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2024 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2024

作者： Weldergs, Gebrhans Weldegebriel Adigrat Univesity College of Engineering and Technology Departmnet of Computer Science Ethiopia

ISBN: (纸本)9798331527792

Orange fruit cultivation is a significant economic activity in Ethiopia. However, many diseases pose a major threat to its productivity and production in many areas of our country. Currently, the detection process is done manually, which is inaccurate, inefficient, expensive, time-consuming, and requires special people. Therefore, this research study is AI-Based Orange Fruit Disease Detection and Classification with Python 3.12.3. This has great potential to enhance early disease identification, reduce crop losses, and increased food security in the country. The paper started by collecting 1252 orange fruits from different environmental locations in the Tigray Region using digital camera. These images were collected as disease categories such as orange canker (201 images), orange scabs (206 images), orange melanosis (88 images), orange greening (369 images), as well as healthy (388 images).After that, the dataset is divided into 20% for testing, for validation 10% and 70% for training. Then, image preprocessing technique is applied on the image to removing the noise and then color threshold technique is used to segment. In feature extraction, texture, shape and color features are considered to facilitate disease detection and classification. Subsequently, a deep learning approach using Convolution Neural Networks (CNN) is then implemented, along with three machine learning approaches - Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM), to recognize orange fruit diseases in the early stages of growth. The proposed CNN model achieves an overall accuracy rate of 99%, recall of 99% and precision of 99%, outperforming SVM, RF, and ANN in disease identification. © 2024 IEEE.

关键词： Citrus fruits

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Microplastic Identification in Seawater using Generative Adversarial Networks

Microplastic Identification in Seawater using Generative Adv...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Rajasekar, M. Geetha, Angelina Hindustan Institute Of Technology And Science Department Of Computer Applications Chennai India Departmnet Of Computer Science And Engineering Hindustan Insitute Of Technology And Science Chennai India

ISBN: (纸本)9798350364828

In recent years, microplastics-plastic particles smaller than five millimeters-have become a significant environmental contaminant. These tiny plastic pieces have been found permeating aquatic ecosystems globally, posing a threat to marine life upon ingestion. However, identification and quantification of microplastics in seawater samples remains an arduous task. The small size and translucent nature of microplastics makes them difficult to distinguish from natural particulates in seawater under a microscope. This has hindered efforts to accurately monitor microplastic pollution levels in the marine environment. Our goal in this study was to use cutting edge deep learning techniques to create an automated image based system for detecting microplastics in seawater. We chose to employ a generative adversarial network (GAN) architecture for its proven capability in generating highly realistic synthetic images. The GAN model was trained on a dataset of microscope images of microplastics and natural particulates extracted from seawater samples. Coastal seawater samples were first filtered to isolate particulates of a size range containing microplastics. Density separation was then used to separate the plastic microparticles from denser natural particulates. We implemented a deep convolutional GAN with a generator network to produce synthetic microplastic images, and a discriminator network to differentiate real from synthetic images. Through iterative adversarial training, the two networks evolved to produce and accurately classify images of microplastics with a high degree of realism. Advanced image processing techniques were used to enhance the training data. After training, the GAN model was evaluated on an annotated testing dataset of images containing real-world microplastics and natural particulates extracted from seawater. Our proposed GAN-based technique achieved a microplastic classification accuracy of 92.5% on the test set, demonstrating its effectiveness. The

关键词： Convolutional neural networks

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Electrical Motor Fault Detection System using AI's Random Forest Classifier Technique 6

Electrical Motor Fault Detection System using AI's Random Fo...

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6th IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2023

作者： Yaqub, Raziq Ali, Hassan Bin Abd Wahab, Mohd Helmy Alabama A&m University Departmnet of Electrical Engineering and Computer Science HuntsvilleAL United States Qatar Department of Sustainable Systems Engineering Doha Qatar Departmnet of Computer Engineering Johor Malaysia

ISBN: (纸本)9798350321029

Induction motors are very commonly used to create traction for electric vehicles. In electric vehicles and other rotating machine systems, faults develop during regular operation, which, if unaddressed, can accelerate the damage to machines. Therefore, early detection and prediction of faults hold the key importance in predictive and preventive maintenance. This work focuses on collecting sounds and vibrations data of an electric motor, which are the critical indicators of fault initiation in electric vehicle systems. We use artificial intelligence (AI) to predict and classify the types of faults associated with different vibration frequencies and sound levels. We explore random forest classifier technique for faults prediction and derive promising results for different fault conditions. Results show that we correctly predicted and classified different types of manually induced faults associated with different vibration frequencies and sound levels with a 92% accuracy and 86 to 91 % precision level. © 2023 IEEE.

关键词： Fault detection

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Orange Fruit Disease Detection and Classification Using AI-Based Techniques

Orange Fruit Disease Detection and Classification Using AI-B...

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Information and Communication Technology for Development for Africa (ICT4DA), International Conference on

作者： Gebrhans Weldegebriel Weldergs Departmnet of Computer Science College of Engineering and Technology Adigrat Univesity

ISBN: (数字)9798331527792

ISBN: (纸本)9798331527808

关键词： Support vector machines Radio frequency Training Accuracy Image color analysis Artificial neural networks Feature extraction Vectors Colored noise Diseases

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Histopathological Image Classification of Breast Cancer using EfficientNet 3

Histopathological Image Classification of Breast Cancer usin...

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3rd International Conference for Emerging Technology, INCET 2022

作者： Venkatesh Sheela, Rachel K Nagaraju, Y Sahu, Divya Anwesh Bangalore University University Visvesvaraya College of Engineering Department of Computer Science and Engineering Bangalore560 001 India National Institute of Technology Departmnet of Computer Applications Tiruchirappalli620015 India

ISBN: (纸本)9781665494991

Deep learning algorithms help achieve promising results in diagnosis of cancer patient. However, obtaining patient confidential data and medical images of cancer patient from medical hospitals and diagnosis centre is challenging thus preventing the deep learning usage in analysis of medical data of patient. Among woman, Breast cancer is prevailing cancer and it has become common health problem. The classification of Breast cancer focuses on identifying breast tumours sub-classes. Deep learning is among the current trends in computer science research and it enhances neural networks. The layers in neural networks are more, deep which allows higher abstraction levels and help to get accurate analysis of data. Google's recently proposed architecture-EfficientNet, has less parameters which results in faster convergence for classification using ImageNet which showed that EfficientNet outperformed prevailing architectures such as DenseNet and ResNet. This research work include comparing the performance of EfficientNetV2-B0 architecture with DenseNet and ResNet architectures and few others for multiple class tumour classification on BreakHis dataset, which contains around 8000 histopathology images of breast that vary in magnification. This research work offers a methods that is based on transfer learning that improves existing architecture in multi-class classification by relying on pretrained DCNN trained on ImageNet dataset. Pretrained weights of EfficientNetV2-B0 are transferred as initial weights and the model is fine tuned in order to identify different benign and malignant tissue samples in tumor cells and classify them into multiclass. The model proposed has been tested with optimised hyperparameters irrespective of magnification of the images and was able to achieve accuracy upto 98%. The results surpass earlier studies in terms of accuracy across the performance metrics defined for CAD systems of breast cancer based on histological images. © 2022 IEEE.

关键词： Image classification

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A Smart Insole for Remote Gait Monitoring of Paralyzed Patients

A Smart Insole for Remote Gait Monitoring of Paralyzed Patie...

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2024 IEEE MetroCon, MetroCon 2024

作者： Oyetunji, Oluwaseyi Rain, Austin Eckert, Austin Feris, William Zabihollah, Abolghassem Priest, Joe Abu-Ghazaleh, Haitham Tarleton State University Department of Mechanical. Environmental and Civil Engineering Mayfield College of Engineering StephenvilleTX United States Tarleton State University Department of Electricaland Computer Engineering Mayfield College of Engineering StephenvilleTX United States College of Health Science Department of Kinesiology StephenvilleTX United States Tarleton State University Department of Departmnet of Electrical and Computer Engineering Mayfield College of Engineering StephenvilleTX United States

ISBN: (纸本)9798331507640

Paralysis occurs when the nervous system is disrupted, preventing patients from making voluntary movements. The most common causes of paralysis are stroke and spinal cord injuries. Paralysis can result in a partial or complete loss of control over movement, often leading to an unsteady gait. Side falls, a severe consequence of this imbalance, frequently cause broken bones and head injuries. Real-time gait monitoring with integrated analysis could greatly improve the rehabilitation process for such patients. The proposed smart insole is designed to provide real-time and remote gait monitoring for paralyzed individuals. This innovative approach holds promise for enhancing personalized rehabilitation programs and enabling early detection of impaired movement and balance *** study will assess various locomotion scenarios to detect potential gait impairments and early signs of balance loss. A smart insole, featuring multiple sensors precisely designed and strategically placed, is developed to measure key gait parameters. A proof-of-concept model of the insole has been created to conduct experimental tests and demonstrate its functionality in different human activities. The model includes a 3D-printed insole equipped with a nano Arduino controller and integrated gyroscopes and accelerometers embedded in the insole. Additional sensors are positioned at specific locations to measure other critical parameters. A Fiber Bragg Grating (FBG) sensor array is embedded in the insole to provide accurate, real-time detection of foot deformation, allowing for the identification of imbalances. © 2024 IEEE.

关键词： Remote patient monitoring

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Enhancing Query Expansion with ML-SSO: A Machine Learning, Semantic, Statistical, and Ontological Framework

Enhancing Query Expansion with ML-SSO: A Machine Learning, S...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Surabhi Solanki Seema Verma Pulakesh Roy Sachin Kumar Rajib Banerjee Ajay Prasad Department of Computer Science & Engg Banasthali Vidyapith Tonk India Department of Electrical & Electronics Engineering Education NITTTR Bhopal India Department of Computer Science & Engineering Brainware University Kolkata India Departmnet of Computer Science & Engneering Parul University Vadodara India School of Computer Science UPES Dehradun India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This paper presents a Machine Learning -Semantic Statistical and Ontological query expansion approach to enhance the performance of information retrieval systems. The proposed methodology ML-SSO looks to combine the strengths of each technique to obtain relevancy and accurately expanded queries. In this, pre-trained word-embed models were used for contextually relevant term expansions, and domain-specific ontologies were used to provide accurate and custom query expansions. Large-scale experiments conducted on benchmark datasets demonstrated that the ML-SSO outperformed traditional ones in MAP, Precision at k (P@10), Recall, and F-measure scores. These results make clear that the ML-SSO query expansion method improves the relevance and accuracy of the Retrieved Document. Future work will be directed towards optimizing the scalability, incorporating dynamic ontology driven into it, and using other advanced models in machine learning to further increase the performance of the system.

关键词： Technological innovation Accuracy Scalability Semantics Machine learning Ontologies Benchmark testing Information retrieval Context modeling

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Electrical Motor Fault Detection System using AI's Random Forest Classifier Technique

Electrical Motor Fault Detection System using AI's Random Fo...

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International Conference on Advanced Systems and Electric Technologies (IC_ASET)

作者： Raziq Yaqub Hassan Ali Mohd Helmy Bin Abd Wahab Departmnet of Electrical Engineering and Computer Science Alabama A&M University Huntsville AL USA Department of Sustainable Systems Engineering University of Doha for Science and Technology (UDST) Qatar Doha Departmnet of Computer Engineering Universiti Tun Hussein Onn Malaysia (UTHM) Johor Malaysia

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Optimizing microgrid performance a multi-objective strategy for integrated energy management with hybrid sources and demand response

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Scientific Reports 2025年第1期15卷 1-24页

作者： Moosavi, Mohsen Olamaei, Javad Shourkaei, Hossein Mohmmadnezhad Department of Electrical and Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Departmnet of Electrical and Computer Engineering South Tehran Branch Islamic Azad university Tehran Iran

This study tackles a key challenge in modern energy management: how to optimize energy distribution when expanding the network is not economically or practically feasible. It explores the integration of hybrid renewable energy sources into a microgrid (MG) and proposes an energy dispatch strategy for MGs operating in both grid-connected and standalone modes. The study incorporates various energy sources, including solar panels (PV), wind turbines (WT), fuel cells (FC), microturbines (MT), diesel generators (DG), and energy storage systems (ESS). Unlike many existing studies that focus only on reducing operating costs, this research also considers energy losses, environmental impacts, and demand response—a vital but often overlooked factor. The research introduces a new method using a mixed-integer linear programming approach to solve the microgrid energy management (MGEM) problem. This method provides a multi-objective solution that includes demand response scheduling and optimizes factors such as PV and WT capacities, energy storage strategies, battery usage, power exchange with the grid, and overall costs and environmental impacts. When compared to leading optimization algorithms, the proposed approach showed better performance. The study also highlights the benefits of demand response programs in improving MG operations. For instance, using a Real-Time Pricing (RTP)-based demand response program reduced operating costs by 3.31%, emission penalties by 2.61%, and power losses by 0.62%. Similarly, a Direct Load Control (DLC)-based program achieved reductions of 2.25%, 2.1%, and 3.56%, respectively. This work advances MG energy management by addressing overlooked factors and demonstrating the benefits of integrating demand response programs into energy optimization strategies. © The Author(s) 2025.

关键词： Demand response Energy storage systems (ESSs) Microgrid energy management (MEGM) Renewable energies

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Overcoming the limitations of gallium oxide through heterogeneous integration

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science China(Physics,Mechanics & Astronomy) 2021年第1期64卷 118-119页

作者： Yuhao Zhang Kevin JChen Center for Power Electronics Systems Virginia Polytechnic and State UniversityBlacksburgVA24060USA Departmnet of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina

Power electronics,processing over 50%of world's electric energy,enables very efficient electric energy conversion for a wide range of applications such as electric vehicles,data centers,autonomous driving,robotics and smart *** availability of low-cost,efficient,and reliable power semiconductor devices that can conduct high current,block high voltage,and switch at high frequencies are key to improving the performance of power electronics systems.

关键词： integration enable autonomous

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