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International Conference on Computing and Networking technology (ICCNT)

作者： John T Mesia Dhas Utpal Saikia K K Arun C.Vimala Josphine M.Theodore Kingslin N Padmavathi Department of Computer Science and Engineering School of Computing Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology (deemed to be university) Chennai Tamilnadu India Department of Mathematics Silapathar College Silapathar Dhemaji Assam India Department of Mechanical Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India Artificial Intelligence and Data Science R.M.K Engineering College Kavaraipettai Department of Electronics & Communication Engineering R.M.K College of Engineering & Technology Puduvoyal Tamilnadu India Department of Electronics and Instrumentation Engineering R.M.D. Engineering College Tamilnadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The optimization of welding techniques for oil rig components is crucial to upholding the structural integrity and safety of offshore drilling activities. In this study, we utilize a residual VGGNet architecture, which is a modified version of the well-known VGGNet, for the purpose of extracting crucial information from images of welding processes. The methodology employed in this study involves the collection of a complete dataset comprising welding process images obtained from diverse oil rig welding activities. The provided images are accompanied by annotations that provide the associated welding parameters, including current, voltage, and welding speed. The residual VGGNet is subsequently trained to acquire knowledge about the complex associations between picture features and welding parameters. In order to develop a reliable mathematical model, regression techniques are utilized to precisely establish the relationship between the extracted features and welding parameters. The performance of the model is evaluated using cross-validation and compared to traditional regression models to emphasize its greater ability to capture intricate patterns within the data.

关键词： Accuracy Welding Oils Voltage Feature extraction Mathematical models data models Safety Reliability Optimization

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An Encouraging System based on Student Attitude Classification

An Encouraging System based on Student Attitude Classificati...

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IEEE International Workshop on Computational intelligence & Applications (IWCIA)

作者： Naoki Kuwahara Naoki Takahashi Toru Nakata Takashi Sakamoto Toshikazu Kato Graduate School of Science and Engineering Chuo University Tokyo Japan Faculty of Science and Enginnering Aoyama Gakuin University Tokyo Japan Artificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Human Augmentation Research Center (HARC) National Institute of Advanced Industrial Science and Technology (AIST) Chiba Japan Faculty of Science and Enginnering Chuo University Tokyo Japan

Studying encouragement, while challenging, is essential for students, and many learning support systems have been developed to address the issue. This paper proposes a new strategy in which each learner’s attitude type is identified to determine an encouragement strategy that is suitable for each student. Forty university students were given a questionnaire regarding their lifestyles and values. The survey results were classified into four groups: "time-efficiency conscious," "internet-addicted," "out-of-class studying," and "dependence." We developed a system that supports study scheduling and provides reminders twice daily to assist the user with their schedule and progress. An experiment was conducted for students classified as "out-of-class studying" and "dependence," increasing study time and motivation.

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Hybrid Movie Recommendation System using Neural Collaborative Filtering with Gradient Decent and Association Rule Mining

Hybrid Movie Recommendation System using Neural Collaborativ...

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Software, Systems and Information technology (SSITCON), International Conference on

作者： J Cindhamani Rajkumar Pogaku M. P Sahana Myasar M. Adnan M. Suganya Department of Artificial Intelligence and Data Science Vel Tech Multi Tech Dr. Rangrajan Dr. Sakunthala Engineering College Avadi Chennai India School of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Maisammaguda Hyderabad India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

In recent days, most of the people are entertained by watching movies and shows, so developing a recommendation system help people across the world in saving the time to search for high rated movies. Usually, movies are recommended based on the user interests in the different movie genres such as horror, drama, romantic and comedy. Previous researchers suggested various methods for movie recommendation using Content Based Filtering (CBF), Collaborative Filtering (CF) which failed to predict the non-linear relationship recommendation due to the availability of many genres. To overcome this challenge, we proposed hybrid model namely Neural Collaborative Filtering (NCF) with gradient decent and Association Rule Mining (ARM). Initially, MovieLens dataset is employed and then data cleaning data organization is incorporated in pre-processing. Further, the features are extracted through the Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) to convert the text data into numerical data and increase the performance respectively. Finally, the developed model was measured using Root mean square error (RMSE), precision, recall, and F 1 score. Hence, the proposed hybrid recommendation model provides higher accuracy value when compared to Knowledge-aware Attentional Neural Network (KANN).

关键词： Collaborative filtering Semantics Neural networks Organizations Motion pictures Feature extraction Frequency conversion Numerical models Association rule learning Recommender systems

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The Application of the New Pythagorean Fuzzy Entropy to Decision-Making using Linguistic Terms

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Procedia Computer science 2022年 207卷 4525-4534页

作者： Parul Thakur Aleksandra Kaczyńska Neeraj Gandotra Namita Saini Wojciech Sałabun Yogananda School of AI Computers and Data Science Shoolini University Solan HP INDIA Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Żołnierska 49 71-210 Szczecin POLAND

As a new conception of IFS (Intuitionistic Fuzzy Sets), Pythagorean Fuzzy Sets can manage conflicted details more fexibly in decision-making. This extension has been used repeatedly for decision making. This is an important trend in decision making that is worth further study in combination with the study of entropy properties. This paper proposes a new entropy for the Pythagorean Fuzzy Sets and an application measure using the TOPSIS method. The new entropy approach was used to estimate the objective weights in the decision-making procedure. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique is presumed that the optimum alternative has the least distance from the positive and farthest from the negative ideal solution. This approach has been widely adopted to solve MCDM issues in multiple fields. We present a simple study case where we use TOPSIS and new entropy to solve the group decision-making problem.

关键词： Entropy MCDM TOPSIS Pythagorean Fuzzy Sets Intuitionistic Fuzzy Sets

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Advancing Telestroke Services technology with Cloud and AI Solutions for Enhanced Care

Advancing Telestroke Services Technology with Cloud and AI S...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Mothiram Rajasekaran R. Meenakshi P. K. Dhal R. Aiyshwariya Devi S. Sujatha R. Kamalakannan Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tami Nadu India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electrical and Electronics engineering S.A. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Telestroke services have become an essential component of stroke therapy, especially in areas with few specialists' medical facilities. To further improve telestroke services, this paper investigates the possibility of integrating cloud computing with artificial intelligence (AI) technologies. With the scalability, accessibility, and cost-effectiveness of cloud infrastructure, healthcare professionals worldwide may communicate and share data easily. Machine learning algorithms, image analysis tools, and other forms of AI are enhancing telestroke services by giving doctors access to sophisticated decision-making capabilities. Better patient outcomes result from these systems' capacity to facilitate quicker and more precise diagnosis and treatment planning. Predictive analytics powered by AI can identify patients at risk early on and provide individualized treatment plans that can better meet each patient's requirements. By combining cloud and AI technology, telestroke services can improve patient care by analyzing large datasets, allocating resources more efficiently, and fostering better teamwork among specialists. It may perform beyond geographical restrictions and ensure that stroke patients all around the globe have equal access to high-quality treatment. The potential is to transform stroke care delivery using data-driven insights and improved decision-making skills. It simplifies processes and improves efficiency.

关键词： Cloud computing Technological innovation Decision making Medical treatment Transforms Stroke (medical condition) Prediction algorithms Teamwork artificial intelligence Predictive analytics

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Cost-Effective Communication in UDN in Indoor and Outdoor Environment via Machine Learning

Cost-Effective Communication in UDN in Indoor and Outdoor En...

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artificial intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： K Nattar Karman V. Velmurugan Kommisetti Murthy Raju T. Sajana V. Vijayalakshmi JoshuvaArockia Dhanraj Departmeru of Artificial Intelligence and Machine Learning Saveetha School of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala R&D Institute of science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women West Godavari Andhra Pradesh India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra Dense cloud Network (UDN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra Dense cloud Network (UDN). The proposed method achieves 93.28% of Cost-effective data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management.

关键词： Costs Mobility models Machine learning Knowledge discovery Scheduling Energy efficiency Concurrent engineering

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Fish Farm Monitoring System Using IoT

Fish Farm Monitoring System Using IoT

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： K.V. Kishore D. Dhinakaran N. Jagadish kumar S. M. Udhaya Sankar K. Chandu L. Maria Michael Visuwasam Department of Information Technology Velammal Institute of Technology Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSBS Sri Sairam Engineering College Chennai India Department of CSE(Cyber Security) R.M.K College of Engineering and Technology Chennai India Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Chennai India

Fish aquaculture is an essential sector of the economy; it has seen rapid expansion in recent years. It can be challenging to manage a fish farm, though, as it demands continual attention to environmental parameters like water quality and temperature. In this study, we offer a monitoring system for fish farms that leverages IoT technology to gather and process data from sensors placed inside a fish farm. When any parameters are outside the specified range, the system notifies the farmer and provides real-time monitoring of water quality, temperature, and other environmental conditions. The method comprises a network of wireless sensors connected to a central computer and installed across the fish farm. The server gathers and analyses the sensor data before giving the farmer real-time access via a web-based interface. The fish farmer can utilize this interface to keep an eye on the fish farm's water quality and temperature as well as to change the feeding schedule and other environmental factors as needed. The system has undergone testing in a commercial fish farm, and the outcomes indicate that it successfully keeps track of the environmental conditions there. The technology can increase fish farming's productivity and profitability while minimizing its adverse environmental effects.

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Cloud Computing Based POC Diagnostic Device: Rapid Infectious Disease Testing by data Analytics

Cloud Computing Based POC Diagnostic Device: Rapid Infectiou...

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artificial intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： H. Anwer Basha L. Ramalingam MD Ashfaqul Hasan M.H Ananda S. Velmurugan S.P. Maniraj Department of Computer Science Saveetha College of Liberal Arts & Sciences SIMATS University Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Loyola Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India School of Electrical and Electronics Engineering REVA University Bangalore Karnataka India Department of Electronics and Communication Engineering TJS Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

To meet the growing need for affordable, efficient, and accurate diagnostic tools, the system has developed a Raspberry Pi-based Point-of-Care (POC) gadget for speedy and reliable detection of infectious illnesses. This groundbreaking gadget, built on the flexible and powerful Raspberry Pi computer, ushers in a new era of state-of-the-art POC diagnostics by combining molecular and image analysis methods. Complex testing for pathogens like COVID-19 and malaria may be carried out using this state-of-the-art system, which integrates a camera module and custom biosensors. The device's software parses sensor data, improves diagnostic precision using machine learning techniques, and presents a straightforward interface for clinicians and patients alike. This study takes a holistic approach by considering all relevant factors, including adherence to regulations, user education, upkeep, and data management. Our POC diagnostic gadget driven by a Raspberry Pi provides a low-cost, high-throughput method for diagnosing illness, which may have far-reaching implications for the future of healthcare delivery in both under-resourced and well-endowed regions.

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Collaborative Filtering ML Technique for Recommendation in E-Commerce Energy System

Collaborative Filtering ML Technique for Recommendation in E...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Hew Sok Yen Samuel-Soma M. Ajibade Angela Lee Siew Hoong Rex V. Culpable Celestino A. Quirante Johnry P. Dayupay Adedotun O. Adetunla Oluwadare Joshua Oyebode Department of Data Science and Artificial Intelligence School of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Sunway University Selangor Malaysia College of Technology Cebu Technological University Cebu Philippines Cebu Technological University Cebu Philippines Dept. of Mechanical and Mechatronics Engineering Afe Babalola University Ado Ekiti Nigeria Dept. of Mechanical Engineering Science University of Johannesburg Johannesburg South Africa Civil and Environmental Engineering Department Afe Babalola University Ado Ekiti Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

The growing integration of e-commerce into daily life has highlighted the essential need for recommender systems to enhance user experience and drive business success. This study examines machine learning techniques for developing effective recommender systems, with a particular focus on collaborative filtering (CF) and content-based filtering (CBF) methods. This study employs exploratory data analysis (EDA) to analyze e-commerce statistics, identifying trends in consumer behavior, product ratings, and user engagement, culminating in the development of a hybrid recommender system. The hybrid method amalgamates the benefits of Continuous Feedback (CF) with Context-Based Feedback (CBF) to deliver customized recommendations, hence improving accuracy and relevance for users. The application of hybrid models yields a notable improvement in predictive performance, evidenced by reduced mistake rates and heightened customer satisfaction. This research offers distinctive insights on user-product interactions, thereby advancing the comprehension of how machine learning could transform e-commerce personalization.

关键词： data analysis Consumer behavior Accuracy Filtering Collaborative filtering Transforms Predictive models User experience Electronic commerce Recommender systems

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Autonomous Vehicles: Steering the Future of Transportation with Self-Driving technology and Advanced AI Systems

Autonomous Vehicles: Steering the Future of Transportation w...

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Global Conference for Advancement in technology (GCAT)

作者： Daniel Nareshkumar. M Desidi Narsimha Reddy A. Sowmiya V. Samuthira Pandi Rayala Sateesh N. Hindumathy Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of EEE Rajalakshmi Engineering College Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Autonomous vehicles (AVs) are a game-changer in the transportation industry; they will alter cityscapes, cut down on accidents, and reimagine how people experience mobility. This study investigates the complex relationship between autonomous vehicles and high-tech AI systems, looking at how these two technologies can influence transportation in the future. The paper highlights crucial developments in sensor fusion, machine learning algorithms, and real-time data processing that are essential for the development of fully autonomous systems by analyzing the present status of AV technology. In the first part of the study, the basic technologies that support AVs are described. These technologies include LiDAR, radar, cameras, and GPS. When taken together, they allow AVs to perceive their surroundings and locate themselves. After that, it delves into how AI is helping AVs understand sensor data, make judgments in real-time, and carry out complicated maneuvers, showcasing how advancements in deep learning, neural networks, and predictive analytics are powering these capabilities. Integrating AVs into current transportation infrastructures presents both potential and obstacles, which the paper delves into. Critical situation safety, legal frameworks, cybersecurity risks, and AI decision-making ethics are all covered. In addition to the environmental advantages of improved traffic flow and decreased emissions, the study delves into the possible socioeconomic implications, including shifts in urban design, public transit, and the labor market. The research delves into the existing deployment tactics and the path for scaling AV operations through a thorough analysis of case studies, pilot projects, and industry reports. It determines how prepared different groups are to accept this revolutionary technology, including the general public, lawmakers, and manufacturers. To sum up, the study highlights how important it is for academic institutions, business executives, and governm

关键词： Ethics Machine learning algorithms Urban planning Decision making Sensor fusion data processing Real-time systems Safety Computer security Autonomous vehicles

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