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5th International Conference on Circuits, Control, Communication and computing, I4C 2024

作者： Karthigeyan, S. Diviya, M. Bhuvaneswari, R. Sabhanayagam, T. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Galgotias University UP Greater Noida India

ISBN: (纸本)9798331528539

The primary issue of global health today is Cardio-vascular diseases (CVDs) and thus requires accurate predictive models for detecting it early. Quantum technology combined with regular deep learning techniques are used by researchers here to improve the accuracy of forecast on heart diseases. While using an expanded dataset with clinical and demographic records, an innovative approach was found to use Quantum Convolutional Neural Network (QCVNET) structure that merged quantum layers into classical CNN framework with Principal Component Analysis (PCA) that allowed for consistency of features with respect to the quantum bits' number. The performance of the QCVNET was evaluated by comparing it to Quantum LSTM, Quantum DNN, Quantum GRU, and Quantum RNN, which are other quantum models enhanced with conventional neural networks, and other traditional non-quantum models like SVM, RF, RNN and DNN, using different indicators of quality like f1-score or recall. In early CVD prediction, the QCVNET model produced an impressive accuracy of 91.73%, beating all other models, showcasing the potential impact of quantum-enhanced deep learning for timely diagnosis and personalized treatment strategies while providing means to reduce the global burden of cardiovascular diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Formal Modeling of Hybrid System Based on Semi-continuous Colored Petri Net: A Case Study of Adaptive Cruise Control System

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ACM Transactions on Embedded computing Systems 2025年第3期24卷 1-21页

作者： Yu, Wangyang Guo, Qi Cheng, Yumeng Liu, Lu Hao, Fei Zhai, Xiaojun Chen, Minsi The School of Computer Science Shaanxi Normal University Shaanxi Xi'an China The School of Computer Science Shaanxi Normal University Xi'an China University of Exeter Department of Computer Science Exeter United Kingdom University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom

Many Next-Generation consumer electronic devices would be distributed hybrid electronic systems, such as UAVs (Unmanned Aerial Vehicles) and smart electronic cars. The safety and risk control are the key issues for the sustainability of such consumer electronic systems. The modeling of hybrid electronic systems is difficult to be abstracted by traditional Petri Nets. This also makes the reachable marking graph unable to be applied to Petri Nets of the hybrid electronic systems. This paper proposes a novel Petri Net to model and analyze the hybrid electronic systems. We name it a Semi-continuous Colored Petri Net (SCPN) that inherits the excellent modeling capabilities and analysis methods of Petri Nets, and can formally depict hybrid quantities. In addition, we propose the construction algorithm for an SCPN reachable marking graph and prove its finiteness. Finally, we model and analyze an Adaptive Cruise Control (ACC) system of smart electronic cars as an example to prove the validity of SCPN. We use the proposed SCPN to model and analyze the running process of an ACC system under the continuous deceleration scenario of the front vehicle. The application study shows that the ACC system has logic flaws under the constant headway strategy when the front vehicle continues to decelerate. Based on this analysis, improvements to the SCPN of the ACC system are made, effectively enhancing its safety and logical correctness. Copyright © 2025 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adaptive cruise control

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ESOA-Based 1D-CNN Classification for Amazon Textual Review Sentiment Analysis of Mobile Phones

ESOA-Based 1D-CNN Classification for Amazon Textual Review S...

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International Conference on computing and Machine Learning, CML 2024

作者： Kosala, N. Nirmalrani, V. School of Computing Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9789819775484

A trend toward the use of digital platforms for product marketing and profit margin enhancement is evident in the contemporary corporate environment. As a result, using deep learning methods to evaluate consumer sentiment in tweets based on product reviews becomes essential. The study's proposal is to use a big dataset and a one-dimensional convolutional neural network (1D-CNN) for the classification process in order to extract lexical characteristics for sentiment analysis. In order to do this, analysis is done on customer review datasets of Amazon items with an emphasis on reviews pertaining to mobile phones. To improve the performance and accuracy of the classifier, data preprocessing is first done. A lexical method is then used to efficiently compress the text via feature extraction. For the purpose of correctly assessing review emotions, efficient classification is essential. So, classification is done using the 1D-CNN method. The Egret Swarm Optimization Algorithm is used to adjust the 1D-CNN model's hyperparameters (ESOA). After this procedure, the research evaluates the 1D-CNN method's performance in relation to other techniques currently in use. The research concludes that the suggested method performs well overall, achieving 98.77% accuracy, 97.37% precision, 97.42% sensitivity, and 97.21% F-measure, respectively. These performance measures outperform those of current algorithms created in earlier research. The sentiment analysis of mobile phone evaluations inside the Amazon e-commerce system shows improved accuracy and efficiency when using the suggested 1D-CNN neural network approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Edge-Enabled Metaverse:The Convergence of Metaverseand Mobile Edge computing

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Tsinghua science and Technology 2024年第3期29卷 795-805页

作者： Nyothiri Aung Sahraoui Dhelim Liming Chen Huansheng Ning Luigi Atzori Tahar Kechadi Information Engineering College Xinjiang Institute of EngineeringUrumqi 830023Chinaand also with School of Computer ScienceUniversity College DublinDublinD04 V1W8Ireland School of Computing Ulster UniversityBelfastBT151EDUK School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electrical and Electronic Engineering University of CagliariSardinia 09124Italy School of Computer Science University College DublinDublinD04 V1W8Ireland

Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical *** state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering *** current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access,such as low-quality *** this end,we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing *** applications leverage edge devices’computing power to perform the required computations for heavy tasks,such as collision detection in the virtual universe and high-computational 3D physics in virtual *** computational costs of a Metaverse entity,such as collision detection or physics emulation,are performed at the device of the associated physical *** validate the effectiveness of the proposed architecture,we simulate a distributed social Metaverse *** simulation results show that the proposed architecture can reduce the latency by 50%when compared with cloud-based Metaverse applications.

关键词： industrial Metaverse edge computing Blockchain virtual environment task offloading fog computing

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Weighted Concept Factorization Based Incomplete Multi-view Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5699-5708页

作者： Khan, Ghufran Ahmad Khan, Jalaluddin Anwar, Taushif Ashraf, Zubair Javed, Mohammad Hafeez Diallo, Bassoma Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China VIT Bhopal University School of Computing Science and Engineering Kothrikalan Madhya Pradesh Sehore466114 India

The primary objective of classical multiview clustering (MVC) is to categorize data into separate clusters under the assumption that all perspectives are completely available. However, in practical situations, it is common to encounter cases where not all viewpoints of the data are accessible. This limitation can impede the effectiveness of traditional MVC methods. The incompleteness of the clustering of multiview data has witnessed substantial progress in recent years due to its promising applications. In response to the aforementioned issue, we have tackled it by introducing an inventive MVC algorithm that is tailored to handle incomplete data from various views. Additionally, we have proposed a distinct objective function that leverages a weighted concept factorization technique to address the absence of data instances within each incomplete perspective. To address inconsistencies between different views, we introduced a coregularization factor, which operates in conjunction with a shared consensus matrix. It is important to highlight that the proposed objective function is intrinsically nonconvex, presenting challenges in terms of optimization. To secure the optimal solution for this objective function, we have implemented an iterative optimization approach to reach the local minima for our method. To underscore the efficacy and validation of our approach, we experimented with real-world datasets and used state-of-the-art methods to perform comparative assessments. © 2024 IEEE.

关键词： Iterative methods

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GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer's Classification

GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer'...

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2024 IEEE International Conference on Signal Processing and Advance Research in computing, SPARC 2024

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwavidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India University of North Texas Denton Department of Computer Science and Engineering TX United States

ISBN: (纸本)9798350385199

Accurate detection of skin cancer, particularly melanoma, is crucial for effective treatment and patient survival. This study explores the use of Convolutional Neural Networks (CNNs) enhanced by Generative Adversarial Networks (GANs) for skin cancer detection. CNNs have shown remarkable performance in image classification tasks but often require extensive labeled datasets, which are scarce in medical domains. To address this, GANs are employed to generate synthetic skin lesion images, augmenting the dataset and improving the model's generalization capability. The proposed approach integrates a pre-trained CNN architecture fine-tuned with a combination of real and GANgenerated images, enhancing the network's ability to distinguish between benign and malignant lesions. Experimental results demonstrate that the inclusion of GAN-augmented data improves the model's accuracy by 7%, achieving an overall accuracy of 94%, with precision and recall rates of 92% and 93%, respectively. These findings suggest that the GAN-augmented CNN model offers a promising solution for automated skin cancer screening © 2024 IEEE.

关键词： Generative adversarial networks

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Denoising Graph Inference Network for Document-Level Relation Extraction

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Big Data Mining and Analytics 2023年第2期6卷 248-262页

作者： Hailin Wang Ke Qin Guiduo Duan Guangchun Luo School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Computing and Artificial Intelligence Southwestern University of Finance and EconomicsChengdu 611130China

Relation Extraction(RE)is to obtain a predefined relation type of two entities mentioned in a piece of text,e.g.,a sentence-level or a document-level *** existing studies suffer from the noise in the text,and necessary pruning is of great *** conventional sentence-level RE task addresses this issue by a denoising method using the shortest dependency path to build a long-range semantic dependency between entity ***,this kind of denoising method is scarce in document-level *** this work,we explicitly model a denoised document-level graph based on linguistic knowledge to capture various long-range semantic dependencies among *** first formalize a Syntactic Dependency Tree forest(SDT-forest)by introducing the syntax and discourse dependency ***,the Steiner tree algorithm extracts a mention-level denoised graph,Steiner Graph(SG),removing linguistically irrelevant words from the *** then devise a slide residual attention to highlight word-level evidence on text and ***,the classification is established on the SG to infer the relations of entity *** conduct extensive experiments on three public *** results evidence that our method is beneficial to establish long-range semantic dependency and can improve the classification performance with longer texts.

关键词： Relation Eextraction(RE) document-level denoising linguistic knowledge attention mechanism

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Sentiment-aware drug recommendations with a focus on symptom-condition mapping

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5195-5212页

作者： Anbazhagan, E. Sophiya, E. Prasanna Kumar, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology

The adoption of digital health records and the rise of online medical forums resulted in massive volumes of unstructured healthcare data. Most of the data used by traditional drug recommendation systems is obtained from patient Electronic Health Records (EHR) and subjective feedback and experiences included in patient evaluations. Nevertheless, the current systems based on sentiment analysis fail consider Symptom based diagnosis whereas researches that proposes Graph models doesn’t not include patient satisfaction and Health History as some has specific needs. To address the draw backs of existing drug recommendation systems, this study suggests a novel approach that combines symptom-disease mapping with sentiment analysis of patient reviews. The primary objective of the research is to utilize machine learning classifiers to make symptom-based predictions about probable medical conditions as Phase I. Then, before being fed into sequence network and machine learning models, patient reviews that are relevant to the predicted condition are filtered as Phase II. This method generates probabilities for suggesting certain drugs by evaluating sentiments and incorporating review ratings. With a Performance score of Ensemble Model up to 99.25% in Phase I and accuracy of 99.45% for sentiment analyser in Phase II. The performance of the model was evaluated based on accuracy, Receiver Operating Characteristic Curve (ROC)-Area Under Curve (AUC) score, sensitivity, selectivity. The proposed system helps in recommending the optimal drug for any type of symptom samples which is available in database. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Disease Drug recommendation system Electronic Health Records Personalized healthcare Sentiment analysis Symptom

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Enhancing sarcasm detection through grasshopper optimization with deep learning based sentiment analysis on social media

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1785-1791页

作者： Srinu, Nidamanuri Sivaraman, K. Sriram, M. School of Computing Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai 600073 India

Detecting sarcasm in social media presents challenges in natural language processing (NLP) due to the informal language, contextual complexities, and nuanced expression of sentiment. Integrating sentiment analysis (SA) with sarcasm detection enhances the understanding of text meaning. Deep learning (DL), utilizing neural networks to grasp lexical and contextual features, offers a method for sarcasm detection. However, current DL-based sarcasm detection methods often overlook sentiment semantics, a crucial aspect for improving detection outcomes. Therefore, this study develops a new sarcasm detection using grasshopper optimization algorithm with DL (SD-GOADL) technique. The SD-GOADL technique aims to explore the patterns that exist in social media data and detect sarcasm. To obtain this, the SD-GOADL technique undergoes data pre-processing and Glove based word embedding technique. Next, the classification of sarcasm takes place using deep belief network (DBN) system. For enhancing the detection results of the DBN approach, the SD-GOADL technique uses GOA for hyperparameter selection process. The stimulation outcome of the SD-GOADL technique is tested on a sarcasm dataset and the results highlight the significant performance of the SD-GOADL technique compared to recent models. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep learning Grasshopper optimization algorithm Natural language processing Sarcasm detection Sentiment analysis

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Stationary wavelet transform and SpinalNet trained light spectrum Tasmanian devil optimization enabled DR detection using fundus images

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Multimedia Tools and Applications 2025年第4期84卷 1597-1626页

作者： Tamboli, Mubin Shoukat Dhumane, Amol Prasad, Rajesh Prasad, Jayashree Rajesh Ranjan, Nihar M. Computer Engineering Pimpri Chinchwad College of Engineering Pradhikaran Maharashtra Pune411044 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune India Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Diabetes is a common disease that causes complications in the eyes known as Diabetic Retinopathy (DR). It aids in discovering the DR and tends to be the main cause behind people’s blindness amidst the previous decades. For prevent the complications of DR and to control the level of blood Glucose, the timely and crucial diagnosis is essential. A model is provided for detecting the DR. Here, Light Spectrum Tasmanian Devil Optimization (LSTDO) is utilized with SpinalNet for DR detection. The method undergoes an adaptive median filter and Region of interest mining process to generate a pre-processed image. The Sparse Fuzzy C-Means (FCM) is adapted for segmenting the optic disks followed by feature extraction. Then, the Hough line transformation with Tyler Coye algorithm (HLTCA) is used for blood vessel segmentation. Concurrently, the Stationary Wavelet Transform (SWT) is adapted to input image to overwhelm the deficiency of translation-invariance issue and then the feature mining is done. The feature vector is generated by unifying the outputs of the three-feature extraction phases and is employed to detect DR. The accuracy, sensitivity, and specificity obtained by the LSTDO-based SpinalNet are 91.6%, 89.9% and 92.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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