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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Vaccargiu, Matteo Tonelli, Roberto University of Cagliari Dep. of Mathematics and Computer Science Italy University of Camerino Dep. of Mathematics and Computer Science Italy

ISBN: (纸本)9798350351637

The increasing demand for renewable energy and sustainable development has led to the evolution of peer-to-peer (P2P) energy exchange models, transitioning from traditional centralized distribution to decentralized marketplaces. This paper presents an overview of the P2P energy market and then analyzes the potential of blockchain technology, highlighting how Ethereum smart contracts and oracles can provide significant support in energy trading and certification processes. By integrating data from smart meters through Ethereum oracles, we propose a basic starting theoretical framework for verifying renewable energy production, make transactions within the P2P market and use renewable energy credits within the real community. This approach provides transparency and trust among participants and addresses regulatory and technical challenges by providing a verifiable and decentralized ledger of energy transactions. Our proposal emphasizes the feasibility of a blockchain-based P2P energy trading system that empowers consumers as active participants, promoting a more resilient and sustainable energy landscape. The results of this paper are to show a theoretical blockchain architecture that facilitates renewable energy certification and decentralized trading, aligning with global sustainability goals and providing a basis for the creation of decentralized local markets and energy communities. © 2024 IEEE.

关键词： Clean energy

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Enhanced Multi-Modal Gas Leakage Detection with NSMOTE: A Novel Over-sampling Approach 8

Enhanced Multi-Modal Gas Leakage Detection with NSMOTE: A No...

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8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Azizian, Aref Yousefimehr, Behnam Ghatee, Mehdi Dep. of Math. & Computer Science Iran

ISBN: (纸本)9798350374674

Urbanization and the emergence of smart cities have led to increased industrial activities, raising concerns about safety and environmental hazards. Detecting and monitoring gas leaks accurately is crucial due to the significant risks they pose to public health and the environment. In this study, we propose a novel approach that combines thermal imaging with sensor data to enhance gas detection accuracy in urban and industrial settings. By employing advanced algorithms and analyzing extensive datasets, our methodology achieves an impressive accuracy rate of 99.32%, setting a new standard in gas leak detection. Our innovation lies in the utilization of a unique oversampling technique called Normal Synthetic Minority Oversampling Technique (NSMOTE), coupled with the simultaneous integration of thermal imaging and sensor data within a multimodal framework. This breakthrough not only underscores the potential of integrating diverse data sources for environmental monitoring but also represents a significant stride in mitigating the risks associated with industrial gas emissions. © 2024 IEEE.

关键词： Data integration

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Design of Quality Improvement Technique Through Ridgelet Transform on Watermarked Video

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第1期4卷 204-210页

作者： Ahmed, Rasha I. Ali, Sura Mazin Computer science Dep. University of Technology-Iraq Baghdad Iraq Mustansiriyah University Baghdad Iraq

with the rapid spread of the use of video files cross the world before and during the pandemic, and the availability of capabilities and tools to create, store and transmit video files of high quality. The latest studies illustrate that video files are the highest data types that had been transmitted over the internet, during video transmission or broadcast from one point to the other or over the internet, videos may be damaged for several reasons, such as packet loss or jitter, in this paper, a proposed approach of Ridgelet transformation is presented to improve received video file quality, based on embedded watermark quality present in video frames. It is implemented by extracting three-color channels of video frames, i.e., Red (R), Green (G), and Blue (B). A selected watermark is included in blue channel (B) along with transmitting the video file from source to destination. However, the quality of watermark is measured with execution regarding watermark. Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) were adopted as measurement criteria. The evaluation results show interesting comparative values without noise, in comparison to applying salt, pepper, and Gaussian noise to video frames. © 2023 Iraqi Journal for computer science and Mathematics. All rights reserved.

关键词： Mean square error

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CNN-based Human Activity Recognition on Edge Computing Devices

CNN-based Human Activity Recognition on Edge Computing Devic...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Singh, Amandeep Margaria, Tiziana Demrozi, Florenc University of Limerick Dep. of Computer Science and Information Systems Ireland University of Stavanger Dep. of Electrical Engineering and Computer Science Norway

ISBN: (纸本)9798350346473

Human Activity Recognition (HAR) is a research area that involves wearable devices integrating inertial and/or physiological sensors to classify human actions and status across various application domains, such as healthcare, sports, industry, and entertainment. However, executing HAR algorithms on remote devices or the cloud can lead to issues such as latency, bandwidth requirements, and energy consumption. Transitioning towards Edge HAR can be a more effective and versatile solution, overcoming the challenges of traditional HAR techniques. We present a novel HAR model for computation on edge devices: we design a Convolutional Neural Network (CNN) Deep Learning approach and compare its performance with cloud-computing HAR models. The paper is accompanied by a self-collected dataset. The experiments on this dataset demonstrate that the proposed edge computing model achieves promising results (\geq 92 %) in terms of Precision, Recall, and Fl-score. Furthermore, the model exhibits significantly reduced latency, with only 117 ms, and utilizes minimal memory, with a peak of 18.8 Kb RAM and 956 Kb Flash memory. © 2023 IEEE.

关键词： Edge computing

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Navigating to Tomorrow's High-Tech Landscape: Outlining a Path Based on the Israeli Case

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ACM Inroads 2023年第4期14卷 51-56页

作者： Zohar, Doron Ezer, Judith Gal Dep. of Mathematics and Computer Science The Open University of Israel Raanana43107 Israel

In recent years, an increasing number of university graduates have been seeking senior positions in hightech companies, attracted by the status, occupational satisfaction, and financial success these companies project. Over the past decade, the Israeli Ministry of Education, as well as teachers and parents, have encouraged this trend. In Israel and abroad, such positions usually presume at least an undergraduate degree from a higher education institution. Thus, schools now offer mathematical and computing courses in order to prepare students for university-level courses, with the goal of eventually achieving high-tech positions. The understanding is that the success of future generations, in Israel and world-wide, in the field of computing and its interface with other disciplines will benefit the individual, as well as safeguarding the country s future economy and security. The objective of this article is to describe a pathway based on the Israeli education system which we think can apply to other counties as well. © This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.

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Enhancing data warehouse efficiency through basic statistical function maintenance 3

Enhancing data warehouse efficiency through basic statistica...

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3rd International Conference on Algorithms, Network, and Communication technology, ICANCT 2024

作者： Wu, Anxiong Fang, Chih-Chiang Dep. of Computer Science ZhaoQing University ZhaoQing526061 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Statistical functions play a crucial role in generating valuable summary insights from corporate databases. However, calculating these functions over large datasets can be time-consuming, especially in computerized systems that manage vast amounts of transactional data. To enhance system performance, data warehouses commonly utilize summary tables or materialized aggregate views. When new transactional data is introduced, all corresponding summary tables in the data warehouse must be updated, which can be challenging due to the large number of tables involved. Efficiently managing these updates is essential for optimizing data warehouse performance. This study presents a novel approach to improving the maintenance of statistical functions—such as MIN, MAX, MEAN, and MEDIAN—by incorporating auxiliary tables within the data warehouse. These auxiliary tables are designed to streamline the update process and reduce maintenance overhead. A comparative analysis is conducted to validate the effectiveness of the proposed method in enhancing data warehouse performance. © 2025 SPIE.

关键词： Data warehouses

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Traffic sign recognition based on CNN vs different transfer learning techniques 5

Traffic sign recognition based on CNN vs different transfer ...

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5th Symposium on Pattern Recognition and Applications, SPRA 2024

作者： Ghouse, Mohammed Farag, Saber Butt, Usman Engineering and Environment Dep. Northumbria University London United Kingdom Computer and Information Science Dep. British University in Dubai Dubai United Arab Emirates

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

Traffic sign detection and recognition (TSDR) is a pivotal component of contemporary transportation systems, designed to enhance road safety and operational efficiency. In an era where automation is indispensable, TSDR stands out as a critical tool for optimizing driving experiences. This study presents the development of a TSDR model employing advanced deep learning techniques, with a particular emphasis on convolutional neural networks (CNNs). Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which comprises annotated images of various traffic signs captured under diverse environmental conditions, we achieved remarkable results. Our CNN models demonstrated a 99% detection rate, underscoring their efficacy in traffic sign recognition tasks. Furthermore, transfer learning models yielded impressive accuracies, with VGG19 achieving 99.64%, EfficientNetB7 reaching 99%, and ASNet attaining 99.72%. Despite these promising results, we identified several limitations, including dataset-specific dep.ndencies and the need for further enhancements in model design and training methodologies. Addressing these challenges is necessary for ensuring the robustness and reliability of intelligent transportation systems, thereby significantly improving road traffic management, safety, and efficiency. Copyright © 2025 SPIE.

关键词： Convolutional neural networks

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Design and Implementation of Minkowski Feature Selection for Machine Learning Techniques 5

Design and Implementation of Minkowski Feature Selection for...

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5th International Conference on information technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Hakim, Ekram Al-Shammary, Dhiah Mahdi, Ahmed M. University Of Al-Qadisiyah College Of Computer Science And Information Technology Dep. Computer Science Diwaniyah Iraq

ISBN: (纸本)9798350335118

This paper proposes a design and implementation of a highly efficient feature selection based on Minkowski's mathematical similarity for machine learning. Moreover, the development of the Minkowski feature selection can be general for intelligent datasets and machine learning. Intelligent optimizers and feature selection represent an advanced stage in machine learning and innovative computer applications by reducing the overall number of features to be included in the classification function process. This paper analyses Minkowski's selection of best factors for EEG Bonn University data. Minkowski cancels the random movement and systematically limits the number of features and its point-to-point movement in order to promote exploration and reduce the probability of falling into stagnation. The main measures to analyze this research are Accuracy, precision, Recall, and F1score assessment. Moreover, the accuracy of Minkowski's selection provided better classification results than some of the other approaches (100%) © 2023 IEEE.

关键词： Feature Selection

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Preventing Overfitting on Noisy Labels Through Adaptive Checkpointing 10

Preventing Overfitting on Noisy Labels Through Adaptive Chec...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Azizian, Aref Ahmadian, Rouhollah Ghatee, Mehdi Shamsi, Mostafa Amirkabir University of Technology Dep. of Math. and Computer Science Tehran Iran

ISBN: (纸本)9798331532543

Overfitting and the presence of noisy labels are significant challenges in the training of machine learning models, particularly in complex datasets. This paper introduces a novel checkpointing method designed to mitigate overfitting while maintaining model calibration in the presence of noise. Our approach leverages the entropy of the two largest predicted probabilities from the model's output to determine when to save the model during training. This entropy-based criterion allows for effective checkpointing without necessitating a separate validation set. We evaluate our method across various datasets characterized by different noise levels and model architectures. The results demonstrate that our entropy-based checkpointing offers a more robust solution for managing model performance during training. This work contributes to the ongoing efforts to enhance model reliability in real-world applications where data imperfections are prevalent. © 2024 IEEE.

关键词： Entropy-Based Metric Noisy Labels Overfitting Prevention

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On the classification of weather based on the production of photovoltaic installations 24

On the classification of weather based on the production of ...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Parczyk, Pawel Puslecki, Tobiasz Burduk, Robert Wroclaw University of Science and Technology Dep. of Systems and Computer Networks Wroclaw Poland

ISBN: (纸本)9798331530631

In recent years, there has been an energy transition in which fossil fuel-fired power plants are being replaced by renewable energy sources such as photovoltaics. While the influence of factors such as location and method of installation or the sun's position above the horizon is deterministic, the weather factor is rather random. Weather data can be easily obtained from nearby weather stations, but some weather phenomena are so dynamic that data obtained in this way can become useless. When considering the problem of classifying weather conditions based on production data of photovoltaic installations, we propose two new features extracted from the production data that describe the overall level of sunshine and the variability of sunlight. Three supervised learning methods were used to classify the dataset: CNN, Random Forest classifier and Decision Tree Classifier. Reference values were obtained by generating classification results from the raw output and compared with those generated from the two new features. The experiments showed a statistically significant advantage for the models fitted to the data extended with the new features. © 2024 IEEE.

关键词： Photovoltaic (PV) Renewable Energy Sources (RES) Weather Classification

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