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Transfer Learning-Based Class Imbalance-Aware Shoulder Implant Classification from X-Ray Images

Journal of Bionic engineering 2024年第2期21卷 892-912页

作者： Marut Jindal Birmohan Singh Department of Computer Science and Engineering Sant Longowal Institute of Engineering and TechnologySangrur148106PunjabIndia

Total shoulder arthroplasty is a standard restorative procedure practiced by orthopedists to diagnose shoulder arthritis in which a prosthesis replaces the whole joint or a part of the *** is often challenging for doctors to identify the exact model and manufacturer of the prosthesis when it is *** paper proposes a transfer learning-based class imbalance-aware prosthesis detection method to detect the implant’s manufacturer automatically from shoulder X-ray *** framework of the method proposes a novel training approach and a new set of batch-normalization,dropout,and fully convolutional layers in the head *** employs cyclical learning rates and weighting-based loss calculation *** modifications aid in faster convergence,avoid local-minima stagnation,and remove the training bias caused by imbalanced *** proposed method is evaluated using seven well-known pre-trained models of VGGNet,ResNet,and DenseNet *** is performed on a shoulder implant benchmark dataset consisting of 597 shoulder X-ray *** proposed method improves the classification performance of all pre-trained models by 10–12%.The DenseNet-201-based variant has achieved the highest classification accuracy of 89.5%,which is 10%higher than existing ***,to validate and generalize the proposed method,the existing baseline dataset is supplemented to six classes,including samples of two more implant *** results have shown average accuracy of 86.7%for the extended dataset and show the preeminence of the proposed method.

关键词： Biomedical engineering Artificial intelligence Total shoulder arthroplasty Prosthesis identification Biomedical image classification

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Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication 4

Advancements in Speech Recognition: Exploring Feature Extrac...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Bhavani, R. Kathirvelu, Kalaivani Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Speech Recognition (SR), for all diverse languages, is still a big challenge. SR is predominantly used in telecommunication, medical documentation, car control, aviation, etc. SR involves breaking down an audio signal of spoken words into discrete units and then matching those units to words in a language model. The main target of SR is to attain healthier verbal communication amid humans and machines. Therefore, it is a good technology of the human-machine crossing point. The SR uses the Feature Extraction (FE) techniques for better accuracy. In this paper, various techniques for FE are discussed, and an overview is provided to assist in selecting the most suitable technique for achieving higher accuracy. The review highlights the importance of developing more accurate and robust SR systems to improve the user experience in various domains. Overall, it provides the manuscript deals with five different techniques, and among them, the Mel-Frequency Cepstral Coefficients (MFCC) technique was found to exhibit a high level of accuracy. © 2024 IEEE.

关键词： feature extraction Signal processing Signals Sound Speech recognition

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Image-based rice leaf disease detection using CNN and generative adversarial network

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Neural Computing and Applications 2025年第1期37卷 439-456页

作者： Ramadan, Syed Taha Yeasin Islam, Md Shafiqul Sakib, Tanjim Sharmin, Nusrat Rahman, Md. Mokhlesur Rahman, Md. Mahbubur Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of rice and have a substantial impact on the yield and quality of the crop. In recent times, deep learning methods have gained prominence in predicting rice leaf diseases. Despite the increasing use of these methods, there are notable limitations in existing approaches. These include a scarcity of extensive and diverse collections of leaf disease images, lower accuracy rates, higher time complexity, and challenges in real-time leaf disease detection. To address the limitations, we explicitly investigate various data augmentation approaches using different generative adversarial networks (GANs) for rice leaf disease detection. Along with the GAN model, advanced CNN-based classifiers have been applied to classify the images with improving data augmentation. Our approach involves employing various GANs to generate high-quality synthetic images. This strategy aims to tackle the challenges posed by limited and imbalanced datasets in the identification of leaf diseases. The key benefit of incorporating GANs in leaf disease detection lies in their ability to create synthetic images, effectively augmenting the dataset’s size, enhancing diversity, and reducing the risk of overfitting. For dataset augmentation, we used three distinct GAN architectures—namely simple GAN, CycleGAN, and DCGAN. Our experiments demonstrated that models utilizing the GAN-augmented dataset generally outperformed those relying on the non-augmented dataset. Notably, the CycleGAN architecture exhibited the most favorable outcomes, with the MobileNet model achieving an accuracy of 98.54%. These findings underscore the significant potential of GAN models in improving the performance of detection models for rice leaf diseases, suggesting their promising role in the future research within this doma

关键词： Generative adversarial networks

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An Enhanced Detection System of Autism Spectrum Disorder Using Thermal Imaging and Deep Learning

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SN computer science 2025年第2期6卷 1-11页

作者： Jegan Amarnath, J. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Tamil Nadu Chennai 600117 India

Autism Spectrum Disorder (ASD) is a brain-based condition characterized by social difficulties and repetitive behaviors. Traditional diagnostic methods can be subjective and time-consuming. Current diagnostic techniques for ASD often rely on behavioral assessments and clinical observations, which can be inconsistent and prone to subjectivity. Additionally, there is a need for more reliable and objective methods to differentiate between autistic and non-autistic individuals. Existing imaging techniques require further refinement to improve accuracy and efficiency in detecting ASD. The primary objective of this research is to develop and evaluate an enhanced detection system for ASD using thermal imaging and Improved Mask Faster Recurrent Convolutional Neural Network (IMFRCNN). This research focuses on enhancing the detection of ASD using Deep Learning (DL) methods and thermal imaging. Traditional diagnostic techniques for ASD often rely on subjective behavioral assessments, highlighting the need for more reliable methods. By examining cerebral imaging data from the thermal face image datasets collect facial thermal images using thermal cameras. Ensure the dataset is diverse, including varying expressions and environmental conditions. The study compares the effectiveness of IMFRCNN and the ResNet 50 system in classifying thermal images of 50 autistic and 50 non-autistic individuals. Emotions were evoked using audio-visual stimuli, and temperature variations in facial areas such as the nose, forehead, cheek, and eyes were measured. Anger showed the greatest temperature differences, with a 12.6% variation between autistic and non-autistic individuals. The IMFRCNN achieved a reliability of 96%, while the ResNet 50 achieved 90%. These findings indicate that computer-aided assessment techniques utilizing thermal imaging and DL can provide a consistent and objective method for identifying individuals with ASD. © The Author(s), under exclusive licence to Springer Nature Sing

关键词： Deep learning algorithms Diagnostic tool Improved mask faster recurrent convolutional neural network Performance measures

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Improved Deepmaxoutmodel for Disease Prediction in Healthcare IoT Cloud System 3

Improved Deepmaxoutmodel for Disease Prediction in Healthcar...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Preetha, P. Packialatha, A. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350374643

The Internet of Medical Things IoMT has grown rapidly due to the growing popularity of wearable technology and its applications in health monitoring systems. The IoMT significantly reduces the death rate by facilitating early sickness identification. Predicting cardiac disease is among the most significant issues with clinical dataset analysis. Finding the key components of heart disease prediction is the goal of the suggested investigation, which makes use of machine learning techniques. Despite several studies on the issue, the accuracy of the findings on the diagnosis of heart illness is low. The purpose of this paper is to provide an innovative framework for healthcare monitoring that includes the following phases. Data preprocessing, feature extraction, acquisition of data, and prediction are the primary stages. The process of acquiring data is at the IoTs layer. The medical data is considered in this case (benchmark datasets of different illnesses). The data pre-processing operations of data cleaning and data filtering at the cloud layer are applied to the data obtained from the IoT sensors (considering the dataset), where the data normalization process will take place. Subsequently, statistical features and raw features are extracted. The multi-layered Deepmaxout model will predict the diseases during in the prediction phase. Lastly, the evaluation of the suggested approach is compared to existing methods using several types of metrics. © 2024 IEEE.

关键词： Prediction models

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AI-based small arms firing skill evaluation system in the military domain

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Defence Technology（防务技术） 2023年第11期29卷 164-180页

作者： Rezoanul Hafiz Chandan Nusrat Sharmin Muhaimin Bin Munir Abdur Razzak Tanvir Ahamad Naim Tasneem Mubashshira Mokhlesur Rahman Department of Computer Science and Engineering Military Institute of Science and TechnologyDhakaBangladesh

Evaluation system of small arms firing has an important effect in the context of military domain. A partially automated evaluation system has been conducted and performed at the ground level. Automation of such system with the inclusion of artificial intelligence is a much required process. This papers puts focus on designing and developing an AI-based small arms firing evaluation systems in the context of military environment. Initially image processing techniques are used to calculate the target firing score. Additionally, firing errors during the shooting have also been detected using a machine learning algorithm. However, consistency in firing requires an abundance of practice and updated analysis of the previous results. Accuracy and precision are the basic requirements of a good shooter. To test the shooting skill of combatants, firing practices are held by the military personnel at frequent intervals that include 'grouping' and 'shoot to hit' scores. Shortage of skilled personnel and lack of personal interest leads to an inefficient evaluation of the firing standard of a firer. This paper introduces a system that will automatically be able to fetch the target data and evaluate the standard based on the fuzzy *** it will be able to predict the shooter performance based on linear regression ***, it compares with recognized patterns to analyze the individual expertise and suggest improvements based on previous values. The paper is developed on a Small Arms Firing Skill Evaluation System, which makes the whole process of firing and target evaluation faster with better accuracy. The experiment has been conducted on real-time scenarios considering the military field and shows a promising result to evaluate the system automatically.

关键词： Machine learning Processing Firing system Artificial intelligence

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A novel hybrid authentication protocol utilizing lattice-based cryptography for IoT devices in fog networks

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Digital Communications and Networks 2024年第4期10卷 989-1000页

作者： Kumar Sekhar Roy Subhrajyoti Deb Hemanta Kumar Kalita Department of Computer Science and Engineering Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal576104KarnatakaIndia Department of Computer Science&Engineering ICFAI UniversityAgartala799210India Department of Computer Science&Engineering Central Institute of TechnologyKokrajhar783370India

The Internet of Things(IoT)has taken the interconnected world by *** to their immense applicability,IoT devices are being scaled at exponential proportions ***,very little focus has been given to securing such *** these devices are constrained in numerous aspects,it leaves network designers and administrators with no choice but to deploy them with minimal or no security at *** have seen distributed denial-ofservice attacks being raised using such devices during the infamous Mirai botnet attack in *** we propose a lightweight authentication protocol to provide proper access to such *** have considered several aspects while designing our authentication protocol,such as scalability,movement,user registration,device registration,*** define the architecture we used a three-layered model consisting of cloud,fog,and edge *** have also proposed several pre-existing cipher suites based on post-quantum cryptography for evaluation and *** also provide a fail-safe mechanism for a situation where an authenticating server might fail,and the deployed IoT devices can self-organize to keep providing services with no human *** find that our protocol works the fastest when using ring learning with *** prove the safety of our authentication protocol using the automated validation of Internet security protocols and applications *** conclusion,we propose a safe,hybrid,and fast authentication protocol for authenticating IoT devices in a fog computing environment.

关键词： Internet of things Authentication Post-quantum cryptography Lattice-based cryptography Cloud computing Fog computing Fail-safe

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Recognition of Deep Fake Voice Acoustic using Ensemble Bagging Model 5

Recognition of Deep Fake Voice Acoustic using Ensemble Baggi...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Basha, Shaik Abdul Yunus Priya, K.Ulaga Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350379945

As artificial speech generates technological evolution;fake voice information has become an increasingly prevalent avenue for fraud. Numerous investigations have been carried out into machine learning techniques, signal processing techniques, and the Automatic Speaker Verification System to address such issues. Since there are increasing methods to create fake information, techniques must be developed to determine when audio recordings employed as electronic proof are corrupted. Such recordings contain the potential to be manipulated. Hence, this research work identified the data regarding audio systems as either fake or real by i) extracting features from audio file metadata using oversampling techniques and ii) Developing an Ensemble Bagging Model appropriate for analyzing input audio signal followed by classification of fake (fraudulent) and real (authentic) audio. Our experimental results reveal that the ensemble bagging model attained an accuracy of 99.5%, precision of 99%, 99.6% recall, and 99.9% Fl-measure in the prediction of the audio system compared with traditional approaches. © 2024 IEEE.

关键词： Audio recordings

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Using DTL-MD with GANs and ResNet for Malicious Code Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 63-78页

作者： Li, Yiming Xie, Tao Mei, Dongdong Department of Computer Science and Technology School of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan753000 China

This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature extraction process, the weighted sum method of GIST and LBP features is used to combine the advantages of the two features. Online transfer learning is used to reduce the data distribution difference between the target domain and the source domain. The model uses ResNet50V2 as the backbone network and combines SimAM to enhance the feature extraction and representation capabilities. In addition, in order to further improve the robustness of detection, GAN is used to generate malicious code variants and expand the training data set. In the experiment, the public CICIDS 2017 data set is used for model training and testing. The performance test results show that when the threshold is 0.7, the accuracy of DTL-MD is 95.8% and the F1 score is 0.93. In a performance test involving 30,000 samples, the throughput of the DTL-MD model under Trojans, viruses, worms, and adware is 11, 12, 11, and 12 tasks/s, respectively, and the inference time is 211, 225, 239, and 234 samples/s, respectively. Compared with GAN, DTL-MD increases the throughput by about 10% and the inference speed by about 15%. The research aims to provide new ideas for improving the intelligence and automation level of malicious code detection technology, which has certain application value and practical significance. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Feature Selection

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Advancing mental health detection in texts via multi-task learning with soft-parameter sharing transformers

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Neural Computing and Applications 2025年第5期37卷 3077-3110页

作者： Kodati, Dheeraj Tene, Ramakrishnudu Department of Computer Science and Engineering National Institute of Technology Warangal India Department of Computer Science and Engineering Mahindra University Hyderabad India

In recent years, mental health issues have profoundly impacted individuals’ well-being, necessitating prompt identification and intervention. Existing approaches grapple with the complex nature of mental health, facing challenges like task interference, limited adaptability, and difficulty in capturing nuanced linguistic expressions indicative of various conditions. In response to these challenges, our research presents three novel models employing multi-task learning (MTL) to understand mental health behaviors comprehensively. These models encompass soft-parameter sharing-based long short-term memory with attention mechanism (SPS-LSTM-AM), SPS-based bidirectional gated neural networks with self-head attention mechanism (SPS-BiGRU-SAM), and SPS-based bidirectional neural network with multi-head attention mechanism (SPS-BNN-MHAM). Our models address diverse tasks, including detecting disorders such as bipolar disorder, insomnia, obsessive-compulsive disorder, and panic in psychiatric texts, alongside classifying suicide or non-suicide-related texts on social media as auxiliary tasks. Emotion detection in suicide notes, covering emotions of abuse, blame, and sorrow, serves as the main task. We observe significant performance enhancement in the primary task by incorporating auxiliary tasks. Advanced encoder-building techniques, including auto-regressive-based permutation and enhanced permutation language modeling, are recommended for effectively capturing mental health contexts’ subtleties, semantic nuances, and syntactic structures. We present the shared feature extractor called shared auto-regressive for language modeling (S-ARLM) to capture high-level representations that are useful across tasks. Additionally, we recommend soft-parameter sharing (SPS) subtypes-fully sharing, partial sharing, and independent layer-to minimize tight coupling and enhance adaptability. Our models exhibit outstanding performance across various datasets, achieving accuracies of 96.9%, 97.

关键词： Multi-task learning

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