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International Conference on Biosignals, Images and Instrumentation (ICBSII)

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

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

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.

关键词： Vibrations Temperature Pain Instruments Force Medical treatment Lighting

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A Mango Disease Prediction for Smart Agiculture using Machine Learning Algorithms

A Mango Disease Prediction for Smart Agiculture using Machin...

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

作者： Lavanya K A. Packialatha Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The country's economy relies heavily on agriculture, and the health of the crops is essential to its success. Crop diseases that go undetected can cost the agriculture industry. Thus early detection and identification are crucial. It is possible to avoid crop diseases from destroying the harvest if they are accurately diagnosed and detected. Because healthy and diseased plants appear identical in their early stages, farmers cannot distinguish between the two by watching the crop leaf. India exports vast mangoes, making it an economically and environmentally significant fruit. About 1500 mango species are grown in India, with over 1000 commercial types. There are a lot of diseases that harm mangoes, affecting their look, taste, and economy. The "Prediction of Disease in Mango Fruit Crops" A complex warning system that uses machine learning and IoT. One of the main objectives is to develop a system that can forecast disease outbreaks on mango fruit harvests using historical weather information and crop yield Mango trees in India are plagued by a fungus called Anthracnose, which is the most frequent disease of its kind. Anthracnose, a highly contagious fungus, requires a quick and accurate method of diagnosis. As a result, an in-depth examination of the plants is essential before initiating any control measures. This study examines machine learning(ML) and deep learning (DL) strategies for detecting and classifying mango plant diseases. The performance of ML and DL-based classification models for mango crops and their datasets and feature extraction approachesare examined in this work. Finally, a variety of issues involved in plant disease identification are explored.

关键词： Support vector machines Deep learning Plant diseases Accuracy Costs Biological system modeling Crops Data models Agriculture Internet of Things

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Exploring Convolution Neural Networks for Image Classification in Medical Imaging 2

Exploring Convolution Neural Networks for Image Classificati...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Vishwa Priya, V. Chattu, Padmini Sivasankari, K. Pisal, Dnyaneshwar Tukaram Renuka Sai, Bantupalli Suganthi, D. Vels Institute Science Technology and Advanced Studies Department of Cs Tamilnadu Chennai India Ace Engineering College India Sri Sai Ram Institute of Technology Department of Ece Tamilnadu Chennai India SVPMs Institute of Management Baramati Pune Maharashtra Malegaon India Raghu Engineering College Computer Science and Engineering Visakhapatnam India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Thandalam Chennai India

ISBN: (纸本)9798350306415

Modern healthcare relies on medical imaging to diagnose and cure diseases. Convolution Neural Networks (CNNs) have excelled at image categorization, and their use in medical imaging could change illness diagnosis. This study examines CNN's medical picture categorization performance. This study used X-rays, CT scans, MRIs, and histopathology slides. Traditional CNNs, transfer learning models, and bespoke networks were constructed and trained. The training data included lung ailments, cancer detection, bone fractures, and neurological disorders. Our investigations showed that CNNs can extract complex characteristics from medical photos, improving classification accuracy. Transfer learning, where pre-trained models were fine-tuned on medical data, performed well with 94.8% accuracy. We used cutting-edge data augmentation and attention strategies to improve model generalization and interpretability. In addition to high classification accuracy, we studied model explainability using gradient-based methods and visualization to highlight medical picture regions of relevance that influenced model predictions. Building trust with medical practitioners and understanding deep learning model decision-making requires interpretability. In clinical settings, CNN deployment raises ethical and practical issues such as data privacy, model robustness, and regulatory compliance. According to our research, CNNs may increase medical picture classification accuracy and speed. X-rays had the best accuracy at 95.2%, followed by CT scans at 92.7% and MRIs at 94.1%. Histopathological slides exhibited 88.6% accuracy;however, this shows that CNNs can diagnose diseases from microscopic images. © 2024 IEEE.

关键词： Convolutional Neural Networks Image Classification Medical Data Analysis Medical Imaging

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Fe single atom trigger asymmetric In-In polarized site pairs boosting near-infrared N2 photoreduction

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Materials Today 2025年 86卷 96-103页

作者： Liang, Huimin Ye, Caichao Wu, Yao Li, Yezi Long, Ran Xiong, Jun Jiang, Wei Di, Jun School of Chemistry and Chemical Engineering National Special Superfine Powder Engineering Research Center Nanjing University of Science and Technology Nanjing210094 China Academy for Advanced Interdisciplinary Studies & Department of Materials Science and Engineering Guangdong Provincial Key Laboratory of Computational Science and Material Design Southern University of Science and Technology Shenzhen518055 China School of Materials Science & Engineering Nanyang Technological University Singapore639798 Singapore National Synchrotron Radiation Laboratory CAS Center for Excellence in Nanoscience School of Chemistry and Materials Science University of Science and Technology of China Hefei230026 China Institute for Energy Research Jiangsu University Zhenjiang212013 China

Building differentiated charge distributed polarized atomic pairs may be an alternative for N2 activation. In this work, CuInS2 atomic layers with asymmetric In-In polarized site pairs are prepared by doping Fe single atoms, as proved by X-ray absorption fine structure spectroscopy, quasi in situ X-ray photoelectron spectra and calculated atomic charge distribution. The engineered Fe single atoms tuned the surface atomic charge distribution, building local polarization center to boost directional surface charge separation. The formed In-In polarized site pairs with asymmetric charge distribution creates distinct interaction with two N atoms in N2 molecule, contributing to the enhanced activation of N2. The alternating pathway is regarded as hydrogenation pathway over Fe-CuInS2 with *NNH formation as rate-determining step. The charge-redistributed surface atomic structure after engineered Fe single atoms strengthens intermediate interaction and lower the rate-determining step energy barrier. Benefiting from these features, Fe-CuInS2 exhibits excellent photocatalytic activity under near-infrared (NIR) irradiation with an ammonia synthesis rate of 94.1 μmol g−1 h−1. In comparison, the N2-NH3 conversion rate of Fe-CuInS2 under ultraviolet–visible (UV–Vis) irradiation could reach 137.4 μmol g−1 h−1, which is about 6.8 times higher than that of CuInS2. The apparent quantum efficiencies of Fe-CuInS2-2 at 380, 400, 450, 500, 550, 650, 700, 800, 850 and 1064 nm were 3.0 %, 3.3 %, 2.7 %, 2.1 %, 2.0 %, 1.5 %, 1.3 %, 0.4 %, 0.7 % and 0.6 %, respectively. © 2025 Elsevier Ltd

关键词： X ray absorption fine structure spectroscopy

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Diabetic Retinopathy Identification From Retinal Images With The Use Of A Deep Learning-Based Transfer Training Framework

Diabetic Retinopathy Identification From Retinal Images With...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： Jayaprakash Venugopal Kalaivani Kathirvelu Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

Diabetic retinopathy (DR) is a major contributor to vision loss worldwide, highlighting the urgent need for early identification and prompt care to prevent further deterioration of vision. Diagnosing DR is intrinsically difficult, since it requires the thorough evaluation of complex retinal images by skilled professionals. DR, a consequence of diabetes, has the potential to cause vision loss if not identified in its early stages. research presents a novel model that utilizes transfer learning to detect diabetic retinopathy. The first trials yielded poor accuracies for both training and validation. The categorization of class-4, especially those depicting severe instances, yielded unexpected outcomes, since several images were categorized as moderate. Consequently, more investigation is required to ascertain the accuracy of these classifications. Therefore, our suggested approach allows for easy classification of the retina pictures of both Diabetic and Healthy individuals, resulting in a decrease in the number of evaluations required by medical specialists.

关键词： Training Diabetic retinopathy Accuracy Transfer learning Refining Retina Data augmentation Robustness Research and development Medical diagnostic imaging

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Mechanical properties of silicon nitride fortified aluminium based metal matrix composites – A review

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Materials Today: Proceedings 2022年 68卷 1733-1736页

作者： Kukanur, Vijayendra Ajith Arul Daniel, S. Sivaganesan, S. Algur, Veerabhadrappa Department of Mechanical Engineering S L N College of Engineering Karnataka Raichur 584135 India Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai 600117 India Department of Mechanical Engineering RYMEC Karnataka Ballari India

Aluminum metal matrix composites (AMMCs) are in potential demand in numerous field such as automotive, defence and sports recreation sectors owe to important properties compared to traditional aluminium alloys. AA7055 is generally embraced and it is the ideal composite between Al7XXX series arrangement mainly used for different vehicle applications. AA7055 metal matrix composites properties can be improved by incorporating suitable reinforcement. The motivation behind this assessment is to give an outline of silicon nitride in the AA7055 MMC as a base material and to clarify changes in mechanical properties due to the inclusion of reinforcements that can be further improved in combination with the addition of appropriate reinforcement materials to the base matrix. © 2022

关键词： Wear of materials

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Intrusion Analysis System of DDOS Attack Using Python

Intrusion Analysis System of DDOS Attack Using Python

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Advancement in technology (ICONAT), International Conference for

作者： M. Monisha K.Sathya Sai M.Bharath Kumar Department of Electronics and Communication Engineering Vels Institute Of Science Technology and advanced studies(VISTAS) Chennai India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Preserving the security and continuous accessibility of online services and networks is critical in today’s digitally interconnected world. Distributed Denial of Service (DDoS) assaults are one of the most well-known and ever-changing threats. This work presents an Intrusion Analysis System that efficiently detects and analyses DDoS attacks in real-time by utilizing Python’s powerful capabilities. This cutting-edge solution combines machine learning, anomaly detection, and network traffic monitoring components to create a strong defence against the disruptive and incapacitating effects of DDoS attacks. The Intrusion Analysis System reduces downtime and service interruptions while providing firms with real-time capabilities to enable them to react quickly to new *** system is adjustable and easily integrable into existing network infrastructures thanks to its usage of Python, a versatile and widely used programming language. This research project is a trailblazer in the field of cybersecurity, attempting to enhance digital defences against the tremendous threat of DDoS attacks. It is not just a proactive reaction to the dangers of the digital era; it is also a declaration of our steadfast dedication to preserving the security and resilience of online networks and services in our globalised society.

关键词： Organizations Telecommunication traffic Machine learning Network security Denial-of-service attack Real-time systems Vectors Computer crime Python Resilience

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Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar(3D)

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Computer Modeling in engineering & Sciences 2023年第4期135卷 211-237页

作者： Jing He Haonan Chen Lingxiao Li Yebin Zou Institute for Advanced Studies in Humanities and Social Sciences Beihang UniversityBeijing100083China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing100000China College of Geoscience and Surveying Engineering China University of Mining and Technology-BeijingBeijing100083China School of Literature Capital Normal UniversityBeijing100089China School of Civil and Hydraulic Engineering Ningxia UniversityYinchuan750021China

There are many sources of geographic big data,and most of them come from heterogeneous *** data sources obtained in this case contain attribute information of different spatial scales,different time scales and different complexity *** is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more *** this case,visualizing high-dimensional spatiotemporal trajectory data is extremely ***,i-tStar and its extension i-tStar(3D)proposed,a trajectory behavior feature formoving objects that are integrated into a view with less effort to display and extract spatiotemporal conditions,and evaluate our approach through case studies of an open-pit mine truck *** experimental results show that this method is easier to mine the interaction behavior of multi-attribute trajectory data and the correlation and influence of various indicators of moving objects.

关键词： Trajectory data multidimensional multivariate visualization technology behavioral characteristics

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Characterization of squash polysaccharide and the anti-diabetic effect on type 2 diabetic rat revealed by urine metabolomics analysis

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Food Science and Human Wellness 2024年第5期13卷 2937-2946页

作者： Li Liang Fuming Zhang Quanhong Li Baoguo Sun Yuyu Zhang Robert J.Linhardt Beijing Advanced Innovation Center for Food Nutrition and Human Health Beijing Key Laboratory of Flavor ChemistryBeijing Technology and Business University(BTBU)Beijing 100048China Departments of Chemical and Biological Engineering Chemistry and Chemical BiologyBiomedical Engineering and Biological ScienceCenter for Biotechnology and Interdisciplinary StudiesRensselaer Polytechnic InstituteTroy 12180USA College of Food Science and Nutritional Engineering China Agricultural UniversityBeijing 100083China China National Engineering Research Center for Fruit and Vegetable Processing Beijing 100083China

The present study reports the structural characteristics of 3 polysaccharide fractions(SPS-F1,SPS-F2 and SPS-F3)isolated and purified from ***-F1(molecular weight(Mw)=12.30 kDa)and SPS-F2(Mw=19.40 kDa)were likely to contain HG and RG-I domain of pectic polysaccharide,***-F2(Mw=270.4 kDa)was mainly composed of rhamnose,galactose and *** treatment with SPS decreased body weight gain,glucose and TG levels in type 2 diabetes ***,25 differential metabolites were identified based on urinary metabolomics analysis,which are crucial to the anti-diabetic effect of *** regulation of nicotinamide N-oxide,histamine,cis-aconitate,citrate,L-malic acid,3-(3-hydroxyphenyl)propanoic acid and N-acetyl-L-aspartic acid were mainly associated with energy metabolism,gut microbiota and *** of surface plasmon resonance revealed the binding kinetics with galectin-3(Gal-3)and fibroblast growth factor 2(FGF2).The K_(D)values of SPS-F2 and SPS-F3 to Gal-3 were 4.97×10^(-3)and 1.48×10^(-3)mol/L,indicating a weak binding *** 3 fractions showed moderate binding to FGF2 and the affinity was SPS-F3>SPS-F2>***,the metabolomics and SPR approach were proved to be a promising tool in exploring the anti-diabetes effects of SPS and provided a deep understanding of the mechanisms.

关键词： Polysaccharide Metabolomics Type 2 diabetes Surface plasmon resonance

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Sharing and Collaborating When Teaching Automatic Control: An Experience-Based Needs Analysis 32

Sharing and Collaborating When Teaching Automatic Control: A...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Knorn, Steffi Garone, Anja Varagnolo, Damiano Garone, Emanuele Technische Universität Department of Control Berlin Germany Brussels Institute for Advanced Studies Belgium Norwegian University of Science and Technology Department of Engineering Cybernetics Norway University of Padova Department of Information Engineering Italy Free University of Brussels Department of Automation and Systems Analysis Belgium

ISBN: (纸本)9798350395440

We discuss two specific issues that affected the workflows in our career as automatic control teachers and that are related to how teachers may establish teaching-oriented collaborations with colleagues from the same or different institutions. The first issue we identify is the systematic difficulty that one encounters when wanting to exchange instructional material with colleagues, while the second is the complexity of establishing students exchange programs across different institutions and nations. We discuss the nature and structure of the two problems, discuss why and how they are connected, and draw practical examples of how these problems may affect workflows. We then present a strategy that aims at solving these issues based on the introduction of an opportune indexing of instructional material in terms of contents and complexity of such contents, and how this information may be graphically displayed so to ease human and machine interpretability. We further discuss how this approach was used on a smaller scale to ease collaborations among close colleagues, and how it may be extended and adopted on a larger scale. © 2024 IEEE.

关键词： Process control

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