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IEEE data Descriptions 2024年 1卷 95-102页

作者： Leonardo O. Iheme Sevgi Onal Yusuf S. Erdem Mahmut Ucar Ozden Yalcin-Ozuysal Devrim Pesen-Okvur Behcet U. Toreyin Devrim Unay Epam Systems Istanbul Türkiye Department of Biotechnology and Bioengineering Izmir Institute of Technology Graduate School of Science and Engineering Izmir Türkiye Center for Advanced Biomaterials for Health Care Istituto Italiano di Tecnologia Napoli Italy Department of Electrical and Electronics Engineering Faculty of Engineering Izmir Democracy University Izmir Türkiye Department of Molecular Biology and Genetics Faculty of Science Izmir Institute of Technology Izmir Türkiye Department of Artificial Intelligence and Data Engineering Istanbul Technical University Informatics Institute Istanbul Türkiye

This article introduces two novel datasets for cell motility and wound healing research: the wound healing assay dataset (WHAD) and the cell adhesion and motility assay dataset (CAMAD). WHAD comprises time-lapse phase-contrast images of wound healing assays using genetically modified MCF10A and MCF7 cells, while CAMAD includes MDA-MB-231 and RAW 264.7 cells cultured on various substrates. These datasets offer diverse experimental conditions, comprehensive annotations, and high-quality imaging data, addressing gaps in existing resources. The collection methods, experimental designs, and annotation processes are detailed, emphasizing the reliability and utility of our datasets. WHAD and CAMAD enable researchers to develop robust algorithms for cell tracking, segmentation, and behavior analysis in phase-contrast microscopy images. The applications of these datasets span cancer research, image processing technique development, and automated analysis of wound healing and cell motility assays. By providing these resources, we aim to accelerate progress in biomedical image analysis and cell biology research. IEEE SOCIETY/COUNCIL engineering in Medicine and Biology Society (EMBS) data TYPE/LOCATION Images; Izmir, Türkiye data DOI/PID 10.21227/f77j-j355

关键词： Wounds Microscopy Substrates Image segmentation Phase change materials Annotations Breast cancer Biomedical imaging Adhesives Pipelines

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An Improved Driver Safety System based on Drunk and Drive Analyzing Architecture using Internet of Things Assisted Alcohol Identification Sensor

An Improved Driver Safety System based on Drunk and Drive An...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： M. Swarna G. Saranya A. Vanathi R. Ashwini B. Arunsundar Department of ECE Dr. M.G.R. Educational And Research Institute University Chennai Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Department of ECE Rajalakshmi Institute of Technology Chennai Department of ECE S.A. Engineering College Chennai Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The recently survey government is said 80 percentage of road accident is done by drunk and drive and the small cities accident range is 50 percentage to 80 percentage on this survey government declare overall of 100 accident and 30 to 35 deaths occurs on the road due to not wearing the seat belt. The Alcohol Sensor we are implement on the steering and this paper is totally depends on embedded System. This research is IoT and Arduino based system. There has been a dramatic rise in the likelihood of traffic accidents caused by the proliferation of automobiles on the road. Drunk driving is a major contributor to traffic accidents worldwide. The major goal of this research is to develop a system that can detect how much alcohol a driver has consumed. The goal of the proposed system is to decrease the frequency of drunken driving accidents by restricting users from operating motor vehicles while under the influence of alcohol or drugs. A NodeMCU ESP8266 WiFi Modules, an Arduino Controller, an Eye Blink Sensor, and a MQ3 Alcohol Detection Sensors are the main components of the suggested model. To ensure everyone’s safety, when the blood alcohol concentration (BAC) rises beyond a certain threshold, the vehicle’s ignition system will be disabled and the appropriate authorities will be notified via the IoT module; this process has previously been carried out using the GSM module. The proposed work is termed as Improved IoT based Driver Safety Model (IIoTDSM), in which it is cross-validated with the conventional model called GSM based Driver Safety Model (GSMDSM) to evaluate the efficiency of the proposed work.

关键词： Surveys GSM Roads Government Urban areas Safety Automobiles

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Image-Based Soil Health Analysis Using Deep Learning for Precision Agriculture

Image-Based Soil Health Analysis Using Deep Learning for Pre...

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International Conference on Communication and Electronics systems (ICCES)

作者： V. S. Saranya Saikiran Mangali K Srinija Galeiah Medabalimi Meena Devi R Venkata Ramana N Ajanthaa Lakkshmanan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Department of CSBS B V RAJU INSTITUTE OF TECHNOLOGY Medak Telangana Department: Data science MLRIT DUNDIGAL Sagi Ramakrishnam Raju Engineering College Sagi Ramakrishnam Raju Marg Bhimavaram Andhra Pradesh India Department of electrical and electronics engineering KCG college of technology Chennai Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Soil health is a critical component of agricultural productivity and sustainability. Traditional methods of assessing soil health, including physical sampling and laboratory testing, are labor-intensive, time-consuming, and costly. This paper proposes a novel image-based approach for soil health analysis using deep learning techniques. By utilizing aerial images captured by drones or satellites equipped with multispectral and thermal sensors, the system non-invasively assesses key soil health indicators such as moisture content, nutrient levels, organic matter, and erosion patterns. The proposed method integrates image processing techniques with machine learning models, particularly Convolutional Neural Networks (CNNs), to extract relevant soil features from the images. These CNNs are trained to predict soil health metrics, and the system's performance is evaluated through accuracy, mean squared error (MSE), and F1-score, showing significant improvements over traditional methods. This scalable solution offers a real-time tool for precision agriculture, aiding farmers and agronomists in making informed, data-driven decisions regarding soil health management.

关键词： Precision agriculture Deep learning Moisture Soil Thermal sensors Nutrients Feature extraction Real-time systems Convolutional neural networks Monitoring

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Improved Imperialist Competitive Algorithm Based Energy Efficient Peak to Average Power Ratio (Papr) in Cooperative Mimo-Af systems 12

Improved Imperialist Competitive Algorithm Based Energy Effi...

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12th International Conference on Computer Communication and Informatics, ICCCI 2022

作者： Kanithan, S. Ananthanarayanan, Arun Vignesh, N. Arun Kannapiran, Thirunavukkarasu Srinivasarao, N. Kumareshan, N. Madhusudhanan, S. Mvj College of Engineering Department of Ece Bengaluru560067 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ece Hyderabad500090 India Head of Data Science Unitedworld School of Computational Intelligence Karnavati University Gandhinagar382422 India Bvrit Hyderabad College of Engineering for Women Department of Ece Hyderabad500090 India Sri Shakthi Institute of Engineering and Technology Department of Ece Coimbatore641062 India Koneru Lakshmaiah Education Foundation Department of CSE Guntur522502 India

ISBN: (纸本)9781665480352

To enhance the performance of communication system, there are many cooperative techniques are available. The multi-input multi-output (MIMO) system has been analysed in this paper and its peak to average power is analysed with optimal available techniques. Such techniques are focused on various parameters and in this paper we have considered energy efficiency (EE). Practical implementations of realizing MIMO amplify and forward technique has some difficulties. In recent times, an algorithm such as Firefly fuzzy hybrid algorithm is being used. But in realizing the amplification and forward system, the performance may not be enhanced if the energy is not considered. The energy of the non linear system is associated with the average power and power efficiency. An improved imperialist competitive algorithm is proposed in this paper. The focus is mainly kept on energy efficiency. Algorithms such as EE HFFA (Energy efficient Hybrid Fuzzy Firefly Algorithm), amplify and forward system (AF) are analysed to bring out an algorithm called Improved Imperialist Competitive algorithm (IICA). It has been analysed in this paper that the simulation results of energy efficient IICA induces peak average power ratio (PAPR) which showed significant improvement in energy efficiency. © 2022 IEEE.

关键词： Energy efficiency

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Development of A Multipurpose Healthcare Mobile App Using JAVA

Development of A Multipurpose Healthcare Mobile App Using JA...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R K Pongiannan S. Nikkath Bushra Syed Ismail Abdul Lathif Saahil Pradhan R Brindha J Franklin Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India Data science and business systems School of computing SRM Institute of Science and Technology Kattankulathur India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur India

Health care has become an essential part of present human life. The preliminary requirement to select the basic information about medical facilities through a mobile app will serve the needs of the common people. This paper presents an integrated mobile app developed using JAVA. This app is user-friendly in terms of login, registration, and bookings. This caters to the facility to select information regarding doctors, medical lab facilities, pharmacy details, and health articles. This app will provide communication.

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Optimizing Load and Routes in Supply Chain Management with Travelling Salesman Problem and CapsNet Attention

Optimizing Load and Routes in Supply Chain Management with T...

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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： Rajasekar P M Shabeena Begam Shouvik Chattopadhyay S. Chand Basha Shanker Chandre Vijay Kumar Dwivedi Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India Department of MBA SNS College of Technology Coimbatore India Department of MBA Institute of Engineering & Management Kolkata India University of Engineering & Management Kolkata India Department of Business Administration St Ann's College of Engineering and Technology Chirala India Department of Computer Science & Artificial Intelligence School of Computer Science & Artificial Intelligence SR University Warangal India Department of Mathematics Vishwavidyalaya Engineering College Ambikapur India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Ever since the industrial revolution, industries have partnered to solve marketing problems and create new channels. Because they don't understand their clients' needs, sales centers often pursue conflicting aims. Lack of information has extended delivery times. Due to supply time, quality, or bulk freight depreciation agreements, facility expenses rise to satisfy demand, resulting in excess inventory. Supply chain management, particularly regional inventory management, has considerably impacted beer manufacturing profitability. The recommended method involves training the model and extracting features. Dynamic and PCA feature extraction are different. While PCA's main purpose is to dissociate correlated from uncorrelated data, dynamic PCA considers auto- and cross-correlation in time-series observations. A revised CapsNet approach trained the models. The Improved CapsNet model outperformed the Attention and baseline models in every test. The model solved brewing supply chain issues with 94.68% accuracy. The most successful advanced machine learning approaches for improving SCM include augmented CapsNet, PCA, and dynamic PCA, according to research. These techniques boost industrial profitability when applied to major issues like excess stock and supply unpredictability. The suggested strategy is more reliable and superior than present solutions, according to results.

关键词： Training Surveys Supply chain management Accuracy Profitability Time series analysis Feature extraction Integrated circuit modeling Surges Principal component analysis

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Nutrient Deficiency Detection using a MobileNet: An AI-based Solution

Nutrient Deficiency Detection using a MobileNet: An AI-based...

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

作者： S.Hrushikesava Raju S. Adinarayna N.Merrin Prasanna Shaik Jumlesha U. Sesadri Chindu Hema Department of CSE KoneruLakshmaiah Education Foundation Guntur Dept of CSE(Data Science) Raghu Engineering College Visakhapatnam Dept. of ECE Annamacharya Institute Of Technology & Sciences(Autonomous) Andhra Pradesh Department of CSE Annamacharya Institute of Technology and Science Tirupati AP Department of CSE Vardhaman College of Engineering Hyderabad Telangana KoneruLakshmaiah Education Foundation Guntur

Crop yield remains a pivotal determinant in the field of agriculture, susceptible to the adverse effects inflicted by various plant diseases during growth. The attainment of desired yields hinges upon the provision of adequate nourishment to plants. Nonetheless, insufficiencies in vital nutrients such as iron, magnesium, calcium, nitrogen, and potassium can significantly curtail production output. The identification and mitigation of these deficiencies require a multitude of machine learning methodologies such as artificial neural networks, convolutional neural networks, InceptionResNet, Densenet, VGG, and MobileNet. Among these techniques, MobileNet has demonstrated unparalleled accuracy, achieving an impressive 98% precision. DenseNet and InceptionResNet also exhibit formidable accuracies, registering at 96% and 94%, respectively. To facilitate a comprehensive comparison of these methods, the depiction of outcomes through graphical representations and diagrams proves instrumental in providing lucid visual comprehension. The MobileNet methodology entails the utilization of captured plant images as input, where the model undergoes training and testing to ascertain the presence of nutrient deficiencies or the plant's healthy state. This approach harnesses depthwise and pointwise convolutions, culminating in streamlined processing and optimal functionality. By harnessing the capabilities of MobileNet, the farmers are empowered to accurately detect and address nutrient inadequacies in crops, consequently leading to amplified yield and overall plant vitality.

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Optimizing Load and Routes in Supply Chain Management with Travelling Salesman Problem and CapsNet Attention 3

Optimizing Load and Routes in Supply Chain Management with T...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Rajasekar, P. Begam, M. Shabeena Chattopadhyay, Shouvik Basha, S. Chand Chandre, Shanker Dwivedi, Vijay Kumar Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India Sns College of Technology Department of Mba Coimbatore India Institute of Engineering & Management Department of Mba Kolkata India University of Engineering & Management Kolkata India St Ann's College of Engineering and Technology Department of Business Administration Chirala India School of Computer Science & Artificial Intelligence Sr University Department of Computer Science & Artificial Intelligence Warangal India Vishwavidyalaya Engineering College Department of Mathematics Ambikapur India

ISBN: (纸本)9798331508456

关键词： Supply chain management

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Large Model for Small data: foundation Model for Cross-Modal RF Human Activity Recognition

arXiv

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arXiv 2024年

作者： Weng, Yuxuan Wu, Guoquan Zheng, Tianyue Yang, Yanbing Luo, Jun Department of Computer Science and Engineering Southern University of Science and Technology China Smart City Center Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China College of Computer Science Sichuan University China College of Computing and Data Science Nanyang Technological University Singapore

Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep semantic insights from unlabeled visual data become viable, yet these vision-based FMs fall short when applied to small RF datasets. To bridge this gap, we introduce FM-Fi, an innovative cross-modal framework engineered to translate the knowledge of vision-based FMs for enhancing RF-based HAR systems. FM-Fi involves a novel cross-modal contrastive knowledge distillation mechanism, enabling an RF encoder to inherit the interpretative power of FMs for achieving zero-shot learning. It also employs the intrinsic capabilities of FM and RF to remove extraneous features for better alignment between the two modalities. The framework is further refined through metric-based few-shot learning techniques, aiming to boost the performance for predefined HAR tasks. Comprehensive evaluations evidently indicate that FM-Fi rivals the effectiveness of vision-based methodologies, and the evaluation results provide empirical validation of FM-Fi’s generalizability across various environments. © 2024, CC BY.

关键词： Zero-shot learning

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Adhesive-less bonding of incompatible thermosetting materials

RSC Applied Polymers

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RSC Applied Polymers 2024年第1期3卷 247-256页

作者： Khairkkar, Shhyam Pansare, Amol V. Pansare, Shubham V. Chhatre, Shraddha Y. Sakamoto, Junji Barbezat, Michel Terrasi, Giovanni P. Patil, Vishwanath R. Nagarkar, Amit A. Naito, Masanobu Data-Driven Polymer Design Group Research and Services Division of Materials Data and Integrated System (MaDIS) National Institute for Materials Science (NIMS) 1-2-1 Sengen Ibaraki Tsukuba 305-0047 Japan Composites Group Mechanical Systems Engineering Swiss Federal Laboratories for Materials Science and Technology-Empa ETH Domain Dübendorf 8600 Switzerland Department of Chemistry University of Mumbai Santacruz (E) Mumbai 400098 India National Chemical Laboratory (NCL) Dr Homi Bhabha Road Pune 411008 India Advanced Material Technology Center Technology Division Panasonic Holdings Corporation 1006 Kadoma Kadoma City Osaka 571-8508 Japan Datacule Inc. 529 Main St. Boston 02129 MA United States

We show that dynamic covalent exchange at the interface of two thermosetting polymers results in strong bonding between the materials via creation of a new material at the interface. Thus, polymers of significantly different polarities can be bonded without the use of adhesives. We also show that such dynamic covalent exchange is not only limited to the interface but also penetrates into the bulk material (ca. 20 microns), thereby creating a strong bond. The creation of a new material at the interface was confirmed by Energy Dispersive X-ray (EDX) elemental mapping as well as a new glass transition temperature at the interface. Using this phenomenon, we show that hydrophobic, compliant polymers can also be used as adhesives for polar, stiff materials. We also show that such dynamic exchange also takes place in the presence of fillers like nano-silica. Lastly, using this technique, we demonstrate the adhesive-less fabrication of layered materials where each layer has vastly different polarities and mechanical properties, thereby tuning the failures modes of the resulting composite material. © 2025 RSC.

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