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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Deepika, J. Reddy, Pappuru Manikanta Murari, Kothaluri Rahul, Boyana School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu Vellore India

ISBN: (纸本)9798331522667

In the realm of predictive maintenance for aircraft engines, ensuring precise and timely predictions of Remaining Useful Life (RUL) is paramount to enhancing safety, reducing costs, and minimizing downtime. While deep learning models dominate this field, their complexity, high computational demand, and lack of interpretability can hinder practical deployment in real-world scenarios. This study demonstrates the potential of machine learning models as an efficient and interpretable alternative for RUL prediction, leveraging the publicly available C-MAPSS dataset from NASA. Using C-MAPSS dataset, we integrated PostgreSQL for scalable data storage and Flask for interactive real-time visualization, creating a seamless pipeline from data ingestion to result interpretation. Preprocessing steps, including normalization, noise removal via box plots, and feature engineering, ensured a clean and reliable dataset. Among the models evaluated—XGBoost, Random Forest, SVM, KNN, and Linear Regression—XGBoost emerged as the best performer, achieving an RMSE of 23.8 and an R2 score of 0.67. These results nearly matches the accuracy typically associated with deep learning models while maintaining computational efficiency. Feature importance analysis through Explainable AI (XAI) further revealed critical insights into sensor contributions, enhancing interpretability and trust in the predictions. This work underscores the practicality and effectiveness of machine learning in predictive maintenance, offering a robust, transparent, and resource-efficient approach to RUL prediction. The findings highlight its potential to meet industry needs where simplicity, accuracy, and interpretability are critical for decision-making and operational efficiency. © 2025 IEEE.

关键词： Predictive maintenance

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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of computer science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

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A Construction of Object Detection Model for Acute Myeloid Leukemia

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Intelligent Automation & Soft Computing 2023年第4期36卷 543-560页

作者： K.Venkatesh S.Pasupathy S.P.Raja Department of Computer Science and Engineering Annamalai UniversityChidambaram608002India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India

The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection model using a Deep Convolutional Neural Network(D-CNN).The proposed Faster R-CNN(Faster Region-Based CNN)models are trained with Morphological *** proposed Faster R-CNN model is trained using the augmented *** overcoming the Imbalanced Data problem,data augmentation techniques are *** Faster R-CNN performance was com-pared with existing transfer learning *** results show that the Faster R-CNN performance was signiﬁcant than other *** number of images in each class is *** example,the Neutrophil(segmented)class consists of 8,486 images,and Lymphocyte(atypical)class consists of eleven *** dataset is used to train the CNN for single-cell morphology classiﬁ*** proposed work implies the high-class performance server called Nvidia Tesla V100 GPU(Graphics processing unit).

关键词： Acute myeloid leukemia(AML) convolutional neural network(CNN) and nvidia tesla v100 gpu

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A Comprehensive Exploration of Real-Time 3-D View Reconstruction Methods

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5915-5927页

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati781039 India Indian Institute of Technology Kanpur Department of Electrical Engineering 208016 India

Real-time 3-D view reconstruction in an unfamiliar environment poses complexity for various applications due to varying conditions such as occlusion, latency, precision, etc. This article thoroughly examines and tests contemporary methodologies addressing challenges in 3-D view reconstruction. The methods being explored in this article are categorized into volumetric and mesh, generative adversarial network based, and open source library based methods. The exploration of these methods undergoes detailed discussions, encompassing methods, advantages, limitations, and empirical results. The real-time testing of each method is done on benchmarked datasets, including ShapeNet, Pascal 3D+, Pix3D, etc. The narrative highlights the crucial role of 3-D view reconstruction in domains such as robotics, virtual and augmented reality, medical imaging, cultural heritage preservation, etc. The article also anticipates future scopes by exploring generative models, unsupervised learning, and advanced sensor fusion to increase the robustness of the algorithms. © 2020 IEEE.

关键词： Generative adversarial networks

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Brain Tumor Detection and Classification Using Deep Neural Network and Interpretation Using XAI Techniques

Brain Tumor Detection and Classification Using Deep Neural N...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy systems, SPICES 2024

作者： Anitha, A. Nair, Anjana Kamaraj, Balakrishnan Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India Madurai Medical College Tamilnadu Madurai India

ISBN: (纸本)9798350376135

Clinical decision-making relies heavily on accurate brain tumor diagnosis from MRI imaging. Manual interpretation is time-consuming and error-prone. To enhance efficiency, automated techniques using deep neural networks (DNNs), particularly Convolution Neural Networks (CNNs), are proposed. This study incorporates explainable artificial intelligence (XAI) for interpretability. By addressing the need for automated and accurate diagnosis, this research aims to improve patient care and treatment outcomes, providing medical professionals with a dependable tool for brain tumor identification in clinical practice. © 2024 IEEE.

关键词： Deep neural networks

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Correlation Attention Registration Based on Deep Learning from Histopathology to MRI of Prostate

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Critical Reviews in Biomedical engineering 2024年第2期52卷 39-50页

作者： Wang, Xue Song, Zhili Zhu, Jianlin Li, Zhixiang School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

Deep learning offers a promising methodology for the registration of prostate cancer images from histopathology to MRI. We explored how to effectively leverage key information from images to achieve improved end-to-end registration. We developed an approach based on a correlation attention registration framework to register segmentation labels of histopathology onto MRI. The network was trained using paired prostate datasets of histopathology and MRI from the Cancer Imaging Archive. We introduced An L2-Pearson correlation layer to enhance feature matching. Furthermore, our model employed an enhanced attention regression network to distinguish between key and nonkey features. For data analysis, we used the Kolmogorov–Smirnov test and a one-sample t-test, with the statistical significance level for the one-sample t-test set at 0.001. Compared with two other models (ProsRegNet and CNNGeo), our model exhibited improved performance in Dice coefficient, with increases of 9.893% and 2.753%, respectively. The Hausdorff distance was reduced by approximately 50% and 50%, while the average label error (ALE) was reduced by 0.389% and 15.021%. The proposed improved multimodal prostate registration framework demonstrated high performance in statistical analysis. The results indicate that our enhanced strategy significantly improves registration performance and enables faster registration of histopathological images of patients undergoing radical prostatectomy to preoperative MRI. More accurate registration can prevent over-diagnosing low-risk cancers and frequent false positives due to observer differences. © 2023 by Begell House, Inc.

关键词： Magnetic resonance imaging

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Use of IoT sensor devices for efficient management of healthcare systems: a review

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Discover Internet of Things 2024年第1期4卷 8页

作者： Gopichand, G. Sarath, T. Dumka, Ankur Goyal, Himanshu Rai Singh, Rajesh Gehlot, Anita Gupta, Lovi Raj Thakur, Amit Kumar Priyadarshi, Neeraj Twala, Bhekisipho School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science Women Institute of Technology VMSBUTU Uttarakhand Dehradun India Department of CSE Graphic Era Deemed to Be University Uttarakhand Dehradun India Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Lovely Professional University Punjab Phagwara Jalandhar144001 India Department of Electrical Engineering JIS College of Engineering Kolkata741235 India Digital Transformation Portfolio Tshwane University of Technology Staatsartillerie Rd Pretoria West Pretoria0183 South Africa

With an aging population and an increase in chronic sickness, healthcare systems are under increasing pressure. Internet of Things (IoT) technology has recently garnered a lot of interest for its ability to alleviate this burden. The IoT is a network of interconnected wireless digital devices that can gather, transmit, and store data automatically, without the need for human or computer intervention. In order to better anticipate health problems, diagnose, treat, and monitor patients in and out of the hospital, the IoT offers numerous advantages that could streamline and improve health care delivery. As a matter of urgency, decision-makers and heads of state around the world are enacting policies to provide healthcare services through the use of technology. Thanks to recent advancements in the IoT, healthcare has been able to evolve at a rapid pace. Research on Internet IoT uses in healthcare and newly developed emergency equipment are summarized in this article. © The Author(s) 2024.

关键词： Internet of things

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Defending Machine Learning and Deep Learning Models: Detecting and Preventing Data Poisoning Attacks 2

Defending Machine Learning and Deep Learning Models: Detecti...

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2nd IEEE Global Conference on Communications and information Technologies, GCCIT 2024

作者： Sardana, Sehan Gupta, Sushant Donode, Aditya Prasad, Aman Karthik, G.M. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore India

ISBN: (纸本)9798350388916

Data poisoning attacks present a significant threat to machine learning (ML) and deep learning models, compromising their reliability and effectiveness during inference. This paper draws insights from recent studies to explore various aspects, including the vulnerabilities of ML architectures, especially deep learning models, to these attacks. It introduces novel methodologies to detect and mitigate data poisoning, considering factors such as model robustness, performance degradation, and poisoning ratio thresholds. Special attention is given to backdoor attacks in multimodal learning scenarios, proposing strategies like Clean CLIP to counter spurious associations. By synthesizing findings across different attack types and defenses, the paper underscores the importance of standardized benchmarks and realistic evaluation settings in assessing countermeasures' efficacy. Ultimately, this work enhances our understanding of data poisoning vulnerabilities in deep learning, offering practical strategies to bolster the resilience of ML systems against evolving adversarial threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of Convolutional Neural Networks on Indian food detection and estimation of calories

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Materials Today: Proceedings 2022年 62卷 4665-4670页

作者： Sathish, Suriyakrishnan Ashwin, S. Abdul Quadir, Md. Pavithra, L.K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Indian Cuisine has a peculiar aroma and flavour distinct from other cuisines. On the other hand, Obesity, Diabetes, and Hypercholesterolemia are severe problems in the Republic of India. This research aims to develop and analyze a Deep Learning model based on OpenCV for identifying Indian cuisine and determining their calories. Indian Cuisine dataset was built by extracting images from the internet and preprocessing them to fine quality. From the dataset, the classification of Indian Cuisine was done by Convolutional Neural Network, and with the help of image processing techniques, calories of the classified food were calculated. After an enormous amount of analysis, the developed model detects Indian food cuisine with an accuracy of 99.19% on training data and 95.30% on testing data;also, the estimation of calories was the accuracy with an error variation of ±10 calories to the actual food. © 2022

关键词： Convolution

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Multimodal Deep Learning for Cardiovascular Risk Stratification: Integrating Retinal Biomarkers and Cardiovascular Signals for Enhanced Heart Attack Prediction

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IEEE Access 2025年 13卷 99672-99689页

作者： K. Sathya G. Magesh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Cardiovascular diseases, particularly myocardial infarction, have remained a significant cause of death around the world. Therefore, dedicated non-invasive risk prediction frameworks are required. Conventional measures for risk prediction among most heterogeneous patients, such as the Framingham Risk Score and ASCVD calculator, are often less precise and poorly generalizable as they fail to capture individual variations in the mechanisms. This study seeks to present a new multimodal deep learning model developed for cardiovascular risk stratification by fusing retinal microvascular features with cardiovascular physiological signals. The pipeline includes a Hierarchical Retinal Vessel Graph Transformer (HRV-GT) for graph-based vascular representation, Spectro-Temporal Cardiovascular Transformer (STC-T) for capturing short- and long-term signal variability, Sparse Manifold Multimodal Fusion (SMM Fusion) for joint alignment of features, Evolutionary Feature Selection with Clinical Prior Constraints (EFS-CP), and a Contrastive Hierarchical Risk Classifier (CHRC-Net) for decision modeling structured. The model was evaluated across the UK Biobank and SEED datasets (n = 55,000), achieving an AUC of 0.97, sensitivity of 91%, and specificity of 89%, with more than 35% better performance than existing models in reducing misclassification. Clinically interpretable biomarkers that combine high computational efficiency (inference time $\approx \; 37$ ms) affirm its design for real-world deployment in preventive cardiovascular screening processes.

关键词： Feature extraction Deep learning Biomarkers Biological system modeling Transformers Predictive models Imaging Heart rate variability Accuracy Retinal vessels

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