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Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification

Intelligent Automation & Soft Computing 2023年第2期35卷 2353-2366页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Hyperspectral(HS)image classiﬁcation is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,*** recognition of features from the HS images is important for effective classiﬁcation ***,the recent advancements of deep learning(DL)models make it possible in several application *** addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of *** this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classiﬁcation(RDADL-HIC)*** proposed RDADL-HIC technique aims to effectively determine the HSI *** addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad ***,RDA with gated recurrent unit(GRU)approach is used for the identiﬁcation and classiﬁcation of *** design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models *** experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different *** comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.

关键词： Hyperspectral images image classiﬁcation deep learning adagrad optimizer nasnetlarge model red deer algorithm

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Enabling technology Integrated Learning for Autistic Children Using Augmented Reality Based Cognitive Rehabilitation

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SN computer science 2024年第1期5卷 151页

作者： Abdullah, A. Sheik Karthikeyan, J. Gomathi, V. Parkavi, R. Rajarajeswari, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering National Engineering College Kovilpatti India Department of Information Technology Thiagarajar College of Engineering Madurai India

Augmented reality (AR)-based rehabilitation is an excellent teaching tool (ICT tool) for simulating daily activities for autistic children. Children with autism may become more mentally capable with this type of AR-based rehabilitation. It is intended to observe the children's performance in terms of concentration, attention, and identification. The observation has been done through placards as a target image to display the 3D objects on a mobile phone or tablet. In this project, observations are made for 21 autism children in the age group of 7–14, out of whom 17 are boys and 5 are girls. Those 21 children are given practice identifying 15 different objects in an augmented reality environment. Their performance was initially evaluated using conventional instructional techniques. The majority of the kids were having more difficulty identifying things during that observation. Then, with an Augmented Reality environment, the identical observation has been made once more. Using a mobile device or tablet, the 3D objects from the provided placard photos are produced in an augmented reality environment with animation and voice in the languages of English and Tamil. Children with autism are able to recognize and also grasp the behaviors of those objects while viewing them in 3D. Their efforts are measured using a two-point scale (0, 1, 2). The pre-assessment and post-assessment reports for the above observations are tabulated. All the observations are made in the presence of the special education teacher (therapist). However, the children observed in this project fall into three different categories: mild, moderate, and severe. In the Mild category, statistical significance is evident with p values of 0.002 in pre-assessment and 0.014 in post-assessment. Likewise, in the Moderate category, where p values are 0.023 in pre-assessment and 0.033 in post-assessment, significance is observed, as all p values fall below the chosen significance level of 0.05. This leads to rejecti

关键词： Augmented reality (AR) Autism ICT tools Observation Rehabilitation Teaching methodology

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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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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Farmease: Bridging the Gap for Farmers in the Digital Ages 2

Farmease: Bridging the Gap for Farmers in the Digital Ages

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Parvathi, S. Gurla, Vedavyas Jhansi, Ch. Srikanth, V. Vijayasri, N. Srividhya, M. Mlr Institute of Technology Department of Computer Science and Information Technology India Geethanjali College of Engineering and Technology Department of CSE-AIML Hyderabad India Aar Mahaveer College Department of Computer Science & Engineering India Mlr Institute of Technology Department of Computer Science & Information Technology Hyderabad India

ISBN: (纸本)9798350370188

FarmEase is an innovative web application designed to help farmers overcome key challenges in the agricultural sector. The platform provides farmers with access to weather forecasting, market information, pest and disease management, and training and education resources. In addition to these features, FarmEase also integrates with government APIs to provide farmers with information on schemes and subsidies. This helps farmers take advantage of government programs that can support their operations and improve their profitability. With FarmEase, farmers can manage their operations more efficiently, sell directly to consumers through an online marketplace, and improve their skills and knowledge through training and education resources. Overall, FarmEase has the potential to transform the agricultural sector by harnessing the power of technology to support farmers and promote sustainable agriculture. © 2024 IEEE.

关键词： Weather forecasting

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Collaborative Service Provisioning for UAV-Assisted Mobile Edge Computing

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Chinese Journal of Electronics 2024年第6期33卷 1504-1514页

作者： Yuben QU Zhenhua WEI Zhen QIN Tao WU Jinghao MA Haipeng DAI Chao DONG The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Colllege of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Xi'an Research Institute of High Technology Department of Information and Communication Noncommissioned Officer Academy of PAP National University of Defense Technology Department of Computer Science and Technology Nanjing University

Unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC), as a way of coping with delaysensitive and computing-intensive tasks, is considered to be a key technology to solving the challenges of terrestrial MEC networks. In this work, we study the problem of collaborative service provisioning(CSP) for UAV-assisted MEC. Specifically, taking into account the task latency and other resource constraints, this paper investigates how to minimize the total energy consumption of all terrestrial user equipments, by jointly optimizing computing resource allocation, task offloading, UAV trajectory, and service placement. The CSP problem is a non-convex mixed integer nonlinear programming problem, owing to the complex coupling of mixed integral variables and non-convexity of CSP. To address the CSP problem, this paper proposes an alternating optimization-based solution with the convergence guarantee as follows. We iteratively deal with the joint service placement and task offloading subproblem, and UAV movement trajectory subproblem, by branch and bound and successive convex approximation, respectively,while the closed form of the optimal computation resource allocation can be efficiently obtained. Extensive simulations validate the effectiveness of the proposed algorithm compared to three baselines.

关键词： Energy consumption Multi-access edge computing Collaboration Programming Autonomous aerial vehicles Minimization Trajectory Resource management Optimization Convergence

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Dysphonic Voice Pattern Based Parkinson Disease Detection Using Machine Learning Models 2

Dysphonic Voice Pattern Based Parkinson Disease Detection Us...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rhythm Khandelwal, Rajat Badlani, Prerna Saxena, Bhawna Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350371055

Parkinson's Disease stands as a widespread cerebral degenerative disorder impacting millions of people across the world. Timely and precise diagnosis of PD is of paramount importance, enabling early interventions and enhancing overall patient care. The presented work focuses on non-invasive detection of PD through voice analysis, targeting early intervention. Dysphonic voice patterns, indicative of PD progression, are analyzed using advanced signal processing and machine learning. Methodology involves collecting voice recordings from PD-affected individuals and healthy controls, with rigorous analysis of acoustic features. This approach minimizes patient discomfort and reduces healthcare costs, potentially revolutionizing PD care and enhancing patient quality of life. This study explored the efficacy of 4 machine learning models - Decision Trees, k-Nearest Neighbors, Support Vector Machine, and Naive Bayes- in Parkinson's Disease (PD) detection. k-Nearest Neighbors classifier demonstrates much better performance compared to the other three, achieving an accuracy of 96.61%. © 2024 IEEE.

关键词： Dysphonic Voice Pattern KNN Machine Learning Parkinson's Disease

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Transaction transmission model for blockchain channels based on non-cooperative games

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science China(information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Stacking Enabled Ensemble Learning Based Intrusion Detection Scheme (SELIDS) for IoV

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SN computer science 2024年第8期5卷 1000页

作者： Singh, Abhijeet Pratap Chaurasia, Brijesh Kumar Tripathi, Ashish Department of Information Technology Pranveer Singh Institute of Technology Kanpur India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

A revolutionary approach for enhancing driving efficiency and safety in intelligent transportation systems (ITS) is deploying autonomous vehicles. Vehicle-to-everything (V2X) technology facilitates interactions between vehicles and infrastructure. However, the Internet of Vehicles (IoV) is susceptible to many cyberattacks, encompassing impersonation, denial of service (DoS), and fuzzy assaults. This paper proposes an intelligent network intrusion detection system (NIDS) using machine learning algorithms. The usage of the ML approach in a Stacking-enabled Ensemble Learning-based Intrusion Detection Scheme (SELIDS) for IoV is proposed. We additionally examine each technique’s shortcomings and how they affect the NIDS efficiency. Deploying the proposed NIDS on the benchmark dataset demonstrates the capacity of the system to recognize different kinds of assaults. Finally, we explore the potential for NIDS to collaborate with additional security technologies in the future. Empirical results prove the efficacy of the proposed mechanism. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attacks Intelligent transportation systems (ITS) Internet of vehicles (IoV) Machine learning (ML) Network intrusion detection system (NIDS)

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Fruit Quality Monitoring System using CNN 1

Fruit Quality Monitoring System using CNN

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ravikumar, B.N. Vidya, M.S. Gowda, Naveen Chandra NarendraBabu, C.R. Veena, H.N. Ramani, D. Roja Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering Reva University Bengaluru India Sjb Institute of Technology Department of Computer Science and Engineering Bengaluru India New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529246

The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and color, limitations arise as different fruits may share similar color, size, and shape values. To enhance accuracy, the system combines size, shape, and color analysis with Convolutional Neural Network (CNN) technology. Upon capturing the fruit image, properties like size, shape, and color are extracted, followed by CNN training and testing processes. The proposed work have implements UNet and En-UNet chosen over other architectures like ResNet or MobileNet for segmentation. This innovative approach utilizes CNN to extract comprehensive features, yielding promising results in fruit freshness detection with accuracy of 99% and loss rate of 0.0123. © 2025 IEEE.

关键词： Convolutional neural networks

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