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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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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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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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Blockchain-Based Information Supervision Model for Rice Supply Chains

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Computational intelligence and Neuroscience 2022年第0期2022卷 2914571页

作者： Wang, Jian Zhang, Xin Xu, Jiping Wang, Xiaoyi Li, Haisheng Zhao, Zhiyao Kong, Jianlei School of Artificial Intelligence Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China

Rice is a major food crop around the world, and its various quality and safety problems are closely related to human health. As an important area of food safety research, the rice supply chain has attracted increasing attention. Based on blockchain technology, this study investigated problems of data privacy and circulation efficiency caused by complex rice supply networks, long circulation cycles, and various risk factors in each link. First, we deconstructed the quality and safety of each link of the rice supply chain at the information level and established a key information classification table for each link. On that basis, we built a rice supply chain information supervision model based on blockchain. Various encryption algorithms are used to secure the sensitive data of enterprises in the supply chain to meet regulators' needs for efficient supervision. Moreover, we propose a practical Byzantine fault-tolerant consensus algorithm that scores the credit of enterprise nodes, optimizes the selection strategy of master nodes, and ensures high efficiency and low cost. Then, we built a prototype system based on the open-source framework of hyperledger fabric, analyzed the model's viability, and implemented the system using cases. The results indicated that the proposed system can optimize the information supervision process of rice supply chain regulators and provide a feasible solution for the quality and safety supervision of grain and oil. © 2022 Jian Wang et al.

关键词： Supply chains

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An Effective Learning Management System for Revealing Student Performance Attributes

arXiv

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arXiv 2024年

作者： Zhang, Xinyu Lee, Vincent C.S. Xu, Duo Chen, Jun Obaidat, Mohammad S. The School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China The Department of Data Science and Artificial Intelligence Faculty of IT Monash University Melbourne3800 Australia Guangyuan Foreign Language School Guangyuan628018 China Chongqing Institute for Brain and Intelligence Guangyang Bay Laboratory Chongqing400064 China The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Department of Computational Intelligence School of Computing SRM University SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India

Contribution: A learning management system (LMS) incorporated with an advanced educational data mining module is proposed, as a means to mine efficiently from student performance records to provide valuable insights for educators in helping plan effective learning pedagogies, improve curriculum design, and guarantee quality of teaching. Background: An LMS streamlines the management of the teaching process in a centralized place, recording, tracking, and reporting the delivery of educational courses and student performance. Educational knowledge discovery from such an elearning system plays a crucial role in rule regulation, policy establishment, and system development. However, existing LMSs do not have embedded mining modules to directly extract knowledge. As educational modes become more complex, educational data mining efficiency from those heterogeneous student learning behaviours is gradually degraded. Intended outcomes: The design and application of the LMS enable educators to learn from past student performance experiences, empowering them to guide and intervene with students in time, and eventually improve their academic success. Application design: This study proposes an effective LMS which processes the stored data through an advanced educational data mining module. The mining module utilizes probability-based frequent pattern mining for generating candidate itemsets and updating LMS databases in each iteration for complexity reduction. In addition, the conditional probability increment ratio (CPIR) is deployed as an exceptionality measure to mine effective common and exception rules simultaneously. The iteratively generated rules can reveal academic performance patterns so that have merits in learning personal goal achievement. Findings: Through two illustrative case studies, experimental results demonstrate increased mining efficiency of the proposed mining module without information loss compared to classic educational mining algorithms. The mined knowl

关键词： data mining

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Correction: Divine Religions algorithm: a novel social-inspired metaheuristic algorithm for engineering and continuous optimization problems

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Cluster Computing 2025年第5期28卷 1-2页

作者： Mozhdehi, Ali Toufanzadeh Khodadadi, Nima Aboutalebi, Mohaddeseh El-kenawy, El-Sayed M. Hussien, Abdelazim G. Zhao, Weiguo Nadimi-Shahraki, Mohammad H. Mirjalili, Seyedali Faculty of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Civil Architectural and Environmental Engineering University of Miami Coral Gables USA Department of Computer Engineering Azad University of Lahijan Lahijan Iran School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic Isa Town Bahrain Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer and Information Science Linköping University Linköping Sweden Faculty of Science Fayoum University Fayoum Egypt School of Water Conservancy and Hydropower Handan China Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley Australia University Research and Innovation Center (EKIK) Obuda University Budapest Hungary

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Novel Deep Learning Approaches to Environmental Management with Sustainability

Novel Deep Learning Approaches to Environmental Management w...

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Communication, Computer sciences and engineering (IC3SE), International Conference on

作者： Chunchu Suchith Kumar Chinnem Rama Mohan B Santhosh Kumar Navdeep Singh Pemmadi Lavanya Dyuti Banerjee School of Agriculture SR University Warangal Telangana India Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara MBA Vignan's Institute Of Information Technology Visakhapatnam Artificial intelligence & Data Science (AI&DS) Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur District Andhra Pradesh

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The application of Earth observation data to Deep Learning (DL) has increased dramatically over the last ten years, with ground-breaking initiatives made possible by the creation of foundational models. Environmental concerns have been addressed with the use of these technologies, which have been used to detect air quality, enhance precision agriculture, and provide alerts about deforestation in near real time. The objective of this research is to present cutting-edge studies and uses of these technologies in areas with limited data. The objective is to tackle the problems associated with environmental development while advancing cloud computing, data democracy, open data access, and capacity-building initiatives. This research concludes by analysing the most current breakthroughs and uses of DL and AI in order to encourage sustainability in a range of domains, such as, renewable energy, smart building energy management, environmental health and accomplishing the sustainable developments Goals(SDGs).

关键词： Industries Deep learning Renewable energy sources Ethics Wireless networks Predictive models data models

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The Performance Analysis of Daffier-Hellman Key Exchange Algorithm for Secure Transmission of data in Cryptography Networks

The Performance Analysis of Daffier-Hellman Key Exchange Alg...

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

作者： Arsalan Ahmad Nitin N. Sakhare Dipannita Mondal M. Vigenesh G. Indira Pravin R. Futane Department Of Management Mangalayatan University Aligarh India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Artificial Intelligence And Data Science Department Dr. D.Y. Patil College Of Engineering And Innovation Talegaon Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India

This paper examines the overall performance of the Daffier-Hellman key trade algorithm for the relaxed transmission of data in cryptography networks. Two types of Daffier-Hellman key exchanges are considered: the traditional scheme without elliptic curves and the current model utilizing elliptic curves. The parameters and their respective performance of both schemes are compared. In addition, it is located that the elliptic curve-primarily based Daffier-Hellman set of rules offers better overall performance than the conventional one, with better security and a drastically shorter key-status quo time. An in-depth analysis of the velocity and protection of both algorithms is supplied. The consequences of the have look indicate that the elliptic curve model of DaffierHellman is the fine-acting set of rules for secure records switches in cryptography networks. Moreover, the paper additionally discusses the safety problems and challenges arising from implementing the Daffier-Hellman key trade algorithm and indicates some possible solutions. Ultimately, the paper also offers an end on the overall performance of DaffierHellman Key's alternate set of rules regarding pace, safety, and reliability.

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A Comparative Study of Predicting Mental Distress in Patients via Social Media Interactions: artificial intelligence versus Ensemble Machine Learning Approaches

A Comparative Study of Predicting Mental Distress in Patient...

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Communication, Security and artificial intelligence (ICCSAI), International Conference on

作者： Aakansha Saxena N Kavya Shruthi N R N V Gowripathi Rao School of Information Technology Artificial Intelligence and Cyber Security (SITAICS) Rashtriya Raksha University Gandhinagar Gujarat India Department of Computer Science and Engineering North Eastern Regional Institute of Science and Technology Nirjuli Arunachal Pradesh India Department of Agricultural Engineering Faculty of Agricultural Sciences Rajiv Gandhi University Rono Hills Arunachal Pradesh India

Mental health of a person is a reflection of their emotional, psychological, and social well-being. It establishes one's feelings, thoughts, and reactions to circumstances. With sound mental health, one may reach their full potential and do tasks effectively. But nowadays people are reluctant to discuss their mental issues which makes it very challenging to diagnose mental health. In addition to lowering a person's quality of life, mental illness also increases medical expenses. The potential of AI in mental health is becoming increasingly apparent in that cases. The only way through which a person's mental health can be diagnosed without discussing it is by analysing their behaviour specifically analysing the interactions on social media platforms. In the current scenario, people are proactively involved on many social media platforms on daily basis and hence social media platforms are used to model mental health and evaluate health results accordingly. To foresee the occurrence of specific mental illnesses including anxiety, depression, and suicidal, Researchers are currently applying quantitative methodologies. When these applications are used in real-time, they can help society by acting as a surveillance tool for people who exhibit abnormal behaviour. The focus in this paper is to make a comparative analysis of how different ensemble machine algorithms and methodologies are used to study the behaviour through social media and hence by determining their performance. An effective system for early major depressive system diagnosis can be created with the help of advanced natural language processing (NLP), deep machine learning, and transfer learning techniques.

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Converter Networks for Multi-Scale Tokens-Aware Multi-Region and Multi-Sequence MR-to-CT Synthesis in a Single Model

Converter Networks for Multi-Scale Tokens-Aware Multi-Region...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Parikshit N. Mahalle Manoj Chandra Lohani S. Hemelatha Divya Sharma Chitra S Sujayaraj Samuel Jayakumar Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India School of Computing Graphic Era Hill University Haldwani India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Sciences Forensic Science JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Synthetic computed tomography (sCT) based on magnetic resonance imaging (MRI) streamlines the planning of radiation therapy treatments by doing away with CT simulation and error-prone picture registration, which in turn reduces radiation dosage for patients and setup uncertainty. To create sCT images that matched the MRI anatomy, an optimally trained MultiHead Attention was used in conjunction with the reverse diffusion process. We generated sCT using MRI on two datasets—one for the brain and one for the prostate—to test the suggested strategy. The following metrics were used for qualitative evaluation: ground truth CTs and sCTs normalized cross correlation (NCC) indices, multi-scale Structure Similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE) of Hounsfield unit (HU). A new method for creating CT images from standard MRIs utilizing a transformer-based convolutional neural network (CNN) has been designed and verified by us. Synthetic CT (sCT) pictures of excellent quality and robustness may be produced in a few minutes using this model, which successfully depicts the intricate interaction among CT and MRI images. By cutting down on patient time spent on treatment planning, improving accuracy of treatment delivery, and doing away with the necessity for extra CT scans, this method may considerably streamline radiation treatment planning.

关键词： PSNR Uncertainty Magnetic resonance imaging Computed tomography Diffusion processes Streaming media Transformers Planning Convolutional neural networks Standards

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