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An effective Image-based Improved Deep Neural Network Framework for Coal and Gangue detection

An effective Image-based Improved Deep Neural Network Framew...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： M. Praveena Afsana A T. Durga Devi S. Sudharsan V. Arulkumar Department of Computer Science Dr SNS Rajalakshmi College of Arts and Science Coimbatore Department of Database Systems School of Computer science and engineering Vellore Institute of Technology Vellore

In coal power plants, gangue is a by-product that is not wanted, even though coal is the energy source utilized the most worldwide. As a consequence of this, the removal of gangue from coal during the pre-processing stage of the mining technique is an essential step. With the development of new computer technologies, a method may be discovered that will allow for an improvement in the efficiency of gangue separation. This study presents a method for identifying gangue using improved deep neural networks. The method involves building a coal-gangue filtration system based on the distinctions between gangue and coal in terms of its rough surface and grayscale feature. In addition, this tactic will make it possible for robots, rather than people, to identify the gangue. Suppose the technology described in this article is applied in the coal industry. In that case, it will lead to the computerization of the extraction of coal gangue, an increase in the raw coal processing efficiency, and an improvement in the quality of the coal. According to the findings of experiments, the strategy enhances the accuracy results of coal and gangue materials by as much as 98.87% compared to the currently used approaches.

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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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Facial Landmarks detection system with OpenCV Mediapipe and Python using Optical Flow (Active) Approach

Facial Landmarks Detection System with OpenCV Mediapipe and ...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Narendra Kumar Rao B Nagendra Panini Challa E S Phalguna Krishna S. Sreenivasa Chakravarthi School of Computing Mohan Babu University Tirupati India School of Computer Science and Engineering (SCOPE) VIT-AP University Amaravati India Dept of CSE GITAM School of Technology GITAM (Deemed to be University) Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidayapeetham Chennai India

To achieve positive detection results, a variety of face landmark methods supported by the convolutional neural network have been developed. The instability landmarks thus emerge in video frames as a result of CNNs, on the other hand, are extremely sensitive to input picture noise. This paper provides a light and effective face landmark identification technology based on a lightweight U-Net model based on semantic segmentation and an Optical Flow (Active) Approach (OFA) for solving the problem of landmark shaking. The OFA employs a quick optical flow approach to determine the motion path of the landmark, as well as a route to increasing landmark maintenance. A lightweight U-Net model is used to predict face landmarks with a reduced size of the model and lower computational. To subsume unstable shaking, the predictable face landmarks are given into the OFA technique as well. Finally, various benchmark datasets are used to produce a comparison of many common methodologies as well as the proposed detection process. A lightweight U-Net model is used to model face landmarks in reduced model size and lower computational. To subsume the unstable shaking, the predicted face landmarks are given into the OFA technique as well. Finally, various benchmark datasets are used to produce a comparison of many common methodologies as well as the proposed detection process.

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detection and Removal of Non-Medical Reports from User Provided Paper-Based Documents for Generating an Efficient EHR system

Detection and Removal of Non-Medical Reports from User Provi...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Nadia Binte Rahman Peeya Prajat Paul Forhad Hossain Ashir Ahmed Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

The global healthcare landscape is rapidly transitioning to Electronic Health Records (EHR) systems. In this digital evolution, individuals store their historical medical records in both analog and digital formats. However, as EHR technology gains traction, users often inadvertently upload unrelated documents alongside their medical records. This unintended inclusion of irrelevant data introduces noise into the EHR databases, leading to decreased accuracy when employing machine learning models. While extensive research focuses on medical data extraction and analysis, the critical task of effectively filtering non-medical documents remains relatively under-explored. This study presents a simple machine learning and deep learning approach to discriminate legitimate medical documents from user-provided uploads. Using machine learning algorithms such as Logistic Regression, Support Vector Machines (SVM), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Bidirectional Encoder Representations from Transformers (BERT), and Clinical BERT, our study identifies BERT as the most effective tools for this task achieving an accuracy of 98%.

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An Improved Fuzzy Logic Based Alcohol detection system to Preserve Road Safety using Smart Sensors Association

An Improved Fuzzy Logic Based Alcohol Detection System to Pr...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： V. R. Kavitha Annamalai Muthu B. Geetha V. Samuthira Pandi M Dinesh B. Anni Princy Department of Computer Science and Business Systems R.M.K. Engineering College Chennai Tamil Nadu India Department of Engineering University of Technology & Applied Science Nizwa Oman Department of Science & Humanities Prince Shri Venkateshwara PadmavathyEngineering College Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India

The increased risk of being involved in a traffic collision is directly proportional to the rate at which the number of vehicles on the road is growing. The majority of people in the world believe that driving under the influence of alcohol is a significant factor in the number of people killed in car accidents. The fundamental purpose of this investigation is to develop a device that is able to determine the degree of drunkenness that is present in the driver of a motor vehicle. The purpose of the proposed system is to discourage users from getting behind the wheel while under the influence of alcohol or drugs in order to reduce the number of accidents that are caused by intoxicated drivers. As part of this investigation, we propose the Fuzzy Assisted Alcohol detection Mechanism (FAADM), which is a logical fuzzy-based alcohol detection mechanism. It uses a Direct Current (DC) motor as the vehicle's engine and a push button as its ignition system. This mechanism detects the presence of alcohol using a fuzzy-logic approach. The efficiency of this system is always being evaluated to ensure that it performs as designed. If this system were put into place, there would be a reduction in the number of accidents on the roads of smart cities that were caused by drivers who were under the influence of alcohol. It is strongly suggested that this system be fitted and installed inside the vehicle. In the event of an unexpected emergency, the engine should be turned off and the alarm should be activated. If there has been no drinking or accidents, the security system can be disarmed and the vehicle's engine should be started. If alcohol is found, the car will be turned off and an alarm will ring. As the number of incidents caused by intoxicated drivers continues to rise, the implementation of a system that combines alcohol detection and vehicle immobilization has been proposed as a potential solution. Engineers have developed a gadget that can detect the amount of alcohol in a d

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Artificial intelligence Based Air Quality Monitoring system with Modernized Environmental Safety of Sustainable Development

Artificial Intelligence Based Air Quality Monitoring System ...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： P. William Gandikota Ramu Lavish Kansal Pravin P Patil Ahmed Alkhayyat A. Kakoli Rao Department of Information Technology Sanjivani College of Engineering Savitribai Phule Pune University Pune India Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Lovely Professional University Phagwara India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Faculty of Engineering The Islamic University Najaf Iraq Department of CSE Lloyd Institute of Engineering and Technology Greater Noida India

The article suggests that one method for enhancing the system of environmental monitoring of atmospheric air quality in the Indian Federation is to increase the number of permanent control stations for atmospheric pollution in each city. It is possible that the system in the Indian Federation may be enhanced with the assistance of this particular technique. Developing computational methods for monitoring air quality that incorporate data on pollution levels in terms of compliance with hygienic standards as well as assessment of pollution levels from the perspective of environmental risk to; and increasing the number of cities in which pollutant concentrations are continuously measured. Directly as a result of the growing usage of computerized Air Quality Monitors (AQM) system for air quality check using AI, the total number of pollutants that are subject to regulation has also grown. The number of pollutants that are subject to regulation has grown as a direct result of the deployment of computerized monitoring systems for air quality. Animals and people are vulnerable to air pollution, but plants are far more sensitive. The informative value of monitoring the state of green areas and the atmosphere will rise as a result. It may be possible to contribute to the modernization of ecological environment management if recent technological improvements and discoveries based on artificial intelligence are adapted and implemented in the form of effective policy instruments. It has been suggested that, while assessing the quality of the air in the atmosphere, not only should the influence on human health be taken into consideration, but also the impact on the health of the surrounding flora and fauna. This would provide precise data that could be used in the development of environmentally responsible policies.

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A Data Selection Method for Domain-specific Machine Translation

A Data Selection Method for Domain-specific Machine Translat...

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Frontiers of Electronics, Information and Computation Technologies (ICFEICT), international conference on

作者： Na Ye Ling Jiang Dongfeng Cai Human-Computer Intelligence Research Center Shenyang Aerospace University Shenyang China

Neural machine translation performs well in general domains with large-scale bilingual corpora, but it is not very effective in specific domains with only a small amount of bilingual corpora. Data selection technology is one of the main methods to alleviate the problem of bilingual data scarcity in specific domains by selecting corpus that is close to the domain corpus from large-scale general domain corpora for data expansion. Due to the large scale of general data, traditional dimension reduction methods are inefficient when dealing with massive data. This paper proposes a two-stage data selection strategy, which improves the efficiency of data selection and the data quality at the same time. First, the Inverted File system Product Quantization (IVFPQ) method is used to preliminarily select data, which improves the computational efficiency and the quality of data selection. In order to obtain data with higher quality, on this basis, this paper trains a domain classifier to fully consider the domain characteristics, and further selects higher quality data. Experimental results show that compared with the baseline method, a better domain-specific machine translation model can be trained using the expanded data obtained by this method.

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Social Distance detection for Smartwatch Using Arduino

Social Distance Detection for Smartwatch Using Arduino

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Pooja Anand Anil Saroliya Nirmal Vatsyayan Amity University Greater Noida India Amity University Tashkent Uzbekistan Skidos Labs Noida India

Half of the population was impacted by the coronavirus last year, and the number of cases is still rising daily. Direct and indirect contact with infected people is how COVID-19 spreads. This paper presents a novel strategy for reducing the spread of the disease: a clever social distancing technique that works in both indoor and outdoor environments. This system consists of a unique, affordable, and small wearable prototype that uses human sensing technology to determine the social distance between people and send out alerts when it drops below a certain safe level. It advises the individual to maintain their social distance. The apparatus uses ultrasonic sensors to function. The watch's thermal detector may be used to determine a person's body temperature and to send out a warning to stay away from them. The intelligent variable gadget alerts people to the need to protect themselves against COVID-19 contact. This clever and original idea may lower the number of COVID-19 instances. Additionally, this gadget may sometimes be useful in signaling to someone to maintain their distance. Our research showed that compared to the commonly used Bluetooth-based product, the purpose system is much more dependable.

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Research of Properties of Deformed Steel Bar Based on Multiple Stepwise Regression and BP Neural Network

Research of Properties of Deformed Steel Bar Based on Multip...

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Electronic technology, Communication and Information (ICETCI), IEEE international conference on

作者： Meihui Wang Xingpeng Chen Zhenyu Chen Jinsheng Liu College of Artificial Intelligence Nanjing Agricultural University Nanjing China Institute of Technology Nanjing Agricultural University Nanjing China School of Power and Architecture Shanxi University Taiyuan China

Excluding secondary factors and reserving decisive factors of steel properties, relationship between the properties of deformed steel bar and chemical elements is analyzed by applying stepwise multiple linear regressions. The scatter diagram is drafted with SPSS; possible fitted curves of various categories are established (e.g., exponential model, logarithmic model, logistic model, linear model). Then the quantitative curve relationship is determined. Research shows that the influence added by chemical elements is neither a simple linear relationship nor a nonlinear one, but belongs to a complex multivariable coupled system. B.P. (abbr. for Backpropagation) neural network, a feedforward network formed of non - linear transformation units, is able to realize adjustment of multi-layer feedforward neural weight and handle highly-nonlinear problems intelligently. Therefore, the paper utilized MATLAB to design and modify B.P. neural network model. Reflection between steel properties and chemical elements are trained by the model repeatedly. According to the requirements of problem, the mathematics model of is obtained on condition that the error of training is extremely small. The modified model is inspected with systematic-sampling extracted data from attachment 1. The high degree of fit of the modified model is proved with the comparison between the predictor and practical value.

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Resisting Visual Hacking: A Novel Graphical Password Authentication system

Resisting Visual Hacking: A Novel Graphical Password Authent...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Jebakumar Immanuel D Ramalingam V Tharun Kumar P Akash Iyer Narayanan M S Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Coimbatore Department of Computer Science and Engineering SNS College of Engineering Coimbatore India

Password-based authentication is a widely used method to ensure security and privacy in computer applications. However, the selection of weak passwords and insecure input methods by users is considered the “weakest link” in the authentication process. Instead of employing arbitrary alphanumeric sequences, individuals tend to opt for passphrases that are either mnemonic or abbreviated in nature. With the proliferation of web and mobile applications, users can access these applications at any time and from anywhere with various devices. Although this convenience is highly desirable, it also increases the likelihood of passwords being exposed to Visual Hacking. The forms of attack may be executed through either direct visual observation or the utilization of external recording devices in order to obtain the user's login information. In response to this issue, our team has devised an authentication system that centers on graphical passwords and is made to combat the threat of Visual Hacking. The system incorporates a three-layered security mechanism, comprising color and pattern matching functionalities in conjunction with password verification. This makes it highly challenging for potential attackers to deduce or limit the password even amidst numerous camera-based hacking attempts. Additionally, we have created and put to the test a prototype of the system to assess its user-friendliness. Our experimental results indicate that our system can resist Visual Hacking more effectively than previous authentication systems while still maintaining a high degree of usability and security.

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