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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Suganiya Murugan S. Pradeep Kumar Kalaiarasi G B. Saritha B. Rubini Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu Department of EEE Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu Department of ACSE Vignan foundation for Science technology and Research Andhrapradesh India Department of Biomedical Engineering Erode Sengunthar Engineering College Thudupathi Erode Tamil Nadu India Department of EEE Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Running is a favoured exercise for millions, but it carries a risk of musculoskeletal injuries, particularly to the lower extremities. Identifying and predicting potential injuries is crucial for prevention. This study explores the use of machine learning techniques to predict injuries among runners, leveraging data on demographics, running habits, health conditions, and previous injuries. Using models like Random Forest, K-Nearest Neighbours, Decision Trees, and Logistic Regression, the study examines the relevance of various attributes and assesses the predictive power of these models. Remarkably, our model achieved an impressive accuracy of over 99%, setting a new benchmark for predictive precision in injury prevention. Visualizations provide insight into attribute interactions and their influence on injury risk, facilitating the development of targeted prevention strategies.

关键词： Training Measurement Logistic regression Prevention and mitigation Data visualization Machine learning Predictive models Injuries Random forests Wearable sensors

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Asymmetric Two-Component Alkenyl Catellani Reaction for the Construction of C—N Axial Chirality

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Chinese Journal of Chemistry 2024年第7期42卷 699-704页

作者： Chenggui Wu Ze-Shui Liu Yong Shang Chang Liu Shuang Deng Hong-Gang Cheng Hengjiang Cong Yinchun Jiao Qianghui Zhou Sauvage Center for Molecular Sciences Engineering Research Center of Organosilicon Compounds&Materials(Ministry of Education)Hubei Key Lab on Organic and Polymeric OptoElectronic MaterialsCollege of Chemistry and Molecular SciencesThe Institute for Advanced Studiesand TaiKang Center for Life and Medical SciencesWuhan UniversityWuhanHubei 430072China Key Laboratory of Xin'an Medicine Ministry of EducationAnhui University of Chinese MedicineHefeiAnhui 230038China School of Chemistry and Chemical Engineering Hunan University of Science and TechnologyXiangtanHunan 411201China

Herein,we report an asymmetric two-component alkenyl Catellani reaction for the construction of C—N axial chirality through a palladium/chiral norbornene cooperative catalysis and an axial-to-axial chirality transfer *** partially aromatic iodinated 2-pyridones,quinolones,coumarin and uracil substrates react with 2,6-disubstituted aryl bromides with a tethered amide group,to afford a wide variety of polycyclic C—N atropisomers(38 examples,up to 97%e.e.).The obtained C—N axial chirality originates from the preformed transient C—C axial chirality with high *** synthetic utility of this chemistry is demonstrated by facile prepa-ration of complex quinoline and pyridine based C—N atropisomers through a N-deprotection and aromatization *** addi-tion,a remote axial-to-central diastereoinduction process dictated by C—N axial chirality is observed with excellent diastereocontrol.

关键词： Alkenyl Catellani reaction Asymmetric catalysis Atropisomerism Axial-to-axial chirality transfer Heterocycles

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Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using Computer Vision and Deep Learning 5

Multi-Modal Emotion and Gesture Recognition System for Human...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Shivaswamy, Smitha Aruna, S. Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Application Karnataka Mangaluru India Acharya Institute of Technology Department of Computer Applications Bengaluru India School of Science and Computer Studies CMR University Bangalore India Hyderabad India

ISBN: (纸本)9798331518097

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like 'need help' or 'need water.' An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare. © 2024 IEEE.

关键词： Gesture recognition

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IoT Implementation at Global Enterprises for Progressive Human Resource Practices 1

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1st International Conference on Mathematical Modeling and Computational Science, ICMMCS 2020

作者： Moyeenudin, H.M. Anandan, R. School of Hotel & Catering Management Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Computer Science & Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

ISBN: (数字)9789813343894

ISBN: (纸本)9789813343887

The human resource division has skillfully improved to identify the employee performance to meet the employer’s commitment through Internet of things (IoT) in advanced multinational establishments, which encounters various jobs at the working environment. The majority of multinational companies is focused on identifying the right tools to improve their recruitment and training processes that deals with the organizational culture, commitment of employee, and overseeing the performance of an employee by IoT. To locate the specific aptitudes of individuals regarding the company’s policies, rules and regulation, the human resource management (HRM) recruits employee using varied channels with the aid of IoT, and the virtual platforms support the HRM for recruitment and training. This paper is focused on the implementation of IoT in human resource management for effective recruitment and training process. Secondly, it highlights the purpose and quantity-wise usage for human resource (HR) practices to determine the attendance, feedbacks, appraisals, and performance of an employee. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Preparation of Nanosize Bone Powder from Waste and Development of Al Composite through Squeeze Casting Process

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Journal of Nanomaterials 2023年第1期2023卷

作者： Kamatchi, R. Muthu Muraliraja, R. Sabari Bharathi, C. Sathish, T. Sathyaraj, S. Kumar, Leevesh Department of Mechanical Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Construction Technology and Management Ambo University Oromia Ambo Ethiopia

Al6061 alloy is most commonly used in automotive, marine, and aerospace applications to lighten the composite and increase its strength. Al6061 alloy is a precipitation-hardened aluminum alloy used as a matrix material. Beef bone is a biowaste that has polluted the environment and the people who live in the vicinity of its manufacturing and disposal sites. Biowaste has been used in a variety of ways by researchers in recent years, including activated carbon, water purification, reinforcement in composites, fillers, additives, etc. Beef bones that had been abandoned as waste were collected, cleaned, and grounded into a fine powder with a particle size of 50-100 nm and used as reinforcement. Squeeze casting process is used to create the newly created aluminum composite (Al6061 + 0%, 5%, 10% of bone powder). The aluminum composite was fabricated and three samples were successfully obtained for further testing and analysis. The prepared Al composites with nanopowder reinforcement are analyzed for surface morphology, elemental identification, hardness, porosity, tensile strength, and compression strength. The percentage of porosity in the composite is improved by 36.7% when compared to the Al6061 alloy. Similarly, the tensile strength of the produced composite is increased to 5.59%. A significant improvement is observed in the wear resistance and hardness of the composite as 54.55% and 48.65%, respectively. © 2023 R. Muthu Kamatchi et al.

关键词： Tensile strength

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Intelligent Kidney Abnormality Detection in CT Images Using GLCM and Neural Networks 4

Intelligent Kidney Abnormality Detection in CT Images Using ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Bajpai, Chhavi Anitha, S. Gaur, Manish Raghini, M. Kaliappan, S. Khan, Imran Centre for Advanced Studies Aktu Department of Computer Science and Engineering Lucknow India Acs College of Engineering Department of CSE-Cyber Security Bangalore India Institute of Engineering and Technology Aktu Department of Computer Science & Engineering Lucknow India Velammal College of Engineering and Technology Department of Artificial Intell igence and Data Science Madurai India Lovely Professional University Department of Research and Development Jalandhar India Sharda School of Nursing Science and Research Sharda University Department of Medical Surgical Nursing Greater Noida India

ISBN: (纸本)9798350352931

Kidney stone illness, one of the most common medical problems worldwide, relieves pain and drives people to the ER. Diagnoses often use imaging, but educated doctors must interpret them. Computer-aided diagnostics (CAD) systems help doctors discover kidney stones more accurately. The suggested technique includes preprocessing, feature extraction, and model training. Ultrasound (US) image preprocessing includes restoration, smoothness, sharpness, and contrast enhancement. The Grey Level Co-occurrence Matrices (GLCM) analyze image texture to extract characteristics. Model training uses KELM. The KELM-based model outperforms Extreme Learning Machine (ELM) and K-Nearest Neighbours (KNN) with an average accuracy of 92.54%. This accuracy demonstrates the method can detect kidney stones in ultrasound images. This proposed shows that computer-aided kidney stone detection can be fast and accurate. The combination of GLCM's texture analysis and KELM's classification skills can help medical professionals diagnose kidney stones using imaging. © 2024 IEEE.

关键词： Computerized tomography

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A Multi-decoder Recurrent Network for Vessel Trajectory Prediction Using Multi-memory LSTMs

A Multi-decoder Recurrent Network for Vessel Trajectory Pred...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Chen, Meng Zhang, Chi Qu, Tengteng Chen, Bo Cheng, Chengqi Deng, Haojiang Academy for Advanced Interdisciplinary Studies Peking University Beijing China College of Engineering Peking University Beijing China Institute of Artificial Intelligence and Applied Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

Vessel trajectory prediction is the foundation for constructing the marine intelligent traffic management system. Deep learning algorithms driven by massive AIS data provide more possibilities for accurately predicting vessel trajectories. Currently, LSTM models widely used for this task have limited ability to extract temporal features, and prediction errors accumulate rapidly over time. To address the above issues, we design a multi-memory state LSTM (MM-LSTM) unit by introducing global memory states and use a multi-decoder structure to capture features for different prediction periods. Experimental results on the real AIS dataset provided by NOAA show that: first, the MM-LSTM unit can effectively capture the temporal information and reduce the prediction error of vessel trajectory by 1.93km (11.5%) than the standard LSTM;second, the multi-decoder structure can universally improve the trajectory prediction accuracy for each prediction period, which has promising applications in trajectory prediction tasks. © 2022 IEEE.

关键词： Forecasting

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Simultaneously optimizing exciton diffusion length and nonradiative energy loss in organic solar cells via ternary strategy

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Science China Chemistry 2024年第9期67卷 3004-3011页

作者： Huanxiang Jiang Hao Lu Zezhou Liang Yonghai Li Guangliu Ran Chenyu Han Yuqiang Liu Hongxiang Li Xichang Bao Zhishan Bo College of Textiles and Clothing State Key Laboratory of Bio-fibers and Eco-textilesQingdao UniversityQingdao 266071China Qingdao Institute of Bioenergy and Bioprocess Technology Chinese Academy of SciencesQingdao 266101China Key Laboratory for Physical Electronics and Devices of the Ministry of Education&Shaanxi Key Lab of Photonic Technique for InformationSchool of Electronics Science&EngineeringFaculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Department of Physics and Applied Optics Beijing Area Major Laboratory Center for Advanced Quantum StudiesBeijing Normal UniversityBeijing 100875China College of Polymer Science and Engineering State Key Laboratory of Polymer Materials EngineeringSichuan UniversityChengdu 610065China Laboratory of Solar Energy Shandong Energy InstituteQingdao 266101China

Significant nonradiative energy loss and short exciton diffusion length in organic solar cells(OSCs)are two major obstacles to achieving state-of-the-art *** is crucial to conduct a study on the intensive mechanism and improvement strategies for future breakthroughs in the efficiency of *** this work,nonradiative energy loss and exciton diffusion length are optimized simultaneously by incorporating a guest acceptor(LA15)to construct ternary OSC(D18:L8-BO:LA15).Firstly,LA15 exhibits excellent compatibility with the host acceptor L8-BO,and effectively improves the fluorescence quantum efficiency(FLQY),resulting in suppressed non-radiative energy ***,LA15 effectively prolongs the fluorescent lifetime of the acceptor phase from 0.85 to 1.12 ns,leading to larger exciton diffusion length,which is beneficial for reducing geminate ***,the addition of LA15 optimizes the crystallinity of the active layer with amplified charge transport *** a result,the optimized D18:L8-BO:LA15 device achieves ultralow nonradiative energy loss of 0.18 e V and improved fill factor(FF)with high efficiency up to 19.13%.These results highlight the crucial roles of regulating FLQYand exciton lifetime in achieving highefficiency OSCs.

关键词： organic solar cells nonradiative energy loss exciton lifetime exciton diffusion length

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Artificial Neural Network-based Relay Selection in Underwater Wireless Sensor Network

Artificial Neural Network-based Relay Selection in Underwate...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Anoop Sreeraj Vijayalakshmi. P V. Rajendran Department of Electronics & Communication Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

As one of the most challenging and promising wireless communications, underwater communication has received much attention. According to the distance, there may be significant delay, limited bandwidth and attenuation, which reduce the system's overall performance. To solve these problems, Artificial Neural Network based Relay Selection (ANNRS) in Under Water Wireless Sensor Networks (UWSN) is introduced. This mechanism uses the Artificial Neural Network (ANN) algorithm to choose the relay nodes to pass the data from sender to receiver. The ANN algorithm picked out the relay using four input features: Energy, bandwidth, depth and packet received ratio (PRR). The UWSN may dynamically pick the optimum relay node depending on the current network circumstances by employing an ANN-based relay selection system, which improves network performance, lowers energy consumption, and increases data transmission reliability. The extensive simulation outcomes demonstrate that the ANNRS mechanism enhances the performance, such as PRR, and energy, and minimizes the delay and Assemble Propagation Distance in UWSN.

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Energy Transfer Data Communication in Wireless Sensor Network Using Improved Firefly Algorithm

Energy Transfer Data Communication in Wireless Sensor Networ...

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Advances in Computational Intelligence and Communication (ICACIC), International Conference on

作者： S Sreejith N Shanmugasundaram V Rajendran K Sushita Department of Electrical and Electronics Engineering Technology & Advanced Studies (VISTAS) Vels Institute of Science Chennai Tamil Nadu

The wireless transmission of energy via directed radio frequency waves has the potential to produce always working sensor nodes by refilling the energy stored in the restricted on-board battery. The sensor nodes in WSNs collect environmental data, which is subsequently progressively sent to the networks. The sensor gathers signals and transmits them as data. The data are then relayed over a number of wireless nodes before being sent to the originating node. The large data transfer and high energy consumption of WSNs was not be successfully balanced by the existing routing techniques. In this paper the improved Firefly optimization approach is introduced to avoid this problem. The potential of the newly developed, nature-inspired Firefly algorithm for optimization reduces and addresses a variety of issues in WSN. The improved firefly technique is implemented in this study to find the correct path in sensor networks to transmit the data. In this paper the comparison of numerous network performance measures including average energy consumption (8.6J) end-to-end latency (2.2 ms), packet delivery ratio (1%), throughput (400 kbps), packet loss ratio (0.1%), are used to assess or analyse this routing system. To demonstrate the protocol’s superiority over current protocols, it was compared to them.

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