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For the Processing of Laser-Cut Materials, Seam Tracking Using Texture-Based Image Analysis

For the Processing of Laser-Cut Materials, Seam Tracking Usi...

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

作者： Kadambari Raghuram K.S. Raghuram Nageswari P Kapil Joshi T. Selvamuthukumar Dasari Veera Reddy (On Contract) Department of Computer Science and Engineering University College of Engineering Kakinada(Autonomous) JNTUK Kakinada Andhra Pradesh Department of Mathematics Mahendra Institute of Technology Namakkal Tamilnadu India Department of Mechanical Engineering Vignan's Institute of Information Technology (A) Visakhapatnam A. P. India Uttaranchal Institute of Technology Uttaranchal University Dept. of CSE (AIML) St. Martin 's Engineering College Secunderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

A camera-based seams monitoring system for lasers material treatment is the subject of this discussion. The lit working piece area and connection point are both captured by the electronic super-fast cameras. The light beams route incorporates the lens equipment symmetrically. In order to regulate the welding operation in a circular path, it is necessary to watch the contact site & connection distance inside one picture. Specifically, a new picture analysis technique is applied for the union separation observations. Rather than searching for an almost undetectable thin line that is reproduced by the connection space, the basic concept is to identify differences in the rough surfaces of the 2 different parts that will be fused joined. The working item seem images based on its texture is greater reliable & least impacted with.

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Artificial intelligence’s Function In Chatbots That Use NLP And SVM Algorithms

Artificial Intelligence’s Function In Chatbots That Use NLP...

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

作者： Jeidy Panduro-Ramirez MelanieLourens AnitaGehlot Durga Prasad Gangodkar TanushreeSarkar SivakumarSattanathan Universidad Tecnológicadel Perú Perú Department of Human Resources Management Durban University of Technology Berea Durban South Africa Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of English Koneru Lakshmaiah Education Foundation (Deemed to Be University) India Department of Mechatronics Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

A healthy lifestyle depends heavily on proper healthcare. Nevertheless, it might be exceedingly challenging to schedule a doctor’s appointment for any health issue. Before contacting a doctor, the goal is to develop a medical chatbot utilizing machine learning that can identify the illness and offer basic information about it. By using a medical robot, this will save healthcare expenses and increase access to medical information. The bots are computational models that communicate with people by using speech recognition. In order to recognize the language terms, make a query choice, and provide a response, the chatbot saves the data in a database. N-gram, TF-IDF, and mutual information are used to calculate ranking and text similarity. Each verb in the supplied input word will be assigned a score and additional comparable phrases will be found for the specified query. When a bot formulates a query that is unclear or not found in the system, a supplier, an expert software, responds.

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Bio-Inspired Optimization with Transfer Learning Based Crowd Density detection on Sparse Environment

Bio-Inspired Optimization with Transfer Learning Based Crowd...

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

作者： A. Poongodi N. Kopperundevi S. Poorani Mohit Tiwari B. Srinivasa Rao N. Abilash School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu Computer Technology-UG Kongu Engineering College Perundurai Computer science and engineering Bharati Vidyapeeth's College of Engineering Delhi Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering & Technology Bachupally Hyderabad Mechanical Engineering Annai Vailankanni College of Engineering Pottalkulam Kanyakumari District

Crowd density estimation is a major importance for applications including crowd control, public space planning, autonomous driving, visual surveillance, and warning visually distrait drivers previous accident. With strong scale, reflective, and translational symmetry, techniques to estimate the density of the crowd yield a promising outcome. But dynamic scenes with constantly evolving spatial and temporal domains and perspective distortion yet have difficulties. The dynamic nature of scenes and the complexity of demonstrating and integrating the feature space of objects of different magnitudes as predictive prototypes are the primary reason for this. This manuscript presents a Red Fox Algorithm with Transfer Learning based Crowd Density detection (RFOTL-CDD) technique in Sparse Environment. The purpose of the RFOTL-CDD system lies in the effectual identification and classification of distinct types of crowds in a sparse environment. To achieve this, the presented RFOTL-CDD method uses a ResNet prototype for feature vector generation. For the identification and classification of a crowd, the RFOTL-CDD technique applies Naive Bayes (NB) classifiers. In this work, the RFO algorithm is utilized for boosting the performance of the ResNet method. The stimulation outcomes of the RFOTL-CDD technique can be well studied on a crowd dataset and the outcomes confirmed the supremacy of the RFOTL-CDD technique on crowd detection.

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Transfer Learning and CNN-Based Vehicle Identification Approach for Hit-and-Run Cases

Transfer Learning and CNN-Based Vehicle Identification Appro...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Krisha Darji Fenil Ramoliya Chinmay Trivedi Rajesh Gupta Riya Kakkar Sudeep Tanwar Deepak Garg Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

In the swiftly evolving landscape of the modernized transportation, characterized by unprecedented technological advancements, the escalating frequency of Hit-and-Run incidents on the road poses a critical challenge to the people's well being. In response, we present a pioneering vehicle identification approach leveraging Transfer Learning-enhanced Convolutional Neural Networks (TL-enhanced CNN) to address the Hit-and-Run cases which can endanger the people' life. By harnessing the power of continuous traffic monitoring, camera sensors, and location data, the proposed approach adeptly detects and classifies vehicles involved in Hit-and-Run incidents. This model is trained on diverse vehicular images and the TL-enhanced CNN methodology not only identifies vehicle types, but also extracts essential information within the dynamic monitoring environment. The core of proposed approach lies in an Ensembled CNN model, combining the strengths of Visual Geometry Group (VGG-16), Residual Network (ResNet-50) and InceptionV3, enhancing both accuracy and robustness. Rigorous evaluation, encompassing training parameter analysis, loss curves, accuracy curves, and comprehensive performance metrics, establishes the robustness and efficacy of this proposed approach, thus offering a potent solution to the mounting challenge of Hit-and-Run detection and response in modern transportation systems.

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Deep Learning-Based Face Mask detection in the Working Scenario of Nuclear Power Plant: Under the Omicron Pandemic

Deep Learning-Based Face Mask detection in the Working Scena...

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

作者： Jining Zhang Guangzhi Li Yihan Yang Chunxiao Wang Yanyan Cui Zhaoguo Zhang State Nuclear Electric Power Planning Design & Research Institute Co. Ltd Beijing China Department of Electrical Engineering and Electronics University of Liverpool Liverpool UK Shanghai Aerospace Electronic Technology Institute Shanghai Academy of Spaceflight Technology Shanghai China Beijing Tsinghua Changgung Hospital Tsinghua University Beijing China

COVID-19 is a great threat to mankind. At present, the world is trying to reduce the spread of the virus. Omicron virus emerged in 2021 and it is much more contagious. This makes it more difficult for humans to stop the spread of the virus, but wearing a mask is a good way to effectively control the Omicron epidemic. According to China’s data, wearing masks has greatly reduced the spread of Omicron. As a national strategic facility, the personnel of the nuclear power plant should pay more attention to protective measures, but it is not easy to ensure that all personnel wear face masks. In order to prevent Omicron from spreading on a large scale in the nuclear power plant and threatening national security. This paper proposes a face mask detection system based on deep learning instead of manual detection, which can detect whether the personnel in the nuclear power plant wear masks as required. At the same time, we developed a new face mask dataset, MASK-N. Our algorithm achieved 0.951 precision on the MASK-N dataset, outperforming all traditional algorithms.

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Binary Optimization Using Hybrid Owl Optimization For Biomarker Selection From Cancer Datasets

Binary Optimization Using Hybrid Owl Optimization For Biomar...

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technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE international conference on

作者： Bibhuprasad Sahu Chakradharamahanthi Madhavi Sasmita Pani Jvr Ravindra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering(Autonomous) Hyderabad India Department of Computer Science and Engineering Balasore College of Engineering and Technology Balasore Odisha Department of Electronics & Communication Engineering Vardhaman College of Engineering(Autonomous) Hyderabad India

The enhancement of medical technology generates a massive amount of disease data. For accurate disease detection, feature subset selection plays an important role in solving different classification problems. The selection of a good feature subset enhances the accuracy of the machine learning model and reduces the training time. However, it becomes tricky and challenging to identify the optimal feature subset from the high-dimensional datasets. Different hybrid models are proposed by various researchers to deal with these types of issues. The Owl optimization algorithm proves its efficiency in selecting the optimal features. Keeping an eye on the performance of this stochastic optimization, a binary version of the hybrid Owl optimization algorithm with simulating annealing is proposed to detect the biomarker features from the cancer datasets. K nearest neighbors (KNN) are used as a wrapper to find the best biomarkers. Eight benchmark datasets were employed for the proposed model’s performance evaluation. The results show that the proposed model is significantly better than binary Owl optimization, and other counterparts using various performance matrices such as accuracy, selection of best biomarkers, and computational time. The proposed model Owl-SA performs with an accuracy of 100% in the case of CNS and lymphoma datasets. And it retains the accuracy of 99.14%, 97.16%, 98.41%, 97.94%, 98.93%,98.32% for breast, colon, ALL-AML, ovarian, SRBCT, and MLL respectively.

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Design and Implementation of a LoRa-Based Water Quality Monitoring system

Design and Implementation of a LoRa-Based Water Quality Moni...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Praveen Kumar Malik Pooja Malik Gotte Ranjith Kumar Sneha Robin Abraham Rajesh Singh School of Electronics and Electrical Engineering Lovely Professional University Jalandhar Punjab India School of Computer Science & Artificial Intelligence SR University Warangal Raj Kumar Goel Institute of Technology Ghaziabad Uttar Pradesh UIT Division of Research and Innvovation Uttaranchal University Dehradun India

Monitoring the physical, chemical, biological, and radiological properties of water is the process of figuring out whether it is suitable for a certain usage. Monitoring water quality strives to guarantee that water is fit for diverse applications, including drinking, recreation, and irrigation, as well as that it complies with environmental requirements. The conductivity, oxygen, TDS, turbidity, temperature, BOD, COD, pH and the presence of other pollutants like heavy metals, pesticides, and pathogens are among the parameters that are commonly examined during water quality monitoring. Monitoring water quality is crucial since it can have a negative impact on ecosystems, economy, and human health. Authorities can detect potential sources of contamination through monitoring and take the necessary action to safeguard the environment and the general people. Monitoring the quality of our water supply is essential to ensure its security. In this project, we suggest developing and deploying a LoRa-based system for monitoring water quality, which would allow for real-time monitoring of variables including pH, TDS, and temperature. The LoRa-based Water Monitoring Mote and the LoRa Gateway Mote are the two nodes that make up the system.

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Constructing the Evaluation Index system of Chinese-Portuguese Machine Translation using the Delphi and Analytic Hierarchy Process Methods

Constructing the Evaluation Index System of Chinese-Portugue...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Yuqi Sun Lap Man Hoi Sio Kei Im Ministry of Education Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Macao Polytechnic University Macao China Faculty of Applied Sciences Macao Polytechnic University Macao China Macao Polytechnic University Macao China

Quality assessment is an important part of ensuring the quality and long-term development of machine translation. There is still much room for research into the evaluation of machine translation from Chinese to Portuguese, and no specific evaluation metrics have yet been developed. This study uses the Delphi method and Analytic Hierarchy Process (AHP) to construct an index system of Chinese-Portuguese Machine Translation and analyzes the relative index weight. The system includes four first-level indicators and nine second-level indicators. The weight of primary indicators, from high to low, is "Accuracy, fidelity" (52.5%), "Readability" (22.9%), "Fluency" (16.8%), and "Acceptability & Usability" (7.8%). The secondary indicators with the highest weight of each primary indicator are "Correction", "Lexicon", "Readability", and "Utility". AHP was used to transform the qualitative problems in evaluating Chinese-Portuguese Machine Translation into the quantitative problem in order to provide a theoretical basis and practical guidance for the future manual evaluation of Chinese-Portuguese Machine Translation, and for the eventual modification of automatic machine translation evaluation methods.

关键词： Measurement Analytic hierarchy process Transforms Manuals Computer applications Power electronics Quality assessment

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Automating Big Data Analysis Using Artificial intelligence

Automating Big Data Analysis Using Artificial Intelligence

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

作者： Ratnmala N. Bhimanpallewar Satish Kumar Jangid Rakesh Kumar Yadav Arunkumar D T Pooja Dehankar Geetha B Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Electrical Engineering Vivekananda Global University Jaipur 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) Bangalore Karnataka India School of Engineering Ajeenkya D. Y. Patil University Pune Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai

Huge information evaluation, the use of synthetic intelligence, is a manner of using AI structures to automate the system of extracting meaningful insights from massive sets of statistics. A combination of AI-driven algorithms and hardware may be used to quickly perceive styles in unlabeled information that could otherwise take vast quantities of time to extract. This paper presents a method and framework for Automating Big Data Analysis using Artificial intelligence (AI). With the increasing volume and complexity of big data, traditional manual analysis methods are becoming inefficient and often result in inaccurate or incomplete insights. To address this challenge, we propose a systematic approach that leverages AI techniques such as machine learning and natural language processing to automate the process of data analysis. Our framework includes measures for data preprocessing, feature selection, model building, and result interpretation, and adapts to different types and sources of big data. We demonstrate the effectiveness of our approach through experiments on real-world datasets and compare the results with manual analysis methods. The proposed automated approach not only improves the speed and accuracy of big data analysis, but also reduces the human effort and expertise required. This has the potential to greatly enhance decision-making processes in various industries and domains that deal with large amounts of data. These talents can be used to permit higher choice-making and create extra efficient, automatic approaches to conducting huge-scale data analysis.

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Rewarding Reviews with Tokens Using Smart Contracts

Rewarding Reviews with Tokens Using Smart Contracts

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Innovative Practices in technology and Management (ICIPTM), international conference on

作者： Devendran K Vani Rajasekar Vandana Sharma Ahmed A. Elngar Prasanna Kumar K R Department of CSE Kongu Engineering College Erode India Amity University of Technology Amity University India Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City Egypt Department of IT Kongu Engineering College Erode India

Research shows that 68% of online purchases are influenced by the reviews of the products that are being sold. Online retailers know the importance of online reviews and how those reviews impact their business. Online review sites are widely used in the hotel sector and by online retailers. Because these evaluations account for such many sales, there is always the risk that they may be exploited or interfered with. The issue is that these systems are highly centralized, leaving them vulnerable to manipulation and control. In this paper, we discussed the possibility of a decentralized, unbiased review system and an abstract implementation of that system. This review system will be a collection of smart contracts which can be deployed on the Ethereum blockchain. The blockchain's decentralized characteristic might be used to establish an unbiased and fair review system that increases consumer connections and confidence. On Test Networks, we build and test smart contracts in this project. The architectural flow and system components are also provided. In conventional review systems, the user usually adds a new review, and this review is stored in a centralized database of the service provider. The service provider can edit the review, it can also be permanently deleted from the platform. Since reviews drive a major part of the sales, this control over the reviews system can affect users who might have found the deleted review to be useful. A blockchain is a continuously increasing collection of linked and encrypted documents known as blocks. When a blockchain is used, a digital item's ability to duplicate indefinitely is eliminated. Consumer review systems might benefit from blockchain's decentralized and tamper resistant features.

关键词： Resistance Databases Smart contracts Blockchains Business

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