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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

引用

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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A Deep Learning Framework with Attention Mechanism for the Classification of Mild Cognitive Impairment using Brain MRI

A Deep Learning Framework with Attention Mechanism for the C...

引用

2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Illakiya, T. Vidhya, B. Chelladurai, S. Prince Hariprasad, S. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331521394

Mild Cognitive Impairment (MCI) refers to a neurological disorder marked by observable changes in cognitive decline. MCI signifies an intermediate phase linking normal aging and Alzheimer's Disease (AD). It is typically categorized into two types: Early MCI (EMCI) and Late MCI (LMCI), representing varying levels of cognitive impairment. An effective classification between EMCI and LMCI is vital, as it allows timely interventions that can potentially slow the progression of AD, improving patient outcomes and quality of life. Identifying the subtle features and patterns in EMCI and LMCI is essential for early diagnosis and effective intervention. This work proposes a novel Deep Learning (DL) network integrating EfficientNet-B0, a Global Context Network (GCN), and Neighborhood Attention blocks. The EfficientNet-B0 acts as the foundation for feature extraction in the proposed model. It uses its efficient architecture and pre-trained capabilities to capture meaningful patterns. To further enhance feature representation, a Global Context Network (GCN) is employed to capture long-range dependencies and global semantic information. Additionally, a Neighborhood Attention Module is used to refine local features by focusing on important regions in the MRI scans. The proposed framework balances global semantic understanding and localized feature extraction, resulting in improved classification performance. With systematic training and evaluation, the model achieves an accuracy of 95.3%, outperforming existing approaches and providing a better solution for neuroimaging-based MCI subtype classification. © 2025 IEEE.

关键词： Convolutional neural networks

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Security of Images Using DNA Encryption and Lorenz System in Cloud Storage 7th

Security of Images Using DNA Encryption and Lorenz System in...

引用

7th International Conference on Soft computing and Signal Processing, ICSCSP 2024

作者： Gupta, Raghav Gullapalli, Manasa Suseela, G. Department of Networking and Communications School of Computing Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Networking and Communications Faculty of Engineering and Technology School of Computing Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9789819799251

Images are the most valuable yet susceptible type of data since they may be used to track user activity, exploited by hackers, and utilized for fraud and identity theft. Storing images in local storage, on a device’s disk, within a web browser’s local storage, or on a server’s file system can introduce several vulnerabilities and risks, particularly regarding security and data integrity. Using image encryption as a security solution provides a reliable means of protecting sensitive visual data from unauthorized access and potential security threats. The convenience of cloud-based systems, however, comes with the responsibility of protecting data against cyber threats. The widespread usage of cloud storage has led to concerns about the security of images stored there. Addressing the challenges associated with securing images in the cloud, this project brings a novel approach that blends cryptographic techniques, such as DNA encryption, with the dynamism of chaotic systems like the Lorenz system. Such integrative methodologies aim to meet current security demands for image storage and transmission in the digital age. This dual-layer encryption approach ensures that images encrypted and stored in cloud storage are safeguarded with a security measure that is currently unparalleled, making unauthorized access and decryption practically impossible without the specific, uniquely generated decryption key. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensitive data

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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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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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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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Plant Disease Detection and Classification using Convolutional Neural Networks (CNN) 15

Plant Disease Detection and Classification using Convolution...

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

作者： Sreenivasan, Mahalakshmi Selvaraj, P. 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

Farmers and producers may suffer significant financial losses due to plant diseases. Effective control of plant diseases requires early identification and categorization of the illnesses. However, traditional plant disease detection techniques are frequently labor- and time-intensive. Plant disease detection has found a possible collaborator in deep learning and machine learning. Consequently, a novel deep-learning technique is proposed for classifying and diagnosing plant illnesses. It uses a transfer learning methodology built on a convolutional neural network architecture consisting of 5 convolutional, 2 fully- connected, and 3 max pooling layers. The algorithm has a 98% accuracy rate when tested on a publicly accessible dataset of images of plant diseases. The CNN model was trained with the PlantVillage dataset, which is available on Kaggle. From the results and a comparison with previous works, it is evident that the chosen approach performed well in classifying plant diseases with expected accuracy. © Grenze Scientific Society, 2024.

关键词： Plant diseases

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Enhanced Privacy in Data Aggregation with Secret Sharing Techniques 1

Enhanced Privacy in Data Aggregation with Secret Sharing Tec...

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1st IEEE International Conference on computing and Data science, ICCDS 2024

作者： Yugandhar, A.C.K. Hemanth, Mandyam Gnanavel, S. SRM Institute of Science and Technology Faculty of Engineering and Technology Dept of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9798350365337

The effectiveness of wireless sensor networks is greatly impacted by wireless spoofing attacks, which are simple to initiate. The difficult issues in Wireless Sensor Networks are locating many adversaries and eliminating them, identifying spoofing attackers, and estimating the number of attackers. The spoofing attackers are located and detected using the clustering approach. This method is unable to correctly identify the attackers. Intrusion Detection System (IDS) is suggested as a solution to this issue in order to identify spoofing attackers. In order to detect the misbehaving nodes in a wireless sensor network, VANET IDS monitors node behavior in their car, such as packet transfer. The simulation results clearly show that the proposed strategy effectively and consistently detects accidents in wireless sensor networks. © 2024 IEEE.

关键词： Cryptography

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Leveraging HPC with GAN-CNN Integration for Enhanced Tuberculosis Detection using Synthetic Image Generation 3

Leveraging HPC with GAN-CNN Integration for Enhanced Tubercu...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Manasa, Pokala Veeramalla, Vaishnavi Vanusha, D. Vathana, D. Robinsha, Delsi S. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603203 India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603203 India

ISBN: (纸本)9798331532420

Tuberculosis (TB) remains a significant global health challenge, especially in remote areas with limited medical resources. Early and accurate diagnosis is crucial to control its spread. This project harnesses high-performance computing (HPC) to address two key issues: managing unreliable medical data and improving TB diagnosis. Given the imbalance between healthy and TB-affected samples, Generative Adversarial Networks (GANs) are used to generate synthetic images, enriching the dataset and making it more diverse. This helps train Convolutional Neural Networks (CNNs), which are highly effective in medical image analysis. By leveraging HPC, we accelerate the training of CNNs on large, augmented datasets, significantly reducing processing time while maintaining accuracy. This system improves TB detection by combining GAN-enhanced data and CNN models, enabling faster and more reliable diagnosis. © 2024 IEEE.

关键词： X ray apparatus

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