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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dankan Gowda, V. Mary, P. Arockia Ramanan, S.V. Kumar, N.M.G. Kumar, Pullela Svvsr Abdullah, Ibrahim Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Ppg Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Mohan Babu University Sree Vidyanikethan Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh Tirupathi India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Al-Turath University College Department of Computer Science Baghdad Iraq

ISBN: (纸本)9798350367720

This paper focuses on the detection of surface defects in the steel manufacturing industry since these defects inhibit the quality of the manufactured products and the efficiency of the manufacturing processes. Conventional inspection techniques require a extensive amount of work to be done at the inspection sites and perhaps associated with several kinds of errors. The current research work describes an automated system for determining the defects in the steel using image processing and integrating it with computational intelligence in MATLAB. The proposed method consists of pre-processing where the images are enhanced, feature extraction where characteristics of the defects are obtained and classification utilizing the machine learning models. The efficiency of the proposed approach is proved using experiments on the dataset of steel images and a substantial increase in the precision of the defects' recognition compared to other techniques. Thus, the' findings of the apply demonstrate that the use of image processing and machine learning can offer a strong system for real time defect identification in steel production. © 2024 IEEE.

关键词： Image enhancement

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Deep Learning-Based Approach for Calorie Estimation in Indian Foods 2

Deep Learning-Based Approach for Calorie Estimation in India...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Chauhan, Naman Revathy, S. Murali, P. Basker, N. Saranya, S. Natarajan, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Electronics and Communication Engg. Chennai Ramapuram India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engg. Chennai India Sona College of Technology Department of Computer Science and Engg. Salem India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India School of Computer Science and Engg Vellore Institue of Technology Chennai Campus India

ISBN: (纸本)9798350373110

People in the modern era are increasingly concerned with their diet and food choices to prevent developing chronic diseases like high blood pressure and diabetes. With the increasing usage of smartphones and technology, an application to automatically monitor their diet would be a great boon, especially for the younger generation. This research work investigates the process of estimating the calories present in the food images. This research work was carried out in three phases. The first phase involves the categorization of food images, such as boiled, fried in oil, or junk. This research work introduces a novel deep neural model by incorporating the MobileNet-V2 framework. In the second phase, the quantity of the labelled food is estimated. Further, it involves computing the calories present in the food using the Google search API. The details of the ingredients present in each type of food are used for this purpose. Classification accuracy, food amount calculation precision, and calorie estimation accuracy are used to evaluate the workings of the proposed model. This research work has secured plausible results with an overall accuracy of 95.21%, comparatively better than the existing approaches. © 2024 IEEE.

关键词： Calorie Estimation Deep Learning Google Search API Image Classification MobileNet-V2 Neural Network

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Design of an Improved Model for Blockchain Forensics Using Graph Convolutional Networks and Variational Autoencoders

Design of an Improved Model for Blockchain Forensics Using G...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Bokade, Sweta A. Sharma, V.K. Manjre, Bhushan M. Computer Science and Engineering Bhagwant University Rajasthan Ajmer India Information Technology G H Raisoni College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9798331517953

Forensics is more needed to address the increasing sophistication of fraudulent activities within the ever-transacting and opaque nature of blockchains, where advanced and interpretable anomaly detection should ideally be present. Current methods struggle to scale with and handle the graph-structured nature of data in blockchains, usually failing in the interpretability that's necessary for either trust or accountability. The current solutions on anomaly detection are mostly heuristic in nature or at best a black-box machine learning model that offers minimal interpretability. Due to this fact, the stakeholders cannot understand or trust such results. In view of these limitations, this work proposes the development of explainable deep learning models with special appeal in blockchain forensic analysis. Some of the particular options considered are GCNs, VAEs, LSTMs, and a hybrid model of GCN- LSTM. GCNs are suitable to capture the graph structure present in blockchain data, while VAEs can detect anomalies through reconstruction error in high-dimensional spaces. LSTM networks are chosen for their capability to model temporal dependencies in blockchain transactions, while the hybrid GCN-LSTM model combines both graph-based and temporal information in order to improve the accuracy of detection. These models are integrated with state-of-the-art explainability techniques like SHAP, LIME, GNN Explainer, and Integrated Gradients that provide transparent insight into model decision-making processes. SHAP values give feature-level explanations, LIME provides local model interpretability, and GNN Explainer underlines the critical graph structure. Beyond that, these methods confer interpretability on the results of anomaly detection and further build trust in the system. Indeed, the performance of the proposed models was excellent: for GCN, the Fl-score ranged between 0.80 to 0.90, for VAEs it was between 0.85 to 0.90 as measured with AUROC, and for a hybrid GCN-LSTM model, pr

关键词： Blockchain

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UAV Frequency-based Crowdsensing Using Grouping Multi-agent Deep Reinforcement Learning

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计算机科学 2023年第2期50卷 57-68页

作者： Cui ZHANG En WANG Funing YANG Yong jian YANG Nan JIANG College of Computer Science and Technology Jilin UniversityChangchun 130012China College of Information Engineering East China Jiaotong UniversityNanchang 330013China

Mobile CrowdSensing(MCS)is a promising sensing paradigm that recruits users to cooperatively perform sensing ***,unmanned aerial vehicles(UAVs)as the powerful sensing devices are used to replace user participation and carry out some special tasks,such as epidemic monitoring and earthquakes *** this paper,we focus on scheduling UAVs to sense the task Point-of-Interests(PoIs)with different frequency coverage *** accomplish the sensing task,the scheduling strategy needs to consider the coverage requirement,geographic fairness and energy charging *** consider the complex interaction among UAVs and propose a grouping multi-agent deep reinforcement learning approach(G-MADDPG)to schedule UAVs distributively.G-MADDPG groups all UAVs into some teams by a distance-based clustering algorithm(DCA),then it regards each team as an *** this way,G-MADDPG solves the problem that the training time of traditional MADDPG is too long to converge when the number of UAVs is large,and the trade-off between training time and result accuracy could be controlled flexibly by adjusting the number of *** simulation results show that our scheduling strategy has better performance compared with three baselines and is flexible in balancing training time and result accuracy.

关键词： UAV Crowdsensing Frequency coverage Grouping multi-agent deep reinforcement learning

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Synergizing CNN and Random Forest for Accurate Cattle Disease Identification

Synergizing CNN and Random Forest for Accurate Cattle Diseas...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Yashu Kukreja, Vinay Srivastava, Prateek Garg, Ashish Hariharan, Shanmugasundaram Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Vardhaman College of Engineering Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350382693

The current research focuses on the essential undertaking of multiclass classification in the context of diagnosing skin disorders in cattle. It aims to provide a thorough assessment of a complete model. The introduction underscores the need of rapid illness diagnosis in veterinary practises. The aim of this study is to construct a robust model that can successfully identify a wide range of skin diseases in cattle with high accuracy. The precision values, which range from 88.12% to 97.57%, serve as indicators of the model's proficiency in accurately predicting distinct classes. The model's ability to make accurate predictions across several categories is highlighted by its overall accuracy of 91.56%, indicating a high level of competency and reliability. The research use a comprehensive dataset and incorporates sophisticated machine learning methods to improve the detection of diseases. The attainment of a high level of precision and accuracy plays a significant role in enhancing the dependability of the model in real-world scenarios, hence enabling timely intervention and treatment. The results indicate that the model that was created has potential for effective use in actual situations, offering a significant resource for veterinarians in the management and treatment of skin disorders in cattle. This research makes a valuable contribution to the continuous endeavours in using machine learning techniques to enhance cattle health and emphasises the potential for further breakthroughs in disease identification strategies. © 2024 IEEE.

关键词： Animal CNN Deep Learning Disease Recognition Health Random Forest

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Context-Aware Semantic Type Identification for Relational Attributes

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Journal of computer science & technology 2023年第4期38卷 927-946页

作者：丁玥郭雨荷卢卫李海翔张美慧李晖潘安群杜小勇 Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China Tencent(Beijing)Technology Company Limited Beijing 100080China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China College of Computer Science and Technology Guizhou UniversityGuiyang 550025China Tencent(Shenzhen)Technology Company Limited Shenzhen 518057China

Identifying semantic types for attributes in relations,known as attribute semantic type(AST)identification,plays an important role in many data analysis tasks,such as data cleaning,schema matching,and keyword search in ***,due to a lack of unified naming standards across prevalent information systems(*** islands),AST identification still remains as an open *** tackle this problem,we propose a context-aware method to figure out the ASTs for relations in this *** transform the AST identification into a multi-class classification problem and propose a schema context aware(SCA)model to learn the representation from a collection of relations associated with attribute values and schema *** on the learned representation,we predict the AST for a given attribute from an underlying relation,wherein the predicted AST is mapped to one of the labeled *** improve the performance for AST identification,especially for the case that the predicted semantic types of attributes are not included in the labeled ASTs,we then introduce knowledge base embeddings(***)to enhance the above representation and construct a schema context aware model with knowledge base enhanced(SCA-KB)to get a stable and robust *** experiments based on real datasets demonstrate that our context-aware method outperforms the state-of-the-art approaches by a large margin,up to 6.14%and 25.17%in terms of macro average F1 score,and up to 0.28%and 9.56%in terms of weighted F1 score over high-quality and low-quality datasets respectively.

关键词： attribute semantic type(AST)identification context-aware semantic embedding knowledge base embedding

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Fair Machine Learning in Healthcare: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 493-507页

作者： Feng, Qizhang Du, Mengnan Zou, Na Hu, Xia Texas A&M University Department of Computer Science & Engineering College StationTX77843 United States New Jersey Institute of Technology Department of Data Science NewarkNJ07102 United States University of Houston Department of Industrial Engineering HoustonTX77004 United States Rice University Department of Computer Science HoustonTX77251 United States

The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare. However, these methods can perpetuate or even exacerbate existing disparities, leading to fairness concerns such as the unequal distribution of resources and diagnostic inaccuracies among different demographic groups. Addressing these fairness problems is paramount to prevent further entrenchment of social injustices. In this survey, we analyze the intersection of fairness in ML and healthcare disparities. We adopt a framework based on the principles of distributive justice to categorize fairness concerns into two distinct classes: equal allocation and equal performance. We provide a critical review of the associated fairness metrics from a ML standpoint and examine biases and mitigation strategies across the stages of the ML lifecycle, discussing the relationship between biases and their countermeasures. The article concludes with a discussion on the pressing challenges that remain unaddressed in ensuring fairness in healthcare ML and proposes several new research directions that hold promise for developing ethical and equitable ML applications in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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A Multi-Source Approach to Film Recommendations using Social Media, Search Data, and Streaming History 5

A Multi-Source Approach to Film Recommendations using Social...

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

作者： Raj, J. Relin Francis Karthikeyan, M. Saravana Rajkumar, G. Vinoth Shankar, S. Vijay Isaac, R. Augustian Anushya, S. Thanga Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Thandalam Chennai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

In the rapidly evolving OTT platforms, providing highly personalized and relevant film recommendations has become a critical challenge. Traditional recommendation systems primarily rely on user ratings and watch history, neglecting the vast potential of other data sources such as social media interactions and search engine queries. This research presents a multi-source film recommendation framework, integrating data from social media posts, search behaviour, and OTT streaming history to deliver enhanced personalization. A novel algorithm, CoFiSent (Collaborative Filtering with Sentiment Analysis), is proposed, combining collaborative filtering techniques with sentiment analysis of user-generated content from social media. By analysing user sentiments and preferences, the algorithm refines recommendations to match users' dynamic interests more effectively. The performance of CoFiSent is evaluated against existing algorithms, including standard collaborative filtering and content-based filtering models. Simulations are conducted using real-world datasets, and the system's effectiveness is measured through precision, recall, F1-score, and mean reciprocal rank (MRR). Results demonstrate that CoFiSent outperforms traditional methods, particularly in capturing emerging trends and contextual preferences reflected in social media activities. The integration of multiple data sources enhances the overall accuracy and relevance of recommendations, offering a significant improvement in user satisfaction. The research findings suggest that incorporating social media sentiment and search behaviour into film recommendation systems represents a promising approach for next-generation entertainment platforms. © 2024 IEEE.

关键词： Collaborative filtering

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On Combining the Potential of Social Robots and ChatGPT for Enhanced Learning 12

On Combining the Potential of Social Robots and ChatGPT for ...

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12th International Conference on Information and Education technology, ICIET 2024

作者： Jamil, Nuraini Saghir, Fahed Namir Alshanqiti, Hassan Ali Almansoori, Ali Khalifa Saeed, Abdulrahman Ahmad, Ali Belkacem, Abdelkader Nasreddine College Of It Uae University Dept. Of Computer Science And Software Eng. Al Ain United Arab Emirates Uae University Dept. Of Information Technology College Of It Al Ain United Arab Emirates

ISBN: (纸本)9798350371772

This systematic literature review examines the impact of artificial intelligence (AI), specifically that of social robots and ChatGPT, on education. This study thoroughly assesses the diverse applications of AI in pedagogy. It emphasizes the potential synergy between social robots and ChatGPT and their transformative impact on teaching, learning, and academic outcomes. By comprehensively examining various scholarly publications, this review highlights the significant influence of social robots in facilitating emotional involvement, collaborative learning, and immersive educational situations. Additionally, it clarifies the mechanism by which ChatGPT enables personalized instruction, immediate knowledge sharing, and academic assistance. Furthermore, it highlights the difficulties and ethical complexities of incorporating AI technology in education. This necessitates the development of a careful and deliberate strategy to ensure data privacy, reduce excessive reliance on AI, and maintain the indispensable role of human educators. This review comprehensively analyzes existing research to understand the evolution of AI-driven education. It discusses the significant contributions of social robots and ChatGPT in shaping an educational landscape that prioritizes effective pedagogical methods, fosters inclusive learning environments, and achieves a balance between technological advancements and human interaction © 2024 IEEE.

关键词： Ethical technology

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