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Fair k-Center Problem with Outliers on Massive Data

Tsinghua science and Technology 2023年第6期28卷 1072-1084页

作者： Fan Yuan Luhong Diao Donglei Du Lei Liu Beijing Institute for Scientific and Engineering Computing Faculty of ScienceBeijing University of TechnologyBeijing 100124China Faculty of Management University of New BrunswickFrederictonE3B 5A3Canada

The clustering problem of big data in the era of artificial intelligence has been widely *** of the huge amount of data,distributed algorithms are often used to deal with big data *** distributed computing model has an attractive feature:it can handle massive datasets that cannot be put into the main *** the other hand,since many decisions are made automatically by machines in today’s society,algorithm fairness is also an important research area of machine *** this paper,we study two fair clustering problems:the centralized fair k-center problem with outliers and the distributed fair k-center problem with *** these two problems,we have designed corresponding constant approximation ratio *** theoretical proof and analysis of the approximation ratio,and the running space of the algorithm are given.

关键词： machine learning distributed algorithm fairness constraints outlier constraints k-center problem

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Alzheimer's Disease Prediction Using Machine Learning 7

Alzheimer's Disease Prediction Using Machine Learning

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Mishra, Shashwat Sharma, Viplav Ramya, G. SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350372816

Alzheimer's disease is a progressive and debilitating neurodegenerative disorder that poses a significant public health challenge worldwide. Early detection and prediction of Alzheimer's disease are critical for effective treatment and care. This research paper presents a novel approach for predicting Alzheimer's disease using machine learning methodologies. Our study leverages a diverse dataset that includes clinical, genetic, and neuroimaging data from individuals at various stages of the disease. Through rigorous preprocessing and feature selection processes, we extract relevant features and apply advanced machine learning algorithms. Our results demonstrate the efficacy of this approach in accurately predicting the onset and progression of Alzheimer's disease. The model's high accuracy, sensitivity, and specificity suggest its potential as a valuable tool for clinicians and researchers. Furthermore, our study highlights key factors contributing to the prediction and offers insights into the underlying biological processes of the disease. This research holds promise for early intervention and personalized patient care, ultimately contributing to improved management of Alzheimer's disease and enhancing the quality of life for affected individuals and their families. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid CNN-BiLSTM Model for Power Quality Disturbance Classification 1

Hybrid CNN-BiLSTM Model for Power Quality Disturbance Classi...

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1st IEEE International Conference on Advanced Power engineering and Energy, APEE 2024

作者： Lin, Jiajian Tavalaei, Jalal Yeo, Lit Yen Zhou, Yutong Afrouzi, Hadi Nabipour Ektesabi, Mehran Motamed Swinburne University of Technology Faculty of Engineering Computing and Science Kuching Malaysia College of Mechancial Engineering Taishan University Taian China School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC Australia

ISBN: (纸本)9798350360936

With the evolution of intelligent grid tech, the need for monitoring power quality has markedly risen, essential for the continuous functioning of the power grid. Addressing this matter, this research proposes a combined approach combining Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks. The model effectively catches spatial or temporary aspects of power quality disruptions using CNN and employs BiLSTM to document long-term or temporal aspects, thus creating a profound correlation between input and output sequences. By substantially decreasing reliance on conventional physical attributes, this technique markedly enhances the precision in detecting power quality anomalies. Crucially, this technique, independent of particular original samples and existing information, holds widespread applicability and is applicable in identifying patterns in various other intricate systems. Research on experiments indicates that, as opposed to conventional machine learning frameworks, deep learning approaches are more effective in detecting disruptions in power quality. Our hybrid model excels in identifying power quality disturbance (PQD) and produces highly accurate recognition outcomes by autonomously analyzing central features of the disturbance indications. © 2024 IEEE.

关键词： Convolutional neural networks

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Crowdfunding Platform Using Decentralised Application (Blockchain)

Crowdfunding Platform Using Decentralised Application (Block...

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2024 International Conference on Communication, Computer sciences and engineering, IC3SE 2024

作者： Kumar, Ayush Utekar, Swarali Viji, D. College Of Engineering And Technology Srm Institute Of Science And Technology Faculty Of Engineering And Technology Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350366846

Blockchain technology provides a safe, transparent, and effective platform that might completely transform public financing. Each transaction is tamper-proof since it is permanently stored with unique identities and timestamps. Furthermore, agreements are automated via smart contracts, which simplifies procedures and lowers errors usually related to human verification. This encourages accountability and confidence inside the system. For platforms that receive public financing, transparency is crucial. This need is met by this blockchain-based online service, which offers a safe and decentralized environment. Donors can simply monitor the initiatives they support and make sure their money is going to where it's supposed to. Should a project be cancelled, the program enables contributors to receive their money back right away. The application caters to two user groups: campaign creators and donors. Donors have access to other fundraising campaigns, project updates, and insightful information. The website gives campaign designers the ability to monitor the status of their initiative and compare their results to those of other campaigns. This blockchain-powered online application provides a stable, open, and safe environment for public fundraising. By empowering both creators and donors, it promotes an ecosystem that is more reliable and efficient. © 2024 IEEE.

关键词： Smart contract

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Gesture Recognition for Phrase Interpretation: A Deep Learning Approach

Gesture Recognition for Phrase Interpretation: A Deep Learni...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Puyam, Kelvin Singh, Nishant Aruna, M. SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350375237

Gesture recognition holds paramount significance in facilitating communication for individuals utilizing sign language to convey phrases and expressions. We present an innovative approach to gesture recognition in this article tailored for interpreting hand signs to derive meaningful phrases, such as 'I love you,' 'please,' 'sorry,' and 'goodbye.' Our proposed methodology integrates a multi-box detector (SSD) with dynamic hand gesture analysis to achieve real-time and accurate recognition of hand signs across various languages and cultural contexts. By harnessing the power of deep learning SSD for object detection, our system ensures swift responses while maintaining precision. Additionally, the incorporation of dynamic hand gesture analysis techniques enhances the system's adaptability to diverse sign styles and linguistic nuances, thus fostering better communication access. By means of meticulous experimentation, we establish the effectiveness of our methodology in precisely identifying and deciphering real-time sign language gestures, attaining noteworthy results in terms of both speed and accuracy. This study enhances assistive technology for hard-of-hearing and deaf communities, thereby promoting accessibility and inclusiveness in communication environments that are accommodated to the linguistic requirements of a variety of individuals. © 2024 IEEE.

关键词： Gesture recognition

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End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 IEEE.

关键词： Adversarial machine learning

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Mindbrush: A Machine Learning Approach to Virtual Artistry 2

Mindbrush: A Machine Learning Approach to Virtual Artistry

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Kashyap, Shreya Kaviya, A. Yamini, R. SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur India SRM Institute of Science and Technology Faculty of Engineering and Technologies Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350368109

Air Canvas is a breakthrough virtual platform of digital artistry which is able to cross the boundaries of traditional art by allowing users to create their artusing only gestures in the air. The novel technique of computer vision, particularly the hand tracking and finger-tip recognition, has been incorporated and it has provided a completely new definition of HCI (human-computer interaction). Through the absence of the physical tools like pens or keyboards, Air Canvas delivers a liberated experience that helps people develop and communicate in a way that is natural, and simpler. This amazing concept is one more step toward widening accessibility and making sure that people like the deaf, aged, and those with physical disabilities are not left behind. © 2024 IEEE.

关键词： Audition

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Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

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Cyberbullying Advisory Model 15

Cyberbullying Advisory Model

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Dhulipala, Sai Kowshik Jaswanth, Vungarala Ragunthar, T. Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331300579

In the current digital environment, cyberbullying remains a significant threat to online safety and well-being. This study explores the technological advancements and strategies utilized in the project, providing compelling evidence of its effectiveness in reshaping the landscape of cyberbullying recovery. To tackle this urgent issue, we introduce the "Cyberbullying Advisory," an innovative AI-powered solution. Unlike traditional technologies, our platform is specifically designed to offer a dedicated space for cyberbullying victims across different online platforms, allowing them to share their stories and begin a journey towards emotional healing. Notably, it goes a step further by providing legal guidance to assist users in navigating the complexities of online harassment. Incorporating technologies such as Natural Language Processing (NLP), Natural Language Understanding (NLU), and Deep Learning, our models utilize task-specific datasets to implement key features like Toxicity Level Detection, Empathetic Response, and Intent Recognition. By leveraging the BERT Model for improved user interaction, these technologies enable real-time empathetic responses and personalized legal advice, revolutionizing the user experience in combating cyberbullying. The integration of AI-driven analysis and supervised learning techniques ensures accurate identification and categorization of toxic content, allowing users to receive tailored support. The uniqueness of our project lies in the seamless integration of these components, emphasizing precision through the use of task-specific datasets. By providing victims with empathetic and context-aware responses, our platform serves as a steadfast ally, offering assistance to those impacted by cyberbullying across diverse digital platforms. © Grenze Scientific Society, 2024.

关键词： Phishing

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Knowledge augmented significant language model-based chatbot for explainable diabetes mellitus prediction 19

Knowledge augmented significant language model-based chatbot...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Elfayoumi, Mazen AbouElazm, Mohamed Mohamed, Omar Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt Faculty of Computer Science Goldsmith UOL United Kingdom College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confidence, this chatbot provides detailed text-based explanations for its individual predictions and enhances user’s understanding by retrieving and presenting similar cases using case-based reasoning techniques. Utilizing key health indicators such as hypertension status, heart disease presence, smoking history, BMI, HbA1c level, and blood glucose levels, the chatbot not only predicts the presence of diabetes but also educates users about the underlying reasons for each prediction. The system's explanation module promotes transparency and trust in the predictive process. The proposed architecture integrates the retrieval augmented generation (RAG) technique with prompt engineering across multiple LLMs, including Llama 3.1, GPT-3.5, and Gemma2. This approach augments the chatbot’s responses with contextually relevant data from similar past cases, thereby enhancing both relevance and accuracy. Furthermore, RAG minimizes LLM hallucinations while enhancing them with up-to-date, precise, personalized medical information. The objective of this chatbot is to offer healthcare professionals and patients a dependable, educational instrument for early detection of diabetes supported by transparent AI principles. This will have a positive effect on preventative healthcare measures. © 2025 IEEE.

关键词： Prediction models

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