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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Choudhary, Vijay Thakur, Archana Bhalse, Nisha IPS Academy Institute of Engineering & Science Department of Computer Science & Engineering Indore India School of Computer Science & IT Devi Ahilya University Indore India

ISBN: (纸本)9798331512965

Potato crops are vital to global food security, but they are susceptible to several diseases that hinder growth and yield. Traditional methods of detecting these diseases rely on labor-intensive lab tests and human observation. This research presents an enhanced machine learning model for detecting and categorizing potato leaf diseases, specifically early blight and late blight, using Convolutional Neural Networks (CNN). The model is trained on various datasets of potato leaf images, utilizing techniques such as hyper-parameter tuning, data augmentation, and transfer learning. The proposed model attains an accuracy of 96.11%, significantly improving upon existing methods. Image processing techniques, Segmentation is performed using K-means technique and feature extraction is done by utilizing gray-level co-occurrence matrix, along with multi-class support vector machines for classification, are employed. Additionally, the study shows that CNN performs better than Artificial Neural Networks (ANN) in image classification. The study demonstrates how AI and deep learning technology can be used in agriculture to improve crop management and enable early disease prediction. Experiments have been conducted using the real image dataset. © 2025 IEEE.

关键词： Adversarial machine learning

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Advancing Alzheimer’s detection: integrative approaches in MRI analysis with traditional and deep learning models

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Neural Computing and Applications 2025年第14期37卷 8527-8546页

作者： Vanaja, T. Shanmugavadivel, Kogilavani Subramanian, Malliga Kanimozhiselvi, C.S. Department of Artificial Intelligence Kongu Engineering College Tamil Nadu Erode India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Erode India

Alzheimer’s dementia (AD) poses a significant global health challenge, characterized by progressive cognitive decline, memory impairment, and behavioral changes. The critical need for early detection to enable timely intervention and personalized care is emphasized by the current lack of effective treatments. This study aims to develop precise diagnostic models for AD by employing machine learning and a customized deep-convolutional neural network (cDCNN) with three convolution layers, utilizing Magnetic Resonance Imaging (MRI) data. Methods involve analyzing two distinct datasets—Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Kaggle—to explore diverse cohorts and imaging features associated with AD pathology. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied across both datasets. A range of traditional machine learning methods, including support vector machine, k-nearest neighbor, random forest, decision trees, and XGBoost classifier, are evaluated alongside the cDCNN model, which leverages key MRI biomarkers of AD for both datasets. Results show the cDCNN model achieved a specific accuracy of 87% on the ADNI dataset, despite challenges in converting ADNI’s Digital Imaging and Communications in Medicine (DICOM) files to JPEG, impacting image clarity. Conclusions suggest that this research provides critical diagnostic tools for clinicians, offering insights into AD pathology and contributing to the alleviation of AD’s societal impact. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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A semantic driven model for extraction of text using TF-IDF, SFLA and XGBoost

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International Journal of Information Technology (Singapore) 2025年 1-6页

作者： Madhumala, R.B. Vineetha, B. Shree, M. Rajani Sanjesh, Riya Charanraj, B.R. Department of Computer Science and Engineering Dayanand Sagar Academy of Technology and Management (DSATM) Bengaluru India Computer Science and Engineering Presidency University Bangalore India Department of CSE BNM Institute of Technology Bengaluru India Computer Science PES University BANGALORE URBAN India

As people increasingly express opinions, offer feedback, and share suggestions on websites, e-forums, and blogs, consumers have come to rely heavily on online product reviews before making purchases or using services. Some spammers deliberately manipulate reviews to either enhance or discredit products, leading consumers to make misguided decisions based on these misleading reviews. To address this issue, a hybrid approach combining TF-IDF (term frequency-inverse document frequency) with the shuffled frog leaping algorithm (SFLA) is proposed. This approach aims to reduce the high dimensionality of feature sets and select optimized subsets of features. Finally, the sentiments from online reviews are classified into genuine and misleading by using Extreme Gradient Boosting (XGBoost) classifier. By enhancing review credibility, the proposed approach helps customers identify authentic feedback and improves classification accuracy. The performance of the proposed approach is assessed and validated with existing studies based on accuracy (%), precision (%) and recall (%). The results indicate that this hybrid feature selection approach achieves an optimized subset of features, leading to superior classification accuracy. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Classification SFLA Text extraction TF-IDF XGBoost

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Metamaterial inspired axe-shaped terahertz patch antenna design: a tool for early skin cancer detection

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Optical and Quantum Electronics 2025年第2期57卷 1-15页

作者： Sharan, Bhagwati Raja, Manjula Department of Computer Science and Engineering SRM University AP Andhra Pradesh Amaravati522240 India

Skin cancer involves abnormal growth of skin cells, typically caused by ultraviolet radiation exposure. Timely and accurate detection is essential to mitigate significant health risks and ensure effective treatment. This paper proposes a nanoantenna to enhance diagnostic and therapeutic capabilities for skin cancer detection. These antennas, emitting electromagnetic waves in the terahertz band (0.1–10 THz), improve integration for miniaturized wireless systems and serve as a foundation for the Internet of Medical Things (IoMT). We design a miniaturized, metamaterial-inspired gold-patch axe-shaped nanoantenna (121.97×110×17μm3), implemented in CST Studio Software. The antenna resonates at 1.152 THz, with a very low return loss ( © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Diseases

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Data augmentation for blurred-text image deblurring using non-euclidean extrapolations

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2309-2320页

作者： Ranjan, Arti Ravinder, M. Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women (IGDTUW) New Delhi India Department of Computer Science and Engineering SSET Sharda University Greater Noida India

The deblurring of blurred-text images is a critical task in computer vision, especially in applications such as document digitization, OCR (Optical Character Recognition), and image restoration. Traditional deblurring methods often struggle with text images due to the complex structures and high-frequency details inherent in textual content. However, existing datasets are often insufficient to train robust deblurring models due to their limited variety in blur patterns, text formats, and environmental conditions. This insufficiency poses significant challenges in data augmentation, where traditional techniques may distort text structures or fail to simulate the complexity of real-world blurs, leading to models that struggle to generalize effectively. To address these issues, we propose a novel data augmentation method using existing dataset SROIE along with Non-Euclidean extrapolation, designed to create more realistic and varied training examples. It consists the combinations of existing Riemannian mixup (R-Mixup), Hyperbolic Spaces for generating a new dataset SROIE-RH and the combination of Hyperbolic Spaces, and Gaussian mix-up for creation another dataset SROIE-HG to improve the quality and the sources of the augmented dataset using. The validity of these methods is proven through various tests and shows enhanced increases in the model’s resilience and precision leading to sharper and more accurate deblurring of blurred-text images. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Blurred-text image deblurring non-euclidean extrapolation Data augmentation Dataset generation Gaussian mixup Hyperbolic spaces Riemannian manifolds

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Diversified deep hierarchical kernel ensemble regression

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Multimedia Tools and Applications 2025年第15期84卷 15553-15573页

作者： Liu, Zhifeng Xu, Zhengqin Ebhohimhen Abhadiomhen, Stanley Qian, Xiaoqin Shen, Xiang-Jun School of Computer Science and Communication Engineering JiangSu University Zhenjiang212013 China Department of Computer Science University of Nigeria Nsukka410105 Nigeria Department of Ultrasound Affiliated People’s Hospital of Jiangsu University Zhenjiang212002 China

Deep ensemble learning models that combine multiple independent deep learning models with multi-layer processing architectures have proven to be effective techniques for improving the accuracy and robustness of deep learning models. In this paper, we propose a diversified kernel ensemble regression method, which is developed from the well-known kernel ridge regression methods. Motivated by multi-view data modeling ideas, we treat each individual kernel as one view of original data in kernel representation space. Therefore, we develop a deep kernel ensemble ridge regression method in Neural Tangent Kernel (NTK) to address the problem faced by traditional kernel ridge regression methods in finding appropriate types of kernels and their parameters. Specifically, as multiple deep kernel regressors share common information from the multi-view kernel representations, our proposed method is built through a hierarchical modeling method, where deep kernel regressors share a common parameter and also have model-specific parameters in individual regressors, which is further helpful in improving our model’s performance. Furthermore, to achieve better diversified deep kernel representations in our proposed method, the Hilbert-Schmidt Independence Criterion (HSIC) is used to regularize our proposed model. In this way, we can find more diversified kernel representations among multiple kernel ensemble regressors to achieve better generalization performance. Experiments on several classification and regression datasets, such as MNIST, TinyImageNet-200, ORL, Credit and YearPredictionMSD demonstrate that our proposed method can achieve best regression performance than other state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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Combined Multi-Agent and Centralized Resource Allocation in Cloud Radio Access Networks

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IEEE Access 2025年 13卷 79098-79106页

作者： Rodoshi, Rehenuma Tasnim Nazib, Rezoan Ahmed Chun, Chanjun Choi, Wooyeol George Mason University Department of Computer Science Fairfax61452 United States Chosun University Department of Computer Engineering Gwangju61452 Korea Republic of Chung-Ang University School of Computer Science and Engineering Seoul06974 Korea Republic of

The fifth generation (5G) mobile network is designed to facilitate high data rates with massive connectivity with the benefit of small cell technology. The cloud radio access network (C-RAN) is a promising mobile network architecture that can meet the ever-increasing resource demand of a growing number of users. In C-RAN, base station functionalities are separated into baseband units (BBUs) and remote radio heads (RRHs), with BBUs centralized and virtualized via cloud computing. However, this architecture introduces new challenges in efficiently allocating resources to dynamic users. This paper aims to design a resource allocation scheme that improves system efficiency and satisfies dynamic user demands in C-RAN. We propose a hybrid resource allocation approach that combines centralized control and multi-agent-based decision-making. The centralized controller, located within the BBU pool, collaborates with virtual base stations (VBSs) acting as multi-agent system (MAS) agents. The resource allocation solution is derived by jointly considering the real-time resource requests from agents and the historical demand estimates generated by the centralized controller. Through simulation-based evaluation, we compare our proposed scheme with conventional random and fixed resource allocation methods. The results demonstrate improved performance in terms of resource utilization, reduced unfulfilled demand, and fairness among VBS agents. The proposed combined resource allocation strategy effectively meets dynamic user requirements while maintaining system efficiency in C-RAN. Our work highlights the importance of integrating historical demand trends with real-time agent requests for improved long-term resource planning. © 2013 IEEE.

关键词： Radio access networks

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Assessing classification approaches for categorizing Ayurvedic herbs

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Multimedia Tools and Applications 2025年第9期84卷 6711-6735页

作者： Umadevi, V. Kamath, Vaishnavi Department of Computer Science and Engineering B.M.S. College of Engineering Bull Temple Road Karnataka Bangalore560019 India

Ayurvedic herbs hold immense importance in traditional Ayurvedic medicine. These herbs are considered vital elements in maintaining physical, mental, and spiritual balance in the human body. According to ayurveda, diseases arise due to imbalances in doshas namely Vata, Pitta, and Kapha. Ayurvedic practitioners diagnose diseases by analyzing the dosha imbalances in an individual’s body and mind. Ayurvedic herbs play a crucial role in balancing the doshas and promoting overall health and well-being. Each herb possesses specific properties that can either pacify or aggravate the three *** research employs machine learning models to classify Ayurvedic herbs according to their dosha balancing properties. Using a 90-10 split, the data was randomly split into a training set and a test set. Data preprocessing using random oversampling and artificial sample generation using CTGAN is also performed on the dataset. A total of six experiments are performed on the dataset. Various classification models like SVM, KNN, RF, DT and XGBoost were used for classification. Using four target classes without oversampling, the XGBoost classifier yielded the best classification models with an impressive accuracy of 96%, Precision of 97%, Recall of 96%, F1-score of 96% and Root mean square error of 0.16. The experimental results provide compelling evidence that the proposed model, which uses ensemble learning methods, significantly outperforms conventional methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Assessing the predictive power of boosting techniques for diabetes

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Multimedia Tools and Applications 2025年 1-25页

作者： V, Umadevi G A, Yashaswini Department of Computer Science and Engineering B.M.S. College of Engineering Bull Temple Road Karnataka Bangalore560019 India

Diabetes affects more than half a million individuals globally. Diabetes can cause heart attacks, renal damage, and even blindness. Machine learning algorithms, with their intelligence capacity can precisely anticipate diabetes and prevent its complications which will allow premature detection of diabetes. Boosting techniques offer a powerful approach to machine learning by combining weak models to form a strong ensemble. They excel in addressing challenging classification tasks and can significantly improve predictive performance compared to individual models. This study investigates the predictive efficacy of two widely recognized boosting techniques, namely Adaboost (AB) and Gradientboost (GB), across four distinct diabetes datasets. The selected datasets include Pima diabetes dataset (PDD), Early stage diabetes dataset (EDD), Mendeley Diabetes dataset (MDD), and BFRSS Diabetes dataset (BDD), to facilitate comprehensive analysis and investigation for diabetes classification. Among the two boosting techniques, the fine tuned GB showed highest performance with respect to Accuracy, Precision, Recall, F1-score, AUC score, MSE (Mean Squared Error), G-Mean (Geometric Mean), Kappa, MCC (Mathews Correlation Coefficient) for diabetes classification on all four diabetes datasets. The accuracy of prediction on four datasets obtained by GB was 87.66%, 98.07%, 99.00% and 85% respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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Document Summarizer: A Machine Learning approach to PDF summarization 4

Document Summarizer: A Machine Learning approach to PDF summ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Peddarapu, Rama Krishna Anogna, Sudagoni Harshini, M.N.S. Sri Sireesha, J. Rathna, K. Guna Department of Computer Science and Engineering Faculty of Computer Sciences and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India

To address the need for summarizing and extracting information efficiently, this paper highlights the growing challenge posed by the increasing number of PDF files. Reading lengthy documents is a tedious and time-consuming task. To save time and quickly comprehend the key points of a PDF, a PDF summarizer tool has been developed to tackle these issues. In today's professional environments, gathering and managing data from documents is critical. This article introduces an innovative solution called DocSum, which automates the process of summarizing extracted data. Powered by *** Core, the DocSum system allows users to upload PDF documents for processing and receive concise summaries in return. The system features a user-friendly interface that encourages interaction and engagement, utilizing AI and machine learning techniques to streamline document handling. Users can request specific summaries, enabling efficient document management workflows. By empowering users to seamlessly interact with vast amounts of information, DocSum enhances productivity and explores new ways to optimize document management. This solution is ideal for professionals seeking to stay informed and manage data effectively, while also keeping track of advancements in document handling technologies across multiple industries. It is not only helpful for professionals but also helpful for students who need quick revision or examinations, teachers to gain brief knowledge on the concepts. © 2025 The Authors. Published by Elsevier B.V.

关键词： Data handling

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