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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Prudhvi Sai Ganesh Chakali Murthy Gari Keerthi Kiriti P. Rama Faculty of Engineering and Technology Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies Faculty of Engineering and Technology College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

The HealthScan AI is a comprehensive health diagnosis AI system which utilizes deep learning algorithms and the Keras library to determine if a person has certain diseases or not using their chest x-rays and other scans. It utilizes six distinct CNN models for the identification of COVID-19 and pneumonia using the chest X-rays, Glaucoma from internal eye scans. Our methodology involves rigorous training and evaluation processes, resulting in a highly accurate and efficient model. The integration of these models into a user-friendly interface, developed with Streamlit, allows seamless interaction. This interface empowers users to upload medical images, facilitating real-time diagnostic feedback on their health status. By providing a flexible and effective tool for the identification of a range of disorders, this novel technique advances the field of medicine and has the potential to completely transform healthcare procedures.

关键词： Glaucoma COVID-19 Deep learning Pneumonia Accuracy Streaming media Libraries

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A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors

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Annals of Data science 2023年第6期10卷 1607-1634页

作者： Islam, Uwaise Ibna Haque, Enamul Alsalman, Dheyaaldin Islam, Muhammad Nazrul Moni, Mohammad Ali Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh University of Waterloo WaterlooONN2L 3G1 Canada School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol that can ultimately lead a human to disastrous consequences. As patients with this behavior display a high value of relapse, the best intervention approach is to prevent it at the very beginning. In this paper, we propose a framework based on machine learning techniques to identify individual vulnerability towards substance abuse by analyzing socio-economic aspects. We have carefully assessed the commonly involved causes to form the questionnaire for collecting data from healthy people and patients suffering from substance abuse. Using Pearson’s chi-squared test of independence, feature importance is measured to eliminate less significant features using backward elimination. Popular machine learning classification algorithms (Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest neighbors, and Gaussian Naive Bayes) are used to build the predictive classifier. To identify the key risk-factors of individual substance abuse, we extract association rules from the significant features and subsequent factors. Experimental results on real data-set support the effectiveness of the proposed framework. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Predictive analytics

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Spark Booster-An Optimized IoT Architecture for Energy Sector Delving Predictive Analysis on Energy Usage With Stochastic Monte Carlo Method

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IEEE Access 2025年 13卷 103118-103136页

作者： S. Delsi Robinsha B. Amutha Vijayakumar Ponnusamy >Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India Department of ECE SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India

Optimal energy distribution and Energy management in a dynamic environment in smart city applications is a challenging task that requires a more optimised method to predict energy consumption. This research presents a novel data-driven Internet of Things (IoT) architecture called Spark Booster to enhance the prediction accuracy of energy consumption in a dynamic environment. The proposed architecture integrates the Stochastic Monte Carlo method and Markov chain modelling to simulate diversified energy usage scenarios and analyse energy data for accurate prediction of energy consumption. The proposed architecture is an adaptive one with real-time data acquisition and edge analytics for energy consumption prediction. The experimental results demonstrate a significant improvement in the prediction accuracy and robustness compared to the conventional architectures. The Sparks Boost architecture offers a scalable solution for both residential and industrial applications. The proposed optimized architecture enables optimal energy distribution, reducing resource wastage for supporting sustainability.

关键词： Internet of Things Energy consumption Energy management Computer architecture Sparks Accuracy Adaptation models Sustainable development Stochastic processes Renewable energy sources

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

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

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Automation, computing and Renewable Systems (ICACRS), International Conference on

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

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

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.

关键词： Training Accuracy Tuberculosis Computational modeling Parallel processing Generative adversarial networks Data models Convolutional neural networks Reliability Medical diagnostic imaging

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Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera Zafar Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

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Automated heart disease prediction using improved explainable learning-based technique

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Neural computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases 2

Machine Learning Models for Brain Signal Classification: A F...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali, Ali M Nashwan, Shadi Al-Qerem, Ahmad Almomani, Ammar Sakhnini, Mahmoud Al Aldweesh, Amjad AlAhliyya Amman University Communications And Computer Engineering Department Faculty Of Engineering Amman19328 Jordan Cybersecurity Department Faculty Of Information Technology Amman11831 Jordan Zarqa University Computer Science Department Faculty Of Information Technology Zarqa13110 Jordan School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan Shaqra University College Of Computing And It Shaqra Saudi Arabia

ISBN: (纸本)9798350394962

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. This study presents a machine learning framework for classifying normal and epileptic EEG recordings. The approach involves feature extraction using discrete wavelet transform (DWT) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. Specifically, DWT decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. Supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. This epilepsy EEG classification framework demonstrates the potential for AI to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Enhanced Feature Selection for Superior Ensemble DDoS Detection in Software-Defined Networks

Enhanced Feature Selection for Superior Ensemble DDoS Detect...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Alashhab, Abdussalam Ahmed Edrah, Aisha Abbas, Maythem K. Alashhab, Mohamed Alngib, Mohamed Daha, Muhammad Yunis Alasmarya Islamic University Faculty of Information Technology Department of Computer Science Zliten Libya Western University Faculty of Engineering Department of Electrical and Computer Engineering London Canada Asia Pacific University of Technology and Innovation School of Computing Kuala Lumpur Malaysia Universiti Sultan Azlan Shah Faculty of Management and Information Technology Kuala Kangsar Malaysia School of Engineering Ulster University Belfast United Kingdom

ISBN: (纸本)9798350391213

Distributed Denial-of-Service (DDoS) attacks disrupt networks by flooding systems with traffic from multiple sources, making real-time detection essential. Integrating Machine Learning (ML) with Software-Defined Networking (SDN) presents a viable solution;however, existing ML methods often face challenges such as high false positive rates and difficulty detecting sophisticated attacks like low-rate and zero-day DDoS attacks due to inadequate training features. As DDoS attack methods evolve, continuous retraining of ML models becomes necessary. This paper introduces a feature selection approach for ensemble classifiers to improve DDoS detection in SDNs. Through correlation analysis and principal component analysis (PCA), the most relevant features are identified, leading to substantial improvements in model performance. Our evaluation, using diverse DDoS datasets, demonstrates that the proposed method achieves near-perfect accuracy, a 100% true positive rate, and minimal error rates, offering a highly effective solution for DDoS detection in SDNs. © 2024 IEEE.

关键词： Zero-day attack

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Enhancing Wound Care in Healthcare Systems with Logistic Regression for Infection Detection 1

Enhancing Wound Care in Healthcare Systems with Logistic Reg...

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

作者： Ramalingam, L. Amarnath, Raveendra N. Arun, V. Sendhilkumar, N.C. Ravi, Rupavathy Srinivasan, C. Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Mphasis Corporation MelissaTX United States SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

In healthcare systems, wound care management is essential for reducing infections and promoting faster recovery. To improve wound care, this paper proposes a data-driven method that combines machine learning (ML), more particularly, Logistic Regression (LR), with Internet of Things (IoT) sensors to identify early infections. The system provides real-time data streams to healthcare providers by continually monitoring important wound parameters, including temperature, moisture, pH levels, and exudate presence via IoT-enabled sensors. Using LR, the system predicts the potential of wound infections based on these physiological indicators, allowing for more effective patient care and early treatments. When tested for accuracy and dependability in infection prediction, the proposed model shows significant detection skills with few false positives. This approach optimizes clinical processes by reducing the need for manual wound examination, enhancing patient safety, and lowering the possibility of complications from infections. Wound care management might improve patient outcomes and expedite infection control measures in medical institutions. © 2024 IEEE.

关键词： Logistic regression

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SDN Enabled Big Data Analytics and Framework for Sensor Data of Vehicle Health, Safety and Monitoring System 8

SDN Enabled Big Data Analytics and Framework for Sensor Data...

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8th IEEE International Conference on Software engineering and Computer Systems, ICSECS 2023

作者： Ahmad, Tanvir Ismail, Nor Syahidatul Nadiah Binti Safri, Abdullah Mat Rahman, Md. Arafatur Beg, Mohammad Sojon Bin Bakhtiar, M. Taher University Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan Malaysia Unversity of Wolverhampton Faculty of Science and Engineering Wolverhampton United Kingdom University Malaysia Pahang Al-Sultan Abdullah Faculty of Mechanical and Automotive Engineering Malaysia Kulliyyah of Pharmacy IIUM Kuantan Campus Kuantan Malaysia

ISBN: (纸本)9798350310931

Vehicular network enabled vehicle health, safety and monitoring system is gaining attention for its potential application while software defined network (SDN) is supporting vehicular communication for designing core of the network. Vehicular network emits enormous amount of data where most of them are sensor data and that amount of data required to undergo analyzed for productive output. There are two types of sensor data are used in vehicular network, vehicle sensor data (VSD) and infrastructure sensor data (ISD). Flow based SDN controller examines every packet of the network which is responsible of engagement of resources gravely. For vehicular health monitoring and safety applications, SDN doesn't required to check and analyze ISD. In this paper, a framework is proposed that promises to ignore ISD and only consider VSD for vehicle health, safety and monitoring applications. With this connections, this paper proposes big data module along with SDN controller and inside the big data module, a partitioner program is designed. The partitioner program categorize VSD and ISD on the basis of certain parameters like data payload, technology used, packet header and restrict RSD to be processed by SDN controller. A combiner program is designed that bind both VSD and ISD to fed to the application plane of system. Additionally, a big data analytics for vehicle health, safety and monitoring application is derived. © 2023 IEEE.

关键词： Big data

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