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Comparative Analysis of Machine Learning Algorithms for Binary Classification of Tea Leaf Diseases 5

Comparative Analysis of Machine Learning Algorithms for Bina...

引用

5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Shekhar, Nidhi Megha, J.S. Varsha, J.S. Revathi, G.P. Pesu - Isfcr Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356236

India stands as one of the world's largest tea exporters. This requires for an implementation of an efficient and robust system for disease detection and prevention. The most important challenge faced by the tea industry is tea leaf diseases due to continuous pathogen infestation. These diseases can cause full-size crop losses, adversely affecting the economic system and the livelihood of tea farmers. The ability to detect these diseases early is essential. Early detection can cause timely intervention that can notably reduce harm to tea plants and keep them at their best possible quality. Unfortunately, traditional diagnostic techniques often depend on visible inspection and are hence inefficient as those methods can be gradual, labour in-dept. and vulnerable to human errors, reducing the effectiveness in larger fields. This research paper aims to classify whether a leaf is diseased or healthy through a two-step process involving feature extraction using a pretrained Convolutional Neural Network (CNN) and classification using four machine learning models - LightGBM, CatBoost, Autoencoders and Self Organized Maps (SOMs). This paper utilizes a pre-trained VGG16 model, which is trained on the ImageNet dataset. This model is capable of extracting high-level features from images. © 2025 IEEE.

关键词： Self organizing maps

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Stomach Cancer Prediction and Detection using Deep Learning: A Review 2

Stomach Cancer Prediction and Detection using Deep Learning:...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kaur, Sharandeep Goel, Paurav Kapoor, Nitika Chandigarh University dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798331505745

Stomach cancer, sometimes referred to as gastric cancer, is still one of the most common and widespread deadly disease worldwide offers a significant challenge in oncology due to late detection and high mortality rates. The advent of deep learning has revolutionized stomach cancer prediction by leveraging sophisticated models such as convolutional neural net-works (CNNs), multimodal learning and hybrid frameworks that integrate multiple data sources like medical imaging, histological slides, clinical records and genetic profiles and provide better performance with high accuracy. This review comprehensively ex-plores recent advances in deep learning applications for the detection of stomach cancer, highlighting their performance, accuracy, and clinical implications. Comparative analysis demonstrates that different deep learning models, such as ResNet50, DenseNet121, EfficientNetB5, and MobileNetV2, provide impressive results with accuracy up to 99% and higher sensitivity and specificity. It also indicates that the system accuracy needs to be improved to 100% in order to detect stomach cancer as early as possible. Early detection helps in the stoppage of further spreading of the disease and lowers the death rates. © 2025 IEEE.

关键词： Convolutional neural networks

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Renl, Weihang Li, Mingze Fan, Lei Guan, Yongpei University of Florida Dept. of Industrial and Systems Engineering FL United States University of Houston Dept. of Electrical and Computer Engineering TX United States University of Houston Engineering Technology TX United States

ISBN: (纸本)9798331541125

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks. © 2025 IEEE.

关键词： Lagrange multipliers

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Plant Disease Detection using Image Processing and Generative Adversarial Network 14

Plant Disease Detection using Image Processing and Generativ...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Teresha, Y. Shaji, Nikhil John Jarial, Manav Mirjankar, Ved Vishal Patil, Swetha Computer Science Engineering Dept. PES University Bengaluru India

ISBN: (纸本)9798331531935

Agriculture is a key contributor to the GDP of our country, India. Plants play a very important role in the ecosystem and plant diseases can negatively impact the agricultural industry by causing huge losses to the food supply. Plant leaves are prone to a variety of diseases, including blights, leaf spots, and other infections. Therefore, identifying diseases efficiently is crucial to pave the way for sustainable agriculture. In the field of agriculture, traditional and manual methods for detecting diseases in plants have sometimes been found to be inefficient and error prone. We therefore present a novel solution to these issues by introducing a method to automate plant disease detection in the Tomato plant leaf by using advanced Convolutional Neural Network (CNN) frameworks, image processing libraries and Generative Adversarial Networks (GANs). The pipeline of our work involves preprocessing plant image datasets, generating synthetic images using GANs, segmenting region of interests via a U-Net model, and classifying diseases with a CNN-based classifier. A deployed model ensures accurate disease identification from user input. © 2025 IEEE.

关键词： Photointerpretation

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PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier 3

PhishVQC: Optimizing Phishing URL Detection with Correlation...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Shahriyar, Farhan Tanbhir, Gazi Raihan Chy, Abdullah Md Tanzin, Mohammed Abdul Al Arafat Mashrafi, Jisan World University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Southern University Bangladesh Dept. of Computer Science and Engineering Chittagong Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331523893

Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive information. Traditional machine learning techniques struggle to perform well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and variational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows. © 2025 IEEE.

关键词： Qubits

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IHDoc: Secure NFC Mobile-based Digital Document Signatures Prototype

IHDoc: Secure NFC Mobile-based Digital Document Signatures P...

引用

2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Sharma, Himanshu Sethia, Divyashikha Delhi Technological University Dept. of Software Engineering Delhi India

ISBN: (纸本)9798331527495

Digital signatures are essential for secure data interchange. However, most document signing solutions depend on cloud-based infrastructures, which may expose users to security vulnerabilities and significant computational requirements. This paper proposes designing and implementing a proximity mobile-based iHDoc digital document signing prototype to sign documents over a Near Field Communication (NFC) tap. It consists of Signer and Signee applications on two different mobile devices that tap to first mutually authenticate each other and then sign the document. iHDoc utilizes iDSign, a previously proposed secure and lightweight design framework based on Identity-based Cryptography (IBC). IBC eliminates the need for certificates by associating public keys directly with user identities, reducing computational and communication overhead and assisting in lightweight mutual authentication over HCE and resource-constrained mobile devices. NFC proximity reduces the risk of man-in-the-middle attacks and ensures the locality of reference. Performance comparison of iHDoc with IBC and RSA-based mutual authentication indicates that the iHDoc prototype performs better with iDSign due to reduced time and storage. It is, hence, suitable for the proximity-based, resource-constrained digital signature of documents. © 2025 IEEE.

关键词： Public key cryptography

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Mingze Ren, Weihang Fan, Lei Guan, Yongpei University of Houston Dept. of Electrical and Computer Engineering TX United States Engineering Technology University of Houston TX United States University of Florida Dept. of Industrial and Systems Engineering FL United States

ISBN: (纸本)9798331541125

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation. © 2025 IEEE.

关键词： AC power flow clustering local search Unit commitment

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Optimizing Renewable Energy Management and Demand Response with Ant Colony Optimization: A Pathway to Enhanced Grid Stability and Efficiency

Optimizing Renewable Energy Management and Demand Response w...

引用

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Islam, Riadul Kabir, Shaharier Shufian, Abu Rabbi, Md Shahiduzzaman Akteruzzaman, Md Lamar University Dept. of Electrical and Computer Engineering Texas United States American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798331541125

The integration of renewable energy sources into the power grid presents significant challenges due to their inherent variability and unpredictability. Efficient management of these sources and the effective implementation of demand response strategies are crucial for maintaining grid stability and optimizing energy usage. This study presents a comprehensive evaluation of the performance of Ant Colony Optimization (ACO) in optimizing microgrid operations compared to other modern algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The primary metrics of analysis include energy cost, renewable energy utilization, grid stability, and demand response effectiveness. Through a detailed dataset analysis and algorithmic implementation, the results indicate that ACO consistently outperforms its counterparts across all key performance indicators. ACO achieved the lowest energy cost, maximized renewable energy utilization, and maintained superior grid stability and demand response effectiveness. These findings highlight the potential of ACO as a robust optimization technique for enhancing the efficiency and sustainability of microgrid systems. The study underscores the importance of selecting appropriate algorithms to meet the complex and dynamic challenges of modern energy systems, promoting a transition towards greener and more reliable energy infrastructure. © 2025 IEEE.

关键词： Demand response

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Optimizing EV Charging Infrastructure through Behavioral Analysis, Smart Charging, and V2G Integration

Optimizing EV Charging Infrastructure through Behavioral Ana...

引用

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Hossain, Md Ismail Anonto, Hasanur Zaman Hossain Emon, Md Mukter Nandi, Anup Shahriyar, Azad Shufian, Abu American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh Lamar University Dept. of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331541125

The rapid adoption of electric vehicles (EVs) underscores the pressing need for optimized charging infrastructure to meet growing demands. This study investigates the integration of behavioral analysis, innovative charging strategies, and vehicle-to-grid (V2G) technology to improve EV infrastructure efficiency and grid stability. This research identifies key factors that influence charging demand and usage by examining user-specific charging patterns, including differences between private and fleet users. Smart charging and dynamic pricing models are explored to alleviate peak load stress, while V2G solutions enhance energy distribution during demand fluctuations. The findings emphasize the significance of flexible infrastructure planning, which includes Level 1, Level 2, and DC fast chargers strategically deployed to cater to diverse EV user needs. This approach offers a pathway to a sustainable, scalable charging ecosystem, promoting both user convenience and grid resilience. © 2025 IEEE.

关键词： Charging stations

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Protecting Battery Health During Charge and Discharge for Electric Vehicles

Protecting Battery Health During Charge and Discharge for El...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Anonto, Hasanur Zaman Hossain Emon, Md Mukter Nandi, Anup Islam, Sashyni Hossain, Md Ismail Shufian, Abu American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh Lamar University Dept. of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331541125

Lithium-ion (Li-ion) batteries are pivotal to electric vehicles performance, efficiency, and sustainability (EVs), offering high energy density, lightweight design, and the ability to endure hundreds of charge-discharge cycles. However, they are susceptible to suboptimal operating conditions, including overcharging, under-discharging, and exposure to extreme temperatures. These can significantly degrade battery capacity, shorten lifespan, and pose safety hazards such as thermal runaway. This paper investigates advanced battery management systems (BMS) and protection mechanisms to mitigate these risks. Through optimized charge/discharge strategies, real-time monitoring, and precise temperature regulation within safe operating limits (15°C to 45°C), the proposed methods demonstrate the potential to reduce battery degradation by up to 30% and extend overall battery life by 20%. Additionally, integrating solid-state battery technology is explored as a promising solution to enhance battery safety, energy density, and reliability. These advancements improve the long-term performance of EV batteries and contribute to the broader goal of making electric transportation systems safer, more sustainable, and more efficient for future mobility. © 2025 IEEE.

关键词： Solid-State Batteries

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