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Advanced Wound Care Training with IoT and lSTM for dynamic learning Environments 5

Advanced Wound Care Training with IoT and LSTM for Dynamic L...

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5th International Conference on data Intelligence and Cognitive Informatics, ICdICI 2024

作者： Viswanathan, C. lalitha, K. Selvi, J. Jeslin, J. Gnana Vignesh, C. Chandru Murugan, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tami Nadu Chennai India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Shree Sathyam College of Engineering and Technology Department of Civil Engineering Sankari Tamil Nadu Salem India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science and Engineering Tami Nadu Vellore India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

Wound treatment is an essential part of patient care that requires continuous training and education for medical personnel. Using long Short-Term Memory (lSTM) networks for time-series feedback and Internet of Things (IoT), technologies provide a paradigm shift in wound education in this research. The platform combines lSTM-based learning algorithms with real-time data collected from IoT wound monitoring to provide healthcare providers with ongoing training and feedback. A battery of trials illustrates that strategy outperforms the status quo regarding wound assessment accuracy and treatment decision-making. This approach promotes ongoing professional growth in wound care management by allowing for individualized learning experiences. Its work shows that continuous training using lSTM powered by the IoT has great promise for better wound education and patient outcomes in healthcare. © 2024 IEEE.

关键词： decision making

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Utilizing Virtual Reality to Enhance HMI Communication

Utilizing Virtual Reality to Enhance HMI Communication

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing landscape of AI, ICSCAI 2024

作者： Rani, l. Pattathu Karthick, V. Yasmin, A. Chinnadurai, Jayaprakash Ashok, P. Krishnakumar, K. Saveetha School of Engineering SIMATS University Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India India Dept of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Lavale Maharashtra Pune India Chennai India

ISBN: (纸本)9798350355338

In order to advance upcoming breakthroughs, it is necessary to enhance the connection between technology and human beings by utilizing complicated Industry 4.0 techniques. Augmented Reality (AR) has significant promise for this purpose by enhancing interaction in contemporary assistive technology solutions. Nevertheless, AR systems tend to be prohibitively costly, making them impractical for small and medium-sized organizations (SMEs). Moreover, the device's dependable data transmission to the applications in AR with the signaling of user action seem to be difficult. This study presents a resolution to these issues. An uncomplicated and expandable method for transmitting information from industrial equipment to apps for Android has been created. The idea for a prototype showcases an AR application designed for actual time debugging & fault rectification. It can be accessed on smartphone or smartwatches & is specifically tailored for usage in lab environments to mimic and resolve different types of mistakes. The individual parts (compact parking doors) are fitted with detectors. The actual time status of the network can be accessed at any point and is communicated to a smartphone or smartwatch that has an AR application. The user is able to observe the data needed in a visual format, including markings & the cursors. An electronic manual displays and resolves possible mistakes. The technology can be utilized for instruction in order to attain enhanced correction of errors efficiency & expedited fixing procedures. © 2024 IEEE.

关键词： Program debugging

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Improving Tomato disease diagnosis Through ResNet50: An Innovative Approach 4

Improving Tomato Disease Diagnosis Through ResNet50: An Inno...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： dhamotharan, R. Raj, J. Relin Francis Krishnan, R. Santhana Kavitha, S. Soundiraraj, N. Kumar, V. Vinoth Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331540364

Tomato crops are vulnerable to numerous diseases that can severely affect both yield and quality. Early and precise identification of these diseases is indispensable for implementing active management and intervention measures. This research introduces an advanced approach to tomato disease detection by employing the ResNet50 architecture, pretrained on the ImageNet dataset. Through transfer learning, the ResNet50 model leverages its pre-learned features to classify tomato plant images affected by diseases such as Tomato Mosaic Virus (ToMV), Bacterial Spot, Blossom End Rot, Early Blight, and late Blight. The ResNet50 model is fine-tuned with custom classification layers tailored to the specific diseases, allowing it to achieve high accuracy in disease detection. Preliminary results indicate that the ResNet50 model significantly outperforms traditional machine learning approaches, demonstrating its efficacy in accurately diagnosing tomato diseases. This study provides an effective solution for improving disease detection in tomatoes and highlights the transformative role of deep learning in advancing agricultural practices, fostering sustainability, and enhancing productivity. © 2024 IEEE.

关键词： Fruits

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Predicting Patient disease Progression with Cloud-based decision Trees and IoT data Integration 2

Predicting Patient Disease Progression with Cloud-based Deci...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Jeslin, J. Gnana Vijayalakshmi, Kuppan Vignesh, C. Chandru Suresh, G. Kosuri, Gopala Varma Murugan, S. Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Tami Nadu Chennai India Vellore Institute of Technology School of Computer Science and Engineering Tami Nadu Vellore India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

To predict how a patient's disease will progress, this research focuses at how effectively cloud-based decision trees (dTs) combined with Internet of Things (IoT) data integration work. The capacity to use these data streams for proactive healthcare treatments is expanding rapidly with the spread of IoT devices that can monitor different physiological markers. It provides an approach that uses cloud-based dTs models in conjunction with real-time data from IoT devices to build illness progression prediction models. Utilizing a varied dataset that covers a range of patient demographics and medical conditions, this method successfully predicts disease trajectories. Cloud computing and IoT can transform healthcare analytics. This might allow doctors to better predict how a patient's sickness will proceed. Improving patient outcomes while decreasing healthcare expenditures may be possible with this proactive strategy that allows for early interventions. The goal of this research is to predict disease development utilizing IoT-enabled data collecting and cloudbased dTs, resulting in more personalized treatments and better healthcare outcomes with real-time monitoring. Cloud computing and IoT data integration have the potential to enhance predictive healthcare analytics and optimize patient care methods. This novel approaches to illness prediction and management in the age of revolutionary digital healthcare. © 2024 IEEE.

关键词： Health data analytics Patient-centric care Personalized medicine Predictive healthcare Remote patient monitoring Wearable health devices

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Hydrodynamic Modeling for Identifying Flood Vulnerability Zones in Mahi lower Sub-basin

Hydrodynamic Modeling for Identifying Flood Vulnerability Zo...

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International Conference on Construction Materials and Environment, ICCME 2020

作者： Rathod Krina, R. dixit, Sudhanshu Water Resources Engineering-Civil Engineering Department L.D. College of Engineering Gujarat Ahmedabad India

ISBN: (纸本)9789811665561

Floods can cause widespread devastation, resulting in loss of life and damages to personal property and critical public health infrastructure. River flooding is the most common type of flooding in many parts of the world. It occurs when a water body exceeds its capacity to hold water and usually happens due to prolonged heavy rainfall. Mahi River located in Gujarat State of India, being the third major west-flowing river was inundated in August 2006. This present study aims to develop mathematical model using HEC-RAS 6.0 version to analyze one-dimensional and two-dimensional unsteady flow of water in Mahi lower sub-basin. The river stretch for hydrodynamic modeling starts from Khanpur gauge station located 139 km downstream of Kadana dam at Mahisagar district of Gujarat to the Mujpur Village near the mouth of Mahi River in Gulf of Khambhat. The simulated results in 1d modeling show that approximately 64.38% of the total cross section have their bank station in overtopped condition for discharge equal or more than 31,061.91 cumec. It has been observed that right bank of river indicating eastern side of Mahi River near Vadodara City is more prone to water spill for higher water levels in River. 2d modeling results identified vulnerable areas of Kherda, Rajupura, and Bhanpura located between Anand and Vadodara cities for flood event 2006 with the highest water surface elevation of 30.95 m. Model outputs can be used as on-structural method of flood vulnerability assessment by government agencies to reduce flood damage. © 2022, Springer Nature Singapore Pte ltd.

关键词： Rivers

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Optimizing drug discovery for Breast Cancer in a laboratory Environment Using Machine learning 9

Optimizing Drug Discovery for Breast Cancer in a Laboratory ...

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Borkhade, Gauri Singh, Jagendra Shelke, Nitin Arvind Upreti, Kamal Kuwar, Vishakha Tiwar, Mohit Bharati Vidyapeeth College of Engineering Navi Mumbai India Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Delhi-NCR Department of Computer Science Ghaziabad India Dr. D y Patil Vidyapeeth Centre for Online Learning Pune India Bharati Vidyapeeth's College of Engineering Computer Science and Engineering Delhi India

ISBN: (纸本)9798350350845

Breast cancer therapy can be greatly enhanced by the proposed method that combines experimental and computational techniques. Employing a state-of-the-art in vitro system, we evaluated biopsy tissues at different cancer stages, monitoring them for 48 hours. later on, our investigation involved the application of machine learning models including naïve Bayes (NB), artificial neural networks (ANN), random forest (RF), and decision trees (dT). Surprisingly, these models reached high test accuracies - ANN 93.2%, NB 90.4%, dT 87.8%, and RF 85.9%. The dataset's impedance dynamics data provide evidence for treatment efficacy. Therapeutic strategies need to be adjusted for particular patients and their stage of cancer since the results underscore the usefulness of personalized breast cancer therapy. This study will significantly contribute to new tailored treatment options available for breast cancer patients. © 2024 IEEE.

关键词： decision trees

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deep learning for Precision Grade-Quality Classification of Pomegranates

Deep Learning for Precision Grade-Quality Classification of ...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Math, RajinderKumar M. dharwadkar, Nagaraj V. Sajjanar, Vijaykumar deshmukh, M.N. Department of Electronics and Communication Engineering B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijayapur India Department of Computer Science Central University of Karnataka Kadaganchi Kalaburgi India

ISBN: (纸本)9798331505134

The conventional methods of manual grading of fruits after agricultural harvest have always been laborious, time-consuming, and prone to human errors. In order to speed up the grading process without human intervention, an automatic system is the need of the hour. This research has led to the development a Convolutional Neural Network (CNN) based deep learning technique to provide real-time grading of pomegranate based on its visual features. This automatic grading technique would help farmers in minimizing their efforts and time spent on manual sorting, at the same time ensuring error-free and uniform sorting and packaging of quality fruits that would, in turn, enhance the storage time post packaging. The methodology uses nine pre-trained models to train on a custom pomegranate dataset with twelve grade-quality classes of pomegranates. The grade-quality grading is obtained using the fine-tuning approach in transfer learning, reducing the computational resource requirements and drastically improving inference speed, which is necessary for real-time model deployment scenarios. The results reveal that the fine-tuning of denseNet-169 model outperformed the other models by achieving highest values of 99.20% and 1 for (Accuracy, Precision, Recall, and F1-score) and AUC, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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labVIEW Software for design and Implementation of Particle Swarm Optimization Tuned Controller for the Climate Control of Greenhouse System 6th

LabVIEW Software for Design and Implementation of Particle S...

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6th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Gandhi, Shriji V. Thakker, Manish Gandhi, Ravi Instrumentation and Control Engineering Gujarat Technological University Gujarat India Instrumentation and Control Engineering L.D. College of Engineering Gujarat India Electronics and Communication Engineering Silver Oak University Gujarat India

ISBN: (纸本)9789811935701

This paper presents the novel concept of simulation and optimized control of the greenhouse system (GHS). The greenhouse is a climate control system with protection boundaries that allow only solar light and no other outside environment element to enter. To provide a favorable environment to the crop, the dynamic behavior of the greenhouse system, the effect of parameters on each other, and disturbances effects should be determined. For full filling these requirements, the greenhouse simulator has been developed for modeling and control of the GHS. The greenhouse climate control system dynamics based on energy and mass balance is implemented using this simulator. The greenhouse climate control model has been implemented on this simulator which allows changing greenhouse design parameters, climate conditions, and functioning control strategies to understand the dynamic behavior of the greenhouse system. This paper presents the simulation of the greenhouse climate control system with open-loop and closed-loop structure;for a closed-loop control, particle swarm optimized (PSO) tuned controller is implemented to meet the desired climate requirements in the greenhouse system. The simulation results obtained for different scenarios with comparative studies are presented. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Greenhouses

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IoT for Emerging engineering Application Related to Commercial System 3rd

IoT for Emerging Engineering Application Related to Commerci...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Veeraiah, Vivek Ahamad, Shahanawaj Jain, Vipin Anand, Rohit Sindhwani, Nidhi Gupta, Ankur Department of R & D Computer Science Adichunchanagiri University Karnataka Mandya India College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Teerthanker Mahaveer University Uttar Pradesh Moradabad India New Delhi India AIIT Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India

ISBN: (纸本)9789819986453

Present paper is focused on IOT for emerging engineering application related to commercial system. Internet of things has been considered with customer sentiment analysis in order to detect the sentiment of consumer so that relevant action could be taken considering consumer taste. lSTM model has been used for classification of sentiment. The objective of work is to consider lSTM model for classification and prediction of customer sentiment for product. dataset of Amazon consumer feedback has been considered to take decision of IoT based commercial application with support of lSTM based deep learning mechanism. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2024.

关键词： Sentiment analysis

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Enhancing Weed Recognition in Cotton Fields Through Advanced Imaging and learning Techniques 5th

Enhancing Weed Recognition in Cotton Fields Through Advanced...

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5th International Conference on Recent Trends in Machine learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Singh, Jagendra Shaik, Nazeer Sahu, dinesh Prasad Tiwari, Mohit Haque, Mustafizul Upreti, Kamal School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India Dr. D. Y. Patil Vidyapeeth’s Centre for Online Learning Dr. D. Y. Patil Vidyapeeth Pune India Ghaziabad India

ISBN: (纸本)9789819788606

This research investigates the efficacy of weed recognition models in cotton fields through advanced imaging and machine learning techniques. Utilizing 10 trials, the models, namely K-NN and GBM, were evaluated across multiple performance metrics. Results reveal that GBM consistently outperformed K-NN in accuracy, precision, recall, and F1 score, with average values of 0.88, 0.89, 0.86, and 0.88, respectively, compared to K-NN's averages of 0.85, 0.87, 0.82, and 0.85. Moreover, GBM exhibited higher AUC values (0.94) than K-NN (0.92) in ROC curve analysis, indicating superior discrimination ability. Additionally, k-fold cross-validation demonstrated GBM's higher mean accuracy (0.89) and F1 score (0.88) compared to K-NN (mean accuracy: 0.86, mean F1 score: 0.85). Additionally, integrating temporal data analysis could improve the models’ ability to detect weed growth patterns over time. Real-time monitoring capabilities and automated decision-making systems could streamline weed management practices in agricultural settings. Furthermore, expanding the study to encompass diverse geographical regions and crop types would provide valuable insights into the generalizability and robustness of the developed models. Overall, continued research in this domain holds the potential to revolutionize weed management strategies and contribute to sustainable agriculture practices. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2025.

关键词： Cotton

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