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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Dhivya, A. Josephin Arockia Hemalatha, R.J. Rubi, Jaya Vels Institute of Science Technology and Advanced Studies Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

In the last decade, physiotherapists have a remedy or a solution for elders and rehabilitative patients easily in a rapid manner, which makes the physiotherapists get to a conclusion rapidly;previous studies clearly show that when the patients excessively exercise or rehabilitation process, it affects the health of the elders extensively. Walkers play a very important role in the field of rehabilitation. Clinical decisions should be taken rapidly, effectively, and efficiently. These walking aids are highly recommended and suggested for elders suffering from back pain, spondylitis, and sports injury. This smart crutch is used to guide the patients properly so they do not get any pain due to excessive usage. Our device alerts the user in the form of a haptic when he/she is applying excessive force on the walker. This reduces the overuse of the device, which leads to severe pain. Our main motive is to enhance the patient's life by designing a Smart crutch that alerts them if they overuse it. We also implemented a few parameters, such as a body posture management system where the user gets notified when his body bends too forward or backward. Also, it provides automatic lighting when the user is in a dark background. The user will be notified in terms of sound buzzer or light. Our ultimate goal is to help the patients and elders facing issues using rehabilitation aids. Although varicose veins are a prevalent condition, they disproportionately impact individuals who endure extended periods of standing or reclining. One potential treatment for varicose veins is vibration therapy, which involves applying vibration to the affected area to improve blood flow and reduce pain and swelling. The accelerometer sensor will detect the angle of the leg, and the Arduino will process the data and send it to the Node MCU. The Node MCU will be responsible for triggering the vibration therapy based on the angle of the leg detected by the accelerometer. © 2024 IEEE.

关键词： Patient rehabilitation

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Investigation on Mechanical Properties of Al5052/BN Metal Matrix Composite

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Materials Today: Proceedings 2022年 68卷 1252-1255页

作者： Kumar, Patnala Padma Ruban, M. Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 India Department of Automobile Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 India

The paper aims to find the Mechanical properties of HNT and Boron Nitride, reinforced with Aluminium hybrid metal matrix composites. The aluminum matrix material are reinforced with different weight percentage of HNT(3%,5%,7%) and BN(2%,3%,4%).The hybrid matrix are fabricated through sir casting *** mechanical Properties of like hardness, tensile, impact and micro structure of the composites are examined. The hardness and tensile of the composite are increased with increase in weight percentage of boron nitride and HNT. Also the microstructure of the composites shows the clear distribution of the composites and XRD pattern shows the presence of reinforcement in the composites. The hardness and tensile strength increases when 7% of HNT and 4% of boron nitride are added towards the base materials. © 2022

关键词： Microstructure

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Improved Deepmaxoutmodel for Disease Prediction in Healthcare IoT Cloud System 3

Improved Deepmaxoutmodel for Disease Prediction in Healthcar...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Preetha, P. Packialatha, A. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350374643

The Internet of Medical Things IoMT has grown rapidly due to the growing popularity of wearable technology and its applications in health monitoring systems. The IoMT significantly reduces the death rate by facilitating early sickness identification. Predicting cardiac disease is among the most significant issues with clinical dataset analysis. Finding the key components of heart disease prediction is the goal of the suggested investigation, which makes use of machine learning techniques. Despite several studies on the issue, the accuracy of the findings on the diagnosis of heart illness is low. The purpose of this paper is to provide an innovative framework for healthcare monitoring that includes the following phases. Data preprocessing, feature extraction, acquisition of data, and prediction are the primary stages. The process of acquiring data is at the IoTs layer. The medical data is considered in this case (benchmark datasets of different illnesses). The data pre-processing operations of data cleaning and data filtering at the cloud layer are applied to the data obtained from the IoT sensors (considering the dataset), where the data normalization process will take place. Subsequently, statistical features and raw features are extracted. The multi-layered Deepmaxout model will predict the diseases during in the prediction phase. Lastly, the evaluation of the suggested approach is compared to existing methods using several types of metrics. © 2024 IEEE.

关键词： Prediction models

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Intelligent computational ensemble model for predicting cerebral aneurysm using the concept of region localization in multi-section CT angiography

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International Journal of Information technology (Singapore) 2025年第4期17卷 1957-1963页

作者： Khan, Zabiha Kanna, R. Kishore Parthasarathy, K. Vijayaraj, S. Chandrasekaran, R. Jawla, Shashi Nitte Meenakshi Institute of Technology Bengaluru India Department of Biomedical Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Tamil Nadu Chennai India Electrical and Electronics Engineering Indian Naval Academy Kannur India Electrical and Electronics Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai India Biomedical Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai India Swami Vivekanand Institute of Engineering and Technology Banur Chandigarh India

Intracranial aneurysms and hemorrhage, also known as cerebral or brain aneurysms, are abnormal bulges or ballooning in the walls of blood vessels in the brain. When intracranial aneurysm leaks or ruptures, it results in bleeding within the brain, referred to as a hemorrhagic stroke. Multi-section computed tomography angiography (CTA) data is used to study because it is an effective technique for identifying cerebral atherosclerosis, arterial and intracranial hemorrhage. The hybrid model RID_Net is proposed that integrates ResNet15, InceptionV3, and DenseNet201 architectures in collectively, optimizing for precision through individual efficacy testing of each model. This ensemble model enhances feature extraction and classification performance, demonstrating the effectiveness of combining multiple transfer learning paradigms in biomedical image analysis. The performance is validated by comparing the proposed model with existing studies based on metrics such as accuracy (%), precision (%), recall (%) and f1-score (%). © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： Computed tomography angiography (CTA) Densenet201 Intracranial aneurysms (IA) Intracranial hemorrhage (IH) RID_Net Stochastic gradient descent (SGD)

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E-Crutch: A Promising tool for Rehabilitative Patients 10

E-Crutch: A Promising tool for Rehabilitative Patients

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Arockia Dhivya, A. Josephin Hemalatha, R.J. Rubi, Jaya Vels Institute of Science Technology and Advanced Studies Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350951

In earlier days,physiotherapists trained the rehabilitative patients in a progressive manner such as giving them a solution for their aid. But the major disadvantage is to find the issues faced by the elders while using the crutch without their knowledge. Our designed e-crutch helps us to train and alert the user to use the device wisely without any side effects. It majorly helps the patients suffering from long term spondylysis,fracture and spinal stenosis. The pain is in a excessive manner since the patients are unaware of their usage. The area which we highlight in our device is force,acceleratio,non invasive varicose vein therapy and lighting system. The first case is alerting the user when he s putting excessive force on the crutch without his/her knowledge will be notified by a haptic vibration. The posture of the body is a very important factor in the field of physiotherapy where the posture condition of patient moving excessively forward and backward will be displayed in the Lcd display present in the crutch module. A non invasive therapy system is also enclosed with the crutch to give a relief based on temperature and these modules together is termed a E-crutch. The notification of the user parameters will be sent to the care taker via GSM module. This particularly helps in the case of Alzheimer affected individuals. So,our ultimate aim is to design a E-crutch which will be useful to the elderly society. © 2024 IEEE.

关键词： Noninvasive medical procedures

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Comparative analysis of thrust force, roughness and roundness error in drilling of aluminium composites using RSM, ANN and fuzzy logic

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Materials Today: Proceedings 2022年 69卷 908-917页

作者： Babu, S. Senthil Dhanasekaran, C. Department of Mechanical Engineering VELS Institute of Science Technology & Advanced Studies Chennai India

Making holes without any defect in a solid product made of hybrid Aluminium composite material, is a difficult job in assembly industries. Hybrid composite of Aluminium 7075 reinforced with ceramic materials like silicon carbide, boron carbide, graphite and mica are used in automobile and structural industries due to their excellent mechanical properties. Thrust force developed during drilling, Error in the circularity and poor surface finish of the drilled holes are some of the common problems faced in the drilling process. Hence, to optimise the quality of drilling, analysing these responses under various conditions of drilling by varying the drilling factor become essential. This study discusses the development of various models to predict the thrust force developed, roughness and circularity error in the holes drilled on a hybrid metal matrix composite. Testing of drilling is conducted in CNC vertical machining centre using Titanium aluminium nitride (TiAlN) coated carbide drill tool of 5 mm diameter. Various drilling factors considered in our study are point angle of the drill tool, drilling speed and feed rate. Multiple regression equations using RSM, Artificial neural network (ANN) and Fuzzy Logic algorithms are used to develop the prediction models. The predicted values of thrust force, surface roughness and circularity error from these models are found to be matching with the observed experimental values. © 2023

关键词： Hybrid composites

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An improved equilibrium optimiser-based algorithm for dynamic network reconfiguration and renewable DG allocation under time-varying load and generation

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International Journal of Ambient Energy 2023年第1期44卷 280-304页

作者： Raut, Usharani Mishra, Sivkumar Department of Electrical Engineering International Institute of Information Technology Bhubaneswar India Department of Electrical Engineering Centre for Advanced Post Graduate Studies Biju Pattnaik University of Technology Rourkela India

This paper presents an improved equilibrium optimiser (IEO)-based method for dynamic hourly network reconfiguration (NR) with optimal allocation of dispatchable and non-dispatchable renewable distributed generator (DG) units in radial power distribution networks. In this work, solar- and wind-based DGs are considered non-dispatchable DG, while biomass DGs are considered dispatchable DG. The output of biomass DGs is adjusted according to the 24-hour load demand curve and that of solar and wind DG follow their hourly power generation. For this purpose, a multi-objective function is developed with consideration of hourly power loss minimisation and profit maximisation without violating the system’s operational constraints. The viability and efficacy of the proposed method are tested on IEEE 33-bus and 69-bus distribution systems for five distinct scenarios of network reconfiguration and renewable DG allocation. The analysis shows that the simultaneous NR and DG have a more substantial impact than considering the cases separately. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Electric load dispatching

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Drilling parameter analysis of hybrid composites (Al/B4C/graphite) using grey relational and Taguchi techniques

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Materials Today: Proceedings 2022年 68卷 1280-1287页

作者： Senthil babu, S. Dhanasekaran, C. Department of Mechanical Engineering VELS Institute of Science Technology and Advanced Studies Chennai India

Application of composite materials has been increasing worldwide especially in production industries. In drilling operation, standard of hole is the basic demand in numerous applications. The choice of optimized drilling parameters is extremely vital for good hole quality. Our objective is to optimize the parameters involved in drilling of aluminium /boron carbide/ graphite hybrid composite materials to get good quality holes. A series of drilling experiments are conducted in CNC machining centre on the hybrid composite specimen slabs of size 100 mm × 100 mm × 10 mm, made-up by stir casting technique. Our work is to analyse the consequences of drilling parameters like spindle speed, feed and drill tool diameter on thrust force, torque, surface roughness and roundness error. The experimental results are collected individually on L27 orthogonal array and analysed by Signal – Noise (S/N) quantitative relation and grey rational analysis with an objective to optimize the drilling parameters to reveal the dominant factors that have an effect on the responses mentioned. © 2022

关键词： Surface roughness

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Recognition of Deep Fake Voice Acoustic using Ensemble Bagging Model 5

Recognition of Deep Fake Voice Acoustic using Ensemble Baggi...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Basha, Shaik Abdul Yunus Priya, K.Ulaga Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350379945

As artificial speech generates technological evolution;fake voice information has become an increasingly prevalent avenue for fraud. Numerous investigations have been carried out into machine learning techniques, signal processing techniques, and the Automatic Speaker Verification System to address such issues. Since there are increasing methods to create fake information, techniques must be developed to determine when audio recordings employed as electronic proof are corrupted. Such recordings contain the potential to be manipulated. Hence, this research work identified the data regarding audio systems as either fake or real by i) extracting features from audio file metadata using oversampling techniques and ii) Developing an Ensemble Bagging Model appropriate for analyzing input audio signal followed by classification of fake (fraudulent) and real (authentic) audio. Our experimental results reveal that the ensemble bagging model attained an accuracy of 99.5%, precision of 99%, 99.6% recall, and 99.9% Fl-measure in the prediction of the audio system compared with traditional approaches. © 2024 IEEE.

关键词： Audio recordings

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An Enhanced Detection System of Autism Spectrum Disorder Using Thermal Imaging and Deep Learning

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SN Computer Science 2025年第2期6卷 1-11页

作者： Jegan Amarnath, J. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Tamil Nadu Chennai 600117 India

Autism Spectrum Disorder (ASD) is a brain-based condition characterized by social difficulties and repetitive behaviors. Traditional diagnostic methods can be subjective and time-consuming. Current diagnostic techniques for ASD often rely on behavioral assessments and clinical observations, which can be inconsistent and prone to subjectivity. Additionally, there is a need for more reliable and objective methods to differentiate between autistic and non-autistic individuals. Existing imaging techniques require further refinement to improve accuracy and efficiency in detecting ASD. The primary objective of this research is to develop and evaluate an enhanced detection system for ASD using thermal imaging and Improved Mask Faster Recurrent Convolutional Neural Network (IMFRCNN). This research focuses on enhancing the detection of ASD using Deep Learning (DL) methods and thermal imaging. Traditional diagnostic techniques for ASD often rely on subjective behavioral assessments, highlighting the need for more reliable methods. By examining cerebral imaging data from the thermal face image datasets collect facial thermal images using thermal cameras. Ensure the dataset is diverse, including varying expressions and environmental conditions. The study compares the effectiveness of IMFRCNN and the ResNet 50 system in classifying thermal images of 50 autistic and 50 non-autistic individuals. Emotions were evoked using audio-visual stimuli, and temperature variations in facial areas such as the nose, forehead, cheek, and eyes were measured. Anger showed the greatest temperature differences, with a 12.6% variation between autistic and non-autistic individuals. The IMFRCNN achieved a reliability of 96%, while the ResNet 50 achieved 90%. These findings indicate that computer-aided assessment techniques utilizing thermal imaging and DL can provide a consistent and objective method for identifying individuals with ASD. © The Author(s), under exclusive licence to Springer Nature Sing

关键词： Deep learning algorithms Diagnostic tool Improved mask faster recurrent convolutional neural network Performance measures

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