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VGGBM-Net: A Novel Pixel-Based Transfer Features engineering for Automated Coffee Bean Diseases Classification

IEEE Access 2025年 13卷 83580-83590页

作者： Hashmi, Muhammad Shadab alam Qadri, Azam Mehmood Raza, ali Ullah, Saleem Smerat, Aseel Kim, Changgyun Syafrudin, Muhammad Fitriyani, Norma Latif Khwaja Fareed University of Engineering and Information Technology Department of Computer Science Rahim Yar Khan Pakistan University of Lahore Department of Software Engineering Lahore 54000 Pakistan Al-Ahliyya Amman University Faculty of Educational Sciences Amman 19328 Jordan Chitkara University Institute of Engineering and Technology Centre for Research Impact & Outcome Chitkara University Punjab Rajpura 140401 India Mazaya University College Computer Technologies Engineering Nasiriyah Iraq Kangwon National University Department of Artificial Intelligence & Software Samcheok 25913 South Korea Sejong University Department of Artificial Intelligence and Data Science Seoul 05006 South Korea

although coffee is a crucial commodity for the economies of developing nations, defects in processing can significantly impact the safety, commercial value, and quality of coffee beans. Traditional coffee bean grading methods are labor-intensive and prone to error, necessitating automated and accurate classification of bean diseases. This study proposes a novel transfer learning-based deep neural network, VGGBM-Net, for the classification of coffee bean diseases. It focuses on the timely and precise identification of diseases to improve export quality without compromising safety during production. In this research, the proposed approach is trained using a dataset USK-Coffee of coffee beans to classify healthy and unhealthy beans. The performance of multiple machine learning classifiers, including Random Forest (RF), Logistic Regression (LoR), LightGBM (LGBM), and K-Nearest Neighbor Classifier (KNC), is evaluated alongside neural network techniques such as Convolutional Neural Networks (CNN) and VGG-19. A novel transformation of the VGG-19 model for feature engineering based on transfer learning is introduced, where spatial features extracted from coffee bean images are transformed into class prediction probabilities using LGBM. These enhanced features are then used as inputs for advanced machine-learning algorithms. Unlike traditional models this feature extraction enhances classification accuracy and robustness. This transformation improves feature extraction by capturing more discriminative patterns, leading to superior performance compared to conventional methods. Experimental results highlight the superior performance of the LGBM classifier, achieving an impressive 99% accuracy, recall, f1, and precision score of 98% with a computational runtime of just 0.084 seconds. K-fold cross-validation ensured the robustness of the models, and optimization techniques were applied to fine-tune parameters for maximum accuracy. This research establishes a highly effective fra

关键词： Coffee Bean Disease Deep Learning Feature engineering Image Processing Transfer Learning

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Investigating the performance of AI-driven smart building systems through advanced deep learning model analysis

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Energy Reports 2025年 13卷 5885-5899页

作者： Arun, M. Barik, Debabrata Othman, Nashwan Adnan Praveenkumar, Seepana Tudu, Kapura Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam 602105 India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore 641021 India Centre for Energy and Environment Karpagam Academy of Higher Education Coimbatore 641021 India Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri 44022 Iraq Department of Nuclear and Renewable Energy Ural Federal University Ekaterinburg 620002 Russian Federation Department of Mechanical Engineering School of Mechanical Sciences Odisha University of Technology and Research Bhubaneswar 751029 India

The significance of this research rests in its potential to revolutionize building management systems, improve energy efficiency, boost occupant comfort, and encourage sustainable urban growth. These include concerns about personal information leakage, the complexity of building systems, and the requirement for powerful computers. To tackle these challenges, a Multi-faceted Smart Buildings using Deep Learning Framework (M-FSB-DLF) is proposed. It combines a comprehensive sensor network for data acquisition, advanced deep learning models like convolutional and recurrent neural networks, and reinforcement learning algorithms for adaptive control systems. The M-FSB-DLF has several potential uses, such as HVAC control, security systems, energy management, and occupancy optimization. By extensively analyzing simulations, this study shows that the proposed methodology improves building performance indicators such as energy efficiency, occupant comfort, resource utilization, security, and operational cost. Energy efficiency, system dependability, and user satisfaction are all shown to have markedly improved by a 20 % reduction in the simulations. In addition to laying the groundwork for future studies in this exciting area, this research highlights the revolutionary effect that deep learning will have on innovative building technology. More smart, efficient, environmentally friendly cityscapes are within reach due to the M-FSB-DLF, a groundbreaking development in smart building technology. The proposed method increases the security system ratio by 94.8 %, energy management system ratio by 96.2 %, occupancy optimization ratio by 74.3 %, energy efficiency ratio by 94.5 %, and sustainable urban growth ratio by 96.2 % compared to other existing methods. © 2025 The Authors

关键词： Artificial intelligence Building Energy Management Deep learning Predictive building energy modeling Smart buildings

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Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

arXiv

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arXiv 2025年

作者： Bilal, Mohsin Aadam Raza, Manahil altherwy, Youssef alsuhaibani, Anas Abduljabbar, Abdulrahman almarshad, Fahdah Golding, Paul Rajpoot, Nasir Information Systems Department College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Luddy School of Informatics Computing and Engineering Indiana University Indianapolis United States Tissue Image Analytics Centre Department of Computer Science University of Warwick Coventry United Kingdom Frontier AI San Francisco Bay Area United States

From self-supervised, vision-only models to contrastive visual-language frameworks, computational pathology has rapidly evolved in recent years. Generative AI "co-pilots" now demonstrate the ability to mine subtle, sub-visual tissue cues across the cellular-to-pathology spectrum, generate comprehensive reports, and respond to complex user queries. The scale of data has surged dramatically, growing from tens to millions of multi-gigapixel tissue images, while the number of trainable parameters in these models has risen to several billion. The critical question remains: how will this new wave of generative and multi-purpose AI transform clinical diagnostics? In this article, we explore the true potential of these innovations and their integration into clinical practice. We review the rapid progress of foundation models in pathology, clarify their applications and significance. More precisely, we examine the very definition of foundational models, identifying what makes them foundational, general, or multipurpose, and assess their impact on computational pathology. Additionally, we address the unique challenges associated with their development and evaluation. These models have demonstrated exceptional predictive and generative capabilities, but establishing global benchmarks is crucial to enhancing evaluation standards and fostering their widespread clinical adoption. In computational pathology, the broader impact of frontier AI ultimately depends on widespread adoption and societal acceptance. While direct public exposure is not strictly necessary, it remains a powerful tool for dispelling misconceptions, building trust, and securing regulatory support. © 2025, CC BY-NC-SA.

关键词： Visual languages

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Explainable Anatomical Learning and Deep Generative Models for Tumour Diagnosis

SSRN

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SSRN 2025年

作者： Mansouri, Sofiene alazzam, Malik Bader altowijri, albaraa Ahmad al-Musywel, Haifa A. alhemaidi, Seham Sulieman alsadoun, Ahmed Ahmad, Mohammad Department of Biomedical Technology College of Applied Medical Sciences in Al-Kharj Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia Department of Software Engineering Faculty of Information Technology Jadara University Irbid Jordan Department of Surgery College of Medicine University of Tabuk Tabuk Saudi Arabia for the College of Sciences Coordinator of the Training Unit for Medical Physics Students Physics Department Faculty of Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Surgery Division of Ophthalmology University of Tabuk Tabuk Saudi Arabia Department of Medical Surgical Nursing College of Nursing King Saud University Riyadh Saudi Arabia

This study aims to develop an interpretable deep learning model for tumor diagnosis that enhances the accuracy and explainability of anatomical segmentation. Scalar global indices, which are often insensitive to differences in asymmetry or regional configurations, are currently used to quantify changes in tumor and brain area. This is currently accomplished through the use of global indexes, which are currently used. Making an appropriate assessment of pathology-driven anatomical remodelling is one of the most essential phases in the process of diagnosing and treating a wide variety of illnesses. This step is one of the most important steps because it is one of the most important processes. In the last few years, deep learning algorithms have grown gaining popularity for the interpretation of medical images. Despite this popularity, the feature extraction and decision-making procedures of these systems are frequently not interpretable. Within the scope of this investigation, we provide a novel deep learning model that can be interpreted purpose of information analysis. We make use of a deep generative network that is equipped with a hierarchy of conditional latent variables in order to accurately represent a collection of anatomical segmentations. Because of this, we are able to carry out the task with more accuracy. At the very top of this hierarchy is a process in which simultaneous adjustments are made to a two-dimensional latent space in order to differentiate between a variety of clinical situations. A better knowledge of the classification landscape is made possible as a result of this. To further elucidate the classification process, the model's generative properties allow the segmentation space to represent the anatomical variability represented by the discriminative latent space. This is feasible as a result of this situation model may generate new data. © 2025, The Authors. all rights reserved.

关键词： Convolutional neural networks

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Advancing Medical Innovation through Blockchain-Secured Federated Learning for Smart Health

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Khan, Salabat Khan, Mansoor Khan, Muhammad Asghar Wang, Lu Wu, Kaishun Qilu Institute of Technology School of Computer and Information Engineering Jinan China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan China Prince Mohammad Bin Fahd University Department of Electrical Engineering P.O. Box 1664 Al-Khobar31952 Saudi Arabia Shenzhen University College of Computer Science and Software Engineering Shenzhen China Thrust Guangzhou China

The rapid digitization of healthcare systems has led to a vast accumulation of electronic medical records (EMRs), offering an invaluable source of patient data that can significantly advance medical research and improve patient care. However, sharing EMRs for research purposes presents challenges, particularly concerning data privacy, security, and the limitations of traditional centralized data-sharing models. This paper introduces a novel approach that leverages blockchain technology to facilitate federated learning with EMRs, thereby addressing these challenges. Federated learning enables multiple institutions to collaboratively train a robust machine learning model without sharing raw data, preserving privacy and security. By integrating blockchain, this framework enhances data integrity, immutability, and trust, all in a decentralized environment. The blockchain serves as a transparent and secure ledger, recording model updates and aggregating them through a consensus-based mechanism. Smart contracts further enforce data usage policies, allowing only authorized access and maintaining control over data ownership and sharing. This approach empowers medical researchers and institutions to collaborate more effectively, accelerating the discovery of treatments, advancements in personalized medicine, and insights into rare diseases. It also enables patients to contribute to medical research while retaining control over their personal data, fostering a patient-centered approach to healthcare innovation. Experimental results confirm the efficacy and efficiency of this blockchain-enabled federated learning framework, highlighting its potential to transform medical research and adhere to stringent privacy and security standards. This study emphasizes the pivotal role of blockchain in enhancing big data analytics within healthcare, paving the way for improved collaboration, innovation, and patient outcomes. © 2025 IEEE.

关键词： Federated learning

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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection Control algorithms

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IEEE Transactions on Consumer Electronics 2025年

作者： Bilal, Hazrat Aslam, Muhammad Shamrooz Tian, Yibin Ullah, Inam Ayouni, Sarra Vasilakos, Athanasios V. College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou2211106 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences Saudi Arabia Grimstad Norway

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency. © 1

关键词： Agricultural robots

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A Hybrid ResNet50-Random Forest Model for Accurate Eye Disease Classification

A Hybrid ResNet50-Random Forest Model for Accurate Eye Disea...

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

作者： Mohit Kumar Goel Gurpreet Singh Jayapal Lande Amaar Haamid Shnaeen Gaurav Sharma Chitkara University Institute of Engineering & Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Early and accurate detection of eye diseases is essential for preventing vision loss. This paper proposes a hybrid model that combines ResNet50 for feature extraction and Random Forest (RF) for classification to improve the automated detection of retinal diseases, including Diabetic Retinopathy, Cataract, and Glaucoma. The dataset used includes 4217 retinal fundus images across four classes: Normal, Diabetic Retinopathy, Cataract, and Glaucoma. The model addresses challenges such as imbalanced datasets and complex decision boundaries. ResNet50 is utilized to extract deep features from the images, while Random Forest efficiently classifies these features with high accuracy. The proposed model achieved strong results, with Precision, Recall, Macro-F1, and Weighted-F1 scores all exceeding 92%, and a low Misclassification Rate of 7.96%. Additionally, explainable AI techniques like Grad-CAM were employed to enhance the model's interpretability, which is vital for its use in clinical settings. The hybrid ResNet50-RF model outperformed standalone models, demonstrating its effectiveness in eye disease classification. This approach offers a promising, reliable solution for automated retinal disease diagnosis and has significant potential for broader application in healthcare.

关键词： Glaucoma Cataracts Radio frequency Diabetic retinopathy Accuracy Retina Feature extraction Reliability Random forests Residual neural networks

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A Comprehensive CNN Approach for Reliable Breast Cancer Diagnosis

A Comprehensive CNN Approach for Reliable Breast Cancer Diag...

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

作者： Rahul Singh Sheifali Gupta Gotte Ranjith Kumar Haayder M. Abbas Gaurav Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Breast cancer is a common type of cancer in women worldwide, and early detection is critical for effective treatment. Conventional breast cancer diagnostic methods, such as mammography and biopsy, rely heavily on human interpretation, which can be time-consuming and error-prone. Recently, deep learning methods, specifically Convolutional Neural Networks (CNNs), have shown promise in improving the precision and effectiveness of breast cancer diagnosis. This study describes an innovative Convolutional Neural Network (CNN) method for detecting breast cancer using histopathology images. A total of 8,598 images were collected and divided into two datasets: training (6,860 images) and testing (1,738 images). The CNN structure was explicitly designed for analyzing breast cancer images with Conv2D, MaxPooling2D, Dropout, Flatten, and Dense layers. The model underwent 25 training epochs and was evaluated using loss, accuracy, precision, recall, and F1-Score metrics. A confusion matrix was constructed to assess the model's classification accuracy, and performance metrics were calculated. The CNN model achieved an overall accuracy of 85% when diagnosing breast cancer, demonstrating its effectiveness in automated breast cancer diagnosis.

关键词： Deep learning Training Technological innovation Accuracy Histopathology Predictive models Breast cancer Convolutional neural networks Reliability Testing

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Hand Fracture Detection Using Convolutional Neural Networks: A Deep Learning Approach for Automated Diagnostics

Hand Fracture Detection Using Convolutional Neural Networks:...

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

作者： Mohit Kumar Goel Gurpreet Singh G Sunil Hasssan Muhamedale Gaurav Sharma Chitkara University Institute of Engineering & Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This paper presents a Convolutional Neural Network (CNN) model developed for the automated identification of hand fractures from X-ray images, aiming to enhance diagnostic accuracy and efficiency in clinical settings. Hand fractures are common injuries that require prompt and precise diagnosis to prevent complications. Traditional diagnostic methods rely on manual interpretation, which can be time-consuming and prone to human error. Leveraging a dataset of 10,000 annotated hand X-ray images, the proposed CNN model was trained and validated using advanced preprocessing techniques and a carefully designed architecture. The model accomplished a training accuracy of 99.45% and a validation accuracy of 96.68% by the 18th epoch, demonstrating its ability to accurately distinguish between fractured and non-fractured hand images. Additionally, the model achieved a training loss of 0.0127 and a validation loss of 0.1408, indicating strong generalization to new data. These results suggest that the CNN model can significantly improve the speed and reliability of hand fracture diagnosis, offering a valuable tool for clinicians, particularly in resource-limited settings. The model's effectiveness highlights the potential of deep learning in medical image analysis and paves the way for its integration into clinical workflows as a decision support system.

关键词： Hands Training Deep learning Adaptation models Technological innovation Accuracy Data models Convolutional neural networks Reliability X-ray imaging

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Proposed Convolutional Neural Network for the Detection of Guava Leaf Diseases

Proposed Convolutional Neural Network for the Detection of G...

引用

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

作者： Rahul Singh Sheifali Gupta Srinivas aluvala Ramee RiadHwsein Gaurav Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Fruit cultivation is essential for domestic consumption and global trade, as agriculture continues to be a fundamental pillar of numerous economies worldwide. Guava (Psidium guajava) is a highly regarded fruit for its nutritional benefits, including high levels of dietary fiber, antioxidants, and vitamins A and C. Nevertheless, guava cultivation is encountering substantial obstacles due to various leaf diseases, which can harm the quality of the fruit and the yield. It is imperative to effectively manage these diseases to guarantee the optimal fruit production and guava crops' health. Timely and precise disease identification is critical to conventional disease management strategies, including applying fungicides, cultivating resistant varieties, and implementing cultural practices. Recent developments in deep learning, particularly Convolutional Neural Networks (CNNs), present promising solutions for the automated detection and classification of plant diseases based on visual symptoms. A CNN model that is specifically designed for the classification of guava leaf diseases, such as Canker, Dot, Mummification, Rust, and Healthy leaves, is proposed in this study. The model was trained on a comprehensive dataset that included 2108 training images and 527 testing images, and it was subsequently evaluated over a period of 30 epochs. It achieved an overall accuracy of 86.91%, with high precision, recall, and F1 scores across various disease categories. The model's potential for practical applications in agriculture, such as mobile apps or field-based systems, to assist farmers in the timely and effective management of diseases is evident in its robustness and reliability.

关键词： Training Deep learning Visualization Technological innovation Accuracy Agriculture Mobile applications Convolutional neural networks Diseases Testing

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