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PODBoost: an explainable AI model for polycystic ovarian syndrome detection using grey wolf-based feature selection approach

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Neural Computing and Applications 2024年第30期36卷 18627-18644页

作者： Moral, Poonam Mustafi, Debjani Sahana, Sudip Kumar Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi835215 India

Polycystic Ovary Syndrome (PCOS) is a recurring endocrine disorder that primarily affects women of reproductive age. It is difficult to diagnose due to its heterogeneous characteristics and overlapping symptoms with other illnesses. As a result, accurate and trustworthy prediction models are required to detect PCOS early. This research work aims to develop ML methods that predict the risk of PCOS among women based on demographic and clinical features. The entire framework is divided into four phases: in Phase I of the study, the SMOTE-Tomek Links (SMTL) technique balances the data set by combining oversampling and undersampling approaches. A novel meta-heuristic-based feature selection approach, the Grey Wolf Optimization (GWO) method, has been employed to select the most crucial features from the dataset, explained in Phase II. Subsequently, in Phase III, a hybridized classifier PODBoost algorithm (Polycystic Ovarian Disorder Boosting algorithm) is devised for faithful early prediction of PCOS using the concepts of different classical supervised learning algorithms. Finally, Explainable AI (XAI) such as the Local Interpretable Model-Agnostic Explanations (LIME) tool has been implemented to interpret relevant predictions made by the proposed classifier. The proposed algorithm is examined utilizing numerous metrics such as Accuracy, Error-Rate, ROC-AUC Score, Recall, Precision, and F1-Score. Among all the evaluated models, the proposed hybridized model has shown an impressive performance with an exceptional accuracy of 97.42%, indicating its superiority by delivering outstanding results. Based on the findings, the novel meta-heuristic-based feature selection method significantly impacts the outcomes of the proposed hybridized PODBoost algorithm. This algorithm may be recommended to predict PCOS or other relevant diseases having datasets which are multimodal in nature. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 202

关键词： Feature Selection

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Performance Evaluation of Natural Language Processing Algorithms for Sentiment Analysis

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SN computer Science 2024年第6期5卷 724页

作者： Silviya, S. H. Annie Faith, S. Julia Seetha, R. Hemalatha, M. Department of Computer Science and Engineering Rajalakshmi Institute of Technology (RIT) Department of Information Technology S.A.Engineering College Department of Information Technology Vellore Institute of Technology Department of Computer Science and Engineering RMD Engineering College

Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, computer science, and artificial intelligence called “natural language processing” studies how computers and human language interact, with a focus on how to teach computers to process and analyze vast volumes of natural language data. Sentiment analysis, sometimes referred to as opinion mining or emotion artificial intelligence, is the methodical identification, extraction, quantification, and study of affective states and subjective data using natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is frequently used in online and social media platforms, voice of the customer materials (such as reviews and survey replies), and healthcare materials for marketing, customer service, and clinical medicine purposes. This paper discusses different sentiment analysis methods and selects the best approach for a certain task. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics Computational linguistics Natural language processing Sentiment analysis Text analysis

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A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

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computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

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LEViT- Leaf Disease identification and classification using an enhanced Vision transformers(ViT) model

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Multimedia Tools and Applications 2024年 1-32页

作者： Prashanthi, Bh. Krishna, A. V. Praveen Rao, Ch. Mallikarjuna Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram522502 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad500090 India

The variety of crops, differences in climate, and the multiplicity of disease symptoms make early identification and evaluation of leaf diseases a challenging task. Although deep-learning methods have been created for this very reason, they often need adjustments to suit various regions and types of crops. This study presents a vision transformer-based deep learning model incorporating Explainable Artificial Intelligence (XAI) features to effectively detect and classify leaf diseases. The model is evaluated using the New Plant Disease Dataset, encompassing data from 14 plants with 38 leaf disease classifications. The primary objective is to enhance accuracy in leaf disease classification through the utilization of vision transformer learning. The proposed model, LEViT (Leaf Disease Identification and Classification using an Enhanced Vision Transformers Model), builds upon the ViT_B_32 model. It incorporates additional layers such as dense layers, dropout, and L2 regularization to improve performance and mitigate overfitting. Specialized preprocessing techniques tailored for transformers are employed to refine data quality. Incorporating Grad-CAM (Gradient-weighted Class Activation Mapping) enhances interpretability, enabling the identification of crucial regions influencing the model’s decisions within images. The model exhibits remarkable efficacy, boasting a training accuracy of 95.22%, validation accuracy of 96.19%, and test accuracy of 92.33%. These results underscore its robust ability to accurately identify leaf diseases across diverse crop types. Comparative analysis with state-of-the-art models in the field demonstrates LEViT’s superior performance, establishing it as a leading solution for leaf disease detection and classification. LEViT’s advanced architecture, coupled with fine-tuning, positions it as a highly promising tool for advancing crop management and yield improvement, thereby significantly contributing to the field of precision agriculture. © The

关键词： Crops

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Improving coronary heart disease prediction with real-life dataset: a stacked generalization framework with maximum clinical attributes and SMOTE balancing for imbalanced data

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Multimedia Tools and Applications 2024年第37期83卷 85139-85168页

作者： Dubey, Madhuri Tembhurne, Jitendra Makhijani, Richa Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India

Heart disease increases the strain on the heart by reducing its ability to pump blood throughout the body, which can lead to heart attacks and strokes. Heart disease is becoming a global threat to the world due to people’s unhealthy lifestyles, prevalent stroke history, physical inactivity, and current medical background. In predictive analytics, many studies were proposed to get alerts about forthcoming heart disease based on various attributes. However, the performance metrics were good, but the model was trained with few features. This study aims to train the model with all the essential attributes for heart disease prediction on Framingham Heart Study (FHS) dataset. The dataset is pre-processed with IQR (Inter Quartile Range) outlier detection followed by data oversampling using Synthetic Minority Oversampling Technique (SMOTE). We proposed a stack generalization approach, wherein various machine learning classifiers, namely logistic regression, random forest, K-nearest neighbour, Naïve Bayes, support vector machine, XGBoost, and decision tree with optimized hyperparameter trained the model to offer the best learner for the prediction of Coronary Heart disease with improved performance. The proposed model is tested on the original imbalanced and SMOTE-balanced FHS dataset. It is observed that logistic regression on the original (imbalanced) FHS dataset provides 86.51% accuracy, while the support vector machine on the SMOTE (balanced) FHS dataset outperformed the other models with an accuracy of 93.07%. Also, the proposed approach of stacked generalization with cross-validation provided 97.2% accuracy on SMOTE (balanced) dataset, which is remarkable. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Anomaly detection

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Person re-identification using vision transformer and centroid triplet loss

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Multimedia Tools and Applications 2024年第29期83卷 73777-73788页

作者： Ijjina, Earnest Paul Medipelly, Rampavan Beerukuri, Santosh Kumar Vinnakota, Sowmya Nelakurthi, Vijay Chowdary Department of Computer Science and Engineering National Institute of Technology Warangal Telangana Warangal506004 India

In the current digital era, video surveillance has become a part of daily life. The person re-identification(re-ID) task involves choosing a person as a target in one camera feed and recognizing that target in footage from a different camera or the same camera at various points in time. The goal is to accurately identify a person despite variations in their appearance due to changes in pose, illumination, and occlusions. Person re-ID is a key component in video surveillance with practical applications in public safety, retail, and transportation, among others. However, it remains a difficult problem due to the inherent variability in appearance and the lack of robust features to capture the subtle differences between individuals. Even the existing Convolution Neural Networks (CNNs) based approaches for person re-ID task struggle to address the issues due to variations in pose, occlusions, and background clutter. To tackle these issues in person re-ID task, we propose an approach using Vision Transformers (ViT) with Centroid Triplet Loss (CTL). Experimental studies conducted on Market1501 and DukeMTMC datasets, yielded better results than the existing approaches, indicating the effectiveness of our approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cameras

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A proposed deep learning model for multichannel ECG noise reduction

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Discover Artificial Intelligence 2025年第1期5卷 1-16页

作者： Maurya, Jay Prakash Manoria, Manish Joshi, Sunil Department of Computer Science Engineering Samrat Ashok Technological Institute Madhya Pradesh Vidisha India Director & Professor Department of Computer Science Engineering Rungta College of Engineering & Technology Bhilai Chhattisgarh Durg India

Heart disease is a critical concern of healthcare for everyone in today’s era. An effective and noninvasive indication of heart disease is an electrocardiogram (ECG). Understanding regular ECG signal patterns and comparisons with irregular, patterns may help to identify the serious nature of heart diseases such as arrhythmia. Comparison of ECG signal patterns is very difficult manually, and machine-based interpretation is a demand of society. Errors in ECG interpretation might result from noise contamination of ECG signals. ECG pretreatment from noise is therefore needed for precise analysis. This article proposed a novel deep learning-based solution for multichannel ECG noise reduction, through utilizing the capabilities of fully convolutional neural network along with the Jacobin regularization to ensure confining and preserving local information. Cascaded layered approach was framed in encoder and decoder sections of model where denoising and reconstruction process worked and compared on standard performance parameters with recent denoising deep autoencoders. This proposed work FCN-DAE with Jacobin regularization uses the noise stress test database (NSTDB) for the noise signal of ECG data sourced from the PhysioNet repository. The proposed model achieves 4.763 * 10–2 mv2 for signal space diversity (SSD), 0.288 mv for the median absolute deviation and 1.859 for the root mean square error (RMSE) in the conducted experiment. The experimental findings demonstrate that complex noise from the ECG signal may be removed up to 97.02%. © The Author(s) 2025.

关键词： Deep learning

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Unleashing the potential of ChatGPT in education: Revolutionizing learning and collaboration

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IEEE Potentials 2024年第6期43卷 13-22页

作者： Chandrasekaran, Balaji Nair, Smrithy Girijakumari Sreekantan College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai600025 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and collaboration within educational settings. By leveraging natural language processing and machine learning, ChatGPT offers an interactive and personalized learning experience, enabling students to engage in dynamic conversations and receive instant feedback. Moreover, ChatGPT may facilitate collaborative learning by promoting effective communication and knowledge sharing among students and educators. This article examines the various applications of ChatGPT in education, including virtual tutoring, adaptive learning, and intelligent chatbots. It also discusses the benefits and considerations associated with implementing ChatGPT, such as ethical considerations, data privacy, and potential biases. While acknowledging the limitations and challenges, this article emphasizes the promising potential of ChatGPT in transforming education by enhancing student engagement, fostering critical thinking skills, and expanding access to quality education. © 1988-2012 IEEE.

关键词： Adversarial machine learning

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An efficient deep learning model for early disease detection in vegetable crops

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Neural Computing and Applications 2025年 1-20页

作者： Bhola, Amit Kumar, Prabhat School of Computer Science and Engineering Sharda University Greater Noida201310 India Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna800005 India

Plant diseases significantly threaten global food security and economic stability by reducing crop yields, increasing production costs, and exacerbating food shortages. Early and precise detection of plant diseases is essential for mitigating these risks. This study introduces a lightweight deep learning model for early and efficient disease detection in key vegetable crops, including eggplant, potato, tomato, and soybean. The model leverages a comprehensive dataset containing 23 classes of healthy and diseased plant images. With only 388,055 parameters and a model size of 1.48 MB, it achieves an impressive accuracy of 92.75%, outperforming 17 state-of-the-art deep learning models in terms of performance, efficiency, and size. The novelty lies in the model’s compact architecture, making it highly suitable for deployment in resource-constrained environments such as mobile and edge devices. The primary objective is to provide a scalable and cost-effective solution for early disease detection to enhance agricultural productivity. The findings underscore the model’s superiority over traditional methods and emphasize its potential for real-world applications in smart and sustainable agriculture systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Smart agriculture

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Neural network-guided motion blur estimation and patch representation method for face hallucination

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Neural Computing and Applications 2025年 1-28页

作者： Kumar, Banti Rajput, Shyam Singh Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

Face hallucination (or super-resolution) is a technique of generating high-resolution face images from low-resolution (LR) inputs. The current face hallucination algorithms struggle with motion blur, a common issue in captured images due to various factors, including camera defocusing, objects in motion, etc. To address this problem, a new motion blur robust face hallucination algorithm via neural network-guided motion blur estimation and patch representation (NNMEPR) is proposed in this paper. The NNMEPR algorithm first estimates the motion blur kernels i.e., length (λ) and angle (α) from a motion-blurred LR test faces using a neural network fitting approach with scale conjugate gradient (SCG) learning algorithm. Then, the estimated factor is embedded in the LR dictionary face images to make them compatible with the test images and mitigate the impact of motion blur from the reconstruction process. Moreover, the proposed algorithm employs a neighboring position patch representation in the correlation coefficient calculation process to sustain the sharp edges and texture details in the resulting HR face images. Experiments on standard datasets and locally captured real-world faces demonstrate the superior performance of the proposed NNMEPR algorithm over the existing state-of-the-art techniques. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion estimation

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