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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Aravindan MK X. Mercilin Raajini Yashwant S. Ingle Pradeep Kumar Jangid Ashu Katyal Preeti Naval Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Electrical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

Assessing machine-learning algorithms for texttype responsibilities is critical to advancing the world of herbal language processing (NLP). As using applications that use NLP grows, so does the want to pick the maximum accurate algorithms out of a pool of to-be-had solutions. This paper reviews the current United States of America research concerning assessing system reading algorithms for textual content-type obligations. Numerous evaluation metrics, consisting of accuracy, precision, consider, and f-degree, are discussed, in addition to the strategies used to examine algorithms and determine the foremost solutions. The diverse challenges confronted in this venture are highlighted, giving readers an outline of the challenges related to assessing the tool and getting to know algorithms for text-kind responsibilities.

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Learnable Operational Design Condition Monitor for Failure Prediction in Autonomous Driving

SSRN

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SSRN 2025年

作者： Xu, Haowei Bian, Yougang Li, Yang Qin, Hongmao Zhou, Hanchu Chang, Fangrong Wang, Shaofei Ye, Qing State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Research and Development Center of Transport Industry of Self-driving Technology China Merchants Chongqing Communications Technology Research and Design Institute Co. Ltd. Chongqing China School of Traffic and Transportation Engineering Central South University Changsha China Department of Data and System Engineering The University of Hong Kong Hong Kong School of Resources and Safety Engineering Central South University Changsha China School of Data Science China

Ensuring the safety of Autonomous Driving systems (ADS) across diverse and unpredictable operational scenarios remains a significant challenge for accident prevention. Central to this effort is the continuous monitoring of Operational Design Conditions (ODC), which encompass both in-vehicle factors and external Operational Design Domain (ODD). However, the absence of robust methodologies to precisely identify ODC boundaries hinders the deployment and adoption of high-level ADS. This paper introduces a learnable ODC monitor designed to identify implicit operational design condition boundaries to prevent ADS failures. By integrating a state-space duality encoder and a Bayesian neural network decoder, we propose an efficient learnable ODC monitor, termed ODCNet. Furthermore, to further enhance prediction reliability, residual error analysis with linear discrimination is applied for calibrating prediction. Experimental results demonstrate ODCNet's superiority in failure prediction compared to baseline models, achieving a balance between safety and coverage. These findings contribute to advancing data-driven accident prediction and prevention strategies, ensuring safer deployments of ADS in complex environments. © 2025, The Authors. All rights reserved.

关键词： Autonomous vehicles

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IoT-Enabled Smart Manhole Management system for Real-time Status, Water Level, and Gas Detection

IoT-Enabled Smart Manhole Management System for Real-time St...

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Intelligent systems for Cybersecurity (ISCS), International Conference on

作者： Md Asif Imran S M Masfequier Rahman Swapno Gunjan Chhabra Keshav KaushiK Md. Arif Anzum Mahi Md Babul Islam Rezaul Haque Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring system improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city.

关键词： Surveillance Urban areas Robot vision systems Transforms Cameras Real-time systems Maintenance

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2-D GCANet Applied to Denoise 1-D EEG Signals in Online Remote Teaching Scenarios

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1289-1302页

作者： Huang, Ting-Qing Wang, Chuan-Sheng Chen, Zhao-Qi Zhang, Fu-Quan Meng, Xiang-Long Grau, Antoni Chen, Yang Huang, Jing-Wei College of Computer and Control Engineering Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulong Jiangbei Avenue Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music No. 2 Wannianchang Street Chenghua District Sichuan Province Chengdu City China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town NO. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 599 Quanxiu Road Juyuanzhou Ecological Civilization District Fujian Province Fuzhou City China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fujian Province Fuzhou City China

When EEG signals are used to assess the level of student engagement in online teaching tasks, they are often interfered by noise. It is a challenge to effectively remove these noises. Currently, deep learning methods have been applied to the field of EEG denoising. However, existing methods still have some problems. The denoised signals still have obvious noise residue, or the original EEG signals are damaged, and the model fitting speed is too slow. Image dehazing, as a typical denoising task in the field of image enhancement, has achieved great success in recent years. Therefore, inspired by advanced models in this field, we introduce CNN into EEG denoising tasks. In this paper, we take GCANet, an excellent image enhancement model, as an example. The dilated convolutions and gate fusion subnetworks included in GCANet enable more efficient EEG signal denoising. The results demonstrate that the proposed model effectively reduces noise while preserving essential features. Furthermore, in comparison to other state-of-the-art models, our proposed model exhibits enhanced robustness and faster convergence, as evidenced by achieving lower loss values after five epochs. Its good performance provides a new development idea for the field of EEG denoising. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Image enhancement

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Supervised Machine Learning Strategy for detection of covid19 patients

Supervised Machine Learning Strategy for detection of covid1...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： I. S. Hephzi Punithavathi K. Deepa Cheruku Poorna Venkata Srinivasa Rao S. Raja Gopal P. Rajasekar Ashok Kumar Department of Artificial Intelligence Vidya Jyothi Institute of Technology Hyderabad Telagana India Department of Computer Science and Artificial Intelligence SR University Kavaraipettai Hasanparthy Telangana India Department of Information Technology Laki Reddy Balireddy College of Engineering Mylavaram Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Computer Science Banasthali Vidyapith Rajasthan India

The current severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) public health catastrophe, both human lives have been lost and the economy has disrupted severely the current scenario. In this paper, we develop a detection module using a series of steps that involves pre-processing, feature extraction and detection of covid-19 patients based on the images collected from the computerized tomography (CT) images. The images are initially pre-processed and then the features are extracted using Gray Level Co-occurrence Matrix (GLCM) and then finally classified using back propagation neural network (BPNN). The simulation is conducted to test the efficacy of the model against various CT image datasets of numerous patients. The results of simulation shows that the proposed method achieves higher detection rate, and reduced mean average percentage error (MAPE) than other existing methodologies.

关键词： COVID-19 Training Support vector machines Computed tomography Computational modeling Predictive models Sensitivity and specificity

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Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification

Equilibrium Optimizer with Deep Learning Model for Autism Sp...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： A. Praveena N. Senthamilarasi T. S. Karthik Abirami S.K Vijayakrishna Rapaka E Shyamali Das Department of Computer Science and Engineering Jansons Institute of Technology Karumathampatti Coimbatore Tamilnadu India Department of Information Technology Panimalar Engineering College Chennai Tamilnadu India Department of Electronics and Communication Engineering Aditya College of Engineering and Technology Surampalem Andhrapradesh India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamilnadu India Data Science Indian Institute of Technology Madras Chennai Tamilnadu India Department of Computer Science CMR Group of Institutions (SOSS) Bangalore Karnataka India

ISBN: (纸本)9781665460859

Autism Spectrum Disorder (ASD) is a developing disorder if the symptoms develop obvious in the initial years of age but it could be present in some age groups. ASD is mental health problem that affects communicational, social, and non-verbal performances. It could not be cured entirely but is decreased when identified initially. The primary analysis was hampered by the difference and severity of ASD symptoms and containing symptoms usually realized in other mental health problems as well. With the application of machine learning (ML) for the predictive and recognition of several diseases with optimum accuracy, a ray of hope to initial recognition of ASD dependent upon many physiological and physical parameters is projected. This article designs an Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification (EODL-ASDC) technique. The presented EODL-ASDC technique mainly focuses on the identification and classification of ASD. To attain this, the presented EODL-ASDC technique exploits the deep belief network (DBN) system to perform the classification procedure. In addition, the EO algorithm is employed for the optimal hyperparameter tuning of the DBN approach. To demonstrate the enhanced ASD classification result of the EODL-ASDC approach, an extensive range of experimental evaluates was executed. The experimental results demonstrate the improvements of the EODL-ASDC technique over other approaches.

关键词： Deep learning Autism Renewable energy sources Automation Mental health Physiology Classification algorithms

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Exploring Cutting-Edge Machine Learning and data Mining Techniques for Enhancing Big data Management with Advanced Algorithmic Strategies for Optimal data Processing and Analysis

Exploring Cutting-Edge Machine Learning and Data Mining Tech...

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International Conference on Advances in Information technology (ICAIT)

作者： M. Anitha V Sheeja Kumari N. Muthuvairavan Pillai P. Jesu Jayarin D. Beulah David Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India Department of Computer Science and O. Business Systems R.M.D Engineering College Thiruvallur Tamil Nadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

This work focuses on diverse machine learning methods for big data analytics, which works to leverage predictive performance. The techniques like data preprocessing, dimensionality reduction, feature selection, model selection, hyperparameter tuning, and model evaluation are a focus. A number of algorithms including Random Forests, SVM (Support Vector Machines) Gradient Boosting Machines (GBM), Neural Networks, and ensemble methods (Bagging and Boosting) have their performance evaluated. Hyperparameter tuning applies the Grid Search and Random Search algorithms while model evaluation is done by the K-fold Cross-validation. Fleiss' kappa provide differing levels of accuracy, precision, recall, and F1-score for different models and techniques. Ensemble methods actually show how they have all the necessary qualities to win and perform better than individual models. repeatedly testing improve the models' reliability and effectiveness for practical use. The study highlights the crucial significance of choices on the methods and algorithms that suit the peculiarities of the dataset and problem domain. Capitalizing on the machine learning techniques and in-depth evaluation methods, organizations can get the actionable insights from the big data that will ensure the headquarters' decision-making process and accomplish the meaningful outcomes. Future research could instead be exploring new algorithms and techniques to improve the prediction and scalability in big data analytics setting.

关键词： Support vector machines Machine learning algorithms Computational modeling Scalability Software algorithms Big data Prediction algorithms Boosting data models Ensemble learning

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Decision-making model in digital commerce: electronic trust-based purchasing intention through online food delivery applications (OFDAs)

Journal of Trade Science

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Journal of Trade science 2024年第3期12卷 220-242页

作者： Muhammad Asif Zaheer Tanveer Muhammad Anwar Laszlo Barna Iantovics Maryam Manzoor Muhammad Ali Raza Zoia Khan University Institute of Management Sciences PMAS-Arid Agriculture University Rawalpindi Rawalpindi Pakistan Department of Management and Law Faculty of Economics University of Rome Tor Vergata Roma Italy Institute of Preventive Veterinary Sciences and Department of Veterinary Medicine College of Animal Sciences Zhejiang University Ningbo China Research Center on Artificial Intelligence Data Science and Smart Engineering ARTEMIS Electrical Engineering and Information Technology George Emil Palade University of Medicine Pharmacy Science and Technology of Targu Mures Marosvasarhely Romania Department of Business Administration Istanbul Aydin University Istanbul Turkey

Purpose This research aimed to raise awareness about the need for safety measures and features of online food delivery applications (OFDAs) to build electronic trust (e-trust) with augmented purchase intention among customers. Moreover, this study explores the attributes of electronic commerce (e-commerce) and how e-trust influences the purchasing intention of consumers while ordering food through OFDAs including fear of contagious diseases. Determinants of e-commerce in the digital era profoundly impact the performance of enterprises. Design/methodology/approach data from 493 food consumers collected from Federal Capital Territory (FCT) Pakistan, who were regular users of OFDAs. To scrutinize the dataset, confirmatory factor analysis (CFA) was conducted to assess the construct validity in this research study. Structural equation modeling (SEM), which is facilitated by Smart-PLS, was employed to examine the direct, moderation and mediation effects of the proposed model. Findings Results revealed the positive and significant impact of the e-trust on the purchase intention. Additionally, e-trust acted as a mediating factor in the connection between electronic security (e-security), electronic privacy (e-privacy), usability, electronic payment (e-payment), electronic innovativeness (e-innovativeness) and the buyer's purchase intention. Furthermore, the fear of contagious viruses negatively moderated the e-trust and purchase intention that weakened buying behavior. Originality/value This research is primarily centered on enhancing the comprehension regarding safety orientation within the context of an evolving restaurant industry. The findings of this study hold substantial contributions for academics, web developers, application designers, OFDAs, restaurants and other businesses since they indicate the attractiveness of OFDAs in generating feelings of pleasure and boosting users' intentions to keep using the application.

关键词： E-commerce OFDAs E-trust Digital disruption Purchase intention Fear of contagious viruses

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Assessing the Ability of AI-Driven Natural Language Processing to Accurately Analyze Unstructured Text data

Assessing the Ability of AI-Driven Natural Language Processi...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： M. Vigenesh RaviKumar V Vivek D. Patil Sharma Sonu Kumar Nyarik Geyi Subir Chattopadhyay Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India Faculty of Business Mangagement & Commerce Usha Martin University Ranchi India

This technical abstract examines the ability of herbal language processing (NLP)-driven synthetic Intelligence (AI) to analyze unstructured textual content facts. Unstructured textual content records include information from assets with webpages, social media, emails, and consumer comments. They are widely used in sentiment evaluation, hazard detection, and entity extraction programs. NLP-pushed AI answers along with natural language knowledge (NLU), herbal language knowledge (NLG), and natural language understanding (NLP) are used to manner and examine unstructured textual content information. The analysis technique starts evolving with tokenizing the facts, breaking the textual content into smaller factors, including phrases and phrases. After tokenizing the statistics, the AI device discovers styles inside the records. Those styles can then discover the emotions and entities inside the data. NLU and NLG models are used to expand the facts' expertise by assigning sentiment and issue-level scores. The accuracy of NLP-driven AI is similarly progressed through more excellent complex fashions consisting of deep studying and reinforcement.

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TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinquan Yang Jinheng Xie Xuechen Li Xuguang Li Linlin Shen Yongqiang Deng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Stomatology Shenzhen University General Hospital Shenzhen China

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods.

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