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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © 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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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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Revolutionising facility layout: a case study of dynamic facility layout in cable production

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International Journal of Web engineering and Technology 2024年第3期19卷 267-309页

作者： Vineetha, G.R. Rasheedali, Shiyas Chekkot Division of Computer Science and Engineering Department of Mechanical Engineering Cochin University College of Engineering CUSAT Kuttanadu Kerala Alappuzha India

In the competitive landscape of globalised markets, businesses must prioritise cost reduction for sustained competitiveness. This study delves into the dynamic facility layout problem (DFLP) within a cable production company in Kerala, emphasising adaptability to changing production demands. Addressing material handling costs and rearrangement expenses, the research evaluates the efficacy of the current static layout and explores the benefits of transitioning to a dynamic layout. The case study reveals potential cost savings through the strategic restructuring of machine arrangements. The innovative machine learning-based genetic algorithm (ML-GA) integrates machine learning algorithms, genetic algorithms, and a local search method, offering a cutting-edge solution to dynamic facility layout challenges. By considering demand variability and relocation costs, the study provides insights for informed decision-making, emphasising the significance of material flow patterns. This research contributes to enhancing efficiency and profitability, providing practical implications for businesses navigating the complexities of modern manufacturing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Plant layout

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On learning the right attention point for feature enhancement

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science China(Information sciences) 2023年第1期66卷 131-143页

作者： Liqiang LIN Pengdi HUANG Chi-Wing FU Kai XU Hao ZHANG Hui HUANG College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computer Science National University of Defense Technology School of Computing Science Simon Fraser University

We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.

关键词： point convolution feature enhancement attention point deep neural network

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Analyzing Patient Reviews for Recommending Treatment Using NLP and Deep Learning-Based Approaches 1st

Analyzing Patient Reviews for Recommending Treatment Using N...

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1st International Conference on Advancements in Interdisciplinary Research, AIR 2022

作者： Shahid, Tauheed Singh, Suraj Gupta, Shatmanyu Sharma, Shanu Department of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9783031237232

Nowadays technological advancement can be seen in the medical field starting from medical devices, data collection, analysis to diagnosis and treatment recommendations for diseases. Drug and treatment recommendation is one of the most popular applications, which is now observed and used by everyone in this digital era. These types of recommendation systems usually require a huge set of data from the patients and an efficient Machine learning-based model to conclude significant insights that can help in the prediction of the best possible medications for a particular disease. The health-related recommendation systems can be proved as a significant tool in the healthcare sector for speeding up various decision-making processes such as health insurance, clinical pathway-based treatment methods, and assisting doctors by recommending drugs using a patient’s health profile. People frequently utilize social media to explore their health issues, therefore there is now a wealth of information on social networks that may be leveraged to create various health-related recommendation systems. In this research, a deep learning-based based drug recommendation system using N-Gram is provided, which uses patient review data as input and sentiment analysis to choose the appropriate treatment for the ailment. The accuracy achieved, shows the efficacy of the proposed approach for the real time applicability of the model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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An Optimized Transfer Learning Model Based Kidney Stone Classification

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computer Systems science & engineering 2023年第2期44卷 1387-1395页

作者： S.Devi Mahalakshmi Department of Computer Science and Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia

The kidney is an important organ of humans to purify the *** healthy function of the kidney is always essential to balance the salt,potassium and pH levels in the ***,the failure of kidneys happens easily to human beings due to their lifestyle,eating habits and diabetes *** pre-diction of kidney stones is compulsory for timely *** processing-based diagnosis approaches provide a greater success rate than other detection *** this work,proposed a kidney stone classiﬁcation method based on optimized Transfer Learning(TL).The Deep Convolutional Neural Network(DCNN)models of DenseNet169,MobileNetv2 and GoogleNet applied for clas-siﬁ*** combined classiﬁcation results are processed by ensemble learning to increase classiﬁcation *** hyperparameters of the DCNN model are adjusted by the metaheuristic algorithm of Gorilla Troops Optimizer(GTO).The proposed TL model outperforms in terms of all the parameters compared to other DCNN models.

关键词： DCNN GTO kidney stone transfer learning

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Secure UPI: Machine Learning-Driven Fraud Detection System for UPI Transactions 2

Secure UPI: Machine Learning-Driven Fraud Detection System f...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rani, Rupa Alam, Adnan Javed, Abdul Ajay Kumar Garg Engineering College Ghaziabad Department of Computer Science & Engineering U.P. Ghaziabad India Banasthali Vidyapith Department of Computer Science & Engineering Rajasthan India Abes Institute of Technology Department of Computer Science & Engineering-Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Secure UPI specializes in developing an advanced fraud detection gadget the usage of the effective XGBoost device getting to know set of rules to create an advanced fraud identity device. XGBoost is a properly-proper alternative for enhancing the precision of fraud detection fashions because of its reputation for dealing with tricky datasets with efficiency and its music record of success throughout multiple industries. In order to extract pertinent records, like transaction quantity, frequency, and place, our approach preprocesses UPI transaction facts. This article makes use of a labelled dataset to train the XGBoost version in order that we may also take gain of its sturdy prediction talents and capacity to handle imbalanced datasets. To help create a system that is less difficult to apprehend and use, feature importance evaluation is used to discover essential symptoms of feasible fraud. After training, the model is covered right into a real-time UPI transaction tracking device, in which it maintains an eye fixed out for any suspicious traits in incoming transactions. In order to lessen the results of fraudulent activity, the system is constructed with 98.2 % accuracy to send out instant notifications and take preventive steps. This challenge allows in improving UPI transaction security and advancing economic era are accomplished through demonstrating the performance of machine learning in fraud detection. © 2024 IEEE.

关键词： Dimensionality Reduction Fraud Detection Imbalanced Datasets Machine Learning PCA SMOTE UPI XGBoost

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Store Management Security System 5

Store Management Security System

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5th International Conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Khandelwal, Shubh Sharma, Shreya Vishnoi, Sarthak Agarwal, Ms Ashi Computer Science Abes Engineering College Ghaziabad India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

Nowadays big shopping marts are expanding their business all over the world but not all marts are fully protected with the advanced security system. Very often we come across cases where people take the things out of the mart without billing. These marts require some advanced features-based security system for them so that they can run an efficient and no-loss business. The idea we are giving here can not only be implemented in marts to enhance their security but can also be used in various other fields to cope up with the incompetent management system. Several issues of the stores like regular stock updating, placing orders for new products, replacing products that have expired can be solved with the idea we present here. We also plan on making the slow processes of billing and checking out of the mart faster and more efficient that would result in customer satisfaction. © 2022 IEEE.

关键词： Security systems

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Facial Emotion Based Automatic Music Recommender System

Facial Emotion Based Automatic Music Recommender System

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Singh, Mahesh Kumar Singh, Pushpendra Sharma, Amit ABES Engineering College Computer Science Department UP Ghaziabad India Ims Engineering College Information Technology Department UP Ghaziabad India

ISBN: (纸本)9798350329186

Listening to music can change the mood of any individual based on type of song like classic, pop or hip-hop. Music can have very strong influence on an individual's emotions. These emotions can be recognized by extracting various facial image features like Eye or Lip movement. Using facial expressions, meaning and context of words can be clearly understood by Humans. With the quick improvement of versatile systems and advanced interactive media innovations, advanced music has gotten to be the standard customer substance looked for by numerous youthful people. People frequently utilize music for mood regulation, more specifically to change a bad mood, to get motivated, to focus on their work, to increase energy levels or to reduce tension. Moreover, tuning in to the correct kind of music at the proper time may progress mental wellbeing. Recommendation of different songs based on an individual's current facial expressions could be latest research problem within the field of image processing. Very few researchers have proposed the solution of this research problem through automatic facial recognition using convolution neural network (CNN) models. In this paper, the proposed system based on CNN model focused on detecting human emotions from face image to identify list of songs to be played through music player to swing user's mood. The objective of this study is to improve the accuracy and reduce time and cost of proposed system for music recommendation. © 2023 IEEE.

关键词： Emotion Recognition

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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