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Research Method of Blind Path Recognition Based on DCGAN 6th

Research Method of Blind Path Recognition Based on DCGAN

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6th International Conference on Advanced Intelligent systems and Informatics, AISI 2020

作者： Luo, Ling Zhang, Ping-Jun Hu, Peng-Jun Yang, Liu Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9783030586683

In order to solve the problem that there are few blind path data sets and a lot of manual data collection work in the current blind guide system, computer vision algorithm is used to automatically generate blind path images in different environments. Methods a blind path image generation method based on the depth convolution generative adversary network (DCGAN) is proposed. The method uses the characteristics of typical blind path, which is the combination of depression and bulge. The aim of long short memory network (LSTM) is to encode the depression part, and the aim of convolution neural network (CNN) is to encode the bulge part. The two aspects of information are combined to generate blind path images in different environments. It can effectively improve the blind path recognition rate of the instrument and improve the safe travel of the visually impaired. Conclusion generative adversarial networks (GANs) can be used to generate realistic blind image, which has certain application value in expanding blind channel recognition data, but it still needs to be improved in some details. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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An embarking user friendly palmprint biometric recognition system with topnotch security 5

An embarking user friendly palmprint biometric recognition s...

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5th International Conference on Intelligent Computing and Control systems, ICICCS 2021

作者： Balakrishnan, S. Venkatesan, K. Hameed, M. Syed Shahul Sri Krishna College of Engg. and Technology Dept. of Computer Science and Business System Coimbatore Tamilnadu India St. Josephs College of Engineering Department of ECE University of Technology and Applied Sciences - sur PO BOX : 484 PC 411 SUR Oman

ISBN: (纸本)9781665412728

The rapid and widespread development of Information technology results in a tremendous need for the development of reliable, safe and secure identification of individuals. Henceforth, biometric rightly enters into the field to overcome the aforementioned challenge. Palmprint is considered as a relatively new promising biometric trait that has attained an increasing research interest due to its reliability, stability, distinctiveness, environment flexibility, low-resolution imaging, low-cost capturing device, and user-friendly characteristics. This paper attempts to provide an overview of biometric system operations, palmprint biometric recognition concepts and also it compares the various palmprint feature extraction and matching methods and finally the future research directions are discussed © 2021 IEEE.

关键词： Biometrics

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Identifying Inaccurate Stories: Leveraging High Knowledge and Big data

Identifying Inaccurate Stories: Leveraging High Knowledge an...

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International Conference on Computing and Networking technology (ICCNT)

作者： Mohammad Ahmar Khan Pushpendra Kumar Verma Shivangi Ghaldiyal N. V. Balaji G.Arun Francis Swati Bula Patil Department of Management Information System College of Commerce & Business Administration Dhofar University Country Sultanate of Oman School of Computer Science and Applications IIMT University Meerut Department of Computer Applications Invertis University Bareilly Uttar Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore Tamil Nadu India IT Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Research on fake news has gained significant traction in a number of fields, We describe how the issue is tackled from a natural language processing standpoint in this research with the aim of developing identify false material in news. Collecting high-quality data, such as examples of false and actual news stories on a diverse range of subjects, is the key obstacle in this field of study. We examine the available before introducing the site as our lab’s gift to the. We provide the whole text of the news that have already been applied based on human evaluation of the articles’ authenticity. In order to clarify the gaps and causes of imbalance in the datasets that are now accessible and to direct ongoing attempts, we also conduct an appearance-based experiment. We urge the public to gather more information and make it accessible for study.

关键词： Deep learning Accuracy Text categorization Training data Big data Natural language processing Reliability Labeling Fake news

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SVM-Enhanced Dynamic Audiology Screening with IoT-Driven Monitoring for Personalized Hearing Health Management

SVM-Enhanced Dynamic Audiology Screening with IoT-Driven Mon...

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Self Sustainable Artificial Intelligence systems (ICSSAS), International Conference on

作者： Bindu S. Ronald Ramakrishnan Raman Daxa Vekariya V Vijaya Baskar J Rahul C. Srinivasan Symbiosis Law School Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Agni College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

The increasing prevalence of hearing impairment across diverse age demographics, particularly in elderly populations, highlights the need for early diagnosis and ongoing therapy. Traditional audiology screening techniques often lack the necessary dynamic flexibility to meet personalized hearing health requirements over time. This system introduces an innovative method for dynamic audiology screening via IoT-based monitoring, using Support Vector Machine (SVM) algorithms for individualized hearing health management. The incorporation of IoT devices allows ongoing, real-time auditory data acquisition from the user’s surroundings, recording essential metrics such as sound frequencies, noise levels, and auditory reactions. This data is analyzed using SVM to categorize and identify probable hearing impairments, providing great precision in differentiating between various degrees of hearing loss. The SVM model’s strong classification skills render it especially effective in differentiating between varying levels of hearing loss, facilitating early identification and tailored therapy strategies. This tailored method of audiology screening boosts diagnostic precision and facilitates proactive management, perhaps improving long-term results for individuals with hearing impairments.

关键词： Support vector machines Accuracy Heuristic algorithms Medical treatment Auditory system User interfaces Real-time systems Recording Internet of Things Monitoring

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Constructing a Blockchain-Based Loyalty Program Model for Secure and Transparent Customer Rewards in Retail Marketing

Constructing a Blockchain-Based Loyalty Program Model for Se...

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International Conference on Communication systems and Network Technologies (CSNT)

作者： Ch. Anudeep Kathari Santosh M Sandeep Kumar Jahnavi M Sabareesh R B Kiran Bala GITAM school of business GITAM University Visakhapatnam A.P. Department of MBA CMR Institute of Technology Bengaluru India RV Institute of Management Bengaluru India Skill Development & Employability Programs Fortuna Business Solutions(R&D) Coimbatore Tamil Nadu Department of Artificial Intelligence and Data Science K. Ramakrishnan college of Engineering Trichy TN

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

As the retail industry continues to evolve in the digital era, businesses are exploring innovative solutions to enhance customer engagement and loyalty. This research proposes a novel approach to loyalty programs by leveraging blockchain technology to create a secure and transparent model for customer rewards. Traditional loyalty programs often face challenges related to security, transparency, and trust, leading to issues such as fraud and customer skepticism. The proposed model outlines the design and implementation of a blockchainbased loyalty program, ensuring a secure and immutable ledger for tracking customer transactions and rewards. Smart contracts, self-executing contracts with predefined rules, are utilized to automate and enforce the terms of the loyalty program, reducing the potential for disputes and enhancing transparency. The decentralized nature of the blockchain ensures that customer data is securely stored and accessible only to authorized parties, addressing privacy concerns prevalent in centralized loyalty systems. This research employs a mix of theoretical analysis and practical implementation to validate the feasibility and effectiveness of the blockchain-based loyalty program model. The proposed Ethereum Blockchain model stands out with a significantly higher throughput of 5300 transactions per day, accompanied by an exceptional transaction transparency score of 99.5 .

关键词： Industries data privacy Communication systems Smart contracts Throughput Blockchains Fraud

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Fraud Transaction Detection Using Machine Learning on Financial database

Fraud Transaction Detection Using Machine Learning on Financ...

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Progressive Innovations in Intelligent systems and data science (ICPIDS), International Conference on

作者： Hasan Mahmud Sozib Md Omar Farouk Md. Firoz Hossain Md Mesbah Uddin Md. Rakib Mia Tahmina Ali Adrita Md. Mehedi Hasan Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Tejgaon Dhaka Bangladesh Department of Industrial System Engineering Asian Institute of Technology Khlong Luang Pathum Thani Thailand Department of Geriatric and Gerontology Welfare University of Dhaka Shahbag Dhaka Bangladesh Department of Occupational and Environmental Health Bangladesh University of Health Science Dhaka Bangladesh School of Business Ahsanullah University of Science and Technology Tejgaon Dhaka Bangladesh Department of Food and Nutrition Akij College of Home Economics Lalmatia Dhaka Bangladesh Department of Electronics and Communications Engineering East West University Jahurul Islam City Aftabnagar Dhaka Bangladesh

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

Financial fraud poses a significant threat to the digital economy, with credit card fraud being a prevalent challenge. This study evaluates the performance of a Voting Classifier Ensemble, Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and AutoEncoder with Gradient Boosting models in detecting fraudulent transactions using financial datasets. The study uses practical data from 284,807 transactions, but only 492 are fraudulent; the imbalanced class issue is solved using the Synthetic Minority Oversampling Technique (SMOTE). Our findings show that Voting Classifier Ensemble is the best with 99.93% accuracy, 100% precision, 86% recall and 90% F1 Score. XGBoost yielded an accuracy of 99.96%, precision of 95.11%, recall of 79.61%, and F1 score of 86.61%, while for Logistic Regression, the corresponding percentage was 99.92%, 88.1%, 60.5%, and 71.7%. AutoEncoder with Gradient Boosting had an accuracy of 99.98%, precision 99.81%, recall 64%, F1 Score 69%. The AUC statistic of 0.98 for XGBoost and Voting Classifier Ensemble against 0.97 for LR, and 0.46 for AutoEncoder wjth Gradient Boosting classified the models as having better discriminant power. The results show that XGBoost is more suitable for real-time fraud detection. However, computational limitations and explainability issues should be considered. For future work, it is suggested that semi-supervised and supervised learning approaches be investigated and work with larger datasets to improve fraud detection in financial systems.

关键词： Measurement Logistic regression Technological innovation Accuracy Computational modeling Biological system modeling Autoencoders Boosting Fraud Security

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Efficient and Resilient Placement Strategy for Electric Vehicle Charging Stations, Incorporating Renewable Energy and Storage Equipment in Distribution Networks

Efficient and Resilient Placement Strategy for Electric Vehi...

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Technological engineering and its Applications in Sustainable Development (ICTEASD), International Conference for

作者： Rakesh Kumar S. Shajathi Begam Dev Singh Tuychiev Komiljon Lazizovich S. Muthubalaji Mohammed Al-Farouni Srinivas Varanasi Department of Computer Engineering & Applications GLA University Mathura Department of Computer Science and Business System E.G.S. Pillay Engineering College Nagapattinam Tamilnadu India Mathematics Department Allenhouse Institute of Technology Kanpur Department of Marketing Tashkent State University of Economics Uzbekistan Department of Electrical and Electronics Engineering CMR College of Engineering & Technology Hyderabad Telangana India The Islamic University Najaf Iraq GRIET Hyderabad Telangana India

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

The placement of Electric Vehicle Charging Stations (EVCS) is a significant obstacle to the widespread adoption of Electric Vehicles (EVs). However, integrating EVCS with distributed Renewable Energy (RE) sources may help mitigate the variability of RE production. This article focuses on studying a Microgrid (MG) that consists of power distribution sources such as Wind Turbines (WT) and photovoltaic (PV), as well as EVCSs and Energy Storage Structures (ESS). The analysis considers the ambiguities associated with the charging needs of electric vehicles and the production of distributed Renewable Energy Sources (RES). A comprehensive optimization model is provided for computing the best Charging Stations (CS) locations equipped with distributed electricity. This model combines the electrical network and the grid to ensure efficiency and reliability. The usage variation ratio is employed to assess the level of compatibility among the uncertain production curve of RE and the charging request curves to ascertain the suitable size of the WT and photovoltaic-producing method and energy storage system. The kernel prediction function is utilized to address the problem of excessive caution in adaptable optimizing methods. A simulation is conducted on an IEEE 33-nodal energy distributing system and a 50-nodal transit network. The findings from simulations provide evidence of the model's resilience and cost-effectiveness.

关键词： Photovoltaic systems Renewable energy sources Power distribution Production Electric vehicle charging Wind turbines Reliability

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Expression of Concern for: Evaluation of Machine Learning Algorithms for Text Classification Tasks

Expression of Concern for: Evaluation of Machine Learning Al...

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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

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

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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Enhancing the Clarity of Surveillance Footage with Deep Residual Neural Networks

Enhancing the Clarity of Surveillance Footage with Deep Resi...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Prajwal Hegde N Jitendrasinh Jamadar Ravindra Changala Samrat Kumar Mukherjee Bijesh Dhyani B. Sachuthananthan Department of Artificial Intelligence and Data Science NMAM Institute of Technology Nitte Deemed to be University Karkala India G H Raisoni Institute of Engineering and Business Management Mohadi India Department of CSE Guru Nanak Institutions Technical Campus Hyderabad India Department of Management Studies Sikkim Manipal Institute of Technology Sikkim India Department of Mabagement Studies Grahic Era University (Deemed To Be University) Dehradun India Department of Mechanical Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Closed-circuit television (CCTV) camera pictures are often retained with low resolution as a result of technological and data storage constraints, which diminishes the usefulness of these images. This research suggests that by using intelligent design and training techniques, it is possible to boost the resolution of CCTV pictures using an enhanced deep residual network known as (EDSR). The EDSR approach has never before been used to improve the resolution of CCTV images before this study. In order to train and evaluate an EDSR that could generate high-resolution pictures from a low-resolution dataset, they used a dataset consisting of 986 CCTV images. Applying an EDSR method, they show that executing picture super-resolution with 60 layers is very effective, achieving a peak signal-to-noise ratio of 77.84 db. Additionally, this method has broader potential as a file compression approach that allows for the storing of low-resolution photographs with reduced file sizes and the capacity to recreate the same pictures in high resolution (HR) form

关键词： Training Image coding TV PSNR Surveillance Superresolution Memory Signal resolution Image reconstruction Residual neural networks

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Deep Learning Approaches for Enhancing Content Retrieval

Deep Learning Approaches for Enhancing Content Retrieval

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

作者： Saniya Khurana Shweta Singh M K Ranganathaswamy G. Renuka Mandar P. Diwakar Sandeep Kumar Jain 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 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

Deep learning strategies for content material retrieval have been explored and updated to cope with the want for accurate search effects. This machine up to date is used to date apprehend and examine text-up-to-date content material from sources, up-to-date webpages, or virtual up-todate motion pictures. As the quantity and complexity of online content will increase, so up-to-date have the want for higher up-to-date up to date those resources. Recent advancements in deep gaining knowledge have enabled better content retrieval structures that are up to date, updated correctly, and speedy to understand statistics from one-of-a-kind sources. Such structures include natural language processing (NLP), sentiment analysis, entity reputation, and up-to-date updated reputation. Those deep studying technologies have been used to decorate content material retrieval by enhancing search effects' accuracy, robotically updated extracting applicable statistics, categorizing information, and identifying patterns. Additionally, deep-getting, updated systems are up-to-date and quick to examine large volumes of data, generating and relevant consequences than up-to-date methods. In summary, deep up-to-date techniques can beautify the retrieval system of content material efficiently, enhancing the accuracy and pace of seek effects.

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