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1st International Conference on data engineering and Machine Intelligence, ICDEMI 2023

作者： Duraipandian, Kavitha Ambigapathy, Murugan Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9789819776153

Early warning signs of dementia may include memory loss, difficulty with problem-solving, confusion, changes in mood and behavior, and impaired communication skills. People impacted by dementia often make a loss on things, money, etc. In the social environment, the disease is considered as one of the serious issues. In order to correctly identify dementia, automated methods of diagnosis have grown essential. Conventional non-imaging components are capable of accurately identifying dementia as well as readily integrating the practice of medicine, despite the fact that neuro-images are currently fully utilized in the most recent technologies. To develop a powerful dementia screening tool, researchers combined machine learning with an enormous amount of non-imaging variables. In order to dramatically lessen the obstacles to employing the diagnostic tool, this project makes use of the predictive capability of non-imaging data. The suggested system was completely equipped to detect and evaluate cognitive signs associated with dementia using speech data, keystroke evaluation, and so on. The optimized Deep Residual Network (ODRN) is used to identify brain fluctuations, the sound information is subsequently analyzed with frequency extractors, and the voice pitch is further examined. A deep neural network is used to evaluate keystrokes while contrasting them to Kaggle data on odd stroke rates. On the ensemble method developed here, the system at hand has a precision of 96.66% performance. The overall processing delay of 52 s is achieved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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An Empirical Analysis of Load Balancing Issues in Fog Computing Environment 2

An Empirical Analysis of Load Balancing Issues in Fog Comput...

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2nd International Conference on data science and Information System, ICDSIS 2024

作者： Kumar, K. R. Prasanna Siddesh, G.M. Ananda Babu, J. M S Ramaiah Institute Of Technology Department Of Computer Science & Engineering Bangalore India M S Ramaiah Institute Of Technology Department Of Artificial Intelligence & Data Science Bangalore India Malnad College Of Engineering Department Of Information Science And Engineering Hassan India

ISBN: (纸本)9798350381665

Fog Computing (FC) is a distributed paradigm that complements cloud computing in terms of service delivery. By extending storage and computation of the network edge, fog systems enable location awareness and mobility support, while also resolving the issue of service computing for delay-sensitive applications. A crucial component of fog networks is load balancing, which prevents fog nodes from becoming under or overloaded. In recent years, only a limited number of studies have been conducted on load-balancing methods in fog networks. This paper provides a thorough assessment of the load-balancing techniques used in fog computing, covering approximate, accurate, fundamental, and hybrid methods. This paper also examines load balancing metrics in fog networks, including all benefits and drawbacks associated with the selected load balancing algorithms. The evaluation methods considered for each study are investigated. Additionally, the primary unresolved issues and potential developments for these systems are discussed. © 2024 IEEE.

关键词： Virtualization

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Understanding and improving fairness in cognitive diagnosis

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science China(Information sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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Hybrid GCN-LSTM Framework for Enhanced Schizophrenia Diagnosis: Integrating Spatial and Temporal Insights from Brain Connectivity data 1

Hybrid GCN-LSTM Framework for Enhanced Schizophrenia Diagnos...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Anitha, R. Hemalatha Prathyusha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tiruvallur India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Kavaraipettai601206 India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

A new hybrid approach for diagnosing schizophrenia combines graph convolutional networks (GCNs) with long short-term memory (LSTM) networks, leveraging GCNs for spatial connectivity and LSTMs for temporal modeling. This integration allows a simultaneous examination of spatial and sequential brain connectivity patterns, offering a comprehensive view of schizophrenia's complex dynamics. By extracting critical features from brain connectivity data, the hybrid GCN-LSTM model enhances research insights and improves clinical interpretability. Testing and validation with real-world data demonstrate the model's superior performance over existing methods, advancing clinical decision support in psychiatry. Additionally, this approach not only improves our understanding of schizophrenia's mechanisms but also shows potential for broader applications, such as early and accurate dementia diagnosis. The study marks significant progress in applying advanced machine learning to clinical research. © 2024 IEEE.

关键词： Clinical research

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Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass 1

Improving Heart Disease Prediction with Stacked Ensemble Lea...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Amirthaa, M. Mary, Gerardine Immaculate Julian, Anitha Selvarathinam, Anto Lourdu Xavier Raj School of Electronics Engineering Vellore Institute of Technology Vellore India Saveetha Engineering College Computer Science and Engineering Tamilnadu Chennai India Data Science and Analytics Grand Valley State University MI United States

ISBN: (纸本)9798331518943

Heart disease prediction remains a critical area of research due to its significant impact on public health. This paper, titled 'Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass,' presents a comprehensive study comparing the performance of binary and multiclass classification models for heart disease prediction. Utilizing two distinct datasets, one for binary classification and one for multiclass classification. The study evaluates various machine learning and deep learning models, with a focus on stacked ensemble methods. The Binary Classification Results leveraging the Preprocessing-and-PCA-on-Heart-Disease-dataset, the study evaluated several models including Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, Deep Learning model, and Logistic Regression. The results indicate that the Stacking Ensemble model achieved the highest accuracy of 90.2%, outperforming individual models where Random Forest (88.5%), SVM (89.1%), XGBoost (89.7%), Gradient Boosting (88.0%) and Deep Learning (89.1%). The Multiclass Classification study using the UCI Heart Disease dataset, when applied the same models, the Stacking Ensemble model achieved an accuracy of 64.1%, which, although superior to other individual models such as Random Forest (58.1%), SVM (54.3%), Gradient Boosting (59.2%) and Deep Learning model (57.6%) fell short compared to XGBoost, which attained an accuracy of 65.8%. This outcome highlights the challenges associated with multiclass classification in heart disease prediction. © 2024 IEEE.

关键词： Contrastive Learning

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CLERC: An Intelligent Sign Language Translator for Improved Accessibility using Machine Learning 2

CLERC: An Intelligent Sign Language Translator for Improved ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Vijayaganth, V. Santhosh Kumar, S. Davis, L. Pranava Darisan, S.V. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350369960

Clerc's system is a modern smart language interpreter using advanced methods like artificial intelligence, imagery recognition, and natural languages translation. The Clergs System includes these are just some of tools used. The system is able to do this by turning gestures in sign language into verbal and written outputs smoothly thus helping people with hearing impairment to communicate easily in real time. In this research, an object recognition SSD MobileNets and FDN feature extraction models are used for accurate gesture detection. A machine-learning model can also be used to predict accurate translations. The efficacy of the Clerc system has been tested and validated to the utmost extent in different settings such as public places, schools, hospitals amongst others. This research seeks to explore possibilities of improving the system's precision, efficacy, scalability, and use with regards to the practical application. © 2023 IEEE.

关键词： Clerc system computer vision FPN-lite feature extractor scalability SSD Mobile Net Object recognition model

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IoT Authentication Protocols: Classification, Trend and Opportunities

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 515-533页

作者： N.alsheavi, Amar Hawbani, Ammar Wang, Xingfu Othman, Wajdy Zhao, Liang Liu, Zhi Alsamhi, Saeed Hamood Al-Qaness, Mohammed A. A. University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Nankai University Haihe Laboratory of ITAI School of Cyber Science Tianjin300459 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan University of Galway Insight Centre for Data Analytics Galway Ireland IBB University Faculty of Engineering Ibb Yemen Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions. © 2016 IEEE.

关键词： Authentication Protocol

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Fast Parallel Recovery for Transactional Stream Processing on Multicores 40

Fast Parallel Recovery for Transactional Stream Processing o...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhao, Jianjun Liu, Haikun Zhang, Shuhao Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Yu School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Transactional stream processing engines (TSPEs) have gained increasing attention due to their capability of processing real-time stream applications with transactional semantics. However, TSPEs remain susceptible to system failures and power outages. Existing TSPEs mainly focus on performance improvement, but still face a significant challenge to guarantee fault tolerance while offering high-performance services. We revisit commonly-used fault tolerance approaches in stream processing and database systems, and find that these approaches do not work well on TSPEs due to complex data dependencies. In this paper, we propose a novel TSPE called MorphStreamR to achieve fast failure recovery while guaranteeing low performance overhead at runtime. The key idea of MorphStreamR is to record intermediate results of resolved dependencies at runtime, and thus eliminate data dependencies to improve task parallelism during failure recovery. MorphStreamR further mitigates the runtime overhead by selectively tracking data dependencies and incorporating workload-aware log commitment. Experimental results show that MorphStreamR can significantly reduce the recovery time by up to 3.1 x while experiencing much less performance slowdown at runtime, compared with other applicable fault tolerance approaches. © 2024 IEEE.

关键词： Fault tolerance

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Enhancing the Security of Big data based on the New AI & its Techniques 5

Enhancing the Security of Big Data based on the New AI & its...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Singh, Uday Pratap Kumari, Bersha Saxena, Vivek Goyal, Dinesh Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur India Department of Computer Science & Engineering Poornima Institute of Engineering and Technology Jaipur India

ISBN: (纸本)9798400709418

Since that time i.e 1864 till today, research on different security level for the security of data which has been transferred or to be transfer within a network is on the boom day by day because with the advances in equipment innovations just as the quantity of gadgets associated day by day to the Internet, there are enormous data are gathered. Such data, whenever examined, will add significant qualities to the private just as governmental sectors. Simultaneously, Artificial Intelligences (AI) algorithms are demonstrated proficient in taking care of numerous unpredictable issues, particularly Deep Neural Networks (DNNs) on other Big data techniques. In this paper will have survey the efficiency of the system by applying different data value techniques and algorithms on the hardware tools. © 2023 ACM.

关键词： Deep neural networks

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Label Aggregation with Self-Supervision Enhanced Graph Transformer 26

Label Aggregation with Self-Supervision Enhanced Graph Trans...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Jiacheng Tang, Feilong Hou, Xiaofeng Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9781643684369

Aggregating noisy labels produced by the crowd of workers to generate true labels is a challenging problem in crowdsourcing. The key behind label aggregation is to effectively utilize the hidden information (e.g., characteristics of workers and questions which are often missing) in the labeling process. Existing methods mainly generated aggregation models based on the complicated Bayesian model or some strong assumptions. Recently, deep learning-based methods attempt to automate label aggregation but need various labels. These all make them hard to deploy to real-world applications. In fact, abundant information in the process of crowdsourcing itself can be extremely helpful to aggregate the labels. In this paper, we propose ATHENA (lAbel aggregaTion witH sElf-supervision eNhanced grAph transformer) to aggregate labels by utilizing the self-supervision signals in crowdsourcing. Firstly, we propose a transformer-based graph neural network that can learn from the crowdsourcing topology and features. Then, we use self-supervision signals inherently included in the dataset to help to aggregate the labels. To be specific, we identify the answer-based self-supervision signal that can predict the answer of any user given to different tasks. In our evaluations, we compare the proposed ATHENA with the other 11 representative methods on 10 datasets. Our experimental results demonstrate that ATHENA is highly effective in aggregating labels and obtains much better performance than existing methods. © 2023 The Authors.

关键词： Aggregates

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