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Identification of Medicinal Plants and Related Fungal Diseases Based on Deep Learning Model

Identification of Medicinal Plants and Related Fungal Diseas...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Mathina Kani M Mahalakshmi R Shunmugavel V Abdul Kareem M Department of Computer Science and Engineering Sethu Institute of Technology Virudhunagar India Department of Computer Science and Business Systems Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Quantanics TechServ pvt Ltd Madurai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In today's technological landscape, manual methods are increasingly being replaced by automated computer systems, streamlining tasks and enhancing efficiency. Within the realm of agricultural research, two critical areas plant identification and disease classification have shifted toward computerized solutions. With millions of plant species globally, medicinal plants hold particular significance due to their healing properties in traditional medicine. these readily accessible and culturally important remedies not only contribute to affordable healthcare but also sustain livelihoods in indigenous and rural communities. To address these challenges, our study evaluates the performance of two widely used pre-trained deep learning models U-Net and F-Net. By comparing their accuracy levels, we aim to enhance disease detection. Additionally, we present performance metrics such as accuracy and error rates, visualized through graphs. Our user interface (UI) leverages Kivy, an open-source multi-platform GUI development library for Python. Kivy enables cross-device app development, emphasizing code reusability and rapid prototyping, making it a valuable tool for interaction design across iOS, Android, Windows, OS X, and GNU/Linux platforms.

关键词： Medicinal plants Deep learning Measurement Visualization Accuracy Medical services Manuals Rapid prototyping Mobile computing Diseases

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Automated Immune Cell Classification Using Image Processing Techniques

Automated Immune Cell Classification Using Image Processing ...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： B. Sathiyaprasad Arva Hari Vardhan Reddy Usama Abdurrahman A. Annie Micheal Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

~ Advances in immunological research are essential for elucidating immune responses and developing targeted therapeutic approaches. this study proposes an automated method for immune cell classification leveraging machine learning and image processing techniques. A novel framework is introduced, combining high-resolution imaging with computational algorithms to accurately classify various immune cell types from biological samples. the methodology incorporated pre-processing steps, like normalization and noise reduction. then, deep learning models, such as CNNs and SVMs, will be applied for feature extraction and classification. these models were trained on annotated datasets derived from flow cytometry and microscopy, demonstrating better accuracy and reliability. Results demonstrate an improvement in processing speed and accuracy of classification over traditional approaches, which underlines the importance of automation in immunological diagnostics. the data obtained can be used in high-throughput immunotherapy and disease diagnostic screening; it allows for the identification of immune cell populations within a short period and therefore improves research efficiency. the work provides a basis for real-time analysis and incorporation of multiscale datasets that will help to better understand immunity and its associated disorders.

关键词： Deep learning Accuracy Immunology Computational modeling Biological system modeling Real-time systems Vectors Classification algorithms Streams Immune system

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Sustainable Solutions for Organ Donation and Procurement via Regional Collaboration System

Sustainable Solutions for Organ Donation and Procurement via...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Farzana Akter Mily Md Shamsul Arefin Department of Computer Science & Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Electrical & Electronic Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

the severe shortage of organs requires the efficient management and collaboration of practices. therefore, this paper introduces the Regional Collaboration System (RCS) to deal with this critical issue. the system aims at enhancing organ donation and procurement with the perspective of coordination of regional healthcare organizations. the primary purpose is to assist patients in finding suitable hospitals, donors, and doctors, in their region to carry out a seamless transplantation process. this system has been developed using Visual Studio 2022, SQL Server 2019 Express, and C# for a robust and scalable solution. Key features include an administrative panel, advanced search options, and the user interface to make it easy for administrators, patients, donors, and doctors to use. Moreover, the system also provides a medical history management platform that enables the patients to maintain a record of their history for future use via the administrative panel. this comprehensive approach is designed to improve transplant outcomes, minimize waiting times, and enhance organ availability. By effectively addressing the crisis in organ shortages, RCS holds a great potential to bring revolutionary changes in the existing structure of organ donation and transplantation, thereby saving more lives and hence fostering equality in access to health resources.

关键词： Procurement Visualization Collaboration Organizations User interfaces Organ transplantation History Servers Informatics Mobile computing

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Performance Evaluation of Load Balancer in Storage Level Service Offering Applications 5th

Performance Evaluation of Load Balancer in Storage Level Ser...

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5th international conference on Recent Developments in control, automation and Power engineering, RDCAPE 2023

作者： Vianny, Maria Manuel Bhardwaj, Rajat Bhardwaj, Vivek Sharma, Vikrant Virender Manikandan, S. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India Department of Computer Science and Engineering ASET Amity University Karnataka Bengaluru India School of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Chandigarh University Punjab Mohali India Department of Information Technology E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam India

ISBN: (纸本)9789819600465

Cloud computing is a world view appropriately characterized for IT contributions where data and resources are aggregated by web-based cloud administration organizations using planned hardware and packages, primarily based on the web. People could use at a reasonable cost. Combining resources may affect the ease with which those resources are accessed, which might result in an issue. Load control is the process of distributing network traffic among several servers. No server will be overburdened as a result of this. In order to increase user responsiveness, load regulation equitably distributes the tasks. Additionally, it improves client accessibility to projects and websites. this archive is being kept in order to better understand charge control. It is connected to other structures via correspondence bodies like the Internet. Load balancing is an important distributed computing space to stay away from work beyond the workload and provide equally important support. Different calculations are used to determine the complexity of the system. Our suggested technique selects 100 virtual computers and, with 92% accuracy, determines where an ideal storage facility is for use in real time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud storage

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Enhancing Predictive Maintenance with AI: Applications and Impact

Enhancing Predictive Maintenance with AI: Applications and I...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Chitranjanjit Kaur Anurag Sharma dept. School of Engineering Design and Automation (Computer Science and Engineering) GNA University Phagwara India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Industrial maintenance is crucial for companies due to its significant impact on operational costs and efficiency. Many industrial firms find that a substantial portion of their expenses stems from equipment breakdowns, failures, or suboptimal performance over time. these costs can sometimes account for up to 50% of overall expenditures. Additionally, companies face various losses, including downtime from breakdowns, challenges in inventory and purchase management, and potential risks of injuries. the objective of this writing is to investigate how Predive maintenance is a footstep towards industry 5.0 where automation of predictive maintenance is evolved to autonomy in maintenance. In addition to it various machine learning algorithms are suggested to identify between healthy and poor data of a machine which is first step in Predictive maintenance then Deep learning algorithms to construct XAI (Explainable artificial Intelligence). Predictive maintenance, production scheduling, problem detection, predictive quality, and increased energy efficiency are highlighted in the typical use cases of the selected AI applications. Data from the real environment is transmitted to be virtually recreated. Efficient industrial maintenance helps mitigate these costs by reducing downtime, improving equipment reliability, and lowering the need for emergency repairs. By implementing a well-designed and optimized maintenance strategy, production plants can ensure their equipment operates as reliably as possible, minimizing disruptions and enhancing overall operational efficiency. Machine Learning (ML) methods have been appeared as a crucial tool in Predictive Maintenance (PdM) applications to prevent failures in equipment that make up the production lines. However, the performance of PdM applications depends on the appropriate choice of the ML method. To save expenses, find inefficiencies, duplicate tool tracking systems, and do other tasks, Digital Twin evaluates material utilization

关键词： Adaptation models Costs Machine learning algorithms Electric breakdown Stacking Companies Writing Vectors Maintenance Predictive maintenance

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Adversarial Encoder-Driven Filter for Targeted Image Tagging: A Novel Approach to Visual Content Manipulation

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

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international Image Processing, Applications and Systems conference (IPAS)

作者： Cole Mckee Dominic Flowers Jesse Wood Ethan Shafer Department of Electrical Engineering and Computer Science United States Military Academy West Point New York

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. this paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon’s Rekognition image tagging algorithm using a black box approach.

关键词： Training Resistance Computer vision Visualization Law enforcement Military computing Computational modeling Autoencoders Image annotation Closed box

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Construction of PCOS Prediction Model Based on BP Neural Network

Construction of PCOS Prediction Model Based on BP Neural Net...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Xianqing Hu Amit Yadav Asif Khan Abhishek Pratap Sah Sami Azam School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP N eural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients.

关键词： Training Adaptation models Accuracy Biological system modeling Computational modeling Neural networks Predictive models Data models Robustness Tuning

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Wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse control 8th

Wind Power Scheduling Deviation Compensation Based on Dual-F...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhu, Jianhong Li, Han Gu, Juping Mao, Linxin Chen, Shaoxuan He, Yu School of Electrical Engineering and Automation Nantong University Nantong226019 China Suzhou University of Science and Technology Suzhou215009 China

ISBN: (纸本)9789819602315

the intermittency of wind power fluctuations and the low reliability of reporting scheduling plans have always affected the friendly access of grid connected wind power. "One machine, one storage" provides an effective solution. An innovative control method for dual-frequency PWM pulses in an improved bidirectional DC/DC Zeta Sepic circuit model is proposed to address the controllability of energy throughput in energy storage systems with dynamic deviation compensation for wind power planning. Firstly, by analyzing the working timing logic mechanism of the energy transfer converter, the feasibility of the energy storage throughput circuit is verified. then, by analyzing the small signal control model of the bidirectional DC/DC converter during charging and discharging operation, the dual-frequency PWM automatic control of charging and discharging conditions is achieved, and the controllability of power exchange of the energy storage system at the wind turbine grid connection point is achieved. Finally, the model predictive control (MPC) method is adopted to effectively compensate the deviation of wind power plans, which can be accurately tracked. the simulation experiment demonstrates the effectiveness and feasibility of the converter model structure and control algorithm. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： controllability

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Human Emotion Recognition from EEG-brain Signals using Enhanced Machine Learning Method

Human Emotion Recognition from EEG-brain Signals using Enhan...

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international conference on Advancements in Computational sciences (ICACS)

作者： Ghulam Ghous Shaheryar Najam Ahmad Jalal Dept. of Electrical & Computer Engineering Riphah International University Islamabad Pakistan Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

this study explores the application of advanced machine learning techniques to EEG data for detecting emotions in individuals with cognitive disabilities. Utilizing the SEED-IV dataset, we analyze EEG recordings from 15 participants across multiple sessions to identify emotional states such as happiness, sadness, fear, and neutrality. To ensure data quality, we employ preprocessing techniques including bandpass filtering and downsampling. Our proposed model integrates multi-class Support Vector Machine (SVM) with AAFST, an innovative feature selection and transformation mechanism. the effectiveness of this approach is demonstrated by an SVM accuracy of 85%, showcasing its capability to extract subtle emotional responses from EEG data. this research contributes to the growing field of affective computing, emphasizing the advantages of combining machine learning with EEG analysis to enhance the detection and understanding of emotions in individuals with cognitive disabilities.

关键词： Support vector machines Emotion recognition Accuracy Filtering Machine learning Feature extraction Electroencephalography Physiology Recording Emotional responses

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Quantum Generative Adversarial Network for Autonomous Drone Navigation in Urban Wind Zones

Quantum Generative Adversarial Network for Autonomous Drone ...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Anumula Swarnalatha Nalluri Archana Naveen Mukkapati Subramanian Selvakumar Eneyachew Tamir Manikandaprabu P Department of Aeronautical and Aerospace Engineering KCG College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vignan's Foundation for Science Technology and Research Andhra Pradesh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Mechanical Engineering Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Major challenges are observed when autonomous drones fly in the complex, changeable environment such as wind zones regarding accurate path planning, avoiding the object, and completing tasks. Lack of flexibility in different situations reduces the reliability of conventional models in real time. To overcome these difficulties the Savitzky-Golay Denoising CSWin Quantum Generative Adversarial Network with Emperor Penguin Optimizer known as SGDCQGANNet-EPO was developed. the RGB data and GPS-estimated wind forces in this model undergo Savitzky-Golay Denoising; this model does not rely on global data, only RGB camera data and GPS data. Recently a CSWin transformer (CSWin-T) makes feature extraction and obstacles detection as well as wind angle, wind speed and wind position identification easier. Quantum Generative Adversarial Networks (QGANet) are then used for optimized path planning, and the model is enhanced by the Emperor Penguin Optimizer (EPO) to fine tune the generator and discriminator loss functions. More accurate compared to current approaches in accuracy and efficiency, the model achieves an accuracy of 99.28%, a low RMSE of 0.38, a recall of 99.25%, and EVA of 0.95. In Confidence using this new technique, the free roaming and self-organizing drone maneuvers in complex environments achieved remarkable results.

关键词： Accuracy Navigation Wind speed Noise reduction Generative adversarial networks Transformers Path planning Data models Real-time systems Drones

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