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17th International Conference on Signal-Image technology and Internet-Based Systems, SITIS 2023

作者： Singh, Vinay Kaur, Simran Jot Shanckin, Shyam Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370911

Vision-based deep neural networks are crucial components for autonomous driving in terms of visual understanding and decision making. These models need to be temporally consistent and visually interpretable. Most recent works focus on using static images for understanding visual semantics, ignoring the temporal consistency of driving conditions;or adopt end to-end architectures for learning the pilot networks, providing limited interpretability. In this paper, we raise the question of can we predict other traffic participant's future trajectory given previous egomotion visual input? The proposed system stems from the issue of human driver reaction time in the autonomous driving context. Our dual-staged model firstly applies modern convolutional object detectors for spotting traffic participants (i.e., cars in this paper) and robustly tracks the targets of interest. Then, a novel SEG-LSTM network is incorporated to fuse the multiple-streams from past frames and then predict targets' future trajectories. We demonstrate the feasibility of predicting future trajectories of vehicles for assisting mediated perception. We also show the effectiveness of combining various levels of abstractions (e.g., scene semantics, detection bounding boxes) further boost the prediction accuracy across diverse traffic conditions. © 2023 IEEE.

关键词： Trajectories

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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Exploration of alzheimer disease using design of experiments

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Multimedia Tools and Applications 2025年第3期84卷 1079-1101页

作者： Gupta, Pranav Pandey, Kavita Gupta, Sanskar Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

This study examines the use of experimental designs, specifically full and fractional factorial designs, for predicting Alzheimer’s disease with fewer variables. The full factorial design systematically investigates the effects of selected factors, providing a comprehensive analysis of their individual and interactive impacts on Alzheimer’s prediction. On the other hand, the fractional factorial design simplifies the model by selecting key factors and removing non-significant ones. For Alzheimer’s, among all the factors examined, the MMSE (Mini-mental state Examination) score emerged as the most significant, which was further cross-validated using Minitab Software Analysis. Importantly, the findings suggest that the fractional factorial design is a better alternative, particularly when time and cost constraints are present. The results highlight the effectiveness and efficiency of these experimental designs, demonstrating their potential for early detection, prevention, and treatment of Alzheimer’s disease. By contributing to the expanding knowledge base on experimental designs in medical research, this study emphasizes their crucial role in understanding and combating complex diseases like Alzheimer’s. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Design of experiments

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A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

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computers, Materials & Continua 2024年第5期79卷 3111-3138页

作者： Sujithra Sankar S.Sathyalakshmi Department of Computer Applications Hindustan Institute of Technology and ScienceChennaiTamil NaduIndia Department of Computer Engineering Hindustan Institute of Technology and ScienceChennaiTamil NaduIndia

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable *** predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce ***,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and *** paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present *** study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction *** the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the *** original dataset is used in trainingmachine learning models,and further used in generating SHAP values *** the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based *** new integrated dataset is used in re-training the machine learning *** new SHAP values generated from these models help in validating the contributions of feature sets in predicting *** conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making *** this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the *** study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of *** proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area un

关键词： Explainable AI machine learning clinical decision support systems thyroid cancer association-rule based framework SHAP values classification and prediction

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AdaFedProx: A Heterogeneity-Aware Federated Deep Reinforcement Learning for Medical Image Classification

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1432-1441页

作者： Sahoo, Pranab Tripathi, Ashutosh Saha, Sriparna Mondal, Samrat Singh, Jyoti Prakash Sharma, Bhisham Computer Science and Engineering Department Indian Institute of Technology Patna India Electrical and Electronics Engineering Department The Rajiv Gandhi Institute of Petroleum Technology India Indian Institute of Technology Computer Science and Engineering Department Patna India National Institute of Technology Computer Science and Engineering Department Patna India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India

In the realm of smart healthcare, vast amounts of valuable patient data are generated worldwide. However, healthcare providers face challenges in data sharing due to privacy concerns. Federated learning (FL) offers a privacy-preserving solution by enabling collaborative model training without direct access to patient data. This decentralized approach utilizes data from diverse sources, resulting in a globally learned model with satisfactory performance across individual sites. However, federated training encounters challenges such as "system heterogeneity"in client specifications and "data heterogeneity"in imbalanced data distribution. Standard FL methods become unstable and require extensive hyperparameter tuning for optimal performance when facing heterogeneous clients, posing challenges in real-world applications. This research introduces a novel heterogeneity-aware federated learning approach named AdaFedProx to address the performance degradation resulting from heterogeneity. AdaFedProx utilizes a reinforcement learning-driven strategy, introducing a tailored proximal term into the objective function to incorporate partial client information in heterogeneous settings. Dynamically determining the proximal term based on individual client states, including critical characteristics like data distribution, system specification, and performance feedback, empowers AdaFedProx to make regularized decisions throughout the federated training. AdaFedProx consistently outperforms four state-of-the-art methods on three medical datasets across diverse heterogeneity settings (0%, 50%, and 75% straggler). The code can be found at https://***/Pranabiitp/AdaFedProx. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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An Ensemble Machine Learning Method Highlights Possible Parkinson’s Disease Genes and Accessing Performance of Re-sampling Techniques

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SN computer science 2024年第5期5卷 1-11页

作者： Arora, Priya Mishra, Ashutosh Malhi, Avleen Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala 147004 India Department of Computer Science Aalto University Espoo Finland

Identification of genes that lead other genes towards disease with neurological disorders like Parkinson's disease (PD) is an important factor in biomedical research. Machine learning techniques have been extensively used in recent years for effective identification of genes associated with the disease. However, the data used in these methods were based on protein–protein interactions, gene expression, and gene ontology. These data may contain incomplete previous knowledge that is used to construct features for each gene. Therefore, in this study, the physicochemical properties of amino acid as a universal knowledge are used to extract features from the sequences. Also, the several machine learning models are used to classify genes associated with PD. In this study, the ensemble method is designed in such a way, so as to improve the diagnosis accuracy based on top four highest performing classifiers. The comparative analysis reveals that gradient boosting performs better having accuracy of 77.50% and area under curve of 0.774 with respect to other six methods. However, ensemble method achieves an accuracy of 83.75%. Ensemble method is evaluated against existing disease gene identification methods;the results suggest that this approach is more accurate and effective for identification of PD genes. Re-sampling techniques for resolving class imbalance issues have been shown to increase classification accuracy by reducing the bias introduced by class size differences. The proposed model can also be used as a prediction tool for diagnosis Alzheimer’s disease protein sequences. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning Parkinson’s disease Physicochemical properties of amino acids Protein sequences Re-sampling

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A Disease Prediction Framework Based on Predictive Modelling

A Disease Prediction Framework Based on Predictive Modelling

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International Conference on Data science and Network engineering, ICDSNE 2023

作者： Kaur, Harmohanjeet Shah, Pooja Muhuri, Samya Kumari, Suchi Computer Science & Engineering Department Thapar Institute of Engineering and Technology Punjab India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819967544

The rise of chronic diseases has become a major public health challenge globally. Early prediction and prevention of these diseases can help reduce their prevalence and improve patient outcomes. The proposed disease prediction system, which is based on predictive modeling, may anticipate the user’s illness by using the user’s symptoms as input. The framework evaluates the symptoms taken as input by the user and generates the likelihood of developing the disease. The disease prediction framework based on machine learning (ML) techniques can help in a more accurate diagnosis than conventional methods. In the current manuscript, we have designed a disease prediction methodology using multiple ML techniques. The proposed framework also has the potential to enhance disease surveillance and support public health interventions, including disease management and resource allocation. The accuracy of our approach is shown over the benchmark data sets, which consist of more than .230 diseases. The suggested diagnostic algorithm outputs the disease name that a person might be experiencing based on the symptoms taken into consideration. The proposed framework provides a scalable and effective solution for public health decision-makers to manage chronic diseases and improve patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Building a Robust and Efficient Defensive System Using Hybrid Adversarial Attack

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4470-4478页

作者： Dhanaraj, Rachel Selva Sridevi, M. Goa College of Engineering Computer Engineering Department Goa 403401 India National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli620015 India

Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence (AI) researchers are constantly trying to find a better balance to develop new techniques and approaches to minimize loss of accuracy and increase robustness. To address these gaps, this article proposes a hybrid adversarial attack strategy by utilizing the Fast Gradient Sign Method and Projected Gradient Descent effectively to compute the perturbations that deceive deep neural networks, thus quantifying robustness without compromising its accuracy. Three distinct datasets - CelebA, CIFAR-10, and MNIST - were used in the extensive experiment, and six analyses were carried out to assess how well the suggested technique performed against attacks and defense mechanisms. The proposed model yielded confidence values of 99.99% for the MNIST dataset, 99.93% for the CelebA dataset, and 99.99% for the CIFAR-10 dataset. Defense study revealed that the proposed model outperformed previous models with a robust accuracy of 75.33% for the CelebA dataset, 55.4% for the CIFAR-10 dataset, and 98.65% for the MNIST dataset. The results of the experiment demonstrate that the proposed model is better than the other existing methods in computing the adversarial test and improvising the robustness of the system, thereby minimizing the accuracy loss. © 2020 IEEE.

关键词： Deep neural networks

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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A Comparative Study of Object Detection Techniques for Automatic License Plate Recognition for Indian License Plates 5

A Comparative Study of Object Detection Techniques for Autom...

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5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Arora, Shivam Prakash, Chandra Rana, Prashant Singh Sood, Kartik Thapar Institute of Engineering and Technology Department of Computer Science & Engineering Punjab Patiala India

ISBN: (纸本)9798350313123

Automatic License Plate Recognition (ALPR) is a technology that allows for the automatic detection and recognition of license plates on vehicles. Deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been applied to enhance license plate detection and character recognition capabilities, improving performance in situations involving distorted or skewed license plates. This paper compares the accuracy of various object detection techniques for License Plate Detection step in ALPR, and their results and performance are analyzed. These object detection techniques include traditional canny edge and contour segmentation, YOLOv5, Single Shot Detector (SSD), FasterRCNN, and RetinaNet. After comparison, it is found that YOLOv5 performs the best in accuracy and inference speed out of all the above-stated techniques. © 2023 IEEE.

关键词： Object detection

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