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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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Semi-supervised classification of bird vocalizations

arXiv

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arXiv 2025年

作者： Hexeberg, Simen Chitre, Mandar Hoffmann-Kuhnt, Matthias Low, Bing Wen ARL Tropical Marine Science Institute National University of Singapore Singapore Department of Electrical & Computer Engineering National University of Singapore Singapore National Parks Board Singapore

Changes in bird populations can indicate broader changes in ecosystems, making birds one of the most important animal groups to monitor. Combining machine learning and passive acoustics enables continuous monitoring over extended periods without direct human involvement. However, most existing techniques require extensive expert-labeled datasets for training and cannot easily detect time-overlapping calls in busy soundscapes. We propose a semi-supervised acoustic bird detector designed to allow both the detection of time-overlapping calls (when separated in frequency) and the use of few labeled training samples. The classifier is trained and evaluated on a combination of community-recorded open-source data and long-duration soundscape recordings from Singapore. It achieves a mean F0.5 score of 0.701 across 315 classes from 110 bird species on a hold-out test set, with an average of 11 labeled training samples per class. It outperforms the state-of-the-art BirdNET classifier on a test set of 103 bird species despite significantly fewer labeled training samples. The detector is further tested on 144 microphone-hours of continuous soundscape data. The rich soundscape in Singapore makes suppression of false positives a challenge on raw, continuous data streams. Nevertheless, we demonstrate that achieving high precision in such environments with minimal labeled training data is possible. © 2025, CC BY.

关键词： Self-supervised learning

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Predicting Blood Glucose Levels in Type 1 Diabetes Using Deep Learning and Regression Techniques

Predicting Blood Glucose Levels in Type 1 Diabetes Using Dee...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Jagendra Singh Brijendra Pratap Singh Monika Dandotiya Pongkit Ekvitayavetchanukul Manoj Rana Bakshish Singh School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Computer Science & Engineering Poornima University Jaipur India The Board of Khonkaen University Affaneirs Khonkaen University Thailand Computer Science & CDOE Teerthanker Mahaveer University Moradabad India Department of Information Technology Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Accurate blood glucose prediction is very critical for the effective management of Type 1 Diabetes as it allows timely interventions and prevents fluctuations in blood sugar levels. Due to such shortcomings, the traditional methods of monitoring that are followed are consistently unable to provide real-time insights and often cannot inform the patients about the stable levels of their glucose. This research studies advanced deep learning applications based on Long Short-Term Memory (LSTM), Gated recurrent unit (GRU), Autoregressive Integrated Moving Average (ARIMA), and linear regression models to predict blood glucose levels in terms of insulin intake dosage, consumption of carbohydrates, level of physical activity, and sleep patterns. The model performances with respect to Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean absolute percentage error (MAPE), and accuracy are deployed for training and performance evaluation. Results were obtained, which show that LSTM performed well in comparison to others with an accuracy of 97.68%, followed by ARIMA (92.23%), GRU (91.23%), and Linear Regression at an accuracy of 89.45%. Real-time testing also gave the results consistent with the above, showing accuracy at close par with the glucose values. Therefore, LSTM in diabetes management systems may allow for blood glucose monitoring in real-time and customised intervention. This research contributes towards developing intelligent systems with the capacity to predict problems and enhance patient outcomes and quality of life for those with Type 1 Diabetes by embedding deep learning models into predictive accuracy and timeliness.

关键词： Deep learning Accuracy Linear regression Predictive models Real-time systems Glucose Diabetes Long short term memory Blood Monitoring

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Predictive Modeling of Solar Power Generation Using Deep Learning for Real-Time Optimization in Photovoltaic System

Predictive Modeling of Solar Power Generation Using Deep Lea...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Minal Bafna Hardeo Kumar Thakur Jagendra Singh P. Srinivas Reddy Pongkit Ekvitayavetchanukul Manoj Rana Department of Physics Agrawal P. G. College Jaipur India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Mechanical Engineering CVR College of Engineering Hyderabad India The Board of Khonkaen University Affaneirs Khonkaen University Thailand Computer Science & CDOE Teerthanker Mahaveer University Moradabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

This research addresses the need for precise solar power generation forecasting and optimization of energy management within photovoltaic systems; this is important as there is a greater demand for renewable energy sources. Reliable predictions of the output of solar power would enhance stability in the grid and thus ensure efficient distribution of the energy. This research uses deep learning techniques, the Long Short-Term memory (LSTM) model, to predict solar power generation from several environmental variables, including solar irradiation, air temperature, wind speed, relative humidity, air pressure, module power, and module temperature. To validate the performance of the model, rigorous use of metrics like Mean absolute error (MAE), Root Mean Square Error (RMSE), and Mean absolute percentage error (MAPE) was made. The proposed LSTM model gave an MAE of 0.25 kW, an RMSE of 0.35 kW, and a MAPE of 5%. This was better compared to other models like Gated recurrent unit (GRU), recurrent neural network (RNN), and Artificial neural network (ANN). A real-time implementation of the LSTM model depicted the fact that it is feasible to use the model to make correct predictions of the amount of solar power produced throughout the day. At peak periods, such as midday hours, accuracy levels hit 98% in some cases, whereas the model is compelling enough to pinpoint and respond accordingly to changing solar power production volumes for optimizing load management and energy supply. This research verifies the deep learning model and its efficiency in energy forecasting, with intense stress on the practical application of the LSTM model in real-time energy management, contributing a lot to the advancement of renewable energy integration and optimizing power systems. Finally, this research provides valuable insight into improving the operational efficiency of photovoltaic systems in order to make the future energy scenario more sustainable.

关键词： Deep learning Photovoltaic systems Renewable energy sources Atmospheric modeling Predictive models Real-time systems Forecasting Long short term memory Optimization Load modeling

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Predicting Blood Glucose Levels in Type 1 Diabetes Using Deep Learning and Regression Techniques

Predicting Blood Glucose Levels in Type 1 Diabetes Using Dee...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Singh, Jagendra Singh, Brijendra Pratap Dandotiya, Monika Ekvitayavetchanukul, Pongkit Rana, Manoj Singh, Bakshish School of Computer Science Engineering & Technology Bennett University Greater Noida India Poornima University Department of Computer Science & Engineering Jaipur India The Board of Khonkaen University Affaneirs Khonkaen University Thailand Teerthanker Mahaveer University Cdoe Moradabad India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India

ISBN: (纸本)9798331527495

关键词： Linear regression

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

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

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

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

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

关键词： Schedules Uncertainty Q-learning Decision making Deep reinforcement learning Real-time systems Delays Automobiles Standards Edge computing

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ROUTABILITY-DRIVEN TECHNOLOGY MAPPING FOR LOOKUP TABLE-BASED FPGAS

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IEEE TRANSACTIONS ON computer-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 1994年第1期13卷 13-26页

作者： SCHLAG, M KONG, J CHAN, PK Computer Engineering Board University of California Santa Cruz CA USA Computer Engineering Board University of California USA

A new algorithm for technology mapping of lookup table-based Field-Programmable Gate Arrays (FPGA's) is presented. It has the capability of producing compact designs (minimizing the number of cells (CLB's)), as well as the flexibility of trading routability with compactness of a design. Research in this area has focussed on minimizing the number of cells. However, minimizing the number of cells without regard to routability is ineffective. Since placement and routing is really the most time-consuming part of the FPGA design process, producing a routable design with a slightly larger number of cells is preferable than producing a design using fewer cells which is difficult to route, or in the worst case unroutable. We have implemented our algorithm in the Rmap program, and studied routability of two other mappers with respect to Rmap. Rmap produces mappings with better routability characteristics, and more significantly Rmap produces routable mappings when other mappers do not.

关键词： Table lookup Field programmable gate arrays Routing Process design Prototypes Logic arrays Design automation Software design Software systems Integrated circuit manufacture

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Predictive Modeling of Solar Power Generation Using Deep Learning for Real-Time Optimization in Photovoltaic System

Predictive Modeling of Solar Power Generation Using Deep Lea...

引用

2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Bafna, Minal Thakur, Hardeo Kumar Singh, Jagendra Reddy, P. Srinivas Ekvitayavetchanukul, Pongkit Rana, Manoj Agrawal P. G. College Department of Physics Jaipur India School of Computer Science Engineering & Technology Bennett University Greater Noida India Cvr College of Engineering Department of Mechanical Engineering Hyderabad India The Board of Khonkaen University Affaneirs Khonkaen University Thailand Teerthanker Mahaveer University Cdoe Moradabad India

ISBN: (纸本)9798331527495

This research addresses the need for precise solar power generation forecasting and optimization of energy management within photovoltaic systems;this is important as there is a greater demand for renewable energy sources. Reliable predictions of the output of solar power would enhance stability in the grid and thus ensure efficient distribution of the energy. This research uses deep learning techniques, the Long Short-Term memory (LSTM) model, to predict solar power generation from several environmental variables, including solar irradiation, air temperature, wind speed, relative humidity, air pressure, module power, and module temperature. To validate the performance of the model, rigorous use of metrics like Mean absolute error (MAE), Root Mean Square Error (RMSE), and Mean absolute percentage error (MAPE) was made. The proposed LSTM model gave an MAE of 0.25 kW, an RMSE of 0.35 kW, and a MAPE of 5%. This was better compared to other models like Gated recurrent unit (GRU), recurrent neural network (RNN), and Artificial neural network (ANN). A real-time implementation of the LSTM model depicted the fact that it is feasible to use the model to make correct predictions of the amount of solar power produced throughout the day. At peak periods, such as midday hours, accuracy levels hit 98% in some cases, whereas the model is compelling enough to pinpoint and respond accordingly to changing solar power production volumes for optimizing load management and energy supply. This research verifies the deep learning model and its efficiency in energy forecasting, with intense stress on the practical application of the LSTM model in real-time energy management, contributing a lot to the advancement of renewable energy integration and optimizing power systems. Finally, this research provides valuable insight into improving the operational efficiency of photovoltaic systems in order to make the future energy scenario more sustainable. © 2025 IEEE.

关键词： Power management (telecommunication)

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Spectral-based multiway FPGA partitioning

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IEEE TRANSACTIONS ON computer-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 1996年第5期15卷 554-560页

作者： Chan, PK Schlag, MDF Zien, JY Computer Engineering Board University of California Santa Cruz CA USA

Recent research on FPGA partitioning has focused on finding minimum cuts between partitions without regard to the routability of the partitioned subcircuits. In this paper, we develop a spectral approach to multiway partitioning in which the primary goal is to produce routable subcircuits while maximizing FPGA device utilization. To assist the partitioner in assessing the routability of the partitioned subcircuits, we have developed a theory to predict the routability of the partitioned subcircuits prior to partitioning. Advancement over previous work is evidenced by results of experiments on standard MCNC benchmarks.

关键词： Field programmable gate arrays Partitioning algorithms Routing Cost function Emulation Constraint theory Logic Integrated circuit yield Design automation

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A SEMI-SYSTOLIC DECODER FOR THE PDSC-73 ERROR-CORRECTING CODE

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DISCRETE APPLIED MATHEMATICS 1991年第1-3期33卷 109-128页

作者： KARPLUS, K KRIT, H Board of Studies in Computer Engineering University of California at Santa Cruz Santa Cruz CA 95064 USA

This paper presents a semi-systolic architecture for decoding cyclic linear error-correcting codes at high speed. The architecture implements a variant of Tanner's Algorithm B, modified for simpler and faster implementation. The main features of the architecture are low computational complexity, a simple, regular arrangement of cells for easy layout, short critical paths, and a high clock rate. A prototype chip has been designed to decode a 73-bit perfect difference set code. This 4600-mu-m x 6800-mu-m chip should achieve 25MHz decoding in 2-mu-m n-well cMOS. The success of the implementation illustrates the value of using technology dependent constraints and cost measures to guide the design of algorithms and architectures.

关键词： ERROR CORRECTION PERFECT DIFFERENCE SET CODE SYSTOLIC ARCHITECTURE DECODER FOR ERROR CORRECTION CODECS DECODING ALGORITHM THRESHOLD DECODING TANNER ALGORITHM

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