learning with reduced labels, the fundamental assumption of data smoothness restricts its scope of application, especially with nonstationary data distributions in different domains. Leveraging transdomain invariant causal relationships, causality has recently been employed to foster robust ML. In this article, we have developed a generic method for self-labeling data that relies on known causality among interactive objects and learned temporal relations among causal events for identifying and associating labels and input data. The causal time-interval between asynchronous cause and effect events is studied to achieve self-labeling. We utilize dynamical system theory in a 1-d setting to demonstrate that our proposed method outperforms traditional feature similarity-based semisupervised learning. A computer simulation experiment was conducted to generate high-dimensional data, and the comprehensive results reveal the potential of learning adaptation in dynamic environments to improve ML robustness against shifts in data distribution. © 2023 IEEE.

关键词： Timing circuits

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Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

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computer Systems science & engineering 2023年第6期45卷 3005-3021页

作者： K.Anuratha M.Parvathy Department of Information Technology Sri Sai Ram Institute of TechnologyChennaiTamilnaduIndia Department of Computer Science and Engineering Sethu Institute of TechnologyMaduraiTamilnaduIndia

The COVID-19 pandemic has become one of the severe diseases in recent *** it majorly affects the common livelihood of people across the universe,it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the *** public opinions are been shared enormously in microblogging med-ia like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics,sports,entertainment etc.,This work presents a combination of Intensity Based Emotion Classification Convolution Neural Net-work(IBEC-CNN)model and Non-negative Matrix Factorization(NMF)for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those *** topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN,based on predefined intensity *** research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 *** the Twitter Application Programming Interface(Twitter API),huge numbers of COVID-19 tweets are retrieved during January and July *** extracted tweets are ana-lyzed for emotions fear,joy,sadness and trust with proposed Intensity Based Emotion Classification Convolution Neural Network(IBEC-CNN)model which is *** classified tweets are given an intensity score varies from 1 to 3,with 1 being low intensity for the emotion,2 being the moderate and 3 being the high *** identify the topics in the tweets and the themes of those topics,Non-negative Matrix Factorization(NMF)has been *** of emotions of COVID-19 tweets has identified,that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also,more than 75%neg

关键词： Twitter topic detection emotion classification COVID-19 corona virus non-negative matrix factorization(NMF) convolutional neural network(CNN) sentiment classification healthcare

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Smoke detection in foggy surveillance environment using parallel vision transformer network

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Neural Computing and Applications 2024年第31期36卷 19499-19514页

作者： Chaturvedi, Shubhangi Thakur, Poornima Singh Khanna, Pritee Ojha, Aparajita Computer Science and Engineering Department PDPM Indian Institute of Information Technology Design and Manufacturing Dumna Road Madhya Pradesh Jabalpur482005 India

Recent years have seen an unprecedented increase in fire incidents, resulting in severe damage to forest regions, loss of human and animal lives, and unwarranted displacement of people. Owing to these issues, artificial intelligence-based fire detection systems have been developed. These systems mostly work with camera-based inputs for fire detection and alarm generation. As fire-smoke can be seen from a distance, methods for smoke detection have also been deployed for identifying fire incidents. But detecting smoke in an outdoor environment can be challenging due to weather conditions such as haze, fog, or clouds. These challenges have led to advancements in the field, and various deep learning architectures have been suggested over the years for fire-smoke detection in normal and foggy or cloudy weather conditions. Many existing methods are either computationally expensive and demand high memory, or if they are lightweight, they suffer from lower smoke detection accuracy and high false alarms. In the present paper, a dual-channel vision transformer model ‘SmokeViT’ is proposed for detecting fire-smoke in an outdoor environment. These channels are interlaced with a convolutional neural network to improve the feature learning capability of the model. ‘SmokeViT’ shows a remarkable accuracy of more than 99% and false alarm rate below 0.20% on two publicly available smoke datasets, outperforming six state-of-the-art methods. The model architecture has only 0.4 million parameters and 0.17 giga floating-point operations, making it suitable for deployment on resource-constrained surveillance devices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smoke

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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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Comparative Analysis of Deep Learning Approaches for Aspect-Based Sentiment Classification 4th

Comparative Analysis of Deep Learning Approaches for Aspect-...

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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Satyarthi, Shailendra Sharma, Sanjiv Department of Computer Science and Engineering Madhav Institute of Technology and Science Gwalior India Department of Information Technology Madhav Institute of Technology and Science Gwalior India

ISBN: (纸本)9789819700363

Social networking-based platforms (e.g., Twitter, Facebook, Amazon) where users feel free to express their opinion/sentiment about any product, service or event, in the form of reviews, tweets, blogs, etc., in the last decade with the enhanced use of social media and social networking, result in huge amount of data in unstructured and unorganized form, and the need of sentiment analysis of user’s opinionated text arises for improvement in the product or service policymaking decisions. Therefore, sentiments in user’s text play crucial role, and classifying the sentiment (positive, negative or neutral) at review/document level or at sentence level is not sufficient for achieving the real solution for improvement in product, service or an event;therefore, there is a need of deeper level and fine-grained analysis at aspect level known as aspect-based sentiment classification (ABSC) which attract researchers. This paper emphasizes on various existing approaches for ABSC specifically deep learning methods and their comparative analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Enhancing Soft Skills in Autistic Children: The Next Generation of Mind Champ's Technological Approach 6

Enhancing Soft Skills in Autistic Children: The Next Generat...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Peiris, Kalna Nelaka, Ruchira Manathunga, Kalpani Department of Computer Systems Engineering Sri Lanka Institute of Information and Technology Sri Lanka Department of Information Technology Sri Lanka Institute of Information and Technology Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information and Technology Sri Lanka

ISBN: (纸本)9798331517878

Autism Spectrum Disorder (ASD) significantly impacts a child's ability to navigate social interactions, regulate emotions, and develop adaptive skills crucial for daily functioning. While various interventions exist to address cognitive and academic skills, the development of soft skills such as communication, emotional regulation, social interaction, and creativity remains an under explored area. This paper builds upon the foundational work of the Mind Champ platform, which originally targeted both learning and soft skills, by focusing exclusively on enhancing the soft skills development of autistic children. This enhanced version of the Mind Champ platform leverages advanced behavioral and emotional analysis techniques to offer a comprehensive technological solution. Through interactive activities centered on painting and music, the platform creates a structured, engaging, and supportive environment tailored to the unique needs of children with ASD. By prioritizing emotional engagement and creative expression, the platform empowers children to improve their social abilities, emotional regulation, and adaptability. The results from our continued research indicate that these technology-driven interventions contribute significantly to the holistic development of soft skills in autistic children, providing them with valuable tools to navigate social environments more effectively. © 2024 IEEE.

关键词： Economic and social effects

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Anonymous and robust biometric authentication scheme for secure social IoT healthcare applications

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Journal of engineering and Applied science 2024年第1期71卷 8页

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

In the era of rapid technological advancement, the Internet of Things (IoT) has revolutionised healthcare through systems like the Telecare Medicine information System (TMIS), designed to streamline patient-doctor interactions and enhance medical treatment. However, the transmission of sensitive patient data over inherently insecure Internet channels exposes it to a spectrum of security risks. Protecting patient medical privacy and ensuring system reliability necessitate mutual authentication between both patients and medical servers. TMIS relies on robust authentication mechanisms, and combining passwords and smart cards has been a recognised approach for mutual authentication. This research introduces an innovative three-factor authentication technique with perfect forward secrecy by leveraging the power of Elliptic Curve Cryptography (ECC) in tandem with smart cards. Additionally, we have incorporated biometric authentication with a Fuzzy Extractor technology to enhance the security and reliability of the system, setting a new standard for user authentication within the realm of Social IoT healthcare. The use of ECC in the method is justified due to its compact key size and robust security measures, making the solution both efficient and secure. The proposed method safeguards user privacy by permitting registered users to change their passwords without divulging their identity to the server. The Burrows–Abadi–Needham logic (also known as the BAN logic) serves as a proof-of-concept for the proposed scheme’s security. Our system provides privacy protection along with mutual authentication and session key negotiation at a considerably low computation cost and communication cost of up to 71.03% compared to the other four relevant techniques, making it more useful in real-world scenarios. © 2023, The Author(s).

关键词： Smart cards

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