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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Karthiga, M. Shiv Navin, V.B. Cibin Kumar, R.M. Sathiya, B. Satheesh Kumar, A. Priyanga, M.A. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India Nandha Engineering College Department of Computer Science and Engineering Tamilnadu Erode India

ISBN: (纸本)9798350367904

B-cell epitope prediction has found its use in understanding B-cell recognition spots on a number of antigens that is vital in vaccine design and immunotherapy. These fields urgently need powerful prediction models, but the existing methods often fail to satisfy the requirement of both high sensitivity and high specificity. In response, this manuscript presents an ensemble model named EnsemBEP that combines Random Forest, Gradient Boosting, and XGBoost for improving the performance of B-cell epitope prediction. New model for epitope prediction, named EnsemBEP shows better performance in comparison with other models including EpiDope and BepiPred-2.0. The proposed EnsembEP model demonstrates superior performance compared to existing models such as EpiDope and BepiPred-2.0. Through rigorous evaluation, EnsemBEP achieved an AUC of 0.945, accuracy of 0.885, sensitivity of 0.823, specificity of 0.926, and an MCC of 0.743. All these findings point out that EnsemBEP yield better results than competing models thereby confirming that it is more accurate and reliable in predicting B-cell epitopes. The results provide evidence for the significance of ensemble learning methodologies in attaining higher predictive performance in immunoinformatic. © 2024 IEEE.

关键词： Prediction models

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A GMM-CGABC Based Energy Replenishment Scheme for On-Demand Wireless Rechargeable Sensor Networks 1

A GMM-CGABC Based Energy Replenishment Scheme for On-Demand ...

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1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024

作者： Mouli, S.Sudhamsu Veeraiah, T. Mahesh, K. Singh, M.P. Ecole Centrale School of Engineering Mahindra University Department of Computer Science and Engineering Hyderabad India National Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350349658

Wireless Sensor Networks (WSNs) are constrained by the limited energy capacity of Sensor Nodes (SNs), which hinders their perpetual operation. The advent of Wireless Energy Transfer (WET) technology has emerged as a promising solution to this challenge, enabling the creation of Wireless Rechargeable Sensor Networks (WRSNs). Recent research has shown growing interest in integrating Wireless Mobile Chargers (WMCs) into WRSNs to replenish the energy reserves of critical SNs through WET. A key aspect of WET involves using one or more WMCs to recharge SNs, with optimizing the charging schedule (i.e., the sequence in which SNs are charged) crucial in on-demand WRSNs. In addition, spatiotemporal constraints imposed by the requesting SNs must be considered when devising the WMC's charging schedule. To address these challenges, this work proposes the integration of a Gaussian Mixture Model with a Chaotic Gaussian Artificial Bee Colony (GMM-CGABC) to develop an efficient recharge schedule that revitalizes SNs. This model is designed to optimize SN deployment, maximize network coverage, minimize energy consumption, and extend network longevity. The proposed method determines the optimal charging path for WMCs within each cluster to minimize Average Charging Latency (ACL) while incorporating a novel strategy for fitness function evaluation. Extensive simulations demonstrate that the proposed system outperforms state-of-the-art algorithms, including First Come, First Served (FCFS), Nearest Job Next with Preemption (NJNP), and the Gravitational Search Algorithm (GSA), in terms of charging latency. © 2024 IEEE.

关键词： Inductive power transmission

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Implementation of Secure and Verifiable Access Control Procedures using the NTRU Cryptosystem to Store Big Data in the Cloud Environment

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Recent Patents on engineering 2025年第2期19卷 57-70页

作者： Babu B, Ravindra Saxena, Swati Widjaja, Gunawan Vinodha D, Vedha Omarov, Batyrkhan Ramaiah, Gurumurthy B. Vats, Prashant Adama Ethiopia Department of Computer Science KCG College of Technology Chennai India Universitas Krisnadwipayana Jawa Barat 13077 Indonesia Department of Electronics and Communication Engineering JCT College Of Engineering and Technology Pichanur Coimbatore India International University of Tourism and Hospitality Turkistan Kazakhstan Technical and Vocational Training Institute Addis Ababa Ethiopia Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India

Background: Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores. Methods: However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility. Results: In this patent paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour. Conclusion: It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users. © 2025 Bentham science Publishers.

关键词： Cloud security

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Face Recognition-Based Door Locking System with OTP Verification 6th

Face Recognition-Based Door Locking System with OTP Verifica...

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6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Rajavel, M. Santhosh Kumaar, R. Suvedha, D. Dharani, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819619801

In the world of the digital era, AI has had a major influence. With the advancement of technology, people can experience extraordinary innovations that not only make life more comfortable, but also make it simpler. As a part of AI, we have created a door locking mechanism that uses face recognition with OTP verification using OpenCV. At the stage of identification of faces, Histogram of oriented Gradients algorithm [HOG] is used for finding the face at the backend. This project is developed using both hardware and software. With the help of smart doorbells, homeowners can get notifications when a visitor is at the door and identify the visitor. They significantly raise people’s quality of life and advance the development of smart homes. The purpose of this project is to make the home or office area secure and can also be used to minimize criminality. Only one form of authentication is offered by the existing systems, which makes them less secure. Thus, for secure access, we have proposed a safe door locking system with two factor authentication that can authenticate and validate the user as well as open the door in real time. We have come up with an idea which should recognize the guest, relatives or a stranger and alert the user. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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Enhancing LLM Performance on Legal Textual Entailment with Few-Shot CoT-based RAG

Enhancing LLM Performance on Legal Textual Entailment with F...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Reji, Sneha Ann Sheik, Reshma Sharon, A. Avisha Rai, M. Jaya Nirmala, S. National Institute of Technology Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli India TKM College of Engineering Department of Computer Science and Engineering Kerala Kollam India

ISBN: (纸本)9798350376135

Large Language Models (LLMs), leveraging extensive data, have become successful in text generation applications. However, their integration into the legal domain presents distinct challenges, prompting deviations in thought processes and potentially misleading outcomes. Such discrepancies can significantly impact the efficacy of legal text applications. In our study, we propose a novel approach that combines retrieval-based methods with generation-based techniques to provide relevant explanations and labels for the given query for legal textual entailment task, leveraging the concept of Chain of Thought (CoT) for reasoning and few-shot prompting. Later, we enhance LLM outputs through the application of various text generation strategies. Our findings demonstrate an enhancement, with a 5% increase in F1 score and a 3% improvement in accuracy when compared with zero-shot baselines. This highlights the effectiveness of utilizing these strategies in specialized fields such as law, where precision and reliability are of utmost importance. © 2024 IEEE.

关键词： Modeling languages

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Transforming High-Frequency Trading Predictions with CNN-LSTM Networks 5

Transforming High-Frequency Trading Predictions with CNN-LST...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Priya, S. Baghavathi Priyanga, S. Kamisetty, Sri Surya Shobith Neelapala, Venkata Sai Vinay Nithila, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Chennai India Vellore Institute of Technology Department of Electronics & Communication Engineering Vellore India Vellore Institute of technology Department of Computer Science & Engineering Vellore India

ISBN: (纸本)9798350356236

High-Frequency Trading (HFT) has transformed financial markets by enabling rapid and automated trading. However, the prediction of critical market metrics, such as trading volumes, remains a challenge due to the noisy and complex nature of financial data. This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model designed specifically for predicting trading volumes in the NIFTY50 index using high-resolution, minutelevel Yahoo Finance data. By combining CNN's spatial feature extraction capabilities with LSTM's temporal modeling strengths, the proposed approach effectively captures the intricate patterns and sequential dependencies inherent in high-frequency trading datasets. Comprehensive data preprocessing, including noise reduction and temporal segmentation, coupled with advanced cross-validation techniques, ensures robust performance across market conditions. Comprehensive evaluations using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) demonstrate the model's efficacy in capturing intricate financial dynamics. This work underscores the potential of hybrid deep learning models in enhancing predictive analytics for high-frequency trading applications in emerging markets. © 2025 IEEE.

关键词： Deep learning

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Ranking-based Diversified Influence Maximization for Dynamic Social Networks 17

Ranking-based Diversified Influence Maximization for Dynamic...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Meena, Sunil Kumar Singh, Shashank Sheshar Singh, Kuldeep University of Delhi New Delhi Department of Computer Science New Delhi India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798331531195

Influence maximization (IM) is the task of selecting the most influential nodes in the network. IM achieves the goal of spreading information, influencing behaviour, or promoting sales of products. Existing studies in IM focus on maximizing the activated nodes in the static network and neglecting the communities. In this paper, we give a novel ranking-based algorithm that ranks the nodes based on their reachability and diversity. This paper proposes an objective function that considers both diversity and activated nodes to determine the diversity capability of the nodes. The proposed algorithm utilizes previous computation and integrates reachability centrality with the objective function to rank the nodes at a timestamp in a dynamic network. We execute the proposed and benchmark algorithms on five datasets. The experiments show that the suggested algorithm produces superior and competitive outcomes in comparison to the benchmark algorithms. © 2025 IEEE.

关键词： Diversified Influence Maximization Dynamic Networks Influence Maximization Local Reaching Centrality Social Network Analysis

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Identification of Real or Fake Currency Using Semi-supervised Generative Adversarial Networks (SSGANs) 6th

Identification of Real or Fake Currency Using Semi-supervise...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Mukherjee, Anupam Ghosh, Anupam Department of Computer Science & Engineering Siliguri Institute of Technology West Bengal Siliguri India Department of Computer Science & Engineering Netaji Subhash Engineering College West Bengal Kolkata India

ISBN: (纸本)9789819780891

Traditional paper money and contemporary electronic money are two significant forms of exchange. However, due to the new and improved methods that counterfeiters are using, it is now becoming an increasingly important issue. This work suggests a machine-assisted method that has been created to distinguish between fake and real currency. Generative adversarial networks (GANs), a cutting-edge machine learning model, are used for this purpose. To develop a model that can be used for supervised prediction, GANs utilize semi-supervised learning. Semi-supervised learning is an exceptional approach in building a model that is fit for supervised prediction. It offers a multitude of benefits, allowing the training of unlabeled data while achieving highly accurate predictions. In this work, detecting counterfeit money using GANs involves a multi-step methodology that leverages the power of deep learning for accurate identification. Preprocess the data by resizing, normalizing, and augmenting the images to ensure uniformity and improve model generalization. The discriminator minimizes the loss when making correct classifications, while the generator minimizes the loss when the discriminator classifies its generated images as genuine. Indian banknotes were subjected to this procedure. Modern feature identification and image processing techniques were employed to develop the overall strategy of a valid input. The trials demonstrate that a high-precision system may be created to identify real paper money. During the training process, the generator network and the discriminator networks update their loss functions. Once training is complete, the model can accurately identify real money notes from fake ones with an 80% accuracy rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Ensuring Trust in Blockchain-Based E-commerce Using Smart Contracts 5th

Ensuring Trust in Blockchain-Based E-commerce Using Smart Co...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Surendar, P.D. Kumar, Adhithi Satish Pavani, Venkata Azhagiri, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India

ISBN: (纸本)9789819773701

In the ever-changing realm of e-commerce, building trust is crucial for both customers and sellers. Reputation systems play a vital role in enabling informed decision-making by providing vendor ratings and reviews. Conventional reputation systems are typically inadequate in dealing with contemporary issues including privacy, identity theft, and feedback fraud. In order to address these challenges, we suggest implementing a decentralized reputation system that places a high value on privacy and effectively counters existing risks. Our architecture utilizes two permissioned blockchains, Hyperledger Indy for decentralized identity management through verifiable credentials and Zero Knowledge Proof (ZKP) technology, guaranteeing anonymity and authenticity, and Hyperledger Fabric for executing smart contracts, facilitating intricate business logic. The system incorporates verified vendor identities, transaction feedback tokens, and discount tokens to encourage genuine evaluations. Smart contracts implemented on the Hyperledger Fabric platform provide the security and transparency of the feedback process, hence reducing the occurrence of biased feedback and identity assaults. This technique provides a strong and reliable answer to the issues faced in e-commerce. It ensures the protection of private information and enables the verification of digital identities while maintaining privacy. The implementation of feedback and discount tokens promotes the provision of precise input, mitigating unjust practices and bolstering the dependability and confidence in the system. We demonstrate the effectiveness of our methodology using Zero Knowledge Proofs (ZKP), highlighting its importance in establishing confidence within the context of blockchain. Our system represents a notable advancement in creating a safe, transparent, and reliable e-commerce environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Drug Targets Prediction of Mycobacterium tuberculosis H37Rv Using In Silico Genome Analysis 2

Drug Targets Prediction of Mycobacterium tuberculosis H37Rv ...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Abhilash Mahapatra, Rajendra Prasad Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India

ISBN: (纸本)9798350364699

Mycobacterium tuberculosis, the causal agent of tuberculosis, is a major global health concern. The most widely studied strain for understanding the mechanism of drug resistance is H37Rv. To identify possible therapeutic targets, in silico analyses of Mycobacterium tuberculosis H37Rv genome were carried out. The genome sequence was downloaded from the website of NCBI (National Center for Biotechnology Information). To identify essential genes, the DEG (Database of Essential Genes) was consulted. Additionally, homology searches with the human genome were conducted using BLASTX. Out of a total of 3924 genes, 594 were determined to be essential for Mycobacterium tuberculosis, of which 366 had no sequence similarity to the human genome. Out of 366 genes, 242 potential drug targets have been found after being screened for fictitious and unidentified genes. © 2024 IEEE.

关键词： Genome

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