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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Dai, Jin Hu, Sunling Zhang, Lei Dept. of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Dept. of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510666597

The big data of coal mine was characterized by large scale, many influencing factors and weak correlation. The existing big data mining based on quantitative data analysis usually adopts fixed framework processing, which is easy to fall into the dilemma of overfitting and cannot acquire elastic knowledge across layers. From the perspective of coal mine monitoring and management, a multi-granularity time-varying cloud model is proposed based on a multi-granularity semantic transformation model and a grey prediction model to achieve the conceptual prediction of coal mine gas concentration risk. The new model first converts the gas concentration data into semantic concepts using a semantic transformation model, then processes the sequence of semantic concept sets for randomness and oscillation, and builds a grey prediction model for them, which is used to project future semantic concepts. The new model is compared with the existing model, and the experimental results show that the new method can not only predict coal mine risk concepts at different granularities, but also has high prediction accuracy. © 2023 SPIE.

关键词： Coal mines

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Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction 4

Reinforcement Learning for Language Grounding: Mapping Words...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adapa, Srinivasa Rao Gupta, Himanshu Mukherjee, Susmoy Mallupalli, Praneeth Reddy Avhad, Priyanka Upreti, Kamal OpenLogix LLC Software Engineer MI United States Software Engineer Meta NJ United States VIT Institute Electrical and Electronics Engg. Tamil Nadu India Arcesium Hyderabad India Veermata Jijabai Technological Inst. Computer Engineering Mumbai India Christ University Dept. of Computer Science Delhi NCR Campus India

ISBN: (纸本)9798350353266

Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. © 2024 IEEE.

关键词： Reinforcement learning

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Forecasting Residential EV Charging Behavior: A Transfer Learning-Enabled Approach to Address Data Scarcity in the Residential Sector

Forecasting Residential EV Charging Behavior: A Transfer Lea...

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Smart Grid Conference (SGC)

作者： Mohammad Rastegar Mehrdad Ebrahimi Ali Forootani Dept. of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University Shiraz Iran Dept. of Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (数字)9798331511333

ISBN: (纸本)9798331511340

Charging a significant number of electric vehicles (EVs) can pose challenges for power systems, and a viable solution is smart charging. Reliable smart charging can be achieved by forecasting the required energy and plug-in duration of a charging event. However, despite the availability of data from public charging stations, the lack of sufficient real data for residential EV charging has consistently been a barrier, particularly when using deep learning methods. Moreover, enhancing the accuracy of forecasting residential EV charging behavior is crucial due to its significant impact on power systems. To address these challenges, this paper proposes an intelligent deep learning-based framework to forecast the required energy and plug-in duration of residential EV charging events by leveraging transfer learning. Additionally, the proposed framework utilizes an auxiliary deep learning model that forecasts charging duration to improve the accuracy of required energy forecasting. Various case studies have been conducted, and the results show that the proposed framework significantly improves the accuracy of residential charging behavior forecasting.

关键词： Deep learning Accuracy Transfer learning Predictive models Power system reliability Reliability State of charge Forecasting Energy management Smart charging

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Flexibility and Privacy: A Multi-Head Federated Continual Learning Framework for Dynamic Edge Environments 11

Flexibility and Privacy: A Multi-Head Federated Continual Le...

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11th International Symposium on Computing and Networking, CANDAR 2023

作者： Chen, Chunlu Wang, Kevin I-Kai Li, Peng Sakurai, Kouichi Kyushu University Graduate School of Information Science and Electrical Engineering Fukuoka Japan The University of Auckland Dept. of Electrical Computer and Software Engineering Auckland New Zealand The University of Aizu School of Computer Science and Engineering Aizuwakamatsu Japan Kyushu University Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798350306705

Federated Learning (FL) and the Internet of Things (IoT) have revolutionized data processing and analysis, overcoming the traditional limitations of cloud computing. However, traditional machine learning strategies lead to increased costs and catastrophic forgetting due to model retraining with new datasets. Continual learning has been proposed to counter this, enabling models to adapt to new data while preserving previous knowledge, which is beneficial for dynamic edge environments. Despite these advantages, the retention of previous knowledge during the continual learning process may lead to the information leakage. To address the inherent challenges of multitask scenarios, we present a Federated Continual Learning (FCL) framework that integrates the privacy-preserving benefits of Federated Learning (FL) into a continual learning system, ensuring both continual learning and privacy preservation in edge computing data processing and analysis. Specifically, our architecture introduces dedicated fully-connected layers for each task. This architecture ensures that distinctive features pertinent to each task are not only captured but also preserved throughout the model's lifespan. Within our framework, data is processed via task-specific layers. Subsequently, the final label is determined by associating it with the paramount prediction value, thus capitalizing on the model's comprehensive knowledge reservoir to bolster prediction accuracy. We subjected our FCL framework to rigorous validation using two benchmark datasets: MNIST and CFAR-10. Experimental outcomes unequivocally substantiate the efficacy of our proposed methodology. © 2023 IEEE.

关键词： Learning systems

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A Review on Data Balancing Techniques and Machine Learning Methods 5

A Review on Data Balancing Techniques and Machine Learning M...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Parmar, Gaurav Gupta, Rimi Bhatt, Tejas Sahani, G.J. Panchal, Brijeshkumar Y. Patel, Hiren S.V.I.T Dept. of Computer Engineering Gujarat Vasad India Software Developer Tüv Süd South Asia Pvt. Ltd India

ISBN: (纸本)9781665474672

Researchers in the discipline of Data Mining (DM) occasionally disregard the need of ensuring a dataset is evenly distributed. As such, it may have a major impact on how things are ultimately sorted. Most classifiers function on the concept that the data is roughly normally distributed. Therefore, the categorization approach is no longer as successful as it once was and fixing this is essential. This research aims to construct an evaluation of several methods for producing labels for minority classes to prevent the model from developing bias toward protected attributes. In this study, different researcher's work learned to determine the most effective method for disseminating the data. There will be a discussion of ways to enhance categorization abilities in the conclusion. The future of data-balancing research is determined by weighing the relative merits of the several possible methodological amalgamations. © 2023 IEEE.

关键词： Data mining

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Feature Selection-driven Bias Deduction in Histopathology Images: Tackling Site-Specific Influences

Feature Selection-driven Bias Deduction in Histopathology Im...

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Congress on Evolutionary Computation

作者： Farnaz Kheiri Azam Asilian Bidgoli Masoud Makrehchi Shahryar Rahnamayan Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada Dept. of Computer Science Wilfrid Laurier University Waterloo Canada Dept. of Engineering Brock University St. Catharines Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The emergence of bias in deep neural models represents a significant reliability concern, which may lead to overoptimistic results on seen data while compromising the model's ability to generalize effectively on unseen datasets. Recent studies conducted on The Cancer Genome Atlas (TCGA), which is a publicly available repository of histopathology images, reveal that the TCGA cancerous features extracted by deep neural networks surprisingly are able to discriminate slides based on their origin sites. This finding undoubtedly indicates the existence of site-specific patterns embedded in the extracted features learned by deep networks rather than focusing on histomorphologic patterns. Consequently, this biased behavior raises concerns about the reliability of these networks. This observation motivates us to conduct a series of experiments in which we present two distinct evolutionary feature selection strategies, each differentiated by its objective function evaluation. The primary goal is to select the features with a minimized foot-print of data source signatures, thereby ensuring a more accurate and site-independent cancer classification. We have conducted nine comprehensive independent experiments across nine cancer types, employing each evolutionary strategy. The comparison between results obtained through evolutionary strategies and the original feature sets highlights the substantial impact of feature selection methods on bias reduction while maintaining accuracy in cancer-type discrimination. Furthermore, the comparison of the two strategies with each other demonstrates the intricate nature of bias and its integration with cancerous features during the training process.

关键词： Training Accuracy Histopathology Training data Feature extraction Prediction algorithms Data models

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Classifying white blood cells using combining different convolutional neural networks

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Multimedia Tools and Applications 2025年 1-24页

作者： Toptaş, Murat Toptaş, Buket Hanbay, Davut Software Eng. Dept. Engineering and Natural Science Faculty Bandırma Onyedi Eylül University Balıkesir Turkey Computer Eng. Dept. Engineering Faculty Inonu University Malatya44280 Turkey

White blood cells are warrior cells that protect the human body against external factors. Each of these warrior cells performs a distinct task, making every piece of information about them highly valuable in the medical field. In this article, a classification framework for the four known types of white blood cells is proposed. It is hoped that the classification of these types will contribute to the prediction of diseases such as AIDS, malaria, leukemia, and many others. In the proposed method, images of white blood cells from the Blood Cell Classification and Detection dataset were used as input to Convolutional Neural Networks. The feature vectors extracted using these Convolutional Neural Network architectures were combined into a single vector. A Minimum Redundancy Maximum Relevance algorithm was then employed to identify the most effective features within the feature vector. Experiments were conducted using these selected features, and the analysis of each experiment was reported in detail. The Support Vector Machines classifier achieved an accuracy of 98.63% in classifying white blood cell types by combining features from multiple deep learning architectures. The experimental results demonstrated that the features obtained from different layers of the Convolutional Neural Networks had varying impacts on the classification performance. © The Author(s) 2025.

关键词： Convolutional neural networks

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Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video Processing 3

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kumar, M. Ashok Jasim, Laith H. Sudhakar, K. Reddy, R. Archana Saranya, N. Naga Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India School Of Computer Science And Engineering Sr University Warangal India Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%). © 2024 IEEE.

关键词： Convolutional neural networks

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A Double MCDM-GIS Analysis for Solar Power Plant Site Selections in Disaster-Prone Area: A case study of Hawkes Bay

A Double MCDM-GIS Analysis for Solar Power Plant Site Select...

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Power and Renewable Energy engineering (PREE), International Conference on

作者： Rizki Dian Rahayani Nirmal-Kumar C Nair Dept. Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Dept. Electrical Computer and Software EngineeringUniversity of Auckland Auckland New Zealand

Selecting an appropriate site for solar power plants is a critical long-term investment decision to maximize solar energy generation while minimizing investment and operational costs and ensuring environmental sustainability. While solar irradiance has traditionally been the primary factor in site selection, accounting for disaster risk assessment is becoming increasingly crucial, particularly in countries prone to natural disasters. Hawkes Bay, New Zealand, holds great promise as a region with high solar potential. Yet, it is susceptible to natural disasters like earthquakes, landslides, and floods. This research aims to identify the most suitable solar farm location in Hawkes Bay by integrating estimated solar energy production with disaster risk assessment. The study employs a two-stages multi-criteria decision-making (MCDM) approach, combining data envelopment analysis (DEA) and the analytical hierarchy process (AHP), alongside the utilization of geographical information system (GIS) technology. The finding, among 15 candidate locations, the area in a 5 km radius of Te Haroto weather station has the most potential for solar PV farms. 82 % of the location has low disaster risk, and only 7% of the area is considered high risk.

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Enhancing Wireless Data Transmission: A GAN-based Approach for Time Series Data Restoration

Enhancing Wireless Data Transmission: A GAN-based Approach f...

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International Conference on Information Networking

作者： Daejin Han Woongsoo Na Dept. of Computer Science and Engineering Kongju National University Cheonan Korea Dept. of Software Kongju National University Cheonan Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Recent technological advancements have led to a significant increase in the use of wireless communication for data transmission, particularly in sensor networks and device-to-device communications, necessitating not only rapid but also reliable data exchange. In conventional wireless communication, if a packet is lost in transmission due to retransmission, it is retransmitted. In this case, the subsequent packets will wait for them so that the order of the packets is not reversed, which is called Head of Line (HOL) Blocking. This problem can lead to overall transmission delays and losses, and is one of the reasons for inconsistent data transfer times. In this paper, we propose a technique to significantly reduce the retransmission rate of the sender and data loss by retransmission using Generative Adversarial Net (GAN). To evaluate the performance of this study, we applied our technique to a portion of SolarCube solar data assuming that it was corrupted, such as NaN processing, value change, and sign change, and the average recovery rate was 92.36%, demonstrating that it is possible to detect and recover losses in the data transmission process. This research is expected to have applications in data loss detection and recovery during transmission of time series data.

关键词： Wireless communication Wireless sensor networks Time series analysis Propagation losses Generative adversarial networks Data transfer Device-to-device communication

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