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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

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Enhanced Cybersecurity: Development and Evaluation of a Secure data Sharing Paradigm Based on Blockchain Technology 1

Enhanced Cybersecurity: Development and Evaluation of a Secu...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Geetha, R. Vijayanandh, T. Ananthi, S.N. Mercilin Raajini, X. Divya Sundar, V.S. Kumari Deepika, P. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of EEE Tamil Nadu Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India S.A Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798350364590

As time goes on and technology gets better, various data-sharing technologies are becoming more and more integrated into many different businesses. Therefore, data exchange and security are crucial for the data's value to be realized. Nevertheless, it is challenging to verify the authenticity of a basic data sharing method in order to detect electronic data usage traces. An further difficulty is the reluctance of data suppliers to provide their data. Employing Blockchain's distributed ledger, smart contract, data trust, and traceability features. For the purpose of facilitating secure access to data, this study introduces a blockchain-based data-sharing architecture that optimizes to circumvent the security and control issues associated with traditional centralized data sharing and administration. In addition, the research evaluates the practicality and safety of the prototype. Furthermore, this paper proposes a Secured data Management Scheme (SDMS) that utilizes blockchain technology to optimize data sharing efficiency. To assess the efficacy of this model, it is cross-validated with the conventional data Privacy Handler (DPH) method. While present centralized security solutions fail to meet security requirements in an IoT setting, blockchain technology provides a decentralized solution. Through the use of a blockchain and a WiFi-enabled network model, this article seeks to offer a secure communication framework for Internet of Things (IoT) devices. According to the results, attitudes and character quirks have a role in how the intelligence community plans to use a blockchain-based data-sharing model for integrating systems. This research demonstrated the viability of using blockchain technology in an intelligence community-centric data-sharing strategy. © 2024 IEEE.

关键词： data consistency

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STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario

arXiv

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arXiv 2024年

作者： Liu, Renyang Lam, Kwok-Yan Zhou, Wei Wu, Sixing Zhao, Jun Hu, Dongting Gong, Mingming The School of Information Science and Engineering Yunnan University Kunming650500 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The School of Software The Engineering Research Center of Cyberspace Yunnan University Kunming650500 China Melbourne Centre for Data Science School of Mathematics and Statistics University of Melbourne ParkvilleVIC3010 Australia

Extensive studies have revealed that deep neural networks (DNNs) are vulnerable to adversarial attacks, especially black-box ones, which can heavily threaten the DNNs deployed in the real world. Many attack techniques have been proposed to explore the vulnerability of DNNs and further help to improve their robustness. Despite the significant progress made recently, existing black-box attack methods still suffer from unsatisfactory performance due to the vast number of queries needed to optimize desired perturbations. Besides, the other critical challenge is that adversarial examples built in a noise-adding manner are abnormal and struggle to successfully attack robust models, whose robustness is enhanced by adversarial training against small perturbations. There is no doubt that these two issues mentioned above will significantly increase the risk of exposure and result in a failure to dig deeply into the vulnerability of DNNs. Hence, it is necessary to evaluate DNNs' fragility sufficiently under query-limited settings in a non-additional way. In this paper, we propose the Spatial Transform Black-box Attack (STBA), a novel framework to craft formidable adversarial examples in the query-limited scenario. Specifically, STBA introduces a flow field to the high-frequency part of clean images to generate adversarial examples and adopts the following two processes to enhance their naturalness and significantly improve the query efficiency: a) we apply an estimated flow field to the high-frequency part of clean images to generate adversarial examples instead of introducing external noise to the benign image, and b) we leverage an efficient gradient estimation method based on a batch of samples to optimize such an ideal flow field under query-limited settings. Compared to existing score-based black-box baselines, extensive experiments indicated that STBA could effectively improve the imperceptibility of the adversarial examples and remarkably boost the attack success rate u

关键词： Structured Query Language

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Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize

arXiv

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arXiv 2024年

作者： Zhang, Tong Li, Chris Junchi Siebel School of Computing and Data Science University of Illinois UrbanaIL United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States

In this paper, we revisit ROOT-SGD, an innovative method for stochastic optimization to bridge the gap between stochastic optimization and statistical efficiency. The proposed method enhances the performance and reliability of ROOT-SGD by integrating a carefully designed diminishing stepsize strategy. This approach addresses key challenges in optimization, providing robust theoretical guarantees and practical benefits. Our analysis demonstrates that ROOT-SGD with diminishing stepsize achieves optimal convergence rates while maintaining computational efficiency. By dynamically adjusting the learning rate, ROOT-SGD ensures improved stability and precision throughout the optimization process. The findings of this study offer valuable insights for developing advanced optimization algorithms that are both efficient and statistically robust. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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Can Large Language Models Predict the Outcome of Judicial Decisions?

arXiv

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arXiv 2025年

作者： Kmainasi, Mohamed Bayan Shahroor, Ali Ezzat Al-Ghraibah, Amani Computer Science and Engineering Dept. Qatar University Doha Qatar School of Computing and Data Science OUC in partnership with LJMU Doha Qatar Biomedical Engineering Dept. Jordan University of Science and Technology Irbid Jordan

Large Language Models (LLMs) have shown exceptional capabilities in Natural Language Processing (NLP) across diverse domains. However, their application in specialized tasks such as Legal Judgment Prediction (LJP) for low-resource languages like Arabic remains underexplored. In this work, we address this gap by developing an Arabic LJP dataset, collected and preprocessed from Saudi commercial court judgments. We benchmark state-of-the-art open-source LLMs, including LLaMA-3.23B and LLaMA-3.1-8B, under varying configurations such as zero-shot, one-shot, and fine-tuning using LoRA. Additionally, we employed a comprehensive evaluation framework that integrates both quantitative metrics (such as BLEU, ROUGE, and BERT) and qualitative assessments (including Coherence, Legal Language, Clarity, etc.) using an LLM. Our results demonstrate that fine-tuned smaller models achieve comparable performance to larger models in task-specific contexts while offering significant resource efficiency. Furthermore, we investigate the impact of fine-tuning the model on a diverse set of instructions, offering valuable insights into the development of a more human-centric and adaptable LLM. We have made the dataset, code, and models publicly available to provide a solid foundation for future research in Arabic legal NLP1 Copyright © 2025, The Authors. All rights reserved.

关键词： Prediction models

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Find truth in the hands of the few:acquiring specific knowledge with crowdsourcing

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Frontiers of Computer Science 2021年第4期15卷 5-16页

作者： Tao HAN Hailong SUN Yangqiu SONG Yili FANG Xudong LIU SKLSDE Lab School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyClearwater BayHong Kong 999077China School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful ***,when we aggregate the crowd knowledge based on the currently developed voting algorithms,it often results in common knowledge that may not be *** this paper,we consider the problem of collecting specific knowledge via *** the help of using external knowledge base such as WordNet,we incorporate the semantic relations between the alternative answers into a probabilisticmodel to determine which answer is more *** formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption,and solve it by the expectation-maximization(EM)*** increase algorithm compatibility,we also refine our method into semi-supervised *** results show that our approach is robust with hyper-parameters and achieves better improvement thanmajority voting and other algorithms when more specific answers are expected,especially for sparse data.

关键词： crowdsourcing knowledge acquisition EM algorithm label aggregation

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Convergent Differential Privacy Analysis for General Federated Learning: the f-DP Perspective

arXiv

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arXiv 2024年

作者： Sun, Yan Shen, Li Tao, Dacheng Faculty of Engineering The University of Sydney Australia School of Cyber Science and Technology Sun Yat-sen University China College of Computing & Data Science Nanyang Technological University Singapore

Federated learning (FL) is an efficient collaborative training paradigm extensively developed with a focus on local privacy, and differential privacy (DP) is a classical approach to capture and ensure the reliability of private security. Their powerful cooperation provides a promising paradigm for the large-scale private clients. As a predominant implementation, the noisy perturbation has been widely studied, being theoretically proven to offer significant protections. However, existing analyses in FL-DP mostly rely on the composition theorem and cannot tightly quantify the privacy leakage challenges, which is tight for a few communication rounds but yields an arbitrarily loose and divergent bound eventually. This also implies a counterintuitive judgment, suggesting that FL-DP may not provide adequate privacy support during long-term training. To further investigate the convergent privacy and reliability of the FL-DP framework, in this paper, we comprehensively evaluate the worst privacy of two classical methods under the non-convex and smooth objectives based on the f-DP analysis. With the aid of the shifted interpolation technique, we successfully prove that privacy in Noisy-FedAvg has a tight convergent bound. Moreover, with the regularization of the proxy term, privacy in Noisy-FedProx has a stable constant lower bound. Our analysis further demonstrates a solid theoretical foundation for the reliability of privacy in FL-DP. Meanwhile, our conclusions can also be losslessly converted to other classical DP analytical frameworks, e.g. (ϵ,δ)-DP and Rényi-DP (RDP). © 2024, CC BY.

关键词： Federated learning

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Healthcare Cost Patterns and Prediction: Investigating Personal datasets Using data Analytics 3rd

Healthcare Cost Patterns and Prediction: Investigating Perso...

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3rd International Conference on Signal and data Processing, ICSDP 2023

作者： Islam, Md. Aminul Nag, Anindya Chandra, Pretam Mishra, Bhupesh Kumar Fahim, S. M. Firoz Ahmed Hoque, Md Mozammel School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom College of Engineering and Physical Sciences University of Birmingham Birmingham United Kingdom Computer Science & Engineering Discipline Khulna University Khulna9208 Bangladesh Department of Computer Science & Engineering Adamas University Kolkata700126 India Centre of Excellence for Data Science AI and Modelling University of Hull Hull United Kingdom Department of Computer Science & Engineering American International University Dhaka Bangladesh Gannon University Erie United States

ISBN: (纸本)9789819795772

The present study introduces a health insurance prediction system that leverages machine learning methodologies. In contemporary times, there has been a notable increase in endeavors focused on tackling this matter, since the significance of health insurance as a research topic has markedly escalated following the occurrence of the pandemic. The dataset employed in this research comprises 1338 observations, 7 columns, and corresponds to individual medical expenditures in the United States which is available at the Kaggle platform. The dataset encompasses a variety of variables utilized in the prediction of insurance prices, including age, gender, BMI, smoking status, and number of children. The researchers utilized machine learning models including neural network, XAI, auto modeling to determine the correlation between pricing and the aforementioned attributes. The training process involved partitioning the dataset into a 80–20 ratio for training and evaluation respectively. As a consequence, the system achieved an accuracy rate of 97% by Gradient Boosting but we corrected it as 92% by Gradient Boosting Regressor by encoding and hyper tuning. Also, among predictive machine learning models, Random Forest had the best accuracy i.e., of 83.44%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Health insurance

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Emotion Classification Through EEG Signals: The EmoSense Model

Emotion Classification Through EEG Signals: The EmoSense Mod...

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computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Yi Ken Chuah Chean Khim Toa Sim Kuan Goh Chow-Khuen Chan Hiroki Takada School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Selangor Malaysia School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Malaysia Department of Human & Artificial Intelligent Systems Graduate School of Engineering University of Fukui Fukui Japan

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

In the Emotion research, there has been extensive work on emotion classification using electroencephalogram (EEG) signals. Despite most studies having achieved high accuracy, there is a potential for improvement in the model, particularly in training time reduction to optimize the modeling efficiency. Hence, our study aims to develop an efficient EEG-based emotion classification model that achieves high accuracy with a reduced number of training epochs. The proposed model, named EmoSense, employs a hybrid deep learning neural network architecture, which comprises a combination of Convolutional Neural Network (CNN), Deep Autoencoder (DAE), Bidirectional Long Short-Term Memory (BiLSTM), and an Attention mechanism. EEG features are first extracted using Differential Entropy (DE) on the EEG channels, which are then structured to form a four-dimensional (4D) representation of features with temporal, spatial, and frequency information. Subsequently, the extracted features will be used as input to the proposed model for training. Experimental evaluation of the publicly available DEAP dataset demonstrated that the proposed model achieved an accuracy of 94.11% with only 35 epochs. This performance is comparable with the result of the Cascaded Convolutional Recurrent Neural Networks model developed by Meng et al., which reported an accuracy of 94.64% with 100 epochs. The primary reason for the comparison with Meng et al. is that our overall process, including feature extraction and formation, is similar to their study, with the only difference being the deep learning architecture. Our finding indicates that the EmoSense model is not only able to achieve comparable accuracy but also reduces the number of training epochs, demonstrating the potential to learn the EEG features in a shorter time and lowering the computational cost. This study will benefit EEG-based emotion recognition to be more accessible and practical for real-world applications.

关键词： Training Emotion recognition Accuracy Attention mechanisms Computational modeling Brain modeling Feature extraction Electroencephalography Convolutional neural networks Tuning

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Multimodal Fusion with Deep Neural Network Architecture for Alzheimer's Disease Prediction 15th

Multimodal Fusion with Deep Neural Network Architecture for ...

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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Venkatesh, Swarna Saritha, Kuppala Gogula, Sreenivasulu Chitteti, Chengamma Yalavarthi, Sikhi Swaraja, K. ECE CMR Technical Campus Hyderabad India CSE Presidency University Bangalore India Vardhaman College of Engineering Hyderabad India Department of Data Science School of Computing Mohan Babu University Andhra Pradesh Tirupati India Lexisnexis Risk Solutions Inc. Oklahomacity United States ECE GRIET Hyderabad India

ISBN: (纸本)9783031810824

Alzheimer's disease occurs due to neurodegenerative disorder, innovative approach is needed for early detection of disease. A method combining multimodal data fusion with adaptive capabilities of Adam optimization algorithm to enhance Alzheimer's disease prediction have been proposed. The proposed multimodal fusion approach combines neuroimaging, clinical, and genetic data into a deep neural network architecture, in order to achieve better accuracy,. Utilization of multimodal data helps to capture the view of disease, from structural brain changes to clinical assessments. This approach not only enhances predictive accuracy but also provides valuable insights into the mechanisms of Alzheimer's disease prediction. Utilization of Adam's optimization algorithm helps the model to converge faster making the proposed method highly effective. Combining the strengths of multimodal fusion and Adam optimization, the proposed method can be utilized for early Alzheimer's disease diagnosis, leading to improved patient outcomes and improved accuracy in Alzheimer's disease prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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