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Autism spectrum disorder based on squeezenet with fractional tasmanian rat swarm optimization

Multimedia Tools and Applications 2024年第41期83卷 89029-89054页

作者： Muppidi, Satish Anuradha, G. Valarmathi, K. Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Kanuru India Department of Computer Science and Engineering Panimalar Engineering College Bangalore Trunk Road Chennai Pooñamallee600123 India

A complicated neuro-developmental disorder called Autism Spectrum Disorder (ASD) is abnormal activities related to brain development. ASD generally affects the physical impression of the face as well as the growth of the brain in children. An early and proper medical diagnosis is essential for ASD affected children to enhance their quality of life. However, the clinical detection of ASD is a difficult task and time-consuming, hence it is essential to design an ASD detection approach for precise diagnosis of ASD children. In this research, an algorithmic approach called Fractional Tasmanian Rat Swarm Optimization driven SqueezeNet (FTRSO-SqueezeNet) is designed for the detection of ASD. The median filter and Region of Interest (RoI) extraction are used to de-noise the input image initially and extract a particular region from the filtered image. Later, the nub regions are extracted by choosing the optimal grid from the pre-processed image and using different feature extractors the features from the input images are determined. Finally, the detection of ASD is done by using SqueezeNet, which is trained using the FTRSO approach. The performance of the method is estimated, where the designed model achieved higher performance with an accuracy of 94.55%, sensitivity of 92.53%, and specificity of 95.22% than other prevailing approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Rats

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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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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

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Multimedia Tools and Applications 2025年第17期84卷 17951-17977页

作者： Karunkuzhali, D. Meenakshi, B. Lingam, Keerthi Department of Information Technology Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering Gitam University Visakhapatnam India

In the Internet of Things (IoT), optimizing machine performance through data analysis and improved connectivity is pivotal. Addressing the growing need for environmentally friendly IoT solutions, we focus on "green IoT." Quality in smart cities heavily relies on efficient data gathering and communication. In this study, we propose a novel software architecture tailored for data collection and communication in IoT-enabled smart applications, emphasizing sustainability from the outset. Specifically, for smart cities utilizing IoT-enabled wireless sensor networks, we introduce a Quality of Service (QoS)-aware routing strategy. In the data collection phase, we employ Chaotic Bird Swarm Optimization (CBSO) for creating IoT sensor clusters, accompanied by Improved Differential Search (IDS) to assess the reliability of individual sensor nodes, ultimately designating the most trusted node as the Cluster Head (CH). In the data transport phase, we implement lightweight signcryption to enhance the security of IoT sensor data. Furthermore, we employ an Optimal Data Routing (ODM) approach to compute the most efficient data transmission paths between source and destination nodes within the IoT network. Finally, the performance of our QoS-aware routing strategy is evaluated through network simulations using NS2 and compared against existing approaches, offering valuable insights into its effectiveness and suitability for smart city IoT applications. The main findings of the study indicate that the proposed QoS-aware routing strategy (QOS-AWARE ROUTING) consistently outperforms existing routing algorithms in terms of energy efficiency, network longevity, and latency, both in scenarios with varying sensor node density and over multiple cycles. Additionally, it demonstrates significant advantages, such as up to 90.8% less energy consumption, 66.8% longer network lifespan, and 80.1% reduced latency when compared to the benchmarked methods. © The Author(s), under exclusive licence to S

关键词： Internet of things

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CVTD: A Robust Car-Mounted Video Text Detector

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computers, Materials & Continua 2024年第2期78卷 1821-1842页

作者： Di Zhou Jianxun Zhang Chao Li Yifan Guo Bowen Li Department of Computer Science and Engineering Chongqing University of TechnologyChongqingChina College of Information and Engineering Jingdezhen Ceramic UniversityJingdezhenChina

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous *** information in car-mounted videos can assist drivers in making ***,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time *** proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary *** model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD *** enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text ***,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s *** further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection *** model holds potential for practical applications in real-world scenarios.

关键词： Deep learning text detection Car-mounted video text detector intelligent driving assistance arbitrary shape text detector

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A Hybrid Regularized Multilayer Perceptron for Input Noise Immunity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 115-126页

作者： Mondal, Rahul Pal, Tandra Dey, Prasenjit National Institute of Technology Durgapur Department of Computer Science and Engineering Durgapur713209 India Cooch Behar Government Engineering College Department of Computer Science and Engineering Cooch Behar736170 India

The immunity of multilayer perceptron (MLP) is less effective toward input noise. In this article, we have focused on the robustness of MLP with respect to input noise where noise can be additive or multiplicative. Here, we have proposed a DropConnect-based regularized MLP to reduce the coadaptation among the neurons of the hidden layer. At first, we have empirically and statistically shown that by reducing coadaptation among the hidden neurons, an MLP can achieve better noise immunity. We have also empirically shown that an MLP with input noise injection and $l_{2}$ regularizer is an effective approach to improve its noise immunity. However, the results indicate that it does not adjust the coadaptation among the hidden neurons. Therefore, for further improvement, we have proposed a hybrid regularized MLP (HRMLP), where DropConnect is combined with the noise injection and $l_{2}$ regularizer. In addition to input noise, we have also verified the robustness of HRMLP with respect to $\mathbf {20\%}$ outliers in the dataset. To justify the effectiveness of the proposed HRMLP, we have compared it with MLP, MLP with noise injection, MLP with $l_{2}$ regularizer, MLP with noise injection and $l_{2}$ regularizer, and MLP with DropConnect along with two state-of-the-art works based on $\mathbf {20}$ standard datasets. The experimental results for both noisy inputs and outliers confirm that the performance of HRMLP is significant compared to other methods. © 2020 IEEE.

关键词： Neurons

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An Ophthalmic Evaluation of Central Serous Chorioretinopathy

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computer Systems science & engineering 2023年第1期44卷 613-628页

作者： L.K.Shoba P.Mohan Kumar Department of Information Technology Jeppiaar Engineering CollegeChennai600119India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal *** this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application *** approach,which was focused on image quality,improves medical image *** enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be *** total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.

关键词： OCT CSCR macula segmentation boosted anisotropic diffusion with unsharp maskingfilter two class support vector machine classifier and shallow neural network with powell-beale classifier

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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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An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

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computer Systems science & engineering 2023年第1期44卷 281-295页

作者： R.Prabhu S.Rajesh Department of Computer Science and Engineering AAA College of Engineering and TechnologySivakasiTamilnaduIndia Department of Information Technology Mepco Schlenk Engineering College SivakasiTamilnadu India

Cloud computing distributes task-parallel among the various *** with self-service supported and on-demand service have rapid *** these applications,cloud computing allocates the resources dynami-cally via the internet according to user *** resource allocation is vital for fulfilling user *** contrast,improper resource allocations result to load imbalance,which leads to severe service *** cloud resources implement internet-connected devices using the protocols for storing,communi-cating,and *** extensive needs and lack of optimal resource allo-cating scheme make cloud computing more *** paper proposes an NMDS(Network Manager based Dynamic Scheduling)for achieving a prominent resource allocation scheme for the *** proposed system mainly focuses on dimensionality problems,where the conventional methods fail to address *** proposed system introduced three–threshold mode of task based on its size STT,MTT,LTT(small,medium,large task thresholding).Along with it,task mer-ging enables minimum energy consumption and response *** proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme(EDS)and Decentralized Virtual Machine Migration(DVM).With a Network Manager-based Dynamic Scheduling,the proposed model achieves excellence in resource allocation compared to the other existing *** obtained results shows the proposed system effectively allocate the resources and achieves about 94%of energy efficient than the other *** evaluation metrics taken for comparison are energy consumption,mean response time,percentage of resource utilization,and migration.

关键词： Cloud computing resource allocation load balance dynamic scheduling dimensionality reduction

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An innovative Telugu text summarization framework using the pointer network and optimized attention layer

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Multimedia Tools and Applications 2024年第37期83卷 84539-84564页

作者： M, Varaprasad Rao Chakma, Kunal Jamatia, Anupam Rudrapal, Dwijen Department of Computer Science & amp Engineering National Institute of Technology Agartala Tripura799046 India Department of Computer Science & amp Engineering CVR College of Engineering Telangana Hyderabad501510 India

Summarizing lengthy text involves distilling crucial information into a concise form by covering the key events in the source text. Previous researchers mostly explored the supervised approaches for the task, but due to its strong reliance on the quality of text features, the resulting summaries often lack precision and coherence. The performance of the state-of-the-art summarizers becomes poor when applied to various Indian languages due to several challenges. The current research paper proposes a summarization approach for Telugu text based on a Pointer Network with an Optimised Attention Layer (PN-OAL). In this approach, the attention layer’s weights are adjusted using a hybrid optimization method that combines the Fusion of Coyote Optimization and Squirrel Search method (FCO-SSA). Weight optimization is conducted in order to address the goal function, which involves the maximization of the cosine similarity between the source text and the summary text. A novel loss function is incorporated within the framework of the constructed network architecture to generate a robust summary. The proposed approach is experimented with alternative baseline approaches in order to validate its efficacy for summarizing Telugu documents. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network architecture

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