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1st International Conference on Intelligent computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Gowdham, C. Ashwin, R. Raj, T. S. Senthil Department of Computer Science Hindustan Institute of Technology and Science Tamil Nadu Chennai India School Of Computing & Information Technology REVA University Bangalore560064 India

ISBN: (纸本)9783031759567

In recent times, it is extremely common for the people to make use of bank credits to satisfy their day to day needs. This is increasing rapidly. There are lot of schemes are provided by the banks. Lending money is one of the most crucial bank programmes. Banks generally provide loans to customers based on their requirements. But, due to some situation, some clients cannot repay their debts on time or they will make it delay because of lack of finances. There are so many people who take advantage of this and misuse the facilities that are provided by the bank. To overcome this issue, Banks should utilize certain methods to help in predicting the status of loan repayment. The financial system needs an appropriate modelling system to handle a variety of problems. For any bank, one of the most challenging tasks is the task of anticipating loan late payers. However, the banks will undoubtedly be able to minimise their non-profit assets in order to decrease their loss as a result of the expected bankruptcies. As a result, it enables the repayment of approved debts to proceed with no losses and contributes to the credit report. This demonstrates the significance of researching this technique to foresee lending decisions. The proposed method utilizes machine learning methods for predicting default on loans. This is because they provide greater accuracy on prediction challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decentralized finance

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Pricing mechanism for constrained driverless shared buses in a smart city

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Journal of Intelligent and Fuzzy Systems 2025年第1-2期48卷 109-122页

作者： Jin, Yong Yang, Huizhen School of Computer Science and Engineering Changshu Institute of Technology Changshu China Changshu Dawei Transportation Technology Co. Ltd Changshu China

The price of passengers is closely related to the travel cost of passengers and the revenue of driverless shared bus. Therefore, the design of pricing mechanism is important to improve the market efficiency of driverless shared bus scheduling. To address these issues, we formulate the social optimization passenger selection problem to maximize the social welfare such that number of passengers taking each driverless shared bus is no more than its capacity and all the driverless shared buses can arrive at the corresponding destinations before the deadlines of the passengers, and propose the auction-based truthful pricing mechanism, Driverless Shared Bus Pricing (DSBP) mechanism, for deadline tightened pricing problem of driverless shared buses. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanism achieves computational efficiency, individual rationality, truthfulness, and guaranteed approximation. © 2025 – IOS Press. All rights reserved.

关键词： Smart city driverless shared bus pricing scheduling

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E-voting system using cloud-based hybrid blockchain technology

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Journal of Safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

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Informative task classification with concatenated embeddings using deep learning on crisisMMD

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International Journal of computers and Applications 2025年第2期47卷 123-140页

作者： Jain, Tarun Gopalani, Dinesh Kumar Meena, Yogesh Department of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

Disastrous situations pose a formidable challenge, testing our resilience against nature's fury and the race against time to prevent the loss of human life. It is noted that in such situations that Microblogging platforms like Twitter(now X) have proven to be an invaluable resource for citizens to share the real-time updates in their locality. Building upon this premise, we are proposing a new hybrid robust approach that integrates transfer learning with selective features from Liwc and manually engineered features to achieve state-of-the-art results on the CrisisMMD dataset on the informative task. In our innovative approach, we harnessed the power of Deep learning combined with traditional ML practices to improve the model, we utilized OpenAI embeddings and minute details from the tweets like the sentiment, text polarity, Retweet address, links included etc. We employed a dense-dropout-based concatenate model for further processing. We established baselines of each technique individually as well as commonly used techniques like Tf-Idf, BoW, Glove embeddings. Notably, our proposed model exhibits a significant accuracy improvement, achieving an accuracy of 87.93%, surpassing the highest accuracy of traditional methods of 82.93% obtained with Tf-Idf and ensemble voting. Through this work, we conclude that there is a scope of improvement in disaster-related NLP work with the introduction of transformer-based architecture and Pretrained GPT which include a massive corpus of text improving the semantic coherency which in turn lead to higher evaluation metrics. This work in future can be extended to incorporate different embeddings as well as using techniques like Multi-headed attention as well as RNNs like GST, GRU, etc. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Tweets

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Framework for classifying gait disorders and fall prevention

Framework for classifying gait disorders and fall prevention

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2025 International Conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Saha, Rupa Karmakar, Koushik Biswas, Suparna Narula Institute of Technology Department of Computer Application India Narula Institute of Technology Department of Computer Science and Technology India Department of Computer Science & Engineering Makaut W B India

ISBN: (纸本)9798331513894

One of the crucial approaches that use tracking and analyzing human movement for the identification of functional disorders and illnesses of the human body is known as the gait analysis method. This method helps diagnose neurological and musculoskeletal conditions. It also tracks the effectiveness of rehabilitation. However, conventional techniques for analyzing gait frequently depend on costly items and methods like fixed lab apparatus, force plates, and motion capture devices. But new developments in wearable sensor technology and machine learning provide new methods that are cheaper, more affordable, portable, and easily accessible. It continuously tracks gait in real-world settings. In this work, the use of wearable sensors for gait analysis, which includes devices like magnetometers, gyroscopes, and accelerometers, is examined. They are integrated into gadgets like smartphones, insoles, and smartwatches and offer real-time kinematic data that machine learning algorithms can analyze. Some of the important gait parameters 1that are extracted from sensor data are stride length, cadence, and gait symmetry. Machine learning is used to process this data and use it for categorization and gait analysis. The suggested method improves patient monitoring outside of clinical settings. It supports individualized rehabilitation plans and also identifies early indicators of gait abnormalities. The use of the wearable sensor-based gait analysis may emerge as a game-changing medical tool. It offers constant, discrete monitoring and prompts intervention in gait-related disorders. This works explains it thoroughly. This report offers insights for upcoming researchers in the field by surveying the state of the art in wearable sensor-based gait analysis. © 2025 IEEE.

关键词： Wearable sensors

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Cross-modal Retrieval with Attention Knowledge Distillation 5

Cross-modal Retrieval with Attention Knowledge Distillation

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Yin, Hanlei The Institute of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350374407

Due to the efficiency of computation and storage, the dual-encoder model has been usually utilized for retrieval tasks involving a large volume of multimodal data. The dualencoder model trains visual tokens and text tokens separately, and ignores the interaction between modalities. Due to the lack of interaction, the cross-model retrieval performance of dualencoder modal is not good. On the contrary, the fusion-encoder model inputs text tokens and visual tokens together into a multimodal encoder, which provides sufficient interaction between modalities. But the search process of fusion-encoder model is both inefficient and impractical. To take advantage the strengths of the both models and avoid their disadvantages, this article proposes CAKD (a cross-modal retrieval method with attention knowledge distillation). This method uses a fusion-encoder model as the teacher model and a dual-encoder model as the student model. CAKD not only ensure the synchronization of predictions from the teacher and the student, but also enable the student to learn interaction information from the teacher by distilling attention knowledge. This method was validated on the MIRFLICKR-25K and MS-COCO. Experiment results show that cross modal retrieval performance of CAKD has decreased by approximately $4 \%$ compared with the teacher model, but the retrieval time of CAKD is ${1 2 0 0}$ times faster than that of the teacher model and the cross modal retrieval performance is superior to current mainstream dual-encoder model. © 2024 IEEE.

关键词： Students

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Gesture and Emotion Detection using Quantum computing for Enhanced Recognition and Analysis 4

Gesture and Emotion Detection using Quantum Computing for En...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Suraj Jai Krishna, S. Anish, M. Posonia, A. Mary Albert Mayan, J. Asha, P. Sathyabama Institute of Science and Technology Department of Computer Science and Technology Chennai India

ISBN: (纸本)9798350385793

The research on 'Gesture and Emotion Detection using Quantum computing' is driven by an increasing need to overcome the linguistic and emotional barriers encountered by the Deaf community. With a deep understanding of the complexity of American Sign Language (ASL), this research bridges the communication gap by seamlessly translating ASL into speech, thereby increasing the accessibility for a wider audience. Going beyond mere translation, this research study aims to integrate the converted speech with the emotional expressions inherent in sign language. The proposed research idea stems from the recognition that existing communication technologies often overlook the rich emotional content conveyed through ASL. By integrating facial emotion identification, this research study aims to capture a diverse range of emotions embedded in sign language communication. This dual approach not only increases human understanding but also enhances the inclusivity in linguistic and emotional experiences for both Deaf and hearing communities. Ultimately, the objective of this research study is to develop a comprehensive system capable of accurately recognizing ASL, transforming it into articulate speech, and simultaneously conveying the emotional depth inherent in sign language expression. Through this research study, the emotion-enhanced sign language to speech conversion research seeks to redefine the communication accessibility, enabling a more interconnected and empathetic society. © 2024 IEEE.

关键词： Emotion Recognition

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Bioinspired activation of silent synapses in layered materials for extensible neuromorphic computing

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Journal of Materiomics 2023年第4期9卷 787-797页

作者： Yan Kang Yabo Chen Yinlong Tan Hao Hao Cheng Li Xiangnan Xie Weihong Hua Tian Jiang College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha410073China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha410073China Institute for Quantum Science and Technology College of ScienceNational University of Defense TechnologyChangsha410073China

Activation of silent synapses is of great significance for the extension of neural plasticity related to learning and *** by the activation of silent synapses via receptor insertion in neural synapses,we propose an efficient method for activating artificial synapses through the intercalation of Sn in layered a-MoO_(3).Sn intercalation is capable of switching on the response of layered a-MoO_(3)to the stimuli of visible and near infrared light by decreasing the *** mimics the receptor insertion process in silent neural *** Sn-intercalated MoO_(3)(Sn-MoO_(3))exhibits persistent photoconductivity due to the donor impurity induced by Sn *** enables the two-terminal Sn-MoO_(3)device promising optoelectronic synapse with an ultrahigh paired pulse facilitation(PPF)up to 199.5%.On-demand activation and tunable synaptic plasticity endow the device great potentials for extensible neuromorphic *** performance of the extensible artificial neural network(ANN)based on the Sn-MoO_(3)synapses are demonstrated in pattern ***,the recognition accuracy increases from 89.7%to 94.8%by activating more nodes into the *** is consistent with the recognition process of physical neural network during brain *** intercalation engineering of MoO_(3)may provide inspirations for the design of high-performance neuromorphic computing architectures.

关键词： Activation of silent synapse Intercalation Layered materials Neuromorphic computing

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm

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Intelligent Automation & Soft computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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