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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Singh, Manu Mohanty, Lopamudra Gupta, Neha Bansal, Yash Garg, Sejal ABES Engineering College Department of Computer Science UP Ghaziabad India Galgotias College of Engineering and Technology Greater Noida India

ISBN: (纸本)9798350353648

Deep Neural Networks offers a high rate of accuracy, extensive, parameters, and intensive computational requirements that often characterize these networks. Due to high memory consumption and energy usage, this results in significant challenges when deploying DNN s on hardware-constrained devices. This paper introduces the 'Q-Net Compressor,' a novel solution for installing substantial neural (Tao, Hou, et al. 2022) network training on constrained resources edge devices. Utilizing adaptive precision levels to maintain model functionality while reducing memory and computation demands. The Q-Net Compressor, implemented with TensorFlow and Keras, offers flexibility across diverse deep-learning architectures, striking a balance between significant model size reduction and the preservation of predictive power. This innovative approach contributes to the evolving deep learning model compression field with quantization, bridging the gap between computational demands and resource constraints. This research paper presents the implementation and evaluation of various quantization techniques on a Convolutional Neural Network (CNN) model trained using a COVID-19 dataset. The study aims to investigate the effects of quantization on model accuracy and size reduction. The CNN model is trained on a dataset containing COVID-19 images, and different quantization techniques are applied to the trained model. The techniques include post-training quantization, quantization-aware training, scalar quantization, vector quantization, centroid quantization, uniform quantization, non-uniform quantization and hybrid quantization methods. The results demonstrate the impact of each technique on the model's accuracy and size, providing insights into the trade-offs between model performance and computational efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Sarcasm Detection in X data Using Node Embedding and Graph Convolutional Networks 4

Sarcasm Detection in X Data Using Node Embedding and Graph C...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Tyagi, Aditya Dayal Garg, Setu Sharma, Swati Tomar, Vivek Verma, Kimmi Computer Science & Application Noida India Its Engineering College Electronics & Communication Engineering Noida India Kiet Group of Institutions Computer Science & Engineering Ghaziabad India Computer Science & Engineering Dehradun India Abes Engineering College Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350364729

An extensive amount of attention has been paid to the implications that the detection of sarcasm on online social networks such as Facebook, X etc. could have for the reason of sarcasm detection, sentiment analysis, content regulation, and public opinion. In light of the fact that traditional methods frequently struggle to comprehend the nuances of sarcasm, there is a growing interest in more advanced methodology that make uses of network architectures and deep learning. For the purpose of identifying instances of sarcasm in X data, this study presents a novel method that combines Graph Convolutional Networks (GCNs) and node embeddings. Our solution makes use of a generalized convolutional neural network (GCN), constructs a user-word interaction network, and incorporates word2vec for initial text embedding in order to capture both textual and contextual characteristics. Using our methodology, we demonstrate that it is superior to traditional methods in terms of both accuracy and robustness. © 2024 IEEE.

关键词： Graph embeddings

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Novel Taxonomy for E-learning Recommender System Using Opinion Mining 14th

Novel Taxonomy for E-learning Recommender System Using Opini...

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12th International Advanced Computing Conference, IACC 2022

作者： Kalpana Chauhan, Shardul Singh Singh, Mahesh Kumar Bagoria, Renu Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science and Engineering Jagannath University Jaipur India

ISBN: (纸本)9783031356438

People constantly seek development to maintain their competitiveness in their careers in this time when every element of society is advancing. E-learning platforms adapt to the always changing environment and offer learners numerous learning resources and remote learning opportunities. Users want assistance in using the myriad resources available online. Choosing a course to enroll in might be difficult, thus recommender systems are used in online learning to give students individualized advice by automatically detecting their preferences, services are provided. This paper thoroughly examines the key E-learning uses recommendation systems to identify new areas for research. This paper reviews the following techniques: collaborative filtering-based, content-based, and knowledge-based recommendations. The paper also talks about the fundamental workings of these methods and how they’re applied to meet the particular requirement. The findings in this research could confirm researchers and practitioners to better comprehend the present state and potential futures of recommender electronic learning systems. © 2023, Springer Nature Switzerland AG.

关键词： Recommender systems

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Flood prediction near Yamuna express way a case study on Delhi 2

Flood prediction near Yamuna express way a case study on Del...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Verma, Shikha Mishra, Priya ABES Engineering College Ghaziabad India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798331530389

The area lies near Yamuna River in Delhi faces a frequent flood event, and with the increasing frequency and intensity of flood events highly impacted the human lives, infrastructure, and agriculture. Hence, there is a great need of accurate and timely flood prediction models. Traditional hydrological models are useful but often inaccurate for effective disaster management. The new technologies like machine learning are showing a prominent solution for the complex problems like flood. Therefore, this study conducts a comprehensive analysis of advanced ML(machine learning) techniques: Logistic Regression or named as LR, Support Vector Machines named as SVM, Random Forest named as (RF)and XG-Boost, in predicting floods in the area lies near Yamuna River by using GIS, Remote sensing data and seven flood influencing factors. The performance of the models is tested by using performance metrics such as accuracy, recall, F1-score and precision. Further, the models were also validated using curve of AUC-ROC. The outputs reveal that XG-Boost out performs the other models in prediction accuracy. Logistic Regression and SVM also perform well, giving them viable options depending on the application scenario. This study examines the benefits and weaknesses of each model and recommends their use in flood-prone area near Yamuna River. © 2025 IEEE.

关键词： Logistic regression

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Steel Surface Defect Detection Using Learnable Memory Vision Transformer

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computers, Materials & Continua 2025年第1期82卷 499-520页

作者： Syed Tasnimul Karim Ayon Farhan Md.Siraj Jia Uddin Department of Computer Science and Engineering BRAC UniversityDhaka1212Bangladesh Department of AI and Big Data Endicott CollegeWoosong UniversityDaejeon34606Republic of Korea

This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel *** awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and *** techniques mitigated overfitting,stabilized training,and improved generalization *** LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,*** findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature *** additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial *** instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often *** study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are *** research m

关键词： Learnable Memory Vision Transformer(LMViT) Convolutional Neural Networks(CNN) metal surface defect detection deep learning,computer vision image classification learnable memory gradient clipping label smoothing t-SNE visualization

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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

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computer Modeling in engineering & sciences 2024年第2期138卷 1469-1483页

作者： Laila M.Halman Mohammed J.F.Alenazi Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11451Saudi Arabia

The healthcare sector holds valuable and sensitive *** amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast *** to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and *** this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe *** attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human *** can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or *** this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various *** propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS *** then evaluate the accuracy and performance of the proposed TBDC *** technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

关键词： Network resilience network management attack prediction software defined networking(SDN) distributed denial of service(DDoS) healthcare

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

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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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iWBFCxr: improved weighted box fusion for analyzing chest x ray images for enhancing thoracic abnormalities

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International Journal of Information Technology (Singapore) 2025年第2期17卷 711-720页

作者： Verma, Sonia Devarajan, Ganesh Gopal Sharma, Pankaj Kumar Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi - NCR Campus Delhi - Meerut Road Modinagar Uttar Pradesh Ghaziabad 201204 India Department of Computer Science ABES Engineering College UP Ghaziabad 201009 India

Chest x-ray studies can be automatically detected and their locations located using artificial intelligence (AI) in healthcare. To detect the location of findings, additional annotation in the form of bounding boxes is required, rather than image-level labeling. Accurate interpretation of chest radiographs is essential for diagnosing thoracic abnormalities. This paper introduces a novel method for abnormality detection in chest radiographs using the VinDr-CXR dataset. Labeled training sets are improved by incorporating data augmentation and advanced pre-processing techniques. To improve the accuracy of anomaly identification, the study makes use of ensemble object detection models, such as the modified weighted boxes fusion (iWBFCxr) technique. With an average precision (AP) of 0.2434 for class 14, experimental results on the VinDr-CXR dataset show an overall mean average precision (mAP) of 0.2791. The iWBFCxr approach, when combined with ensemble predictions, has the potential to improve abnormality localization. By integrating enhanced label merging with ensemble object detection models, our method improves abnormality localization in chest radiographs significantly, improving thoracic abnormality diagnosis accuracy and possibly leading to new medical interventions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Automated classification Automated localization Chest radiographs computer vision Deep learning Machine learning Thoracic anomalies

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A novel cluster based reliable security enhancement in FANET directed by game theory

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Soft Computing 2025年第2期29卷 1091-1106页

作者： Gupta, Shikha Sharma, Neetu Department of Computer Science Engineering College Ajmer Rajasthan Ajmer305001 India

The emergence of interconnected UAVs has given rise to the creation of flying ad hoc networks (FANETs) aimed at efficiently facilitating network-dependent services. However, FANET encountered considerable challenges in achieving reliability due to security issues influenced by the existence of malicious nodes. These nodes continuously engage in communication and present a significant threat. These issues are precisely addressed in this article by introducing a novel methodology enhancing network security through a game theory-driven decision tree approach. The proposed strategy involves the implementation of a cluster mechanism based on path similarity techniques. Additionally, an optimized strategy put forth to minimize cluster overhead. Comparing with state-of-the-art protocols in identifying malicious nodes within the network, the results indicate a notable enhancement averaging at 52.84%. Moreover, in aspects such as delay reduction, precision rate increase, and message drop minimization, the experimental outcomes exhibit average improvements of 42.17%, 65.24%, and 46.63%, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Decision trees

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