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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar, Keerthan G.T. Anoop, R. Koolagudi, Shashidhar G. Rao, Trupthi Kodipalli, Ashwini Department of Information Science and Engineering Siddaganga Institute of Technology Tumkur India Department of Artificial Intelligence and Data Science Global academy of Technology Bengaluru India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

This work examines the performance of various LSTM (long short-term memory) variants on social media text data. This study evaluates the performance of LSTM models with different architectures, namely, classic LSTM, Bidirectional LSTM, Stacked LSTM, gated recurrent unit (GRU), and bidirectional GRU, on a social network dataset comprising texts extracted from multiple social media platforms. We aim to identify the most effective LSTM variant of the five considered LSTM models for text analysis through a comparative study of the models' precision, recall, F1-score, and accuracy. The research findings show that the Classic LSTM and the GRU model perform better than the other models in accuracy. In contrast, the bidirectional models (Bidirectional LSTM and Bidirectional GRU) provide better precision scores than their respective primitive models. This research has significant implications for developing more efficient models for natural language processing applications. It offers beneficial insights into the implications involving the scrutiny of depression on social media platforms through text data analysis. © 2024 Elsevier B.V.. All rights reserved.

关键词： Long short-term memory

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A robust method for malware analysis using stacking classifiers and dendrogram visualization

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International Journal of Information technology (Singapore) 2024年 1-16页

作者： Naveen Kumar, N. Balamurugan, S. Maruthamuthu, R. Devi, P.P. Seshu Kumar, P. Department of Computer Applications Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Avadi Tamil Nadu Chennai India

Malware analysis is a vital and challenging task in the ever-changing cyber threat landscape. Traditional signature-based methods cannot keep up with the fast-paced evolution of malware variants. This underscores the need for developing a more effective malware classification tool helping in exploring new research directions with solutions. In this research work firstly, we present a comprehensive assessment on malware classification using dendrogram clustering techniques. This work proposes, a robust approach to malware analysis by constructing dendrogram that groups malware samples which works based on their ancestral relationships. Semantic analysis of the code also provides deeper insights into the malware’s behavior and functionality, enabling more precise identification. These methods help to detect new variants as well as enhance the recognition of existing malware families. Our proposed model results shows that, the robustness in accuracy and False Positive Rate when compared with other existing models. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Autoencoder decoder Dendrogram Malware classification Semantic analysis

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Enhancement of charging performance of quantum battery via quantum coherence of bath

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Chinese Physics B 2023年第1期32卷 161-171页

作者：于文莉张允李海魏广芬韩丽萍田峰邹建 School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China School of Science Tianjin University of TechnologyTianjin 300384China School of Physics Beijing Institute of TechnologyBeijing 100081China

An open quantum battery(QB)model of a single qubit system charging in a coherent auxiliary bath(CAB)consisting of a series of independent coherent ancillae is *** to the collision charging protocol we derive a quantum master equation and obtain the analytical solution of QB in a steady *** find that the full charging capacity(or the maximal extractable work(MEW))of QB,in the weak QB-ancilla coupling limit,is positively correlated with the coherence magnitude of *** with the numerical simulations we compare with the charging properties of QB at finite coupling strength,such as the MEW,average charging power and the charging efficiency,when considering the bath to be a thermal auxiliary bath(TAB)and a CAB,*** find that when the QB with CAB,in the weak coupling regime,is in fully charging,both its capacity and charging efficiency can go beyond its classical counterpart,and they increase with the increase of coherence magnitude of *** addition,the MEW of QB in the regime of relative strong coupling and strong coherent magnitude shows the oscillatory behavior with the charging time increasing,and the first peak value can even be larger than the full charging MEW of *** also leads to a much larger average charging power than that of QB with TAB in a short-time charging *** features suggest that with the help of quantum coherence of CAB it becomes feasible to switch the charging schemes between the long-time slow charging protocol with large capacity and high efficiency and the short-time rapid charging protocol with highly charging power only by adjusting the coupling strength of *** work clearly demonstrates that the quantum coherence of bath can not only serve as the role of“fuel”of QB to be utilized to improve the QB's charging performance but also provide an alternative way to integrate the different charging protocols into a single QB.

关键词： quantum battery quantum coherence maximal extractable work charging power

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Blockchain for Securing Autonomous Vehicles 2

Blockchain for Securing Autonomous Vehicles

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Rajendar, Sivaramakrishnan Thangavel, Udhayakumar Devendran, Sarvesh Selvi, Vaishnavi Muthumanickam, Shanmuga Sundaram Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350346640

Autonomous Vehicles (AVs) are perceived as a revolutionary development in the worldwide transport sector and are believed to enhance safety significantly. An AV is equipped with a range of advanced technologies that allow it to communicate with other vehicles, as well as with various other systems and infrastructure such as traffic lights, road signs, and other Road-Side Units (RSUs). AVs also employ a variety of communication channels for their operation and safety. The major concerns in such communication channels are reliability, precision, and security of received and broadcasted data. These concerns directly affect the safety and security aspects of an AV which endangers the passenger safety. Thus, AVs require a secure mechanism for the exchange of crucial data. This paper explores the utilization of blockchain technology in AV communication, enabling authorized access to data, facilitating secure data sharing among vehicles and RSUs, tracking the internal status of the vehicle for In-Vehicle Network (IVN) breaches, along with the architecture of the system. Moreover, the paper outlines the implementation challenges and future research of blockchain technology in AVs. © 2023 IEEE.

关键词： Autonomous vehicles

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Optimizing the Selection of Base Learners for Multiple Classifier System in Liver Cancer Identification Using Contribution-based Iterative Removal Algorithm

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SN computer science 2023年第5期4卷 493页

作者： Sabitha, P. Meeragandhi, G. Department of Computer Science & Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India SRM Institute of Science & Technology Ramapuram Tamil Nadu Chennai India

In the healthcare industry, developing an efficient diagnostic system to classify liver cancer cells is a very perplexing and arduous task. Recently, several studies demonstrate that deep ensemble classifiers can achieve better predictive accuracy than individual deep learning classifiers. The deep ensemble learners exploit more than one individual deep learner to achieve better classification results and improved generalization performance. When implementing an ensemble learning (multiple classifier) approach, the selection of the optimum learners from a crew is a critical issue and an effective learner assortment strategy is used to achieve better results. Several researchers have applied different approaches (e.g., rule-based algorithms, evolutionary computing, simulated annealing, etc.) to determine the optimal learners that can increase the performance of the diagnostic system. This work proposes a new classifier selection strategy to construct an ensemble called a contribution-based iterative base learner removal algorithm (CIBRA). The proposed algorithm finds out the best subset of individual learners by considering both prediction accuracy and diversity. The proposed CIBRA enables each base learner in a pool to have multiple chances to partake in an iteration of selection. CIBRA drops the classifiers only if they have no residual opportunities. This procedure is reiterated till no learner in the crew has any remaining possibility to partake in the selection round. In this study, we test various decision synthesis techniques to increase the performance of the ensemble classifier. To assess the performance of CIBRA, 8 standard cancer databases are exploited. Extensive simulation results divulge that two base classifiers are enough to classify liver cancer cells from hematoxylin and eosin (H&E) scans successfully. Based on the results obtained from this study, we construct an ensemble classifier using Dropout Extreme Learning Machine (DrpXLM) and Enhanced Convo

关键词： Base crew Classifier selection Ensemble learner Fusion techniques Liver cancer Voting mechanism

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Machine Learning Technique for Prognosticating Crop Yield Variability with Air Quality Index

Machine Learning Technique for Prognosticating Crop Yield Va...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Kumar, S. Siva Usha, L. Josephine Saranya, K.R. Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350315127

Rice production in India holds significant importance in terms of its contribution to the economy and food security. However, the expansion of this sector is currently facing challenges due to the increasing levels of air pollution, including ground-level ozone and particle matter. The objective of this study is to examine the influence of air quality index (AQI) factors on rice productivity, namely during the winter season, with a particular emphasis on the exceptional year of 2020-2021. By utilizing Internet of Things (IoT) technology, the collection of real-time air quality data is combined with rice yield analysis through the implementation of decision tree regression, k-nearest neighbors (KNN), and support vector regression (SVR) methods. The research findings emphasis the effectiveness of the decision tree model in accurately representing the complex relationship between AQI and rice yield. This model demonstrates higher predictive accuracy when compared to SVR and KNN. This study highlights the importance of managing air quality in order to achieve sustainable rice production. Additionally, it emphasizes the potential of monitoring systems based on the Internet of Things (IoT) to significantly transform agricultural practices. By doing so, these systems may effectively connect environmental stewardship, technological innovation, and food security. © 2023 IEEE.

关键词： Air quality

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Research Challenges and Future Directions for Data Storage in Cloud Computing Environment

Research Challenges and Future Directions for Data Storage i...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Rajalakshmi, K. Sambath, M. Joseph, Linda Hindustan Institute of Technology and Science Department of Computer Science and Engineering TamilNadu Chennai India

ISBN: (纸本)9798350348217

The flexibility and reliability with which cloud computing can deliver a wide variety of computation, memory, communication, and resource management services through the internet have contributed to its rise in popularity in recent years. Increasing interest in cloud computing is due to the growing need for reliable, affordable infrastructure. This essay largely focuses on an overview of problems and unresolved difficulties with cloud databases as storage for cloud computing. This survey's objective is to give a researcher a broad perspective on the problems, unresolved problems, and emerging trends in cloud storage, with a particular focus on cloud databases. This article compares academic approaches and methodologies to address the difficulties related to data availability, including replication, managing, and securing data in cloud enabled technologies. © 2023 IEEE.

关键词： Cloud storage

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CKGLD: Curating Knowledge Graph for Linked Open Data Generation for Cyber Law and Ethics 4th

CKGLD: Curating Knowledge Graph for Linked Open Data Generat...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Aravind Krishnan, A. Deepak, Gerard Santhanavijayan, A. Venugopal, K.R. Department of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India National Institute of Technology Tiruchirappalli India Bangalore University Bengaluru India

ISBN: (纸本)9789819757022

This paper introduces a knowledge graph curation framework for the generation of linked open data in the domain of cyber law and ethics called the CKGLD. This framework utilizes both static metadata and dynamically acquired knowledge from Web 3.0. With advanced methods of term and category extraction, classification using both lightweight machine learning and strong deep learning methods, and clustering, the framework can exceed all similar frameworks in terms of performance. Initial terms are processed from the documents using the TF-IDF strategy. Static metadata stack is established from e-books, query logs, and glossaries. Renyi entropy is used to guide informative term selection for a decision tree classification;in this manner, the scale of the generated knowledge is managed by lightweight computing requirements. Dynamic knowledge from Web 3.0 is classified using GANs and is subjected to DBSCAN clustering. A multi-agent setup with parallelization enhances the efficiency of the clustering process. The knowledge graph is finally created using the Co-SIM rank and is evaluated for specificity using Hellinger's distance and volume of intersection index. These paradigms contribute to this framework significantly outperforming the baseline frameworks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Temporal Community Analysis Using Centrality Measures

Temporal Community Analysis Using Centrality Measures

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Rajeshkumar, Deepak Jaysankar, Pranav Yookeesh, M.R Sree Padma Chandrasekaran, Balaji Girijakumari, Smrithy Anna University College of Engineering Guindy Department of Computer Science and Engineering Chennai India Vellore Institute of Technology Sreekanatan Nair School of Computer Science and Engineering Chennai Campus Chennai India

ISBN: (纸本)9798331505776

This research explores temporal community detection to identify patterns in course grading and offerings at The University of Illinois from Spring 2010 to Spring 2020. Using a dataset that includes course grades, subject codes, instructors, and student enrolment, the study applies four community detection algorithms - Greedy Modularity, Louvain, Leiden, and Walktrap - to examine how courses group based on grading similarities over time. The data is pre-processed, split into time slices, and represented as graphs where nodes are courses and edges are weighted by cosine similarity of grading patterns. Key metrics such as modularity, community size, edge density, overlap, and persistence are used to evaluate algorithm performance. The results provide insights into the evolution of grading structures, course groupings, and instructional trends, offering a deeper understanding of the dynamics in educational data. This analysis contributes to improved decision-making in academic program development and resource allocation. © 2024 IEEE.

关键词： Resource allocation

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Hybrid Optimization for Enhanced Retinal Image Classification Using Neural Networks

Hybrid Optimization for Enhanced Retinal Image Classificatio...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Anwar Basha, H. Deepak, R. Babu, M. Bhanu, Shaik Thasleem Amutha, B. Madhavan, R. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mathematics Tamilnadu Chennai India Jeppiaar Institute of Technology Department of Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798331543617

The present study proposes a hybrid optimization algorithm that involves the integration of Neural Networks (NN), Genetic Algorithms(GA), and Particle Swarm Optimization(PSO) to improve the accuracy and efficiency of retinal image analysis. First, a wide range of features are extracted from retinal images and normalized. Then a NN is constructed with its weights and biases initialized to process these features. The fitness of each feature subset is measured with the help of a network's output, quantified by a Mean Squared Error(MSE)-based fitness function. Receiving fitness values, the GA component picks out the most important subsets of features, which are refined with crossover and mutation operations to retain diversity and improve the search process. In a parallel process, PSO dynamically adjusts the position of particles representing subsets of features through modification of velocity and position vectors under the influences of cognition and society. With the proposed integrated approach, iteratively, an optimal feature subset is generated by minimizing error and maximizing classification performance. Finally, after convergence, this last optimized subset of features is used in the final tuning of the NN to achieve a far-reaching improvement in the accuracy of classification. This method takes advantage of the complementary strengths of NN, GA, and PSO;therefore, it is able to provide an effective solution for the optimization of features within medical image analysis. © 2024 IEEE.

关键词： Ophthalmology

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