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26th International Symposium on Symbolic and Numeric Algorithms for Scientific computing, SYNASC 2024

作者： Ciobanu, Alexandru-Adrian Bacanin, Nebojsa Sanders, Tilman Stoean, Catalin University of Craiova Department of Computer Science Craiova Romania Singidunum University Faculty of Informatics and Computing Belgrade Serbia FH Muenster Department of Electrical Engineering and Computer Science Steinfurt Germany

ISBN: (纸本)9798331532833

Accurate forecasting of solar energy production is highly important for an adequate integration of renewable energy into the power grid. This study explores the importance of various predictors for enhancing the accuracy of solar energy forecasting using two distinct Long Short-Term Memory models: one that relies solely on historical power production data and past exogenous weather data, and another that additionally incorporates future exogenous weather data. Various settings are tried to improve the results and relatively similar parameters are used by both, yielding more accurate results for the model that also uses information from the future. The results also demonstrate that predictor importance varies significantly between the two models. These findings suggest that different models benefit from distinct predictor combinations and incorporating exogenous data can significantly enhance forecasting performance. © 2024 IEEE.

关键词： Prediction models

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Representing Sounds as Neural Amplitude Fields: A Benchmark of Coordinate-MLPs and A Fourier Kolmogorov-Arnold Framework 39

Representing Sounds as Neural Amplitude Fields: A Benchmark ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Linfei Zhang, Lin Wang, Zhong Zhang, Fengyi Li, Zelin Shen, Ying School of Computer Science and Technology Tongji University China Department of Automation Shanghai Jiao Tong University China School of Electrical Engineering and Computer Science The University of Queensland Australia McCormick School of Engineering Northwestern University China

ISBN: (纸本)157735897X

Although Coordinate-MLP-based implicit neural representations have excelled in representing radiance fields, 3D shapes, and images, their application to audio signals remains underexplored. To fill this gap, we investigate existing implicit neural representations, from which we extract 3 types of positional encoding and 16 commonly used activation functions. Through combinatorial design, we establish the first benchmark for Coordinate-MLPs in audio signal representations. Our benchmark reveals that Coordinate-MLPs require complex hyperparameter tuning and frequency-dependent initialization, limiting their robustness. To address these issues, we propose Fourier-ASR, a novel framework based on the Fourier series theorem and the Kolmogorov-Arnold representation theorem. Fourier-ASR introduces Fourier Kolmogorov-Arnold Networks (Fourier-KAN), which leverage periodicity and strong nonlinearity to represent audio signals, eliminating the need for additional positional encoding. Furthermore, a Frequency-adaptive Learning Strategy (FaLS) is proposed to enhance the convergence of Fourier-KAN by capturing high-frequency components and preventing overfitting of low-frequency signals. Extensive experiments conducted on natural speech and music datasets reveal that: (1) well-designed positional encoding and activation functions in Coordinate-MLPs can effectively improve audio representation quality;and (2) Fourier-ASR can robustly represent complex audio signals without extensive hyperparameter tuning. Looking ahead, the continuity and infinite resolution of implicit audio representations make our research highly promising for tasks such as audio compression, synthesis, and generation. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Network coding

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Skin Disease Detection using Convolutional Neural Network 2

Skin Disease Detection using Convolutional Neural Network

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Saraf, Priyanka Tharaniesh, P.R. Singh, Soumyendra Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Mathematics Chennai India

ISBN: (纸本)9798350372892

Skin illnesses encompass a wide range of problems that affect the skin and can have significant effects on an individual's general health and well-being. The skin is a complex organ that affects not only physical protection but also mental well-being and general quality of life. The frequency of skin disorders, which manifest as redness, itching, discoloration, blisters, and rashes, highlights the need for prompt identification and treatment in a variety of demographic contexts. Dermatologists are essential to the diagnosis and treatment procedures since they are specialists in the medical treatment of the skin. Although earlier identification of certain disorders can greatly improve outcomes, the complexity of dermatology makes it difficult to effectively detect certain conditions at an early stage. The complicated nature of the diagnostic process results from the frequent need for intensive testing, which takes a long time. Furthermore, the subjectivity presents in dermatological diagnoses - which is shaped by the unique experiences and knowledge of practitioners - introduces heterogeneity that may compromise the validity of the findings. In order to improve the accuracy and effectiveness of illness identification in dermatology, this research introduces a unique deep learning technique. The technology's astounding 98 percent accuracy rate points to a potential advance in early intervention, opening the door to more potent therapies and, eventually, better patient outcomes. Beyond its direct consequences for dermatology, this study shows how artificial intelligence is revolutionizing the medical industry and is a major step toward utilizing cutting-edge technologies to support healthcare operations. A future where skin diseases are quickly and precisely recognized for the best possible patient care is promised by the promise of improved diagnostics and customized therapies as we negotiate the junction of technology and healthcare. © 2024 IEEE.

关键词： Dermatology

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A Comparative Market Research and Trend Analysis of Volatility in Decentralized and Regulated Markets 3

A Comparative Market Research and Trend Analysis of Volatili...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Mittal, Anamika Hariharasitaraman, S. Subramanian, R. Raja School of Computing Science and Engineering Vit Bhopal University Division of Cyber Security and Digital Forensics Madhya Pradesh India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350361186

Decentralized markets like Bitcoin and Ethereum, which utilize blockchain technology, offer advantages such as increased transparency, lower transaction costs, and quicker settlement times when compared to regulated markets. However, these markets are also known for their higher volatility. On the other hand, traditional regulated markets such as stock and commodity exchanges have also been subject to volatility due to macroeconomic factors such as inflation, geopolitical tensions, and policy changes. Therefore, it is crucial to compare and analyze the volatility of both decentralized and regulated markets to understand their behaviour and potential risks. This paper aims to conduct comparative market research and trend analysis of volatility in decentralized and regulated markets, investigating the contributing factors such as market size, liquidity, regulations, and the impact of market events like economic downturns or regulatory changes. The study will also examine historical trends, explore the correlation between the volatility of these markets, assess the potential impact of market volatility on investors and traders, and analyze how different trading strategies and investor behaviours can affect market volatility. The research could provide valuable insights into the behaviour of decentralized and regulated markets, which could be useful for investors, traders, policymakers, and other stakeholders. The paper will conclude with a discussion of the findings and their implications for traders and investors in both types of markets. © 2024 IEEE.

关键词： Blockchain

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Efficient Method For Automatic Irrigation System using IOT 10

Efficient Method For Automatic Irrigation System using IOT

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10th International Conference on electrical Energy Systems, ICEES 2024

作者： Rajagopal, R. Senbagavalli, M. Sampath, P.V.V.S.S. Sanjeevi Kumar, V. Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bangalore India Alliance School of Advanced Computing Alliance University Department of CSE Bangalore India

ISBN: (纸本)9798350353778

The precise and effective management of water using Internet of Things (IoT) technology in Automatic Irrigation Systems (AIS) has revolutionized agricultural practices. The primary objective is to enhance the watering methods, reduce labor costs, and conserve water. Through the literature review, various components are used in this automatic irrigation system. Identifying gaps in existing approaches leads to the proposal of an integrated framework. This process encompasses the availability and cost-effectiveness of the product. The testing and evaluation of the system will be conducted by testing trails with different soil moisture levels. As a result, we can see an improvement in the quality of the product. The purpose of this project is to show the importance of water conservation in the modern deforested environment. Future work will focus on integrating based on weather conditions by getting the datasets from the weather station. © 2024 IEEE.

关键词： Soil moisture

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Integrating digital twins and neural networks for real-time temperature management in smart homes: An innovative approach using ZigBee networks

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Energy Reports 2025年 13卷 6201-6218页

作者： Teng, Meng Al-Hamdawee, Zahraa Mehssen Agheeb Ali, Ali B.M. Jin, Kai Ahmadi, Monireh School of Computer Science and Engineering Hunan University of Information Technology Changsha410151 China College of Engineering University of Misan Iraq Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Sanya Research Institute of Hunan University of Science and Technology Sanya572000 China Department of Electrical Engineering Male.c. Islamic Azad University Malekan Iran

This paper presents a novel approach to real-time temperature management in smart homes by integrating digital twins with neural networks, leveraging the capabilities of ZigBee-enabled wireless sensor networks. The proposed system continuously monitors and analyzes environmental data collected from strategically placed Xbee S2 modules within a smart home, facilitating dynamic temperature control through an intelligent management system. A digital twin of the smart home is created to simulate and predict temperature variations based on real-time data, while a neural network model optimizes the control strategies to maintain desired thermal comfort levels. Results demonstrate that the integrated system achieves significant improvements in temperature regulation efficiency. Specifically, the proposed method reduced temperature variance by 14.12 % compared to traditional thermostat-based systems, leading to a more consistent indoor climate. Additionally, the energy consumption associated with heating and cooling was reduced by 8.05 %, highlighting the system's potential for energy savings. The neural network model, trained on historical data, achieved a prediction accuracy, enabling precise adjustments to the HVAC system in response to predicted temperature changes. Overall, the integration of digital twins and neural networks with ZigBee-based sensor networks offers a powerful and efficient solution for real-time temperature management in smart homes, demonstrating substantial advancements in both comfort and energy efficiency. © 2025 The Authors

关键词： Smart homes

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Machine Learning-Based Lifespan Prediction Modelling for Electric Submersible Pumps in Oil Wells 2

Machine Learning-Based Lifespan Prediction Modelling for Ele...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rajakala, J. Gupta, Satyaveer Naidu, Pothala Mutyalu Basi Reddy, A. Revathi, S. Selvan, R. Senthamil Sai Spurthi Institute of Technology Department of Computer Science and Engineering Gangaram India Delhi Technical Campus Department of Electrical Engineering Greater Noida India Vignan Institute of Technology And Science Department of Cse Hyderabad India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350364699

To maintain the output flow rate and prolong the life of oil wells, artificial lift is crucial to the petroleum industry. Electric Submersible Pumps (ESP) are a widely used artificial lift technique due to their ability to generate large flow rates in very deep wells. ESPs have a much shorter lifetime than anticipated, even though they are designed to function in harsh environments including corrosion, high temperatures, and high pressure. Because ESP intervention work is far more expensive than other artificial lift techniques, particularly for offshore wells, ESP failures result in production loss and raise replacement costs. Knowing when an ESP may fail is thus highly helpful in the oil production industry and affects how oil wells are designed, built, and managed. Using three machine learning algorithms - Decision Tree, Random Forest, and Gradient Boosting Machine - this study contributes to the construction of prediction models for ESP lifespan that take into account both dynamic and static ESP features. The model that produced the best results for ESP life cycle prediction was determined by comparing the output of various models. To predict the most influential characteristics on the length of ESP, this research also assessed the influence factor of different operational parameters. By knowing more about ESP behavior, early efforts to avert possible ESP failures may be realized thanks to the study's findings. © 2024 IEEE.

关键词： Submersible pumps

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Hybrid Semantic/Bit Communication Based Networking Problem Optimization

Hybrid Semantic/Bit Communication Based Networking Problem O...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xia, Le Sun, Yao Niyato, Dusit Zhang, Lan Zhang, Lei Imran, Muhammad Ali University of Glasgow James Watt School of Engineering Glasgow United Kingdom Nanyang Technological University College of Computing and Data Science Singapore Clemson University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350351255

This paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a novel and practical next-generation cellular network where two modes of semantic communication (SemCom) and conventional bit communication (BitCom) coexist, namely hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we comprehensively develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with several practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by employing a Lagrange primal-dual method and devising a preference list-based heuristic algorithm. Finally, numerical results validate the performance superiority of our proposed strategy compared with different benchmarks. © 2024 IEEE.

关键词： Frequency allocation

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Smart Video Surveillance Powered by AI: A New Era of Public Safety 5

Smart Video Surveillance Powered by AI: A New Era of Public ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Begum, S Ariffa Subbulakshmi, N. Sravani, K. Venkata Kumar, G. Sarath Richith, V. Chaitanya, G. Shiva Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Taminadu Krishnan Koil India School of Computing Kalasalinagm Academy of Reaserch and Education Department of Computer science and engineering Taminadu Krishnan Koil India

ISBN: (纸本)9798331530013

This paper introduces an innovative approach to improving quality of life by addressing a critical issue: the rising number of road accidents due to rapid global population growth and increased automobile sales. As vehicle numbers surge, many administrations face challenges in managing traffic and ensuring road safety. Among the various concerns, road accidents require urgent attention as timely intervention can save numerous lives. This study proposes the use of Python programming combined with Convolutional Neural Networks (CNN) in deep learning to accurately detect accident locations and promptly alert emergency services. By implementing this system, response times are reduced, leading to a decrease in traffic-related fatalities. The solution offers a practical and effective way to safeguard human lives in real-world scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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