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Haemoglobin measurement from Eye Anterior Ciliary Arteries through Borescope Camera

Computer Systems Science & engineering 2023年第2期44卷 1763-1774页

作者： mohamed Abbas Ahamed Farook S.rukmanidevi N.r.Shanker Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering CollegeChennai600062India Department of Electronics and Communication Engineering R.M.D Engineering CollegeChennai601206India Department of Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringChennai600055India

Nowadays,smartphones are used as self-health monitoring devices for ***-health monitoring devices help clinicians with big data for accurate diagnosis and guidance for treatment through repetitive *** measurement of haemoglobin requires for pregnant women,pediatric,pulmonary hypertension and obstetric *** haemoglobin measurement through conjunctiva leads to inaccurate *** inaccuracy is due to a decrease in the density of goblet cells and acinar units in meibomian glands in the human eye as age ***,conjunctivitis is a disease in the eye due to inﬂammation or infection at the *** is in the form of lines in the eyelid and covers the white part of the ***,small blood vessels in eye regions of conjunctiva inﬂammations are not visible to the human eye or standard *** paper proposes smartphone-based hae-moglobin(SBH)measurement through a borescope camera from anterior ciliary arteries of the eye for the above *** proposed SBH method acquires images from the anterior ciliary arteries region of the eye through a smartphone attached with a high megapixel borescope *** anterior ciliary arteries are projected through transverse dyadic wavelet transform(TdyWT)and applied with delta segmentation to obtain blood cells from the ciliary arteries of the ***,the Gaussian regression algorithm measures haemoglobin(Hb)with more accuracy based on the person,eye arteries,red pixel statistical parameters obtained from the left and right eye,age,and ***,the experimen-tal result of the proposed SBH method has an accuracy of 96%in haemoglobin measurement.

关键词： Hemoglobin measurement borescope camera smartphone anterior ciliary arteries region

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Tire Wear Assessment Using VGG16: A Computer Vision Approach 4

Tire Wear Assessment Using VGG16: A Computer Vision Approach

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4th International Conference on Sentiment Analysis and deep Learning, ICSAdL 2025

作者： Shanthi, K.G. rukmani devi, S. Blesslin Sheeba, T. Kalaiselvi, A. Purnananda rao, P.G. Jaswanth, P. Harshal Venkat, K. R.M.K College of Engineering and Technology Dept of Electronics and Communication Engineering Chennai India R.M.D Engineering College Dept of Electronics and Communication Engineering Chennai India R.M.K Engineering College Dept of Electronics and Communication Engineering Chennai India Kumaraguru College of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

This paper addresses the issue of tire bursts caused by uneven pressure and wear by implementing an efficient Computer Vision-Based tire health monitoring system using VGG16 in machine Learning. This system involves capturing tire images using a camera and processing them with a machine learning module, such as VGG 16, to obtain the tire's current state. The system can offer early warning of potential tire failures, allowing drivers to take proactive measures to prevent accidents and minimize maintenance costs. Furthermore, it can assist fleet managers in scheduling tire replacements more efficiently, resulting in improved fleet efficiency overall. The system operates in real-time, generating results almost instantly, and can be integrated with existing vehicle systems for ease of use. The proposed tire health monitoring system is an essential solution to preventing tire bursts, enhancing safety, and minimizing maintenance costs. © 2025 IEEE.

关键词： Convolutional neural networks

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Implementing a robust method for detecting Human Actions in Health monitoring by Employing Sensors on mobile Internet of Things devices and Utilizing a One-dimensional Convolutional Neural Network

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Journal of Technology in Behavioral Science 2025年第1期10卷 79-91页

作者： Vignesh, T. Thyagharajan, K.K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Electronics and Communication Engineering R.M.D Engineering College Chennai India

This research is focused on developing a precise human action recognition system by utilizing accelerometer and gyroscope sensor data. The aim is to analyze various human actions like jogging, sitting, standing, walking, going upstairs, going downstairs, and falling, using multiple sensors found in modern smartphones. Previous studies have explored human action recognition using different sensors, cameras, and machine learning and deep learning techniques. However, some existing approaches, particularly those based on channel state information (CSI), have shown less satisfactory outcomes. In contrast, this paper employs deep learning techniques, specifically one-dimensional convolutional neural networks (1d-CNN), for more accurate human action analysis. One significant advantage of the proposed system is its potential as a wearable device. The paper will introduce a one-dimensional convolutional neural network tailored for human action recognition and an optimized approach for determining the magnitude of accelerometer's X, Y, and Z coordinates, as well as gyroscope sensor values. By utilizing these coordinates, the One-dimensional Convolutional Neural Networks can effectively classify various human actions like jogging, sitting, standing, walking, going upstairs, going downstairs, and falling. To evaluate the performance of the 1d-CNN approach, a comparison is made with other methods, including SOm, SVm, and FCm. The results demonstrate that the proposed method (1d-CNN) outperforms other classification techniques with an impressive accuracy of 92.42 per cent. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Acceleration Gyroscope Human action recognition One-dimensional convolutional neural network Sensors

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An Optimized deep-Learning-Based Low Power Approximate multiplier design

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Computer Systems Science & engineering 2023年第2期44卷 1647-1657页

作者： m.Usharani B.Sakthivel S.Gayathri Priya T.Nagalakshmi J.Shirisha Department of Electronics and Communication Engineering Er.Perunal Manimekalai College of EngineeringKonneripalliHosur635117India Department of Electronics and Communication Engineering Pandian Saraswathi Yadav Engineering CollegeSivagangaiTamilnadu630561India Department of Electronics and Communication Engineering R.M.D Engineering CollegeGummidipundiTamilnadu601206India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur522502India Department of Electronics and Communication Engineering Malla Reddy Engineering CollegeHyderabadTelangana500015India

Approximate computing is a popularﬁeld for low power consumption that is used in several applications like image processing,video processing,multi-media and data *** Approximate computing is majorly performed with an arithmetic circuit particular with a *** multiplier is the most essen-tial element used for approximate computing where the power consumption is majorly based on its *** are several researchers are worked on the approximate multiplier for power reduction for a few decades,but the design of low power approximate multiplier is not so *** seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher *** overcome these issues,the digital circuits are applied to the deep Learning(dL)approaches for higher *** recent times,dL is the method that is used for higher learning and prediction accuracy in severalﬁ***,the Long Short-Term memory(LSTm)is a popular time series dL method is used in this work for approximate *** provide an optimal solution,the LSTm is combined with a meta-heuristics Jel-lyﬁsh search optimisation technique to design an input aware deep learning-based approximate multiplier(dLAm).In this work,the jelly optimised LSTm model is used to enhance the error metrics performance of the Approximate *** optimal hyperparameters of the LSTm model are identiﬁed by jelly search ***ﬁne-tuning is used to obtain an optimal solution to perform an LSTm with higher *** proposed pre-trained LSTm model is used to generate approximate design libraries for the different truncation levels as a func-tion of area,delay,power and error *** experimental results on an 8-bit multiplier with an image processing application shows that the proposed approx-imate computing multiplier achieved a superior area and power reduction with very good results on error rates.

关键词： deep learning approximate multiplier LSTm jellyﬁsh

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Automatic Certificate Generation and Security Implementation Using LSB and Hybrid Watermarking Techniques 2nd

Automatic Certificate Generation and Security Implementation...

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2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Arulananth, T.S. Jose Anand, A. Jayanthi, S. manaswini, Petla Teja, Gogineni Viswa Sai redyy Taruni, G. Prasad, Nalla Krishna Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad500043 India Department of ECE KCG College of Technology Karapakkam Chennai600097 India Department of Electronics and Communication Engineering R.M.D Engineering College Kavarapettai Chennai India

ISBN: (纸本)9789819754113

Process to ensure the authenticity and integrity of digital certificates, LSB and hybrid watermarking are popular techniques used to embed additional information or watermark into digital certificates to enhance their security and prevent unauthorized modifications. This research aims to explore the application of LSB and hybrid watermarking in certificate generation, with a focus on implementing these techniques using mATLAB. Here, we developed a mATLAB code to generate certificates automatically and provide security by watermarking. The process of creating a digital certificate is complicated, and the process of creating an individual certificate which contains a variety of information is much more challenging. To overcome the difficulties with the manual method and reduce the time needed to issue a certificate, a range of certificate generating and verification methods have been developed in the modern day. The certificate design template is uploaded into the folder holding the mATLAB code since the system runs automatically on a template that the system user has already created. To display the details on certificates, this program gathers participant information from excel sheets. An Excel document is used to extract the information that will be written on the certificate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： mATLAB

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Energy Optimized and dynamic design of Task Offloading in mobile Fog Computing 15

Energy Optimized and Dynamic Design of Task Offloading in Mo...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Vedaraj, m. Satwika, A. monisha, B. Namratha, I. R.M.D Engineering College Computer Science and Engineering Department Chennai India

ISBN: (纸本)9798331300579

In this study, we provide a dynamic and energy-optimized task offloading approach for mFC. Our method takes into account how much energy FNs, cell phones, and communication cables use. We employ a dynamic programming technique to choose the job offloading strategy that minimises energy use while satisfying the application's latency requirements. We conduct a simulation-based experiment to assess our suggested strategy. The results show that our solution performs better than others in terms of energy consumption and delay efficiency. Additionally, our method may balance energy usage and delay performance while adapting to dynamic changes in the network *** proposed energy-optimized and dynamic design for task offloading in mFC can enable efficient and effective utilization of computational resources in mobile devices and FNs, This may result in better functionality and longer battery life for portable electronics. © Grenze Scientific Society, 2024.

关键词： Fog computing

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Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

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multimedia Tools and Applications 2025年第18期84卷 19223-19253页

作者： Anusha, N. Tapas Bapu, B.r. S, Selvakumaran Vijayaraj, A. ramesh Kumar, C. P, raji Department of IoT School of Computer science and Engineering VIT Tamil Nadu Vellore India Department of Electronics and Communication Engineering S.A.Engineering College Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Information Technology R.M.K Engineering College R.S.M Nagar Tamil Nadu Kavaraipettai India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India

Wireless sensor network (WSN) is one of the essential components of a multi-hop cyber-physical system comprising many fixed or moving sensors. There are many common attacks in WSN, which can quickly harm a WSN system. In this manuscript, dual Interactive Wasserstein Generative Adversarial Network (dIWGAN) with war strategy optimization (WSO) algorithm is introduced for detecting intrusion in WSN. Initially, the input data is attained from the NSL-Kdd and CICIdS2017 datasets. The data is then passed onto morphological filtering and Extended Empirical wavelet transformation (mFEEWT) based pre-processing. After that, the filtered data is sent to the dIWGAN, which detects the intrusion and classifies the fraud data in the network. Additionally, the WSO algorithm is introduced to enhance the classification parameters of dIWGAN. By then the performance of the proposed dIWGAN-WSO-IdS methodology is evaluated using Python Platform. Also, the performance of the proposed methodology is calculated using various metrics like accuracy, precision, recall, f-measure, mean square error, and attack detection rate. Thus, the proposed technique has attained 29.87%, 31.7%, and 26.8% higher accuracy, 23.54%, 20.09%, and 26.98% higher recall, 12.76%, 10.96%, and 6.7% higher precision. And 6.85%, 4.56%, and 8.56% lower mSE than the existing techniques like BFSGSrF, mHICA-SSA, and CNN-LSTm methods. © The Author(s), under exclusive licence to Springer Science+Business media, LLC, part of Springer Nature 2024.

关键词： Wireless sensor networks

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deep Learning and IoT for COVId detection and Health monitoring 3

Deep Learning and IoT for COVID Detection and Health Monitor...

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3rd International Conference on Innovative mechanisms for Industry Applications, ICImIA 2023

作者： devi, d.rukmani Kingslin, m.Theodore Josphine, C.Vimala manivannan, Kirankumar rajavel, A. ramalakshmi, r. R.M.D Engineering College Department of Electronics and Communication Engineering Chennai India R.M.K College of Engineering College & Technology Department of Electronics and Communication Engineering India R.M.K Engineering College Department of Artificial Intelligence and Data Science Chennai India Easwari Engineering College Department of Electronics and Communication Engineering India Electronics and Instrumentation Engineering Kamaraj College of Engineering and Technology Virudhunagar India Ramco Institute of Technology Department of Electronics and Communication Engineering Rajapalayam India

ISBN: (纸本)9798350343632

Covid-19 has been spreading globally since 2019, and the significant death toll fr.m.delayed detection has been a consequence. One way to solve this issue is to use AI to swiftly identify Covid-19 without having to deal with problems like a shortage of testing kits. To this end, this study has developed a system that uses both deep learning and machine learning techniques to detect the presence of Covid-19 in lung X-ray images. While the KNN method is used in machine learning to classify the image and detect COVId-19, dF-Net is used in deep learning to optimize the image and provide more clarity. Because dL and mL algorithms work together, this method combines the advantages of a high degree of accuracy with a shorter training period. This study also integrated a health monitoring system that uses Internet of Things (IoT) sensors to take a patient's temperature and heart rate;the data is updated on the cloud. © 2023 IEEE.

关键词： deep Learning (dL) dF-Net Global monitoring (Gm) K-Nearest Neighbors (KNN) machine Learning (mL)

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Utilizing machine Learning Techniques for detecting Anomalies in IoT Networks 5

Utilizing Machine Learning Techniques for Detecting Anomalie...

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5th IEEE International Conference on recent Trends in Computer Science and Technology, ICrTCST 2024

作者： Shobha rani, P. Ahamed, mohammad Vajid Chaithresh, Kommi Sree Sai Srinivas, Sakkuru Kundan Vivek, Panguluri Venkata R.M.D Engineering College Department of CSE Kavaraipettai India R.M.D. Engineering College Department of CSE R.S.M. Nagar Kavaraipettai India

ISBN: (纸本)9798350351378

The proliferation of IoT devices has enabled unprecedented data collection and connectivity, revolutionizing various sectors. due to the huge network of linked devices, security and anomaly detection are harder. Our research examines how machine learning may improve anomaly detection in IoT networks to answer the demand for effective cyber security. The proposed method evaluates network traffic patterns using supervised and unsupervised machine learning. Training supervised learning models on annotated datasets lets the system discriminate good and poor behavior. The research also examines how real-time monitoring might assist dynamic Internet of Things systems identify and adapt to new risks. The proposed method is evaluated on IoT network datasets with different devices and communication protocols. results indicate that machine learning algorithms can identify anomalies without false positives. In addition, the system's ability to learn and adapt to new normal and deviant behavior patterns in changing IoT environments is tested. This research helps IoT cyber security become proactive and robust. Organizations can use machine learning to detect and mitigate security vulnerabilities to ensure safe and dependable IoT integration and development. © 2024 IEEE.

关键词： Anomaly detection

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A Light-Weight deep Learning-Based Architecture for Sign Language Classification

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Intelligent Automation & Soft Computing 2023年第3期35卷 3501-3515页

作者： m.daniel Nareshkumar B.Jaison Department of Electronics and Communication Engineering R.M.K.Engineering CollegeKavaraipettai601206India Department of Computer Science and Engineering R.M.K.Engineering CollegeKavaraipettai601206India

With advancements in computing powers and the overall quality of images captured on everyday cameras,a much wider range of possibilities has opened in various *** fact has several implications for deaf and dumb people as they have a chance to communicate with a greater number of people much *** than ever before,there is a plethora of info about sign language usage in the real *** languages,and by extension the datasets available,are of two forms,isolated sign language and continuous sign *** main difference between the two types is that in isolated sign language,the hand signs cover individual letters of the *** continuous sign language,entire words’hand signs are *** paper will explore a novel deep learning architecture that will use recently published large pre-trained image models to quickly and accurately recognize the alphabets in the American Sign Language(ASL).The study will focus on isolated sign language to demonstrate that it is possible to achieve a high level of classiﬁcation accuracy on the data,thereby showing that interpreters can be implemented in the real *** newly proposed mobile-NetV2 architecture serves as the backbone of this *** is designed to run on end devices like mobile phones and infer signals(what does it infer)from images in a relatively short amount of *** the proposed architecture in this paper,the classiﬁcation accuracy of 98.77%in the Indian Sign Language(ISL)and American Sign Language(ASL)is achieved,outperforming the existing state-of-the-art systems.

关键词： deep learning machine learning classiﬁcation ﬁlters american sign language

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