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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Kayade, Parth Pardeshi, Aniket Patil, Suyog Raut, Prashant Shetkar, Pranav Barhate, Madhuri Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350367171

This research work introduces a novel social media platform built on blockchain technology, aiming to address contemporary challenges in privacy, security, and content integrity. Leveraging decentralized ledger technology, the platform ensures transparent and secure data storage, granting users unprecedented control over their personal information. Through cryptographic protocols, identity protection is prioritized, reducing vulnerability to data breaches. The proposed system employs smart contracts for content attribution, guaranteeing immutable and authentic user-generated content. Token-based incentives incentivize community participation and amplify content creators' contributions. By emphasizing decentralization and transparency, the developed platform intends to revolutionize the social media landscape, fostering a more resilient, userdriven digital environment. The fusion of blockchain technology and social media heralds a new era, offering a resilient, transparent, and user-centric digital ecosystem for the future. © 2024 IEEE.

关键词： Dapp NodeJS ReactJS Smart Contract Solidity Web3.0

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Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3564-3578页

作者： Xu, Honghui Fang, Chuangjie Wang, Renfang Chen, Shengyong Zheng, Jianwei Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Tianjin University of Technology College of Computer Sciences and Engineering Tianjin300384 China

Recently, tensor singular value decomposition (TSVD) within high-order (Ho) algebra framework has shed new light on tensor robust principal component analysis (TRPCA) problem. However, HoTSVD lacks flexibility in handling the hidden correlations along different modes of large-scale multidimensional data. Moreover, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimality. For a relief, we propose a dual-enhanced self-learning TSVD along all modes to address computational flaws and learn a lossless transformation that induces a lower average-rank tensor. Specifically, we multiply the learnable semiorthogonal matrices obtained through Tucker compression with the original tensor along all modes, thus obtaining a core tensor with more inherent low rankness. Building upon this foundation, a new TNN is introduced by generalizing HoTSVD to Mode-k TSVD, followed by the facilitation to the core tensor, achieving dual-enhancement. Moreover, a reweighting scheme is imposed on the Mode-k HoTSVD to learn the global low-rank correlation and provide an efficient numerical solution. Finally, an alternating direction method of multipliers (ADMM)-based algorithm is developed as a solver. Experimental results on several types of multidimensional visual data, including light field images (LFI) and color videos, demonstrate the superiority of the proposal over previous state-of-the-art methods. © 2020 IEEE.

关键词： Discrete Fourier transforms

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Innovative Approaches to Cardiovascular Disease: Machine Learning Predictions Unveiled & Interpretation using LIME & SHAP 2

Innovative Approaches to Cardiovascular Disease: Machine Lea...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Reddy, Vonteddu Adharsh Dilish, M. Kodipalli, Ashwini Rao, Trupti Rohini, B.R. Gargi, N. Department of Artificial Intelligence and Data Science Global Academy of Technology Bangalore India

ISBN: (纸本)9798331504960

This study investigates how machine learning classification could revolutionize the field of cardiovascular disease prediction. By utilizing a variety of datasets that include demographic, lifestyle, and clinical information, several algorithms such as Logistic Regression, KNN, SVM, Decision Tree Classifier, GradientBoost, AdaBoost, XGBoost, Hyperparameter tuning, LIME and SHAP are used to create prediction models. The research hopes to facilitate early interventions and preventative measures by improving individualized risk assessments and their accuracy. The findings demonstrate the effectiveness of ML in predicting cardiovascular risks, indicating a paradigm change in the direction of preventive healthcare. With further development, incorporating these models into clinical practice may be able to reduce the prevalence of cardiovascular illnesses worldwide by implementing timely and focused therapies. © 2024 IEEE.

关键词： Risk assessment

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A momental bound algorithm based on precise stepsize adjustment for training deep neural networks 43

A momental bound algorithm based on precise stepsize adjustm...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Haixia Li, Dequan Qian, Zhen Anhui University of Science and Technology School of Mathematics and Big Data Huainan232000 China Anhui University of Science and Technology School of Artificial Intelligence Huainan232000 China

ISBN: (纸本)9789887581581

Adaptive algorithms are extensively employed in the field of deep learning owing to their rapid convergence properties. Adam is the most common adaptive algorithm among them. However, it has revealed that Adam has a poor generalization ability. AdaBelief is an algorithm based on Adam with exact stepsize adjustment. It introduces a new second-order momentum that corrects Adam's second-order moments. These algorithms still fail to converge during training due to the presence of instability and extreme learning rates. In this paper, we propose a new adaptive and momental bounded algorithm, called AdaBMod, which can effectively mitigate the sudden large learning rate problem and is especially suitable for training deep neural networks. By setting an adaptive finite learning rate in AdaBelief algorithm, the obtained AdaBMod can effectively eliminate the problem of high learning rate in the late training of neural networks, so as to make the training process more stable. Numerical simulation experiments on deep neural network tasks also show that our proposed AdaBMod algorithm eliminates the large learning rate during the training process. The results are also better than other current state-of-the-art optimizers. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Adaptive Algorithms Adaptive and Momental Bound Deep Neural Networks Image Classification Robustness

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A Comprehensive Study on Distributed Denial of Service Attack Detection using Deep Learning Technique 1

A Comprehensive Study on Distributed Denial of Service Attac...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Mukku, Bhavana Mlr Institute of Technology Department of Data Science and Cyber Security Hyderabad India

ISBN: (纸本)9798350352931

Distributed Denial of Service (DDoS) attacks are among the most dangerous threats in cyberspace impacting areas such as the Internet of Things (IoT), Cybersecurity, and Cloud Computing, which leads to network interruptions. DDoS attacks target IoT environments by manipulating IoT networks, consuming resources and interrupting services, leading to privacy challenges and performance degradation. However, accurate detection of DDoS attacks is difficult, due to their various DDoS attack types, heterogeneity of network security and complexity of communication protocols. Advanced Deep Learning (DL) approaches have been employed for effective detection of DDoS attacks while reducing false-positive rates and improving high detection rates of the different network traffic patterns. The DL algorithms such as Convolutional Neural Networks (CNN), Bidirectional Long-Short Term Memory (Bi-LSTM), Recurrent Neural Networks (RNN), and Deep Neural Networks (DNN) are used for the detection of DDoS attacks. This survey focuses on analyzing the advantages and limitations of different DL algorithms used for DDoS attack detection. The performance of the DL models is evaluated based on performance metrics such as accuracy, precision, recall, and F1-score. © 2024 IEEE.

关键词： Cyber attacks

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Genetic Algorithm-Driven Weight Adjustment in Ensembles for Advanced Thyroid Classification

Genetic Algorithm-Driven Weight Adjustment in Ensembles for ...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Samson Jebaraj, P. Grace Mary Kanaga, E. Karunya Institute of Technology and Sciences Department of Data Science and Cybersecurity Coimbatore India

ISBN: (纸本)9798350354539

The thyroid is the tiniest gland on the front of the neck, shaped like a butterfly. It generates hormones that regulate the amount of energy the body uses. This research explores the use of machine learning techniques, ensemble learning strategies, and optimization algorithms for ensemble weights in response to the vital importance of thyroid diagnosis. To accurately identify thyroid disorders, a wide range of machine learning methods are used, including logistic regression, Support Vector Classifier (SVC), MLP Classifier, KNN Classifier, Decision Tree Classifier, and Random Forest Classifier. In addition, the application of ensemble learning techniques demonstrates the enhanced performance that results from combining these models. This research provides insights into the effectiveness of Genetic EnsembleWeight Adjuster in changing the ensemble weights, hence leading to improved thyroid classification accuracy. The Random Forest Classifier proved to be effective in identifying thyroid abnormalities in this investigation, as seen by its best accuracy of 98.9%. A remarkable accuracy of 99.9% was attained by adjusting the weight of the Ensemble Approach using the Genetic EnsembleWeight Adjuster and based on the experimental result it gives better accuracy compared to the Ensemble Approach without adjusting weights and other classifiers. © 2024 IEEE.

关键词： Decision trees

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The Social Score: A Social Credit System Using Blockchain 5

The Social Score: A Social Credit System Using Blockchain

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ubale, Ganesh Bawake, Vasundhara Badkas, Kshitij Bhade, Shourya Batkadli, Aarya Belure, Prasad Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331518097

The Chinese Social Credit System (SCS) serves as a large-scale example of behaviour tracking, adapted here for a college-level social scoring framework. The system monitors student activities, achievements, and certificates to promote positive behaviour. Certificates generated by administrators are hashed and stored on the blockchain, creating an immutable, tamper-proof record that cannot be altered or forged. Blockchain technology facilitates decentralized verification, allowing certificates to be authenticated independently when uploaded by students to enhance their social scores. This approach fosters trust through enhanced transparency and security. The student dashboard provides access to certificate uploads, activity records, violation reporting, and reward monitoring, while the administrative interface supports the management of reports, violations, leaderboards, and certificate generation. The blockchain-based design ensures that processes, from certificate issuance to verification, remain secure, transparent, and resistant to tampering, establishing a reliable framework for managing social scores. © 2024 IEEE.

关键词： Blockchain

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Real-Time House Price Predictions with Regression Analysis 6

Real-Time House Price Predictions with Regression Analysis

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Purushotham, K. Bangarappa Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Department of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350381689

Accurate property assessment is essential in the dynamic and ever-changing world of real estate so that both buyers and sellers can make well-informed choices. We are aware that setting the appropriate asking price for a house can be a difficult undertaking. Property values are greatly influenced by factors including location, size, amenities, market trends, and numerous economic indicators. This system supports in determining a property's price based on geographic regions factors, the number of bathrooms, bedrooms, and area (square feet). Forecasting future costs relies on examining historical market trends and value ranges. The primary focus of this analysis is to efficiently utilize regression models in combination with other methodologies to achieve the highest possible accuracy in predictions. This study aims to forecast housing values in Bangalore. Our website is designed to give you reliable and data-driven insights on property values when you're a potential homebuyer looking to identify the best property. © 2024 IEEE.

关键词： Houses

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Design and Evaluation of an AI-Powered Conversational Agent for Personalized Mental Health Support and Intervention (MindBot) 5

Design and Evaluation of an AI-Powered Conversational Agent ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kambare, Shweta M. Jain, Kriya Kale, Ishika Kumbhare, Vibhor Lohote, Sakshi Lonare, Shubham Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331530013

This research study describes the design and evaluation of an interactive MindBot to help a person deal with depressive Sentiments and to support individuals experiencing depressive symptoms by providing timely, interactive, and empathetic engagement. With the growing trend of mental health issues and a lack of adequate resources to cope, the need for innovative solutions is of great importance. In this MindBot, users will be connected to each other to communicate their Sentiments and seek support. The integration of NLP and LLMs by the chatbot fosters meaningful conversations and ensures timely support to users. Computational architecture optimizes response generation to control the processing cost related to LLMs while striking a balance between conversational richness and efficiency. This method allows for real-time communication without sacrificing the caliber of involvement. The chatbot's integration of NLP and LLMs promotes meaningful discussions and guarantees that consumers receive prompt service. The utilization of predefined response templates combined with dynamic responses generated by GPT -3.5-turbo further improved the implementation's performance in conversational capability. The paper covered the system architecture, methodology, and results achieved, with an emphasis on the contribution of sentiment analysis and the contributions of the chatbot towards mental health support. User happiness, emotional correctness, referral appropriateness, and the model's capacity to sustain coherent conversation even in the face of small user input errors are all used to gauge how effective the model is. The results indicate that the effectiveness of mental health interventions and user engagement are increased when sentiment analysis and sophisticated language models are combined. It was demonstrated that the impact of integrating advanced language models along with sentiment analysis can have a huge bearing on enhancing the user engagement factor as well as the ef

关键词： Chatbots

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The dilation of the pupils and the mystery of gaze: Revealing the microcosm of cognitive load

The dilation of the pupils and the mystery of gaze: Revealin...

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2024 International Conference on Future of Medicine and Biological Information Engineering, MBIE 2024

作者： Bao, Caijilahu Zhang, Lulu Inner Mongolia University of Technology Department of Data Science and Applications Hohhot China

ISBN: (纸本)9781510682825

In the process of education and learning, the lack the process of education and learning, the lackers' cognitive load may lead to the lack of accurate assessment of learners' cognitive load may lead to the design of teaching content, the provision of learning resources, and the choice of teaching methods that do not match the cognitive characteristics of learners, thereby causing difficulties in learning and affecting learning outcomes. To better achieve teaching objectives, this article reveals the level of cognitive load faced by participants in searching for specific texts or symbols through tracking and in-depth analysis of eye parameters such as pupil diameter, gaze point, and gaze duration. The results show that when individuals face complex tasks, their pupils dilate sharply, a phenomenon stemming from the demand for more cognitive resources by complex tasks, which triggers adjustments in the autonomic nervous system. In addition, high-frequency scanning behavior was also observed, and this frequent scanning behavior is precisely the user's attempt to quickly capture and process a large amount of information in a limited time, reflecting the tense allocation and efficient utilization of cognitive resources. The effective combination of the two can better assess the cognitive load level of the subjects. © 2024 SPIE.

关键词： Teaching

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