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2024 IEEE International Conference on Big Data, BigData 2024

作者： Wang, Danny Y. C. Jordanger, Lars Arne Lin, Jerry Chun-Wei Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350362480

In the rapidly evolving e-commerce industry, the ability to select high-quality data for model training is essential. This study introduces the High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) model, a utility mining based active learning strategy, to tackle this challenge. We found that the parameter settings for positive and negative SHAP values affect the mining results of the model, introducing a key consideration into the active learning framework. Unlike traditional SHAP, which evaluates individual elements, HUSPM-SHAP utilizes SHAP values in combination with HUSPM to identify the valuable of high-utility sequential patterns for improving prediction models. In experiments to predict behaviors that actually lead to purchases, the developed HUSPM-SHAP model shows its superiority in different scenarios. The model's ability to reduce labeling requirements while maintaining high predictive performance is highlighted. Our results show that the model is able to refine the processing of e-commerce data and lead to optimized, cost-efficient prediction modeling. © 2024 IEEE.

关键词： Active learning

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SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks

SSRepL-ADHD: Adaptive Complex Representation Learning Framew...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Rehman, Abdul Heldal, Ilona Lin, Jerry Chun-Wei Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350362480

Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations of the Attention Deficit Hyperactivity Disorder (ADHD) data and have the potential to improve the model's performance on also downstream different types of Neurodevelopmental disorder (NDD) detection. In this paper, a novel SSRepL and Transfer Learning (TL)-based framework that incorporates a Long Short-Term Memory (LSTM) and a Gated Recurrent Units (GRU) model is proposed to detect children with potential symptoms of ADHD. This model uses Electroencephalogram (EEG) signals extracted during visual attention tasks to accurately detect ADHD by preprocessing EEG signal quality through normalization, filtering, and data balancing. For the experimental analysis, we use three different models: 1) SSRepL and TL-based LSTM-GRU model named as SSRepL-ADHD, which integrates LSTM and GRU layers to capture temporal dependencies in the data, 2) lightweight SSRepL-based DNN model (LSSRepL-DNN), and 3) Random Forest (RF). In the study, these models are thoroughly evaluated using well-known performance metrics (i.e., accuracy, precision, recall, and F1-score). The results show that the proposed SSRepL-ADHD model achieves the maximum accuracy of 81.11% while admitting the difficulties associated with dataset imbalance and feature selection. © 2024 IEEE.

关键词： Self-supervised learning

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Enhancing Customer Behavior Prediction and Interpretability

Enhancing Customer Behavior Prediction and Interpretability

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2024 IEEE International Conference on Big Data, BigData 2024

ISBN: (纸本)9798350362480

This paper leverages insights from my previous works to analyze and predict customer behavior in different areas using data mining and machine learning techniques. The research focuses on identifying and interpreting customer preferences, purchasing patterns and risk factors for customer churn to develop predictive models that support decision making. By analyzing various datasets such as tabular data, clickstream data, and digital interactions, this study aims to provide a comprehensive framework for extracting actionable insights and enhancing the accuracy and transparency of customer behavior predictions. The proposed approach enables businesses to anticipate customer behavior, personalize marketing strategies, and improve the explainability and effectiveness of customer behavior prediction models. Future work will incorporate advanced visualization techniques and various data structures to further improve the model's transparency and predictive capability. © 2024 IEEE.

关键词： Prediction models

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Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

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computers, Materials & Continua 2024年第3期78卷 3909-3927页

作者： Yilei Wang Xin Sun Guiping Zheng Ahmar Rashid Sami Ullah Hisham Alasmary Muhammad Waqas Zhejiang Electric-Power Corporation Research Institute Zhejiang310014China Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Trusty Cloud Technology Co. Ltd.Beijing100022China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and EngineeringTopi23640Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir18050Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent *** sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and ***,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion ***,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart *** response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these *** scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT *** then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy *** comprehensive approach reduces the impact of subjective factors on trust ***,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious ***,in turn,enhances the security and reliability of the smart grid *** effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation ***,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

关键词： IIoT trusted measure energy efficient

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A Blockchain Framework for Securing Examination Results in Higher Institutions of Learning

A Blockchain Framework for Securing Examination Results in H...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Charles, Taiwo LYemi Azeta, Ambrose A. Crawford University Department of Computer and Mathematical Sciences Nigeria Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia

ISBN: (纸本)9798350358155

With data security and transparency guaranteed, stakeholders' confidence and trust in certificates and degrees from our universities and colleges will improve, third party investments in education will also increase and government effort at building a strong and high quality education for all will be adequately complemented. Existing traditional approaches to execution of academic activities lacks capabilities in the area of preventing alterations to academic records after being recorded, preventing single points of failure, providing enhanced data security, encouraging the use of decentralized storage system and most importantly, ensuring distributive transparency. This study provides a blockchain framework for securing examination results in higher institutions of learning. Some of the methodology engaged includes Blockchain technology, Cryptography, schematic diagram, and Unified Modelling Language (UML). Java was used to implement the blockchain component services. Generic flat file was used to implement the database. Having adopted a measurement scale of 1-5 in this study and based on the mean ratings of the usability average rating of 4.18 findings, it can be concluded that the blockchain framework has a "Good Usability"rating. Using blockchain platform for storing students' examination results ensures transparency and provides enhance data security by locking the finalized scores to prevent any alteration. © 2024 IEEE.

关键词： Decentralized finance

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OFDM-structure based waveform designs for integrated sensing and communication

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science China(Information sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

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Decentralised Renewable Electricity Certificates Using Smart Meters and Blockchain 8th

Decentralised Renewable Electricity Certificates Using Smart...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Sato, Yuki Fazekas, Szilard Zsolt Yamamura, Akihiro Department of Mathematical Science and Electrical-Electronic-Computer Engineering Akita University Akita Japan

ISBN: (纸本)9789819932429

Recently, the use of renewable energy has been promoted to decarbonise the world. Renewable energy certificates are issued to prove the authenticity of source of renewable energy. However, it is not easy to deal with this system because of security and privacy issues caused in the process amongst stakeholders. We analyse plausible security and privacy issues caused in related ICT systems and then propose a renewable electricity certificate to be issued in a decentralised manner using smart meters and a blockchain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Smart meters

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Investigation of Colors Categorized into Color Names on a Liquid Crystal Display to Enhance CAPTCHAs

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International Journal of the Society of Material engineering for Resources 2022年第2期25卷 212-217页

作者： Ishizawa, Chikako Kobayashi, Nozomu Kageyama, Yoichi Department of Mathematical Science and Electrical-Electronic-Computer Engineering Graduate School of Engineering Science Akita University Akita010-8502 Japan

This study focuses on the human ability to tolerate subtle diff erences in color and categorize similar colors into a single mass to enhance completely automated public turing test to tell computers and humans apart (CAPTCHA). First, an experiment was conducted to check whether the color selection condition could control the color categorization results. Consequently, it was clarifi ed that when the selection condition was added, the color to be categorized changed, implying the possibility of controlling the categorized result. Subsequently, an investigation was conducted to clarify the range of liquid crystal display colors categorized as major color names. As a result, it was clarified that the hue angle size of the colors categorized by more than half of the participants was sometimes greater than the hue angle size between the primary and complementary colors. It was also clarifi ed that among the categorized colors, there were categorized colors in the two color names. Finally, results from the fi rst and second experiments were collated. It was confi rmed that by using the categorized color range and selection conditions, it is possible to obtain an answer with a color name that is diff erent from the original color name. These results suggest that the ability of humans to categorize similar colors into a single mass enhances CAPTCHA. © 2022 Society of Materials engineering for Resources of Japan. All rights reserved.

关键词： Color

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PolluSenseCheck: Cost Effective TinyML-based Air Quality Monitoring System with In-Built Sensor Fault Detection 17

PolluSenseCheck: Cost Effective TinyML-based Air Quality Mon...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Datta, Arko Chakraborty, Joyita Majhi, Pabitra Mondal, Astom Roy, Satyaki Nandi, Subrata Saha, Sujoy National Institute of Technology Department of Computer Science and Engineering Durgapur India Dr. B. C. Roy Engineering College Department of Computer Science and Engineering Durgapur India University of Alabama in Huntsville Department of Mathematical Sciences AL United States

ISBN: (纸本)9798331531195

Air pollution is a significant contributor to respiratory diseases, posing severe health risks globally. Traditional air quality monitoring stations, while effective, are too expensive, particularly in developing nations like India, where numerous monitoring sites may be required to cover diverse urban and industrial regions. Existing cost-effective air quality monitoring systems are rendered inadequate by their reliance on sensors that are prone to drift, which necessitates frequent recalibration. This study addresses the gap by developing a cost-effective air quality monitoring device capable of detecting particulate matter, carbon monoxide (CO), temperature, and humidity, and incorporating self-diagnostic capabilities. It enhances reliability through a dual-sensing mechanism: it calculates the Air Quality Index (AQI) directly and uses a TinyML model to provide secondary-sensed AQI estimations. Deviations between these two AQI values signal sensor malfunction, which is tracked by a proposed algorithm, Interocept, that assigns fault scores to the sensors. Models were created for eight cities, showing accuracies from 0.69-0.89 for dust and 0.76-0.95 for CO. These models were optimized for real-time performance by converting them into TensorFlow Lite micro-models, allowing deployment on the device itself for continuous, autonomous sensor health diagnostics and improved long-term air quality management systems. © 2025 IEEE.

关键词： Fault detection

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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