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检索条件"任意字段=International Conference on Advances in Computer Science and Engineering"

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Fuzzy-Deep Learning-Based Artificial Intelligence for Edge Computing and Real-Time Decision-Making in Uncertain IoT Environments 1

Fuzzy-Deep Learning-Based Artificial Intelligence for Edge C...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Joshi, B.P. Singh, Akhilesh Kumar, Nagendra Rautela, Siddharth Graphic Era Hill University Department of Mathematics Department of Computer Science & Engineering India VMSB UTU Department of Electrical Engineering Campus Pithoragarh India GL Bajaj Institute of Technology & Management Department of Electrical & Electronics Engineering Greater Noida201306 India Central Board of Secondary Education Department of Mathematics Uttarakhand Bhimtal India

ISBN: (纸本)9798331518578

Owing to the Internet of Vehicles (IoV) quick growth, both academics and the sector have paid close emphasis to vehicular edge computation (VEC). Nevertheless, because of the unbalanced congestion and the strict delay requirements, task offloading in various junction situations continues to struggle from inefficient resource allocated and poor operation implementation standards. This study proposes a task-offloading technique using a fuzzy decision-making method to deal with ambiguity and uncertainties to solve these problems. Roadside Utilities (RSUs) placed alongside remote roadways typically have limited energy resources, thus they must offer energy-effective planning assistance with the distribution of duties to VEC. However, planning decisions for regional task execution incur computational costs, and assigning duties to edge automobiles incurs transmission costs, making energy usage management difficult. Task data transmission to edge automobiles results in increased RSU power usage even while task offloading lowers response delay. To meet task schedule and supply restrictions, this study proposes an energy-effective automobile planning issue for offloading functions to mobile edge units. This study develops a planning technique depending on on-policy deep reinforcement learning (DRL) and a fuzzy-based DRL to address the extremely complex problem brought on by a rise in the number of automobiles under RSU service. When contrasted to the Q-learning method, this FRL not only speeds up the learning procedure but also enhances long-term payoff. © 2025 IEEE.

关键词： Energy utilization

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Uncertainty-Aware Reservoir Permeability Prediction using Gaussian Processes Regression and NMR Measurements 24

Uncertainty-Aware Reservoir Permeability Prediction using Ga...

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8th international conference on advances in Artificial Intelligence, ICAAI 2024

作者： Lawal, Ahmad Yang, Yingjie Baisa, Nathanael L. He, Hongmei School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Science Engineering and Environment University of Salford Manchester United Kingdom

ISBN: (纸本)9798400718014

This study investigates the challenges of permeability prediction in reservoir engineering, focusing on addressing uncertainties inherent in the data and modelling process, and leveraging Nuclear Magnetic Resonance (NMR) log data from the Northern Sea Volve field. The study uses a probabilistic machine learning method called Gaussian Process Regression (GPR) with different kernels, such as Matern52, Matern32, and Radial Basis Function (RBF). LSboost, K-nearest neighbour (KNN), and XGBoost are some of the existing models that are used for comparison. Performance metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination (R2) are utilized for assessment. Additionally, the uncertainty associated with different GPR kernels is analyzed, and confidence intervals are generated to provide insights into model behaviour. The inclusion of confidence intervals enhances interpretability by quantifying the range within which the true permeability value is likely to fall with a specified level of confidence, offering valuable information for decision-making processes in reservoir engineering applications. Findings demonstrate the effectiveness of GPR with Matern52 and Matern32 kernels in permeability prediction, offering competitive performance and robust uncertainty quantification. This research contributes to advancing reservoir engineering by providing a comprehensive and uncertainty-aware approach to permeability prediction. © 2024 Copyright held by the owner/author(s).

关键词： Radial basis function networks

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Estimation of Response Time with Neural Networks in Cloud Computing System 1

Estimation of Response Time with Neural Networks in Cloud Co...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： More, Rajesh Suresh, Sanjna Veerappampalayam Easwaramoorthy, Sathish Kumar Rizvi, Haider Ali Kunnathully, Kannan Gupta, Sahil K. Cloudwerx Wordwide Departmen of AIML Hyderabad India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India School of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Yogananda School of Artificial Intelligence Shoolini University Department of Artificial Intelligence and Machine Learning India Veltech University Department of Research Chennai India Tula's Institute Department of Computer Science and Engineering Dehradun248197 India

ISBN: (纸本)9798331518578

Resource allotment, responsiveness, connection, research of unexplored assets, related resource modeling, and asset scheduling are just a few of the numerous factors and concerns that need to be considered to enhance cloud computing efficiency. The functionality of the cloud has a significant impact on the propagation of cloud architecture. Consequently, it is important to determine the components that influence cloud function to analyze it. Response duration is the primary aspect that significantly affects cloud computation functioning, according to the research. The response duration assistance is among the numerous different variables that may be used to organize resource applications. Artificial neural networks (ANNs) were employed as a technique for evaluating the parameter of pursuit, and a fork-join framework or an architecture with demand partitioning was selected as the queuing paradigm. As a result, this study suggests a load-managing method depending on cloud demand response duration using the ARIMA Estimation of Response Duration Method. The fundamental concept is to employ ARIMA methods to estimate the future response duration, providing a more efficient method of allocating resources with limited values. The results of the investigation demonstrated that the estimations were more precise compared to those that had been recently established. The results of the study indicate that estimation is an outstanding path for load balance and has possibility and value for load balance with displayed response duration. Additionally, the suggested method makes it feasible to acquire estimations for a wider range of cloud structure functioning signs compared to earlier feasible and also to extend the evaluation of the cloud platform to the instance of a structure with a non-Poisson data flow and non-exponential assistance duration. © 2025 IEEE.

关键词： Cloud platforms

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Advancements in Additive Manufacturing: Optimizing Mechanical Properties Through AI-Based Design 1

Advancements in Additive Manufacturing: Optimizing Mechanica...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Prakash, Jyoti Nahar, Rajni Raj, Abhishek Joseph Silvester, K. Pandey, Sujeet Kumar Patil, Pruthviraj Devidas Engineering Institute Kamla Nehru Institute of Physical and Social Sciences Uttar Pradesh Sultanpur India Kalinga University Department of Mechanical Engineering Chhattisgarh Raipur India B.I.T. Sindri Production & Industrial Engineering Department Jharkhand Dhanbad India Bannari Amman Institute of Technology Industrial Safety Engineering Department of Mechanical Engineering Tamilnadu Erode India Rajiv Gandhi Institute of Petroleum Technology Jais Bahadurpur Uttar Pradesh Amethi India JSPM's Rajarshi Shahu College of Engineering Department of Mechanical Engineering Pune India

ISBN: (纸本)9798331518578

Modern engineering has been transformed by additive manufacturing (AM), which makes complicated geometries and customized constructions possible. Nonetheless, there are so many interconnected variables that optimising mechanical properties is still difficult. To improve mechanical performance in AM, this work investigates AI-driven design optimization strategies. Tensile strength, material toughness, and weight reduction efficiency are predicted by utilizing machine learning models to analyze critical aspects like layer height, print speed, infill density, and extrusion temperature. Metrics like MSE, MAE, and R2 scores are used to compare the predictive accuracy of various AI models, such as neural networks, random forests, and support vector machines. The findings show that while using less material, AI-based optimization greatly increases structural integrity. The connections between print parameters and performance results are shown graphically. This study demonstrates how AI may simplify AM procedures, opening the door to more intelligent, high-performance manufacturing systems with improved productivity and material use. © 2025 IEEE.

关键词： Tensile strength

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Real-Time Interaction in AR/VR Environments: A Deep Learning Approach to Human-computer Interaction 1

Real-Time Interaction in AR/VR Environments: A Deep Learning...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Chindiyababy, U. Mehta, Vishal Kakkar, Preetish Dilbar, Matchanova Umidbek, Abdalov Sharma, Aditya SRM Valliammai Engineering College Department of Information Technology India India Urgench state university Uzbekistan Mamun university Department of History Khiva Uzbekistan Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331518578

AR and VR technologies represent Innovative technologies of HCI providing rich and dynamic user experiences. This work proposes a framework based on deep learning for interacting in real-time with AR/VR environments and employing innovative multimodal input handling and flexible response strategies. The implementation consists of convolutional neural networks (CNNs) for gesture recognition, recurrent neural models for natural language understanding, and transformer-based models for gaze tracking. These models are latency-reduced and are deployed with minimal overhead architectures to fit into constrained AR/VR devices. Several assessments are done based on actual data, and mock AR/VR is used to help prototypes. Experimental evaluation confirms that the proposed framework has better interaction latency, accuracy, and adaptability over conventional approaches. For instance, gesture recognition has a success rate of 96.3% the response is facilitated in less than 50 and the natural language commands have a processing delay of 30 ms. The user surveys support the case that the system effectively presents context-based and individualized communication making the system more engaging and easier to use. It focuses on the issues that arise while implementing such methods, including computational load and integration difficulties, and provides a more detailed version of this work that describes solutions to them. The findings map the feasibility of deep learning in augmenting HCI within the AR/VR systems, which will help propel advancements across education, healthcare, and more notably, game-designing and telepresence. © 2025 IEEE.

关键词： Convolutional neural networks

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Optimized Mimo-Ofdm with Sequential Monte Carlo Detection for Low- Complexity IoT Applications 1

Optimized Mimo-Ofdm with Sequential Monte Carlo Detection fo...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sahoo, Girija Shankar Manjunath, Honganur Raju Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Physics Karnataka Ramnagar District India

ISBN: (纸本)9798331518578

An innovative digital modulating method, indices modulation (IM) deactivates certain subcarriers that in orthogonal multiplexing of frequencies (OFDM) in order to transfer bits subconsciously using their indices. This technique has the ability to further enhance the environment and error efficiency. A low-complexity and highly efficient IM-supported technique is required to decrease the computation burden in the receiving part of multiple-input multiple-output (MIMO)-aided Internet of Things (IoT) devices. To achieve two, communicate diversity orders and a substantial decrease in computational workload at the expense of an insignificant loss in spectral efficiency, Presenting a new, highly effective MIMO-OFDM with IM system in this paper. This scheme works through carrying out IM on particular communicate antennas category, that contain 2 antenna that transmits. To make demodulating easier, offer a lower complex sequentially Monte Carlo (SMC) grounded in model detection system using QR break down to determine the most probable transmitted IM sequences & altered symbols. This detector takes advantage of the empty space submatrix in the processed channels reaction matrix. Below the similar group, the suggested SMC detectors have the ability to attain nearby-optimal bit rate faults achievement with significantly reduced demodulating complex actions, and computer simulations outcomes and complex analyses demonstrate that the suggested IM-supported system accomplishes better fault achievement with very simple computation requirements. © 2025 IEEE.

关键词： Demodulation

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Analysis of Feedback and Re-Circulating Buffer Based Optical Packet Switch 1

Analysis of Feedback and Re-Circulating Buffer Based Optical...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sinha, Amit Bhardwaj, Diwakar Shukla, Vaibhav Computer Engineering &Applications GLA University Mathura India Tech Mahindra Mumbai Mumbai India

ISBN: (纸本)9798331518578

In optical packet switching (OPS), contention is major phenomenon where more than one packet tries to occupy the same output port. This paper introduces a new design for an optical packet switch based on hybrid buffering strategy. In this hybrid scheme both Feedback and Re-Circulating buffers are used to hold the contending packets. OPS is a latest transmission technology which is based on the optics and provide high bandwidth and very low latency. However, as the traffic conditions varies continuously, therefore a key challenge is to design buffering strategy. In this study, we explore how combining the benefits of both Feedback and Re-Circulating buffers can improve the overall performance of OPS. The performance of the switch is measured in terms of packet loss probability. By looking at packet loss and buffer usage under different traffic conditions, main aim to understand the impact of this hybrid approach. Our results show that using both types of buffers in the same switch leads to a lower chance of packet loss, improving the overall network performance. We found that the proposed design performs better in terms of packet loss probability compared to other approaches. © 2025 IEEE.

关键词： Packet loss

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Fire Suppression Robot with Image Processing for Warehouse Safety 1

Fire Suppression Robot with Image Processing for Warehouse S...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Base, James Allen V. Dorosan, Jairus F. Ocampo, Kyle Joshua P. Puma, Aeron Joshua M. Mercado, Mark Angelo T. Niega, Juliet O. Nationial University Philippines Department of Computer Engineering Laguna Calamba City Philippines

ISBN: (纸本)9798331518578

Warehouses are highly vulnerable to fire hazards due to the presence of combustible materials, high-energy equipment, and operational complexities. In response, the proponents propose a solution to the pressing issue of warehouse fire which pose a significant threat globally, including Philippines. The prototype that the proponents developed introduces an advance approach to fire detection and suppression. It is an incorporation of the YOLO algorithm to ensure fire detection with high accuracy, the Raspberry Pi 5 for advanced image processing, and the Arduino Mega for effective hardware control. The overall performance capacity of the robot in terms of detection and suppression shows efficiency as it provides faster response with great productivity, compared to using traditional firefighting techniques with human intervention. With an average of 4.0, or interpreted as good, it confirms that it is effective and reliable in suppressing fire. Recommendations for future improvements include expanding the dataset and enhancing hardware scalability to optimize the system's real-world applications. © 2025 IEEE.

关键词： Fire extinguishers

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How Artificial Intelligence Is Impacting on the STEM Education of Students with Disabilities: A Five Years Review 17

How Artificial Intelligence Is Impacting on the STEM Educati...

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17th international conference on computer Supported Education, CSEDU 2025

作者： Buzzi, Marina Pieriboni, Giuditta Leporini, Barbara IIT-CNR via Moruzzi 1 Pisa56124 Italy Department of Computer Science University of Pisa via Fibonacci Pisa Italy ISTI-CNR via Moruzzi 1 Pisa Italy

ISBN: (纸本)9789897587467

Artificial intelligence promises to revolutionize our life, bringing significant advances in any fields: health, education, work and leisure time. This paper analyzes the last 5-year literature concerning the use of AI for supporting people with disabilities in education. The aim is to investigate the current state of art of accessible applications in the STEM (science, Technology, engineering, Mathematics) field and understand if contents and tools are accessible for all, regardless of personal need and abilities. Personalization and adaptation emerge as fundamental factors when designing for people with disabilities. Privacy and ethics aspects often neglected are very relevant. The analysis suggests that the STEM field still suffers from accessibility gaps, and current tools need to evolve and be increased to be exploited by different disabilities and ensure the same opportunities for every student, engaging, motivating, and empowering them.. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Students

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Precision Land Use and Land Cover Mapping with a High-Accuracy Time Series LSTM Model

Precision Land Use and Land Cover Mapping with a High-Accura...

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Aryan, Adla Rv, Prakash Aluvala, Srinivas Chowdary, Venigalla Shashank Ardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal506371 India Nandyal518501 India

ISBN: (纸本)9798331529833

This paper introduces a machine-learning-based LULC classifying model developed with a time series data set, trained on a diverse set of agriculture and non-agricultural classes. It used a strong generalization capability employing an LSTM architecture;that is, it achieved accuracy of 90.04% in training and also validation accuracy of 90.55%. The ultimate accuracy was obtained at the minimal loss of 0.1909 at the accuracy of 95.58%, thereby validating its usability for LULC pattern classification. This model has a wide-range of potential application in environmental monitoring and resource management. Future work involves introducing more variables into the dataset, the inclusion of other environmental factors, and fine-tuning of the model towards more general applications in geospatial applications. Thus, this work advances the current state of remote sensing and LULC classification to present an effective tool in sustainable land management. © 2025 IEEE.

关键词： Environmental monitoring

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