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Analysis of Glass and Carbon Fiber-reinforced Aluminum-Sandwiched Composites in Automotive Applications

Journal of The Institution of Engineers (India): Series d 2024年第2期105卷 725-731页

作者： Selvamuthukumar, M. Bobba, Sujith Padmanabhan, S. Satyanarayana, d. AP Guntur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology TN Chennai India AP Guntur India

The purpose of this research was to improve the safety and performance by reducing the weight of the on-road vehicles with the utilization of reinforced sandwich composite material in automotive doors and panels. By this research conducted, the vehicle guarantees safety, since the dominant force works on bumpers and doors at the moment of impact and the possibility that sandwich panels will be put within the passenger seat, meaning that the safety of the user will be improved and the impact will be maximized. From the results, mechanical vibrations frequencies of Nomex, polyurethane and polypropylene honeycomb core structures showed 10% deviation when compared with the experimental results. With the implementation of the proposed sandwich panel in various automotive components, the vehicle weight will gradually be reduced, which ensures that the vehicles performance improves naturally. © The Institution of Engineers (India) 2023.

关键词： Honeycomb structures

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Secure and reversible Medical Image Watermarking for E-Healthcare Applications 6

Secure and Reversible Medical Image Watermarking for E-Healt...

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

作者： Nandhini, K. rajkumar, r. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

E-healthcare and mobile healthcare technologies have become increasingly important for efficient healthcare delivery in recent years due to the pandemic's needs for real-time data interchange. The electronic transfer of patient health records, including imaging reports, is made possible by the growing public networks connecting diagnostic labs and hospitals. However, this strategy is unsafe since outside attacks on public networks can expose private health information, leading to inaccurate diagnoses and treatments. To overcome such challenges, methods such as Medical Image Watermarking (MIW) and data encoding are employed to safeguard patient identities and identify instances of tampering. data security is crucial because outsourcing patient records and medical reports to servers exposes them to modification and illegal access. This study presents a new reversible watermarking technique with a large payload and minimal computational expenditure. It divides the medical image into a Border region (Br) and a Non-Border region (NBr). A pseudorandom key encrypts the Electronic Patient record (EPr), which is incorporated in the image after the NBr has been upscaled using Neighbor Mean Interpolation (NMI). The technique, which incorporates both local and global tamper detection, produces remarkable results with robustness against a range of threats and an average PSNr of 61.14 dB with average embedding and extraction times of 0.88 and 0.83 seconds, respectively, the approach is incredibly secure and effective, demonstrating its efficacy in comparison to contemporary, widely used techniques. © 2025 IEEE.

关键词： Electronic health record

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CNNs for Early detection of Plant diseases: A deep Learning Perspective 6

CNNs for Early Detection of Plant Diseases: A Deep Learning ...

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

作者： Vinod, dasari Naga Sai revanth, r. Bhaskar, B. Jaya Prakash, P. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

In India, where agriculture sustains over 70% of the population, the impact of millet diseases on crop yield and economic outcomes for farmers cannot be overstated. With the advent of digitalization permeating various sectors, including agriculture, leveraging technology becomes imperative for enhancing productivity and ensuring food security. The ability to detect and manage Millet’s diseases efficiently is crucial for maximizing crop yield and minimizing losses. deep learning, mostly Convolutional Neural Networks (CNNs), has appeared as well as an influential tool in digital image processing, offering significant advantages over traditional methods. Leveraging CNNs for the recognition and categorization of Millet's diseases presents a promising solution to this longstanding challenge. The process of Millet's disease detection using CNN involves several key steps, together with acquiring images, initialization, partition, division, as well as disease identification. In conclusion, the integration of CNN-based Millet’s disease detection systems holds immense potential for revolutionizing agricultural practices in India. By connecting the supremacy of deep learning and digitalization, stakeholders can work towards confirming food security, increasing farm profitability, and promoting supportable agricultural practices. In this article, we obtained a 97.8% accuracy in detecting Plant diseases using a CNN-based approach. © 2025 IEEE.

关键词： Convolutional neural networks

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Optimized Fuzzy Logic Adaptive System with Holographic Convolutional Heterogeneous Graph Neural Network-based Feature Extraction and Classification for Brain Tumor detection 8

Optimized Fuzzy Logic Adaptive System with Holographic Convo...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： regan, M. Srinivasan, P. Santhosh Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Mathematics Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

Cells growing abnormally in the brain or surrounding tissues is called a brain tumor. It can cause symptoms such as headaches, seizures, and physical or cognitive deficits. It can also be benign (non-cancerous) or malignant (cancerous). To control its effects and enhance results, early identification and treatment are essential. It is necessary to detect BC, because of that many techniques are implemented. But the existing methods have lot of disadvantages such as, overfitting high computational complexity and resource demands. To overcome the aforementioned problem, Holographic Convolutional Neural Network with Heterogeneous Graph Neural Networks using Boosted Sooty Tern Optimization Algorithm (HCNN-HGNN-BSTOA) is proposed for accurately detecting BC.. In this input image is taken from Figshare dataset and Brats2020 *** pre-processing and feature extraction are done using Filter based Fuzzy Logic Adaptive System Based Feature Extraction (CFFLAS) is proposed, for the purpose of removing noises and improve the efficiency of feature extraction. Following that future selection are done using Fennec Fox Optimization Algorithm (FFOA). After that classification are done using Holographic Convolutional Neural Network (HCNN)and optimization are done using Boosted Sooty Tern Optimization Algorithm (BSTOA) for detecting normal and abnormal conditions of the BT. The efficiency of the proposed HCNN-HGNN-BSTOA is analyzed using an dataset and attains 99.78% accuracy, 97.34% recall and attains better results compared with the existing methods. Experimental results demonstrate the effectiveness of the proposed approach in accurately detecting tumors in medical images, paving the way for improved diagnostic tools in healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Performance of Machine Learning Models in Predicting Plant Growth using IoT data 5

Comparative Performance of Machine Learning Models in Predic...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： rajathi, K. Alex david, S. ruth Naveena, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of computer science and engineering Chennai India Hindustan Institute of Technology & Science Department of Mathematics Chennai India

ISBN: (纸本)9798331504403

The use of greenhouses in agriculture is greatly facilitated by the deployment of Internet of Things (IoT) technologies, where researchers test novel configurations for monitoring and controlling plant development. In this study, a dataset of conventional and IoT greenhouses in Tikrit University is utilized to predict plant growth variables using machine learning methods. The collection has 30,000 entries in all with a total of 14 characteristics. data pre-processing included label encoding, feature scaling, and treatment for missing values. In this work, performance is compared with four machine learning modules: XGBoost Classifier - random Forest Classifier- dNA Support Vector Machine (SVM)- Logistic regression. The results showed that XGBoost performed better than other models. These results show how complex machine learning techniques could be employed to minutely and efficiently monitor plant growth attributes which in turn can augment the crop yield. © 2024 IEEE.

关键词： Adversarial machine learning

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Exploring deep Neural Networks for Accurate diabetic retinopathy Prediction

Exploring Deep Neural Networks for Accurate Diabetic Retinop...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Kujani, T. Arivubrakan, P. raju, K. Sankar, P. Mani reddy, B. Surekha Bhavana, E. Computer Science And Engineering Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India Artificial Intelligence And Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India Artificial Intelligence And Data Science Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India

ISBN: (纸本)9798350352689

diabetic retinopathy (dr) emerges as a serious complication associated with diabetes mellitus, causing damage to retinal blood vessels, and resulting in vision loss. The critical need for early detection and effective management to curb the progression towards vision impairment and potential blindness underscores the significance of this issue. To address these challenges, have propose a novel deep learning system utilizing a densely connected Convolutional Network (denseNet-201) for the efficient detection of dr. This model is specifically trained to classify fundus images into six distinct categories: no dr, mild, moderate, and severe, proliferative dr, and hard exudates. The datasets utilized in this study include the publicly available diabetic retinopathy detection 2015 and Aptos 2019 blindness detection datasets from Kaggle. The proposed system comprises multiple stages, including data Collection, Preprocessing, Augmentation, and Modeling. during the preprocessing phase, normalization techniques are to eliminate biases or distortions present in the fundus images. Furthermore, the augmentation of data is employed to enhance the diversity and quantity of the training dataset. In the modeling stage, have designed and implemented the deep learning model, with the robust and high-performing denseNet-201 architecture selected as the model's backbone. The model exhibits a significant accuracy of 87.7% in its classification task. Additionally, have introduced a deep Neural Network (dNN) model, achieving an impressive accuracy rate of 95.8%. These results underscore the effectiveness of our proposed system in accurately and efficiently detecting dr. The primary objective of this research is to develop a reliable and automated system for diabetic retinopathy detection, with the overarching goal of improving the quality of life for individuals affected by diabetes. © 2024 IEEE.

关键词： deep neural networks

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IKASF: A Hybrid Approach to denoising the Effect of Electromagnetic radiations on Multimodal Breast Cancer Images Using Improved Kuan Adaptive Spatial Filter

IKASF: A Hybrid Approach to Denoising the Effect of Electrom...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Mahalakshmi, V. Sasikala, G. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Over the decades, breast cancer has played distressing metamorphoses due to the vigorous increase in successive rates of malignancy among the women's community. As per the medical statistics, breast cancer became the second leading life-threatening cancer next to lung cancer. However, the survival rate of the cancer patients could be increased up to 10 years, provided the disease has been identified at an initial stage. Imaging plays a crucial role for breast cancer screening, and those images are more likely degraded by noises such as electromagnetic radiation (EMr), electronic noises, and environmental noises. This makes the oncology experts very challenging to identify the precise tumor location in medical images. Hence, this research mainly focuses on denoising the EMr noises of different intensity levels, such as 0.01, 0.05, 0.1, and 0.2, using the improved Kuan adaptive spatial filter (IKASF) in the mammogram, ultrasound, and histopathology images. In order to analyze the image quality of the denoised image, the parameters such as mean square error (MSE), peak signal to noise ratio (PSNr), and structural similarity index (SSIM) have been measured and compared with the other modality images. The study reveals that the proposed filter improves the quality measures on sample multimodal images around 90%, 50.5%, and 80%, respectively, in comparison with the linear and nonlinear denoising techniques such as mean, median, gaussian, bilateral guided median (BGM), and kuan. The enhanced images using IKASF could be used to develop the artificial intelligence model for the automated process of tumor segmentation and classification problem, thereby improving the false positive and false negative ratios during the clinical trials. © 2025 IEEE.

关键词： Lung cancer

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Advancing Glaucoma detection: Synthetic Image Generation via Generative Adversarial Networks and Classification with Pretrained MobileNetV2

Advancing Glaucoma Detection: Synthetic Image Generation via...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： ramprasad, S.r. rampriya, r. Poongodai, A. Govindharaj, I. Vimal raja, r. Yazhinian, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle India Ck College of Engineering and Technology Department of Computer Science and Engineering Cuddalore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Irreversible vision loss, generally insidious in start and lacking apparent symptoms, is usually attributed to glaucoma. detecting glaucoma in its early stages is crucial since it may reduce disease progression. Conventional diagnostic approaches, depending on manual assessments, are notably prone to mistakes. Hence, automated glaucoma analysis acquires vital relevance for precise and prompt detection. Moreover, medical picture databases frequently display asymmetries, creating a problem. To solve these challenges, this paper presents a unique framework employing generative adversarial networks (GANs) to generate images, thus addressing dataset imbalances. Specifically., in the context of fundus images, standard approaches like image-to-image translation are applied to build synthetic fundus images and associated vascular networks, attempting to boost overall image quality and capture finer details. Gaussian filtering is originally done to pre-process the raw dataset, eliminating unwanted noise. Subsequently, GANs are applied for dataset balancing, providing synthetic images that boost classification accuracy. Optic cup segmentation is conducted using the Enhanced Level Set Algorithm. Finally, Pretrained MobileNetV2 permits accurate classification of glaucomatous images into normal and pathological categories. Experimental results illustrate the efficacy of our suggested framework, obtaining an accuracy of 98.9%, exceeding existing techniques. © 2024 IEEE.

关键词： Generative adversarial networks

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Transforming diabetes Care with Predictive Machine Learning Techniques 4

Transforming Diabetes Care with Predictive Machine Learning ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Mariammal, G. Jasmine, r. Megiba Siva rama Lingham, N. Poongothai, E. Prema, S. Sai Charan, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India Ponjesly College of Engineering Nagercoil India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331508845

diabetes mellitus is a chronic metabolic disorder characterized by elevated blood sugar levels, affecting millions of individuals worldwide. Early detection and management of diabetes are crucial in preventing complications and improving patient outcomes. In recent years, machine learning techniques have shown promise in predicting diabetes risk based on various factors such as demographic information, medical history, and lifestyle habits. Verticaling through a not-too-well designed case study - This paper is an extensive examination and comparison of different ML techniques for diabetes prediction. It goes through the methodologies, datasets, features, performance metrics and challenges of current models that are based on machine learning. In addition, understand strengths and weaknesses of all the algorithms- SVM, decision Trees, random Forests, Logistic regression etc. Here the logistic regression algorithm is used to develop a predictive model for diabetes based on demographic and clinical features. After preprocessing the data, including handling missing values and scaling numerical features, trained a logistic regression model using a portion of the dataset. The model achieved a high accuracy of 90% on the testing set, indicating its effectiveness in distinguishing be- tween diabetic and non-diabetic individuals. © 2024 IEEE.

关键词： Logistic regression

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Antioxidant activity during in vitro gastrointestinal digestion and the mode of action with tannins of cowhide-derived collagen hydrolysates:The effects of molecular weight

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Food Bioscience 2023年第3期53卷 2671-2681页

作者： Lei Bai Xiaojing Tian Yang Wang Kai Zhang Jingjing Guo Chenwei Ma ruixi Shen Xinhua Wang Wenhang Wang College of Food Science and Technology Tianjin University of Science and TechnologyTianjinChina R&D Centre of Collagen Products Xingjia Biotechnology Co.LTD.TianjinChina

In this study,collagen hydrolysates were prepared by autoclaving or its combination with *** it,the digestion pattern and antioxidant activity were estimated in vitro gastrointestinal *** MW(HMW-CH,10-100 kda;MMW-CH,1-10 kda;LMW-CH,<1 kda)of collagen hydrolysates were *** digestion,MW of these samples decreased to some extent,while their total MW composition still show a similar tendency to the undigested samples,***-CH>MMW-CH>*** gastric to intestinal phase,dPPH radical scavenging of HMW-CH increased from 5.45%to 35.19%.However,MMW-CH and LMW-CH increased from 47.35%to 52.90%and 49.84%-64.40%in the gastric phase,and then decreased to 27.94%and 21.69%in the intestinal phase,***,the reducing power of these samples was lower in the gastric phase,but increased within 15 min of intestinal ***,the differences decreased and the final was LMW-CH(1.030)>MMW-CH(0.914)>HMW-CH(0.872).These results indicate that the lower the MW,the higher the antioxidant *** addition,the binding mode of them to tannins was analyzed by thermody-namic *** results suggest that the main mode of interaction between HMW-CH and MMW-CH and tannins was hydrogen bonding,whereas LMW-CH and tannin was ***,these results are helpful for us to better understand absorption process of collagen hydrolysates and provide some theoretical and technological support for collagen consumed in food and medicine.

关键词： Cattle hide Collagen hydrolysis Gastrointestinal digestion Antioxidant Tannin

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