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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Thangiah Sathish Kumar Dharmendra Kumar Roy Pachaivannan Partheeban R Ponnusamy Renganathan Rani Hemamalini M. Ettappan Department of Computer Science and Engineering Hyderabad Institute of Technology and Management (HITAM) Department of Civil Engineering Chennai Institute of Technology Kundrathur Chennai Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur Chennai India Department of Electrical and Electronics Engineering St. Peters Institute of Higher Education and Research Avadi Chennai Department of Electrical and Electronics Engineering Chennai Institute of Technology Chennai India

Recent advancements in DL (Deep Learning) have unlocked the potential for more accurate predictions in various fields, including agricultural crop yield forecasting. This paper presents the NeuroDendritic Networks (NDN) methodology, a biologically inspired approach that emulates the dynamic growth and retraction of dendrites in neurons. This novel method hinges on the NeuroDendritic Units (NDUs) and their ability to grow and prune dendritic branches adaptively based on data stimuli, ensuring optimal processing and information storage. As a result, NDN offers dynamic model adaptation, intrinsic regularization through dendritic retraction, and enhanced focus on pertinent data patterns. Preliminary tests have shown an expected improvement of up to 40% in prediction accuracy compared to traditional methods. This dynamic adaptation ensures the model remains attuned to the most influential factors affecting yield, leading to more informed and timely interventions. The versatility and adaptability of the NDN method not only redefine the standards in agricultural predictive modeling but also hint at its expansive potential across various domains, heralding a promising direction for future research.

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A Novel Approach to Smart Grid Energy management Using the Osprey Optimization Algorithm 4

A Novel Approach to Smart Grid Energy Management Using the O...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rudrapogu, Nissisolomon Hemalatha, N. Warkad, Sanjay B Jayanthi, A. Vinoth, K. Jayaram, B. Lecturer in Eee Nissisolomon Rudrapogu Mbts Government Polytechnic College Department of Electrical and Electronics Engineering Section Andhra Pradesh Guntur522005 India Bharath Institute of Higher Education and Research Department of Electrical and Electronics Engineering Tamil Nadu Chennai600073 India P. R. Pote Patil College of Engineering & Management Department of Electrical Engineering Maharashtra Amravati444602 India Jeppiaar Institute of Technology Department of Physics Tamil Nadu Sriperumbudur631502 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India Rmk Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798331523923

Smart Grid Energy management (SGEM) denotes the function of optimizing energy flow in the grid while enhancing grid efficiency and stability. One major issue is that increased integration of Renewable Energy Sources (RESs) may add a layer of complexity to EM, which may, in turn, reduce the reliability and stability of the grid. Keeping such challenges in mind, this paper proposes the Osprey Optimization Algorithm (OOA) on its part to enhance SGEM through various facets of load balancing and energy distribution. The objective of this method is to minimize the Mean Absolute Percentage Error (MAPE), enhancing the precision of energy predictions and optimizing grid performance. The OOA optimizes energy distribution in micro grids, improving efficiency and reducing power losses. The performance of the suggested OOA method is assessed by contrasting it with other optimization approaches that are currently in use, including Particle Swarm Optimization (PSO), Multi Objective Genetic Algorithm (MOGA), Sparrow Search Algorithm (SSA), and Multi Objective Wind Driven Optimization (MOWDO). The OOA method is implemented on the MATLAB platform. The outcomes show that the proposed OOA method provides superior performance in minimizing MAPE, achieving the lowest MAPE value of 0.28%, indicating its enhanced accuracy in energy demand forecasting and optimization. © 2025 IEEE.

关键词： Clean energy

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Using DL Measure and Distance to Assess Text Mining Document Classification Problems

Using DL Measure and Distance to Assess Text Mining Document...

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Communications and Information technologies (GCCIT), Global Conference on

作者： Vivek Deshpande K. Yuvaraj Akhilesh Kalia Praveena J Shivangi Gupta Uma C Swadimath Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

In this study, we take an asymptotic approach to the issue of document categorization in text mining. Some intriguing aspects may be shown graphically through the formalisation of statistical hypothesis testing as a text mining task. Due to its experimental efficacy in numerous case studies, various heuristics are used to practical analysis in the text mining issue. This method will provide the necessary theoretical explanation for the efficacy of text mining approaches. Documents are sorted using a distance metric in word vector area and deep learning algorithms. The study also examines the effectiveness of distance measures from the fresh perspective of asymptotic analysis.

关键词： Text mining Measurement Deep learning Visualization Documentation Vectors Information and communication technology Classification algorithms Testing

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Preference-Based Regret Three-Way Decision Method on Multiple Decision Information Systems with Linguistic Z-Numbers

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SSRN 2023年

作者： Wang, Han Ju, Yanbing Dong, Peiwu Wang, Aihua Cabrerizo, Francisco Javier School of Management and Economics Beijing Institute of Technology Beijing100081 China Graduate School of Education Peking University Beijing100871 China Department of Computer Science and AI University of Granada Granada18071 Spain

Three-way decision theory (3WD) provides a reasonable solution to solve multi-criteria decision-making (MCDM) problems and reduces more decision risks than two-way decision (2WD). However, the impact of preferences on decision results has not been reasonably considered in 3WD. Thus, this paper proposes a preference-based regret 3WD model on multiple decision information systems (MDISs) with linguistic Z-numbers (LZNs) to address the defect. First of all, a Z-LINMAP method is proposed by extending LINMAP method into the LZNs environment to derive criteria weights, Z-number ideal solutions and preference coefficients. On the basis of preference coefficients, the consistency-order and inconsistency-order are defined and further the consistency and inconsistency equivalence classes are presented to derive the conditional probabilities of alternatives. Then, the regret loss functions based on regret theory are presented and the regret 3WD rules of a single decision information system is designed. Furthermore, the average expected regret loss function and loss score function are defined to classify and rank all alternatives for MDISs. Finally, the proposed 3WD model is applied to the image recognition case with human-computer interaction to verify the effectiveness, and the comparative and sensitivity analyses are carried out to demonstrate the feasibility of our model. © 2023, The Authors. All rights reserved.

关键词： Linguistics

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IoT and wireless sensor network -Based Three- Tier Architecture for Continuous Cardiac Health Monitoring and Alert System

IoT and wireless sensor network -Based Three- Tier Architect...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Swapnil Gulabrao Gorde Archana Singh M Tamilselvi G Ravivarman Rajendiran M Ramya Maranan MIT College of Management MIT Art Design & Technology University Pune Private University Maharashtra India Department of Computer Science and Engineering Roever Engineering College Perambalur Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Research and Innovation SIMATS Saveetha School of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

This study suggests a powerful Three- Tier Architecture that combines machine learning models, Internet of Things (IoT) devices, and sophisticated sensors to provide continuous cardiac health monitoring. A range of physiological data is provided by the sensors in the Data Collection Tier, which measure blood pressure, temperature, acceleration, and electrocardiogram (ECG). In the Data Processing Tier, a local server applies complex algorithms to efficiently assess the gathered data and provide insightful outcomes. By providing easy access, authentication, and alarm production, the Application Tier acts as the interface and guarantees efficient contact with healthcare providers. The unique aspect of the system is the integration of machine learning models, which were trained on recorded data for predictive analysis. k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Decision Trees (DT), and Recurrent Neural Networks (RNN) are some examples of these models. Impressive accuracy levels are seen in the results, with ANN leading at 96.44% The measures of accuracy, precision, recall, and F1 score all demonstrate the algorithms' capacity to anticipate possible health risks, which supports the models' effectiveness. This proactive paradigm in healthcare is made possible by the comprehensive approach of continuous heart health monitoring, which enables prompt interventions and customized treatment strategies. This study increases our knowledge of the dynamics of cardiovascular health and the rapidly growing field of health care technology by demonstrating the synergy of sensor-based monitoring, IoT connectivity, and machine learning analytics in a unified architecture. In the end, patient care is improved.

关键词： Temperature sensors Temperature measurement Machine learning algorithms Accuracy Recurrent neural networks Artificial neural networks Sensors

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Construction Process Quality Control Assisted Mobile Application 8

Construction Process Quality Control Assisted Mobile Applica...

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8th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2021

作者： Kruachottikul, Pravee Cooharojananone, Nagul Phanomchoeng, Gridsada Wongsanao, Keetapat Thabsuwan, Sirada Suwannatrai, Kuptipong Pongsuttisatta, Watcharapong Ganyaporngul, Sirapavee Kovitanggoon, Kittikul Vajarodaya, Napatsapa Graduate School Chulalongkorn University Technopreneurship and Innovation Management Program Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Mechanical Engineering Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Solitech Innovations and Technologies Co. Ltd. Bangkok Thailand

ISBN: (纸本)9781665428958

Digital transformation plays a big role in many industries, including construction. One of the obstacles to manage large-scale and complex construction projects is inefficient communication between involving personnel. Most problems are such as misunderstanding contexts, incomplete information, ignorance, missing multimedia files, etc. In Thailand, organizations adopt digital transformation by utilizing a variety of applications and software to complete their projects, such as LINE, G-Suite, Asana, etc. Users have to spend a lot of time to learn each application and software to be able to achieve maximum performance. Construction process quality control assisted application is the new tool developed for uniting all needs and solving communication problems. Requirements are gathered by five interviews with three major organizations, the Thailand department of Highway, Team Built Co., Ltd., and one major construction company. Requirements were analyzed to extract the most useful features that are beneficial to all users. React Native and the Expo framework were used to develop the frontend part of the application. For the backend part, the application used microservice architecture with various cloud services, like Amazon AWS, Expo Server, and Firebase Cloud Messaging. In the end, the application was put through hand-on tests by 13 on-site personnel. The final results showed the average scores of the user interface, hands-on experience, and benefits for the users were 4, 4.16, and 3.92 (out of 5), respectively, in which the total satisfaction score is 4.03(out of 5). © 2021 IEEE.

关键词： Construction

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Blockchain Innovations in education: Empowering Students with Smart Contracts

Blockchain Innovations in Education: Empowering Students wit...

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Disruptive technologies (ICDT), International Conference on

作者： M. Sheerin Banu A. Sevuga Pandian Bindu Madhavi P. Sasikumar Vishal KS L Sherin Beevi Department of Information Technology R.M.K. Engineering College Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru Karnataka India KL Business School Koneru Lakshmaiah Education Foundation (Deemed to be University) Andhra Pradesh India School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Commerce School of Commerce VET Institute of Arts and Science Erode Tamilnadu India Department of Computer Science and Engineering R.M.D Engineering College

A lot of people are interested in how blockchain technology can be used in education because it has the potential to change everything. The main point of this study is to investigate how the Internet of Things (IoT), smart contracts, and blockchain technology can be used to give students more power in the classroom. Traditional school systems have a lot of problems when it comes to being open, safe, and effective. When old-fashioned ways of keeping records and managing data are used, wrong information, fraud, and not being able to access data are possible outcomes. In the past, though, studies have been done on separate parts of these technologies. This work suggests a complete plan to fix current issues in the education field that makes use of how blockchain technology, smart contracts, and the IoT can work together. The study uses a mixed-methods approach, which means that it uses both qualitative and quantitative evaluations. The results show that there is an effect that is good for student empowerment because it makes it easier for students to get to learning materials and generally lowers the amount of work that needs to be done.

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Model of the Dynamics of the State of educational Content Recommender System

Model of the Dynamics of the State of Educational Content Re...

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IEEE International Conference on Dependable Systems, Services and technologies (DESSERT)

作者： Mateusz Muchacki Oksana Sitnikova Serhii Yenhalychev Institute of Security and Computer Science University of the National Education Commission Kracow Kracow Poland SET University Kyiv Ukraine Cybersecurity and Information Technologies Department Simon Kuznets Kharkiv National University of Economics Kharkiv Ukraine

the report presents a generalized model of the dynamics of the state of the educational content recommender system. It differs from the known ones by taking into account the factor of changes in user preferences over time and the possibility of adapting to them. The relevance of the report is caused by the need to take into account the dynamic nature of user preferences and changes in interests over time. To solve this problem, the authors propose to apply GERT-networks (Graphical Evaluation and Review Technique). GERT-networks allow describing the probability of the system staying in different states over time, which is important for understanding and predicting changes in user interests. As a result of modeling, analytical expressions for calculating the probability-time characteristics of the recommender system dynamics are derived. Experiments were conducted to estimate the probability distribution density function of the formation time of educational content recommendation. The results showed the possibility of estimating the maximum values of the probability distribution density of the task of forming educational content recommendations, and, accordingly, the indicator of the time of forming recommendations.

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Leveraging Machine Learning Algorithms to Optimize Diet Maintenance Based on User Food Intake and Fitness Data

Leveraging Machine Learning Algorithms to Optimize Diet Main...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： K. Clarinda N. Deepa R. Dhamotharan R. Santhana Krishnan J. Relin Fransis Raj J. V. Anchitaalagammai Department of Management Studies SCAD College of Engineering and Technology Cheranmahadevi India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The increasing prevalence of lifestyle-related health issues has necessitated the development of innovative approaches to diet management. This research introduces the Adaptive Eating Pattern Predictor (AEPP), a novel machine learning algorithm designed to optimize diet maintenance by analyzing user food intake and fitness data. The AEPP combines the predictive capabilities of Artificial Neural Networks (ANN) with the probabilistic modeling of Markov Chains, enabling accurate forecasting of future eating patterns based on historical dietary and activity data. A comprehensive simulation analysis was conducted to evaluate the performance of the AEPP against existing algorithms, including traditional recommendation systems and basic machine learning models. Various simulation metrics, such as accuracy, precision, recall, and F1 score, were employed to compare the effectiveness of the AEPP in providing personalized dietary recommendations. The results demonstrate that the AEPP significantly outperforms existing algorithms, highlighting its potential as a valuable tool for individuals seeking to maintain a balanced diet in alignment with their fitness goals. This study contributes to the field of nutritional science by showcasing the advantages of integrating advanced machine learning techniques in personal diet management.

关键词： Adaptation models Machine learning algorithms Accuracy Adaptive systems Collaboration Artificial neural networks Predictive models Prediction algorithms Maintenance Wearable devices

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Grading of Fruit Ripeness Using Arduino in IoT

Grading of Fruit Ripeness Using Arduino in IoT

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Sustainable Emerging Innovations in Engineering and Technology (ICSEIET), International Conference on

作者： Dilip Kumar Jang Bahadur Saini Sk Hasane Ahammad Purushottam Das Ashutosh Bhatt Pragati Malusare Sandeep Dwarkanath Pande Department of Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Pune India Department of E.C.E. Koneru Lakshmaiah Education Foundation Vaddeswaram India CSE Graphic Era Hill University Dehradun India CSE Graphic Era deemed to be University Dehradun India Department of computer Science and Engineering Himalayan School of Science & Technology Swami Rama Himalayan University Uttarakhand India Department of Computer Science and Engineering Indira College OF Engineering & management Pune MIT Academy of Engineering Pune India

Ripening is the process by which the growing fruit will get the required quality and palatable nature. Grading fruits play an essential role in the food processing industry because by knowing the quality of the fruit, the vendor or the farmer can decide when he must sell his product to his customer. If the farmer or the fruit vendor knows the correct quality of the product, then he can sell his product at a better price, and he may get some profit. In this work, we supervise the quality of climacteric fruits like bananas and mango. It has been proposed to permit customers to purchase healthy fruits without actively contacting products and to benefit a predetermined set of individuals. The sensing element is the one that helps to detect the quality of the fruit, and here the sensing element is a gas sensor that is connected to the Arduino Uno. The information about the fruit will be sent to the user Via NodeMCU using Arduino Uno.

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