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Image Search Engine with Recognition 4

Image Search Engine with Recognition

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kumar, Jmsv Ravi Dheeraj, Bellaganti Gowtham, Geddada Babu, Dokala Vivek Kiran, Bonthu Asha Srikanth, M. Srkr Engineering College Department of Information Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798331523923

The Image Recognition Search Engine is one of the modern advances in artificial intelligence and image analysis and processing. This advanced system surpasses typical functionalities in image identification as it captures features to solve new problems faced in existing systems of digital media authentication, content designing, and improvement of images. The system core relies on advanced AI algorithms that can rasterically recognize and analyze various forms of image objects ranging from objects, faces, scenes, and brand logos converting what appears to be simple images into indexical, analyzable data. One feature is that the system can integrate deepfake detection technology, which is relevant in the context of increasing the amount of manipulated content in the media space. Using special algorithms to learn even the least detectable abnormality in images including the presence of unnatural distinctive facial characteristics and changes in lighting conditions, we obtain a highly effective tool for improving the reliability of image identification. Furthermore, the platform allows for new features to convert an image into a video by implementing frame interpolation and motion estimation through animating the images to allow users to create interesting clips for applications regarding marketing or any personal use. © 2025 IEEE.

关键词： Motion estimation

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Multi-Criteria Decision-Making (MCDM) methods for microgrids: An overview

Multi-Criteria Decision-Making (MCDM) methods for microgrids...

引用

international conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Alqaisi, Walid El-Bayeh, Claude Ziad Alzaareer, Khaled Zellagui, Mohamed Flah, Aymen Brahmi, Brahim School of Engineering Technology The University of Doha for Science and Technology Jelaiah St. Doha Qatar Canada Excellence Research Chair Team Concordia University Montreal Canada Electrical Engineering Department Philadelphia University Amman Jordan Department of Electrical Engineering Faculty of Technology University of Batna 2 Fesdis Batna Algeria Processes Energy Environment and Electrical Systems National Engineering School of Gabès University of Gabès Gabès Tunisia Electrical Engineering Department Collège Ahuntsic MontrealQC Canada

ISBN: (纸本)9781032803722

Multi-Criteria Decision-Making (MCDM) methods are great tools for solving complex problems with a huge number of alternatives, conflicting criteria and objectives. The main goal of using MCDMs in engineering, especially in the design of micro-grids (MGs), is to find the best solution for a specific complex problem among many possibilities and alternatives, such as selecting the best solar panel from many alternatives considering contradicting criteria. This paper presents a brief overview of the usage of MCDM methods in MGs. It shows, (a) the most used MCDM algorithms for MG design and components’ selection, (b) the main objective functions in modeling MCDM problems, (c) the main criteria (e.g., technical, technological, economic, social, and environmental), and (d) the prons and cons of each MCDM method. In addition, it points out the available MCDM software in the market and categorizes them. © 2025 The Author(s).

关键词： Solar concentrators

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AI Based Development and Prediction of Energy Prosumers 3

AI Based Development and Prediction of Energy Prosumers

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Goudappanavar, Sangamesh Y. Gowda, Devendra Kandagal, Santoshkumar Basaveshwar Engineering College Department of Electrical and Electronics Engineering Bagalkote India Acharya Institute of Technology Department of Electrical and Electronics Engineering Bangalore India

ISBN: (纸本)9798331508456

Energy prosumers, comprising individuals, businesses, or organizations, play a crucial role in modern energy systems by both consuming and generating electricity. This shift from traditional energy consumption models has led to increased energy imbalances, rising operational costs, and potential grid instability. Prosumers primarily utilize renewable energy sources, such as solar photovoltaic (SPV) systems, small wind turbine generators (SWTG), and micro-scale hydropower, while also relying on the grid when self-generation falls short. The rapid expansion of prosumer-based energy generation necessitates predictive models to enhance energy balance and cost optimization. This study develops an AI-driven energy prediction model to minimize prosumer energy imbalance costs. Historical weather and load data from Estonian energy consumers with solar installations serve as the basis for model training. The research integrates machine learning algorithms, including the Random Forest Regression model, to forecast energy generation and consumption patterns. Factors such as weather conditions, time of day, and user-specific characteristics are incorporated to enhance prediction accuracy. The results demonstrate an effective predictive model with an accuracy of 81.4%, showcasing its potential to encourage wider prosumer participation and support the adoption of renewable energy sources. © 2025 IEEE.

关键词： Solar fuels

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Axial Capacity Prediction of Concrete Externally Strengthened by Low-Cost Natural Fiber Reinforced Polymer Composite Utilizing Machine Learning Intelligence 24

Axial Capacity Prediction of Concrete Externally Strengthene...

引用

8th international conference on algorithms, Computing and systems, ICACS 2024

作者： Antonino, Josiah Davidson Patricio Laroza Silva, Dante School of Graduate Studies Mapua University Manila Philippines School of Civil Environmental and Geological Engineering Mapua University Manila Philippines

ISBN: (纸本)9798400718304

The concept of enhancing concrete's strength and sustainability with fiber reinforced polymer (FRP) composites has drawn the interest of engineering researchers. Previous studies on FRP composites with glass, carbon, and synthetic fibers as fiber material paved the way for the current applications of FRP composites in the construction industry. Despite its effectiveness, there are certain drawbacks, including high cost of fiber material and sustainability of production. A less expensive and more sustainable method of producing FRP can be explored by employing natural fibers as alternative fiber source for FRP composites. Pioneering investigations on the use of natural fiber reinforced polymer (NFRP) as external strengthening for concrete demanded several hands-on laboratory tests, resulting in prolonged preparation times and significant material waste. The study seeks to offer an alternative method for assessing NFRP's strengthening capabilities for concrete, decreasing physical laboratory testing costs, material wastage, and assuring time management. Creating a computational artificial neural network (ANN) model that uses the Levenberg-Marquardt algorithm to forecast the compressive strength of concrete confined with NFRP composites is the key objective of the study. Data that was utilized in the ANN modelling was derived from conducted compressive strength tests on concrete externally confined with NFRP featuring sisal, jute, flax, hemp, and cotton as fiber material. The model inputs were unconfined concrete compressive strength, NFRP tensile strength, NFRP elastic modulus, NFRP strain, and NFRP thickness. The resulting model had excellent predictive capabilities, with a total Pearson's correlation coefficient (R) of 0.9966 and mean square error (MSE) of 0.3826. Parametric analysis revealed that NFRP thickness had the greatest influence on enhancing the compressive strength of concrete. © 2024 Copyright held by the owner/author(s). Publication rights licensed to A

关键词： Sustainable development

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A Research Framework for Energy Efficient Distributed Flow Shop Scheduling in Intelligent Production systems

A Research Framework for Energy Efficient Distributed Flow S...

引用

2025 international conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Ganesh, J. Regi Kumar, V. College of Engineering Trivandrum Apj Abdul Kalam Technological University Department of Mechanical Engineering Kerala Thiruvananthapuram695016 India

ISBN: (数字)9798331507541

ISBN: (纸本)9798331507541

In intelligent production scheduling, advanced algorithms are employed to develop optimal production schedules for the shop floor. Industries all over the world are now adopting various energy conservation policies due to the rapid depletion of energy sources. Energy efficient scheduling has emerged as an economical tool to conserve energy in intelligent production systems. To survive the fierce competition in the context of globalization, manufacturing companies have adopted the strategy of distributed manufacturing, where production facilities are located at different geographical locations. This article presents a research framework for energy efficient distributed scheduling in flow shop environment. The proposed framework has three phases, the formulation phase, the solution methodology phase, and the experimentation phase. The various inputs that need to be included in each phase are detailed with examples. This framework is proposed as an attempt to standardize the research works in the field of energy efficient distributed flow shop scheduling. © 2025 IEEE.

关键词： Location

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RIS-assisted OFDM-based SWIPT System

RIS-assisted OFDM-based SWIPT System

引用

international Communication systems and Networks and Workshops, COMSNETS

作者： Emani N S S Anjana Neha Sharma Sumit Gautam Department of Electrical Engineering Indian Institute of Technology Indore India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

reconfigurable Intelligent Surfaces (RIS) and Simultaneous Wireless Information and Power Transfer (SWIPT) are prominently emerging technologies in the field of wireless communications. RIS enhances signal strength and coverage by intelligently adjusting the phase shifts of electromagnetic waves, while SWIPT enables the concurrent transmission of information and power, addressing the growing demand for energy-efficient networks. In this paper, we explored resource allocation algorithms for RIS assisted SWIPT system, utilizing Orthogonal Frequency-Division Multiplexing (OFDM) for data transmission. The system consists of a single RIS that aids communication between a base station (BS) and the user equipment (UE). Each RIS element reflects multiple orthogonal sub-carriers, optimizing the overall system performance. Two novel algorithms for OFDM sub-carrier allocation and RIS element selection are proposed such that the data rate and the harvested energy are maximized at the user end, where the separated architecture of the SWIPT technology is employed. Numerical results confers that increasing number of RIS elements and OFDM sub-carriers enhances both the data rate and the energy harvested, underscoring the effectiveness of our optimization approach.

关键词： Wireless communication OFDM System performance reconfigurable intelligent surfaces Robustness Hardware Resource management Frequency division multiplexing Simultaneous wireless information and power transfer Optimization

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning algorithms 4

A Music Recommender System to Identify the Emotional State o...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 IEEE.

关键词： Convolutional neural networks

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Predicting Public Transport Passenger Using Machine Learning algorithms 10

Predicting Public Transport Passenger Using Machine Learning...

引用

10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Meza, Angelo Acuna, Annsherly Santisteban, Jose School of Systems and Computer Engineering Lima Peru

ISBN: (纸本)9798331543273

This study compares five predictive models for passenger demand at the Lima Metro's Line 1 stations: Prophet, Linear Regression, Random Forest, Decision Tree, and AutoRegressive Integrated Moving Average (ARIMA). The analysis utilizes a dataset extracted from the OSITRAN website, which includes information on the number of passengers who used the metro at various stations during specific times. Data was collected from July 1 to July 7, 2024, between 5 a.m. and 11 p.m., covering all 26 metro stations. The evaluation metrics used in this study are Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The results indicate that Prophet achieved the highest scores across all three metrics, establishing it as a viable solution compared to the other models analyzed. © 2025 IEEE.

关键词： Random forests

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Quantum neural network quantum state 3

Quantum neural network quantum state

引用

3rd international conference on algorithms, Network, and Communication Technology, ICANCT 2024

作者： Pang, Xin Li, Yanan Wang, Zhimin Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Quantum many-body computation is a central challenge across multiple domains of scientific research, and Monte Carlo method has emerged as one of the most effective numerical techniques for investigating large-scale many-body systems. In recent years, the integration of Monte Carlo methods with machine learning has led to the development of neural quantum state approach. Quantum neural networks (QNNs) offer superior expressiveness compared to classical neural networks. In this paper, we propose a novel Quantum Neural Network Quantum State (QNNQS) method, based on the variational Monte Carlo framework. This approach leverages a quantum neural network to represent the many-body quantum state, and optimizes it iteratively through gradient descent to determine the ground-state energy. To assess the performance of this method, we conduct simulations of the one-dimensional transverse-field Ising model, investigating the influence of quantum circuit architecture on circuit expressibility, entangling capability, and computational accuracy. The results demonstrate that the QNNQS method achieves comparable simulation accuracy while utilizing significantly fewer parameters and requiring less training resources than classical network quantum state methods. © 2025 SPIE.

关键词： Quantum computers

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Computer Vision and Convolutional Neural Networks (CNNs) for Advanced Face Detection and Recognition 2

Computer Vision and Convolutional Neural Networks (CNNs) for...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Ajay, A. Javith Ahamed, M. St. Joseph's Institute Of Technology Department of Computer Science Engineering Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Modern security and law enforcement involve face detection and recognition technologies, which are crucial instruments in criminal identification. It is a comprehensive system integrating advanced face detection algorithms and robust recognition techniques with a view of providing accurate and efficient identification of an individual relating to a criminal activity. It uses the latest CNNs technology for automatic extraction of facial features, matching, and classification, with an advanced algorithm for the matching that compares all identified faces to a database of criminal individuals. Its large processing databases improve the identification speed rate with fewer false positives or negatives. Other issues the paper deals with are lighting conditions, facial expression, etc. © 2025 IEEE.

关键词： Face recognition

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