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2024 international conference on advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Vinothini, C. Abbas, B.I. Mohammed Channabasava, S.M. Chirag, K. Shashank, M.R. Computer Science And Engineering Dayananda Sagar College Of Engineering Bengaluru India

ISBN: (纸本)9798350364828

The growing volume of healthcare data necessitates advanced data mining techniques to extract meaningful patterns and insights. This paper introduces 'RX Assist,' an Intelligent Disease Prediction and Drug Recommendation system employing multiple machine learning algorithms. Leveraging patient symptoms, age, and gender as input parameters, the system predicts diseases using Gaussian Naive Bayes, Random Forest, Logistic Regression, and Sklearn Decision Tree models, achieving high accuracies (98.4% to 100%). The drug recommendation module utilizes Random Forest and Gaussian Naive Bayes models, achieving notable accuracies (100%). The project also incorporates user-friendly interfaces for patients and doctors, enabling appointment bookings and fostering efficient communication. Our work extends the base paper by enhancing disease prediction accuracy and incorporating a sophisticated drug recommendation system. Experimental results and methodologies presented herein contribute valuable insights for future medical applications and research endeavors. © 2024 IEEE.

关键词： Forecasting

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Insights into distorted lamellar phases with small-angle scattering and machine learning

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2025年第Pt 2期58卷 523-534页

作者： Tung, Chi-Huan Ding, Lijie Huang, Guan-Rong Porcar, Lionel Shinohara, Yuya Sumpter, Bobby G. Do, Changwoo Chen, Wei-Ren Oak Ridge Natl Lab Neutron Scattering Div Oak Ridge TN 37831 USA Natl Tsing Hua Univ Dept Engn & Syst Sci Hsinchu 300044 Taiwan Natl Ctr Theoret Sci Div Phys Taipei 10617 Taiwan Inst Laue Langevin BP 156 F-38042 Grenoble 9 France Oak Ridge Natl Lab Mat Sci & Technol Div Oak Ridge TN 37831 USA Oak Ridge Natl Lab Ctr Nanophase Mat Sci Oak Ridge TN 37831 USA

Lamellar phases are essential in various soft matter systems, with topological defects significantly influencing their mechanical properties. In this report, we present a machine-learning approach for quantitatively analyzing the structure and dynamics of distorted lamellar phases using scattering techniques. By leveraging the mathematical framework of Kolmogorov-Arnold networks, we demonstrate that the conformations of these distorted phases - expressed as superpositions of complex waves - can be reconstructed from small-angle scattering intensities. Through the contour analysis of wave field phase singularities, we obtain the statistics of the spatial distribution of topological defects. Furthermore, we establish that the temporal evolution of these defects can be derived from the time-dependent traveling wave field, informed by the dispersion relation of spectral components. This method opens new avenues for investigating the dynamics of distorted lamellar phases using various dynamic scattering techniques such as neutron spin echo and X-ray photon correlation spectroscopy. These findings enhance our microscopic understanding of how defects influence the physical properties of lamellar materials, with implications for both equilibrium and non-equilibrium states in general lamellar systems.

关键词： small-angle scattering machine learning distorted lamellar phases regression analysis Kolmogorov-Arnold networks

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Skin Disease Classification using Transfer learning with a Modified ResNet-50 Architecture

Skin Disease Classification using Transfer Learning with a M...

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2024 international conference on advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Priya Bhaminie, S. Yogesh, C. School Of Computer Science And Engineering Vellore Institue Of Technology-Chennai Chennai India

ISBN: (纸本)9798350364828

This paper presents an advanced approach in skin disease classification using a modified ResNet-50 architecture, applied to a specific subset from the ISIC 2019 Dataset focusing on Benign Keratosis, Basal Cell Carcinoma, and Melanoma. The study integrates deep learning and medical imaging to enhance diagnostic accuracy in dermatology. It leverages transfer learning, adapting the well-established ResNet-50 model, and modifies it by incorporating global average pooling and dense layers, thus tailoring it for dermatological use. This method is inspired by significant strides in skin cancer detection using ResNet and innovative transfer learning techniques in skin disease classification. A notable aspect of this research is the evaluation of data augmentation's impact on model generalization and robustness. The findings of this study contribute to the growing field of deep learning in skin disease classification, providing a refined and effective tool for potential applications in dermatology. © 2024 IEEE.

关键词： Dermatology

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Smart Air and Water Quality Monitoring for Industrial Emissions using IoT and machine learning 2

Smart Air and Water Quality Monitoring for Industrial Emissi...

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2nd international conference on machine learning and Autonomous systems, ICMLAS 2025

作者： Ramya, Ms. R. Amithesh Sharavan, Mr. S. Asfiya Taj, Ms. S. Kaviyarasan, Mr. D. K.S.Rangasamy College of Technology Department of Electronics and Communication Engineering India K.S.Rangasamy College of Technology Department of Electronics and Communication Engineering India

ISBN: (纸本)9798331505745

The system is an integrated and intelligent solution for the tracking and quality management of air and water in industrial environments. As the system utilizes sophisticated sensors of the IoT that can measure key parameters like Total Dissolved Solids, pH, turbidity, dissolved oxygen, temperature, NH3 levels, heavy metals, and harmful gases, it feeds in data at continuous, real-time sites to operators and regulatory bodies. Access and visualization of monitored data can be done through ThingSpeak and the data is stored in google firebase, letting in proactive response and decision-making about keeping emissions and pollutants within legal standards. The dataset can be used for applying machine learning techniques like Random Forest combined with gradient boosting to predict the future pollution levels. © 2025 IEEE.

关键词： Quality of service

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The Performance Evaluation of E-learning During the Emergency Using machine learning 26th

The Performance Evaluation of E-learning During the Emergenc...

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26th international conference on Interactive Collaborative learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： El-Sofany, Hosam F. El-Seoud, Samir A. King Khalid Univ Abha Saudi Arabia Cairo Higher Inst Engn Comp Sci & Management Cairo Egypt British Univ Egypt BUE Fac Informat & Comp Cairo Egypt

ISBN: (纸本)9783031519789;9783031519796

E-learning is one of the educational alternatives available to students who need assistance during an emergency. (e.g., Covid-19 pandemic, bad climate, etc.). Most educational institutions are moving a significant portion of their curriculum toward an online learning paradigm to reduce the amount of face-to-face interaction between students and faculty members during times of emergency (e.g., in the case of Covid-19 pandemic). The success of E-learning is conditional on a wide range of aspects, such as students' and teachers' levels of self-efficacy, attitudes toward, and confidence in making use of the relevant technology;the instructional approaches that are utilized;the capacity to monitor and evaluate educational outcomes;and students' levels of motivation. The performance and circumstances of students who are engaged in e-learning are analyzed in this paper. The research investigates and evaluates predictions made by a model that is based onmachine learning techniques. Predicting the degree to which students are delighted with the online mode of instruction by considering several parameters, including internet capability, and involvement in the online mode of instruction.

关键词： E-learning Education in an emergency learning outcomes machine learning (ML)

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Medicinal Plants Identification Using Deep learning: A Survey 6

Medicinal Plants Identification Using Deep Learning: A Surve...

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6th IEEE international conference on Recent advances in Intelligent Computational systems, RAICS 2024

作者： Prasannan, Revathy Roy, Preethy Jithi, P.V. Amritha, R. Nair, Anjana V Jose, Ann Mariya Adi Shankara Institute of Engineering & Technology Computer Science and Engineering Kalady India

ISBN: (纸本)9798350381689

Medicinal plant identification has shown great benefit from deep learning, especially when CNNs are employed. CNNs are ideally suited for this task since they can extract complex features from photos. The promise of deep learning for medicinal plant identification is outlined in this research, which achieves up to 99% accuracy. This survey shows that deep learning can successfully detect medicinal plants in a variety of real-world contexts, such as open fields, rural places, and botanical gardens. Deep learning's ability to automate the identification process is a key benefit in this industry as it can assist alleviate the scarcity of knowledgeable and proficient botanists. Moreover, deep learning models can be implemented on mobile platforms, hence expanding their user base. This offers strong evidence that deep learning has potential applications in the recognition of medicinal plants. Deep learning models have the potential to aid in the building of an all-encompassing and dependable system for detecting medicinal plants in a variety of contexts with additional study and improvement. © 2024 IEEE.

关键词： Adversarial machine learning

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MRAM-Based Physical Unclonable Function that is Resilient to Deep learning Attacks 1

MRAM-Based Physical Unclonable Function that is Resilient to...

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1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Venkadeshwaran, Kuthalingam Alam, Intekhab Faculty of Engineering and Technology Department of Mechanical Engineering Ramnagar District Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

Electronic devices are everywhere, so strong hardware security is needed to keep private data safe from dangers. A physical Unclonable Function (PUF) device constructed using magneto-resistive random-access memory (MRAM) is shown in this study. The circuit generates appropriate outputs for problem-solving by using built-in properties derived from various manufacturing processes, such as the resistance of the magnetic tunnel junction (MTJ) cell. This proposal differs from Arbiter PUF in that it satisfies the rigorous avalanche criteria (SAC). Additionally, the proposed circuit is impervious to machine learning-based modelling assaults due to its grid-like architecture. Various ML approaches are evaluated for two- and four-array designs, including systems such as support vector machine (SVM), linear regression (LR), and multilayer perceptron (MLP). Accuracy of MLP-attack forecasts remained 53.62% for two array circuit & 49.88% for four array circuit. This demonstrates that the system continues to function well despite substantial process modifications. A convolutional neural network (CNN), a recurrent neural network (RNN), a multi-layer perceptron (MLP), and a Larq are all used in high-dimensional models that are based on deep learning. These nets have an accuracy rating of 50.32%, 50.26%, and 50.32%, along with 50.32%, correspondingly. For a simpler two-array design, the suggested circuit's efficiency at the layout stage is also looked into. Based on the simulations, the suggested circuit has an average intra-hamming distance (HD) of 0.99% and an average inter-hamming distance (HD) of 49.97%. It also has an average diffuseness of 49.08. © 2025 IEEE.

关键词： Hamming distance

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IoT Attack Detection:Using Feature Selection and machine learning Algorithms 6

IoT Attack Detection:Using Feature Selection and Machine Lea...

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6th IEEE international conference on Recent advances in Intelligent Computational systems, RAICS 2024

作者： Seena Binth, K.T. Jayasree, P. Govt. Engineering College Barton Hill Dept. of Information Technology Thiruvananthapuram India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381689

The Internet of Things (IoT) is facing a growing concern regarding cyber-attacks and the need for anomaly detection. As the deployment of IoT devices rapidly expands, the number of attacks targeting these devices increases, posing a continuous threat of exploitation and danger. Various types of attacks, such as malicious operations, denial of service, MITM (Man-in-the-Middle), and scanning, can lead to the failure of IoT devices. In our study, we examine the effectiveness of several machine-learning algorithms, including decision trees, random forests, gradient-boosting machines (GBM), Support Vector machines (SVM), Naive Bayes, Adaboost, and the unsupervised classifier K medoids. The objective is to analyze and predict network attacks targeting IoT devices. These algorithms are implemented in MATLAB platform. The dataset used here is IOTID2020 network intrusion dataset. The accuracy of the model by using adaboost is the highest that is 99.92% for normal and abnormal classification. The accuracy 99.49% for category classification. © 2024 IEEE.

关键词： Network intrusion

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machine learning Techniques in Adaptive and Personalized systems for Health and Wellness

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international JOURNAL OF HUMAN-COMPUTER INTERACTION 2023年第9期39卷 1938-1962页

作者： Oyebode, Oladapo Fowles, Jonathon Steeves, Darren Orji, Rita Dalhousie Univ Fac Comp Sci Halifax NS Canada Acadia Univ Sch Kinesiol Wolfville NS Canada Dalhousie Univ Sch Hlth & Human Performance Halifax NS Canada

Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized interventions or treatments to individual patients. In this paper, we present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being. We conduct a systematic search for articles published between January 2011 and April 2022 and selected 87 articles that met our inclusion criteria for review. The selected articles target 18 health and wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary management, health monitoring, substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile health (mHealth) apps finder, clinician knowledge representation for neonatal emergency care, dental and oral health, medication management, disease surveillance, medical specialty recommendation, and health awareness. Our review focuses on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health and wellness interventions. We also identified various technical and methodological challenges including data volume constraints, data quality issues, data diversity or variability issues, infrastructure-related issues, and suitability of interventions which offer directions for future work in this area. Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multimodality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated learning, explainabl

关键词： Knowledge representation

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Comparison Analysis of Transfer learning Models for Deep Fake Image Detection 2

Comparison Analysis of Transfer Learning Models for Deep Fak...

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2nd international conference on machine learning and Autonomous systems, ICMLAS 2025

作者： Sujanthi, S. Priya, R. Ranga Shree, S. Suruthika, S. Sushmitha, S. M.Kumarasamy College of Engineering Computer Science and Engineering Tamilnadu Karur639113 India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Deepfake identifies fake videos and photographs. To create deepfakes, machine learning algorithms change video or reputation features like faces. Deepfake identifies fake videos and photographs. New deep learning generative algorithms may make fake films and photos seem real, worrying many. Personal and social safety are jeopardized. We urgently need computational methods to detect fake material and alert consumers of picture and video changes. We evaluate deep learning-based deepfake detection. We want to automate field development fake content detection. Assessing the work's strengths and faults, we find deepfakes. AI, NLP, and computer vision use deep machine learning for efficiency and usability. Deepfakes create realistic images and videos using deep learning. We test multiple deep learning-based deepfake detection and generation approaches. Several deepfake detection techniques are examined. Researchers benefit from our paper's social media deepfake detection. Compare current and previous industrial practices and datasets. © 2025 IEEE.

关键词： Deep learning

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