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EPDTNet + -EM: Advanced Transfer Learning and SubNet Architecture for Medical Image Diagnosis

Cognitive Computation 2025年第2期17卷 1-22页

作者： K, Dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MRI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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ANovel Light Weight CNN Framework Integrated with Marine Predator Optimization for the Assessment of Tear Film-Lipid Layer Patterns

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computer Modeling in engineering & Sciences 2023年第7期136卷 87-106页

作者： Bejoy Abraham Jesna Mohan Linu Shine Sivakumar Ramachandran Department of Computer Science and Engineering College of Engineering MuttatharaThiruvananthapuramKerala695008India Department of Computer Science and Engineering Mar Baselios College of Engineering and TechnologyThiruvananthapuramKerala695015India Department of Electronics and Communication Engineering College of Engineering TrivandrumKerala695016India

Tear film,the outermost layer of the eye,is a complex and dynamic structure responsible for tear *** tear film lipid layer is a vital component of the tear film that provides a smooth optical surface for the cornea and wetting the ocular *** eye syndrome(DES)is a symptomatic disease caused by reduced tear production,poor tear quality,or excessive *** diagnosis is a difficult task due to its multifactorial *** of several clinical tests available,the evaluation of the interference patterns of the tear film lipid layer forms a potential tool for DES *** instrument known as Tearscope Plus allows the rapid assessment of the lipid layer.A grading scale composed of five categories is used to classify lipid layer *** reported work proposes the design of an automatic system employing light weight convolutional neural networks(CNN)and nature inspired optimization techniques to assess the tear film lipid layer patterns by interpreting the images acquired with the Tearscope *** designed framework achieves promising results compared with the existing state-of-the-art techniques.

关键词： Dry-eye syndrome tearscope plus tear film deep neural networks

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Autoencoder-Based Architecture for Identification and Mitigating Phishing URL Attack in IoT Using DNN

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Journal of The Institution of Engineers (India): Series B 2023年第6期104卷 1227-1240页

作者： Gopal, S.B. Poongodi, C. Nanthiya, D. Kirubakaran, T. Kulavishnusaravanan, B. Logeshwar, D. Department of Electronics and Communication Engineering Kongu Engineering College Perundurai India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal India Department of Computer Technology-UG Kongu Engineering College Perundurai India

As a new and potentially devastating form of cyberattack, ‘Phishing’ URLs pose a risk to users by impersonating legitimate websites in an effort to obtain sensitive information such as usernames, passwords, and financial details. At least two-thirds of persons were hit by these phishing assaults in 2016, according to a survey (Gopal et al. in IOP Conference Series: Materials Science and engineering, vol. 1055, no. 1, p. 012072. IOP Publishing). Therefore, these attacks must be addressed carefully to prevent financial losses. In order to detect and prevent new kinds of assault, in addition to the ones already in use, a defense mechanism that can learn the possibilities of attacks is required. In the IoT (Internet of Things), everything is managed remotely via a web address (URL). So, intruders can simply gain access to the controls by stealing the URL used to access them from a distant location. To identify and prevent access to phishing sites at the network layer, we offer an approach that combines a deep neural networks (DNN) model with autoencoders. In order to train the model for feature reduction, an autoencoder sends it a dataset that includes both legitimate and phishing domains to analyze. The effectiveness of the suggested approach has been tested on a number of datasets, including Open phish, UCI, Mendeley, and PhishTank. For the multiclassification dataset, these metrics for this design are: 92.89% accuracy, 93.07% recall, 92.75% precision, and 92.21% F1-score. © 2023, The Institution of Engineers (India).

关键词： Deep neural networks

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A geometric-approach based Combinatorial Transformative Scalogram analysis for multiclass identification of pathologies in a voice signal

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Multimedia Tools and Applications 2024年 1-21页

作者： Khumukcham, Ranita Nongmeikapam, Kishorjit Department of Electronics and Communication Engineering Indian Institute of Information Technology Mantripukhri Manipur Imphal795002 India Department of Computer Science and Engineering Indian Institute of Information Technology Mantripukhri Manipur Imphal795002 India

Many researchers have preferred non-invasive techniques for recognizing the exact type of physiological abnormality in the vocal tract by training machine learning algorithms with feature descriptors extracted from the voice signal. However, until now, most techniques have been limited to classifying whether a voice is normal or abnormal. It is crucial that the trained Artificial Intelligence (AI) be able to identify the exact pathology associated with voice for implementation in a realistic environment. Another issue is the need to suppress the ambient noise that could be mixed up with the spectra of the voice. Current work proposes a robust, less time-consuming and non-invasive technique for the identification of pathology associated with a laryngeal voice signal. More specifically, a two-stage signal filtering approach that encompasses a score-based geometric approach and a glottal inverse filtering method is applied to the input voice signal. The aim here is to estimate the noise spectra, to regenerate a clean signal and finally to deliver a completely fundamental glottal flow-derived signal. For the next stage, clean glottal derivative signals are used in the formation of a novel fused-scalogram which is currently referred to as the "Combinatorial Transformative Scalogram (CTS)." The CTS is a time-frequency domain plot which is a combination of two time-frequency scalograms. There is a thorough investigation of the performance of the two individual scalograms as well as that of the CTS *** classification metrics are used to investigate performance, which are: sensitivity, mean accuracy, error, precision, false positive rate, specificity, Cohen’s kappa, Matthews Correlation Coefficient, and F1 score. Implementation of the VOice ICar fEDerico II (VOICED) standard database provided the highest mean accuracy of 94.12% with a sensitivity of 93.85% and a specificity of 97.96% against other existing techniques. The current method performed well despite the d

关键词： Frequency domain analysis

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua Science and technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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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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Advanced Breast Cancer Detection Using Spatial Attention and Neural Architecture Search (SANAS-Net)

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SN computer Science 2025年第1期6卷 1-12页

作者： Souza, Melwin D. Ananth Prabhu, G. Kumara, Varuna Department of Computer Science & Engineering Sahyadri College of Engineering & Management Mangalore India Department of Electronics and Communication Engineering Moodlakatte Institute of Technology Kundapura India

This paper introduces a novel spatial attention neural architecture search network (SANAS-Net), which incorporates a spatial attention mechanism to enhance the model’s ability to focus on critical regions within mammograms. By utilizing multi-head attention within transformer blocks, the model captures diverse spatial relationships and interactions between features. Positional embeddings are incorporated into the transformer blocks, further enriching the model’s capacity to understand global spatial context. Extensive experiments were conducted to validate the proposed methodology. SANAS-Net was shown to successfully identify attentive networks that demonstrate high performance in distinguishing malignant from benign breast cancer cases. The model achieved a test accuracy of 89.95%, significantly surpassing the performance of previously proposed algorithms for breast cancer detection using mammography images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Breast cancer detection Deep learning Mammography Neural architecture search Spatial attention

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Vulnerability Analysis on Third Party Application 2

Vulnerability Analysis on Third Party Application

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Supreetha, H.H. Rajani Suma, H.C. Divya, K.P. Suhasini Computer Science Engineering Department Acharya Institute of Technology bangalore India Electronics Engineering Department BGSIT Institute of Technology BG Nagar India Computer Science Engineering Department Maharaja Institute of Technology Mysore India Electronics Engineering Department Bangalore Institute of Technology Bangalore India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Road traffic has been an open challenge in a highly-populated area in the world. India is one of them where traffic management is a crucial problem to solve. Road traffic congestion, traffic rule violation, road accidents are the outcome of limitless traffic. The paper attempts to highlight the Real-Time Traffic Control System (RTTCS) monitoring vehicles, passengers, detecting objects on the road, and license plate detection. This paper proposes an RTTCS based on Machine Learning (ML) model, which takes traffic streaming as an input, monitors the traffic by detecting objects, finds the count of the objects per class, detects license plates and recognizes license plate characters using Easy-OCR. For depth analysis, the RTTCS analysis is performed for a particular location at various timestamps for easy traffic monitoring. The proposed system uses the you Only Look once version-4 (YOLOv4) for object detection (e.g., bicycle, motorbike, cars). The transfer learning concept uses pre-trained convolutional weights to optimize the model. © 2024 IEEE.

关键词： Traffic congestion

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Privacy-preserving multi-keyword hybrid search over encrypted data in cloud

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 261-274页

作者： Singh, Niharika Kumar, Jitendra Singh, Ashutosh Kumar Mohan, Anand School of Computer Sciences Bennett University Uttar Pradesh Noida India CISIR Department of Electrical and Electronics Engineering University Teknologi Petronas Perak Seri Iskandar Malaysia Department of Computer Applications National Institute of Technology Tiruchirappalli Tamilnadu Tiruchirappalli India Department of Computer Applications National Institute of Technology Kurukshetra Haryana Kurukshetra India Department of Electronics Engineering Indian Institute of Technology-BHU Uttar Pradesh Varanasi India

Data outsourcing to the cloud is gaining popularity day by day in cloud computing. It requires a dynamic architecture that avoids loopholes and stops intruders from entering the cloud environment. To retrieve the enciphered documents from the cloud, the latest technique that is enrolled effectively is multi-keyword fuzzy search. The proposed research work uses this technique along with the n-gram corpus algorithm that helps to achieve relevancy score and sub-linear search time. This algorithmic combination is able to predict the privacy ranking measure of the retrieved data. To validate the opposed approach, simulation analysis has been performed to preserve user-oriented privacy in a cloud environment. The ranking precision of the proposed method is compared with (Fu et al. in IEEE Trans Inf Forensics Secur 12(8):1874–1884. https://***/10.1109/TIFS.2017.2692728, 2017) which uses a stemming algorithm and a matching algorithm based on the inner product. The results express the efficiency of the proposed approach by showing a relative improvement in security precision up to 55%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Privacy-preserving techniques

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