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A novel soft computing based efficient feature selection approach for timely identification of COVID-19 infection using chest computed tomography images: a human centered intelligent clinical decision support system

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Multimedia Tools and applications 2025年第14期84卷 13717-13785页

作者： Singh, Law Kumar Khanna, Munish Garg, Hitendra Singh, Rekha Department of Computer Engineering and Applications GLA University Mathura281406 India School of Computing Science and Engineering Galgotias University Plot No.2 Sector 17-A Yamuna Expressway Gautam Buddh Nagar Greater Noida203201 India Department of Physics Uttar Pradesh Rajarshi Tandon Open University Uttar Pradesh Prayagraj India

The SARS-CoV-2 coronavirus strain’s introduction in December 2019 resulted in the development of the new coronavirus disease, COVID-19. Following its first appearance, the virus quickly spread throughout the world and is now considered to be a pandemic. There were 6,885,962 recorded deaths and 689,853,908 confirmed cases as of April 4, 2023. It is essential to put in place a thorough testing strategy in order to stop the disease from spreading. Nonetheless, a number of testing approaches are presently under consideration due to a restricted inventory and a limited supply of testing equipment. Recent expert analysis suggests that images from chest computed tomography (CT) scans could reveal important information about COVID-19, which is why we are using this modality as a focus of our investigation. Moreover, numerous recent studies have demonstrated that selecting the most informative features from the subject images improves the classification models’ efficiency and shortens the time needed for training and testing. All of this encourages us to present a study that suggests a novel, efficient, and fast feature selection system as the core of the proposed highly competent clinical decision support system for COVID-19 infection prediction. Using a publicly accessible CT image dataset, this four-phase system divides the images into two categories: "COVID-19-infected human" and "healthy human". Pre-processing is the first step in the study, after which features from different categories in the images are extracted in the next phase. In the third phase, the most influential features are then selected using three algorithms: the Teaching Learning-Based Optimization Algorithm (TLBO), the Cuckoo Search Optimization Algorithm (CSO), and a proposed hybrid of these two. To the best of the authors’ knowledge, these algorithms have rarely been applied to feature selection for COVID-19 infection prediction, which highlights the originality and inventiveness of the work. Followin

关键词： COVID-19

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Deep Learning Techniques for Human Resource Management Optimization

Deep Learning Techniques for Human Resource Management Optim...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Ranawat, Pratyaksha Kumar, Manish Karthik, A. Asha, V. Nagpal, Amandeep Shaik, Bazani Jecrc University Computer Science Engineering India Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Computer Applications Bangalore India Lovely Professional University Phagwara India Ramachandra College Of Engineering Department Of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350366846

Because deep learning and machine learning (ML) techniques have the potential to totally change how firms manage their human capital, there has been a lot of interest in developing HRM procedures that use these techniques. Strong DSS (Decision Support System) technology must be incorporated into the HRM (Human Resource Management) profession in order to make judgements that are effective in today's competitive environment. This study focuses on the problem of prediction, decision making, prediction, and testing assistance in a HRM system. The paper discusses a creative decision support system for HR procedures. Machine learning and deep learning techniques have been offered as fundamental instruments for tracking various HR indicators in the systems developed and implemented analytical process. The description of the suggested methodology and a discussion of a outcomes from the experiments are included in the paper. © 2024 IEEE.

关键词： Decision making

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Video Integrity Checking Using X25519 and Nested HMAC with BLAKE2b 2nd

Video Integrity Checking Using X25519 and Nested HMAC with...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Lawrence, Linju Shreelekshmi, R. Department of Computer Science and Engineering College of Engineering Trivandrum Affiliated to APJ Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India Department of Computer Applications College of Engineering Trivandrum Affiliated to APJ Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India

ISBN: (纸本)9789819995202

Latest developments in video editing or manipulation tools have facilitated effortless alteration of video content without any discernible traces left behind. As a result, it is imperative to subject video data to an integrity verification process before utilizing it as evidence. This paper presents a novel, lightweight approach for verifying the integrity of video data. The proposed method uses Hash-based Message Authentication Code (HMAC) and Elliptic Curve Diffie-Hellman Key Exchange utilizing Curve 25519 (X25519) with BLAKE2b. Nodes store video verification codes that are generated for video clips of a specific predetermined size. To enhance the security level, each node stores the nested HMAC value of the prior node. The integrity check involves the regeneration and comparison of nested HMAC of the node. The proposed method’s experimental results demonstrate better performance in terms of both speed and security when compared to state-of-the-art methods. With minimal additional storage requirements, our method can identify any type of forgery on any video file, at any given time, by an authorized individual. Security analysis indicates that the method can withstand a range of attacks, such as timing attacks, key substitution attacks, side channel attacks, and brute force attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Side channel attack

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Predictive Maintenance of Power Grid Infrastructure using Long Short-Term Memory Networks

Predictive Maintenance of Power Grid Infrastructure using Lo...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mittal, Mudit Alsalami, Zaid Kumar, Rakesh Boob, Nandini Shirish Verma, Vikas Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This study examines the application of Long Short-Term Memory (LSTM) networks for the prescient upkeep of a control lattice framework. Leveraging verifiable information comprising sensor readings and support records, the LSTM demonstrate precisely expects potential disappointments or debasement within the control network, empowering proactive support techniques. Through broad experimentation, the LSTM demonstrates accomplishing momentous execution with an exactness of 90%, precision of 88%, review of 92%, F1-score of 90%, and an area beneath the ROC curve (AUC) of 0.95. Comparative examination against standard methods and related works within the writing illustrates the prevalence of the LSTM-based approach in prescient support of control network infrastructure. Optimization of hyperparameters and interpretability examination advance upgrades the model's execution and encourages decision-making in control network administration. This study sheds light on the potential of machine learning strategies to revolutionize control framework upkeep, guaranteeing a more solid, strong, and economic vitality supply. © 2024 IEEE.

关键词： Long short-term memory

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Efficient Load Balancing Algorithms for Edge Computing in IoT Environments

Efficient Load Balancing Algorithms for Edge Computing in Io...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Nainwal, Ankita Almusawi, Muntather Bansal, Saloni Michaelson, Jacob Kumar, Suresh Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This study investigates the accuracy of load balancing calculations in terms of edge computing for IoT. The paper compared four main calculations, namely Round Robin (RR), Weighted Round Robin (WRR), Least Connections (LC) and Ant Colony Optimization ratings in an reproduced edge computing atmosphere. As for the study, it focused on efficiency improvements of asset utilization as well as reducing inactivity and enhancing reasonableness. Weighted Round Robin showed that the predominant execution came about with an astonishing 25% decrease in idleness as compared to a depiction of noncapacity aware task load distribution. The Slightest Associations also showed extraordinary outcomes, achieving 20% decrease in idle period and demonstrated that it could be flexible with workloads. While flexible, Ant Colony Optimization demonstrated an inability to achieve steady variation improvements related with parameter tuning ranging from 15% toward 30%. The discoveries emphasize the significance of calculation choice based on the particular prerequisites of IoT edge situations. © 2024 IEEE.

关键词： Internet of things

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Convolutional Neural Networks for Intrusion Detection in IoT Networks with Limited Resources

Convolutional Neural Networks for Intrusion Detection in IoT...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mittal, Mudit Al-Fatlawy, Ramy Riad Bansal, Saloni Michaelson, Jacob Bhalla, Lalit Veena, Ch. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

In this research, we explore the adequacy of Convolutional Neural Systems (CNNs) for interruption location in resource-constrained IoT systems. Leveraging two freely accessible datasets, we compare the execution of CNNs with conventional machine learning calculations such as Support Vector Machines (SVM) and Random Forests (RF). Our tests uncover that CNNs accomplish competitive location exactness, beating SVM and RF models over both datasets. Particularly, on Dataset A, CNNs accomplish an accuracy of 0.92, a review of 0.88, and an F1-score of 0.90, whereas, on Dataset B, they accomplish an accuracy of 0.86, a review of 0.84, and an F1-score of 0.85. Also, CNNs illustrate prevalent computational proficiency, with shorter preparing times and lower memory impressions compared to conventional calculations. This inquiry contributes to progressing the state-of-the-art in IoT interruption discovery by highlighting the viability of profound learning methods in tending to cybersecurity challenges in IoT situations. Moving forward, the selection of CNNs can upgrade the versatility and security of IoT systems, clearing the way for more strong and effective interruption location frameworks. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid Classifier for Software Defect Prediction by Using Filter-Based Feature Selection Techniques 3rd

Hybrid Classifier for Software Defect Prediction by Using Fi...

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3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Kumar, Raghvendra Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology SRM Nagar Tamil Nadu Kattankulathur603203 India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Gunupur India

ISBN: (纸本)9789819705726

Software’s capacity for error-free operation determines how reliable it is. Unfortunately, mistakes can happen at any stage of the software development process. Predicting software flaws early on in the development process is now of utmost importance in the field of software engineering. Using classification techniques, which are a popular strategy for predicting software faults, modules that are illustrated by a group of metrics or code properties are divided into defective or non-defective categories. However, there are low-quality, unreliable, redundant, and noisy data sources that have an adverse impact on the process of observing information and helpful patterns. Researchers must therefore use feature selection techniques to extract pertinent info from massive databases. In feature selection, the redundant and unnecessary features are eliminated in order to focus on the most significant ones. In this paper, we looked in to the impact of filter-based feature selection on classification techniques in software defect prediction. NASA and the Metric Data Program software repository both include publicly available datasets that were used in this study. Principal component analysis (PCA) and CFS was used to assess the dataset’s top discriminator features. The datasets were classified by using hybrid ensemble learning, for hybrid ensemble learning classifiers like DT, SVM, K-NN, NB, and LR were used. The experimental findings showed that it is preferable and should be encouraged to apply feature selection to datasets prior to classification in order to anticipate software defects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature Selection

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Fog Restoration in Hazy Images using Deep Transfer Learning

Fog Restoration in Hazy Images using Deep Transfer Learning

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agrawal, Navin Kumar Sharma, Viboo Singh, Pranita Sachi, Savya Jain, Arpit Alam, Md Mohtab GLA University Department of Computer Engineering and Applications U.P. Mathura India Hindustan College of Science and Technology Department of IT U.P. Mathura India Roorkee Institute of Technology Department of Computer Science and Engineering U.K. Roorkee India L N Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Eklavya University Department of Computer Science & Engineering M.P. Damoh India

ISBN: (纸本)9798350394412

Issues like low vision and the distortion of spectral and spatial information in photographs can be brought on by unfavorable atmospheric circumstances including smog, rain, overcast sky, fog, and smoke. This piece looks at images taken in a hazy environment. While de-smogging requires veil and transmission map data, optical imaging systems are limited to capturing smoggy images. To restore blurry photographs, effective optical information prediction is necessary. Research has shown that optical information anticipated by the dark channel prior technique frequently produces subpar images in photographs with areas of higher brightness, noticeable pollution gradients, or textured information. In order to estimate optical information, this study makes use of Deep Transfer Learning (DTL) and Optimized Gradient Prior Prediction (OGPP). We gathered benchmark images of both smoggy and smog-free environments in order to train the DTL model. The OGPP technique uses the smog gradient, which the DTL model is taught to anticipate, to recover pictures free of pollution. A comparison analysis shows that when it comes to recovering hazy photos, the OGPP restoration model performs better than other approaches. © 2024 IEEE.

关键词： Restoration

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Mobile-Based Classification and Detection of Diabetic Retinopathy Using TinyML 2

Mobile-Based Classification and Detection of Diabetic Retino...

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Kumar, Manish Mohan Kumar, H.P. Sultana, Husna Shweta, J.S. M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Pes College of Engineering Dept of Master of Computer Applications Karnataka Mandya India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

This paper presents a comprehensive approach to diabetic retinopathy (DR) classification and detection, encompassing the evaluation of ResNet, RegNet, and WideResNet models for DR severity assessment. Leveraging the Kaggle's APTOS 2019 dataset, we implement and analyze these models using PyTorch, highlighting their efficacy in categorizing DR severity levels. Additionally, we leverage Tiny Machine Learning (TinyML) techniques to develop a mobile-based solution, enabling real-time DR severity assessment on mobile devices. This mobile application showcases the potential of TinyML for early detection and intervention in DR cases, ultimately enhancing patient outcomes. By synergizing advanced deep learning models with TinyML, this study contributes to healthcare technology, enabling timely interventions and improved patient outcomes. © 2023 IEEE.

关键词： Equalizers

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Innovations in Mechanical Drawing and computer Graphics: Enhancing Precision and Visualization in engineering Design 4

Innovations in Mechanical Drawing and Computer Graphics: Enh...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Shrivastava, Anurag Goyal, Himanshu Rai Hussein, Layth Bansal, Saloni Sharma, Vishal Harikrishna, M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331518981

This study examines the significant advancements in computer graphics and mechanical drawing, with an emphasis on how these developments have revolutionised engineering design precision and visualisation. For engineers and designers, integrating cutting-edge computer-aided design (CAD) tools and visualisation technologies has become crucial as sectors continue to adapt to issues that are ever more complex. The study focusses on significant developments that improve design accuracy and enable detailed visual representations of engineering concepts, including augmented reality (AR), simulation methods, and 3D modelling. Professionals can streamline the engineering workflow by using CAD software to produce complex drawings that are both exact and easily customisable. Before physical prototyping takes place, decision-making based on examination of the product's performance under different conditions is made possible by the integration of simulation modelling. Furthermore, by offering immersive experiences that let stakeholders see and interact with 3D models in actual settings, augmented reality applications transform design assessments. The research emphasises the importance of these advances in enhancing engineering teams' cooperation, efficiency, and communication through a thorough examination of them. The ultimate goal of this research is to pinpoint future paths for the integration of computer graphics and mechanical drawing, opening the door for improved engineering design and development processes. © 2024 IEEE.

关键词： Precision engineering

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