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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Qi, Lianyong Chi, Xiaoxiao Zhou, Xiaokang Liu, Qi Dai, Fei Xu, Xiaolong Zhang, Xuyun School of Computer Science Qufu Normal University China Faculty of Data Science Shiga University Japan Riken Center for Advanced Intelligence Project Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Big Data and Intelligence Engineering Southwest Forestry University China Department of Computing Macquarie University Australia

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain-LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods. © 2022 IEEE.

关键词： Smart city

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Explainable Ai-Driven Financial Transaction Fraud Detection Using Machine Learning and Deep Neural Networks

SSRN

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SSRN 2023年

作者： Sai, Chaithanya Vamshi Das, Debashish Elmitwally, Nouh Elezaj, Ogerta Islam, Md Baharul Department of Computing & Data Science Faculty of Computing Engineering & the Built Environment Birmingham City University United Kingdom College of Data Science and Engineering American University of Malta Malta

Artificial Intelligence (AI) is revolutionizing and transforming data-driven businesses like financial services, e-commerce, and banking. AI technology and the digitization of banking are rapidly increasing the volume of financial transaction frauds, causing billions in revenue losses and unrecoverable funds. The primary business challenge faced by the finance and banking industry is understanding how their AI systems make predictions and explaining why a transaction is flagged as fraud. Explainable Artificial Intelligence (XAI) is a human-centric AI that accelerates business value through a greater understanding of predictions and makes complex black box decisions more transparent, trustable, explainable. This research involves the development of a novel classification model to classify whether a financial transaction is fraudulent or legitimate using five Machine learning algorithms and two Deep Neural Networks Algorithms like Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). To tackle challenges like data quality issues and high-class imbalance in the dataset, several experimentations like Memory Optimisation, Hyperparameter Tuning methods have been implemented. Based on the literature, this is a novel instance of research investigating and implementing five XAI methods on the IEEE-CIS fraud detection dataset. After hyperparameter tuning, the experimental results showed that the Light GBM model performed better with 98.3% Accuracy, F1- score of 0.70, and AUC-ROC score of 0.96 compared with other models. The outcome of the research can be applied to different areas for better decision-making and generating business value through real-time predictions. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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An improved computational framework using one stage filtration by incorporating knowledge in gene expression clustering 16

An improved computational framework using one stage filtrati...

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2016 International Conference on Artificial Intelligence and Robotics, ICAIR 2016 and 2016 International Conference on Automation, Control and Robotics Engineering, CACRE 2016

作者： Kasim, Shahreen Fudzee, Mohd Farhan Md. Salamat, Mohamad Aizi Ramli, Azizul Azhar Mahdin, Hairulnizam Abdullah, Mohd Hafizul Afifi Soft Computing and Data Mining Centre Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat86400 Malaysia Web Technology Department Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat86400 Malaysia

ISBN: (纸本)9781450342353

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. Given a gene expression dataset, the challenge is to predict gene function given the intensity and redundancy of the data and the lack of similarity of expression profiles without degrading the analysis performance and also without assigning genes at random. At the same time, the computational method must be capable of producing highly compacted clusters with furthest separation, high consistency, and accuracy. In this paper, we present a new computational framework for clustering gene expression data. From this experiment, we can conclude that our new framework capable to improve the accuracy thus determined the dominant gene. © 2016 ACM.

关键词： Gene expression

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Efficient Federated Learning with Adaptive Channel Pruning for Edge Devices 19

Efficient Federated Learning with Adaptive Channel Pruning f...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Jia, Yongzhe Zhang, Xuyun Liu, Bowen Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing China Shenzhen China Macquarie University Department of Computing SydneyNSW Australia Shiga University Faculty of Data Science Hikone Japan

ISBN: (纸本)9798350346558

Federated learning (FL) is an emerging machine learning paradigm that allows distributed participants to train a global model collaboratively. In edge networks, FL involves numerous edge devices updating the local models with the assistance of a central server while keeping the local data on the devices. However, edge devices are commonly resource-limited while neural network models used in FL require significant resource consumption to reach a satisfactory accuracy. Moreover, different devices are various in terms of system capabilities, and employing a uniform model on all devices leads to the derogation of model accuracy. Several pioneer work integrate model pruning techniques into the FL process and focus on reducing the resource consumption on edge devices, whereas neglecting the impact of model generality on accuracy. In our work, we propose FedACP, an efficient FL approach with adaptive channel pruning for edge devices to achieve better resource-accuracy trade-offs. Specifically, we design a two-phase channel pruning approach to adaptively prune local models, in which both the resource constraints and model generalities are taken into consideration. In addition, we also design an aggregation algorithm for aggregating heterogeneous local models produced by the two-phase channel pruning approach. To evaluate the performance of the FedACP, we implement it with a real FL framework FedML and compare it with several state-of-the-art methods. The experimental results show that FedACP achieves better model accuracy while simultaneously reducing 22.41% to 27.34% model parameters. © 2022 IEEE.

关键词： Economic and social effects

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Peer Tutoring and its Impact on Academic Achievements

Peer Tutoring and its Impact on Academic Achievements

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IT and Industrial Technologies (ICIT), International Conference on

作者： Muhammad Aqeel Manahil Hassan Affaf Ghani Manahil Zaheer Bilal Anjum Faisal Bukhari Department of Data Science Faculty of Computing and Information Technologys University of the Punjab Lahore Pakistan

ISBN: (数字)9798331504465

ISBN: (纸本)9798331504472

Peer tutoring is gaining attention rapidly nowadays, providing a learning environment where students have the opportunity to learn and grow through collaboration and effective communication. This study focuses on the impact of peer tutoring by collecting data from different students as well as surveys and then analyzing the pre and post-tutoring assessments to determine the impact of peer tutoring. It employs statistical techniques to study the impact of peer tutoring on both tutee and tutor, their self-confidence, and the overall academic achievement gained through tutoring.

关键词： Training Surveys Job shop scheduling Statistical analysis Collaboration Cultural differences Socioeconomics

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Analysis of Factors Influencing Early Detection of Breast Cancer

Analysis of Factors Influencing Early Detection of Breast Ca...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), International Conference on

作者： Hassal Malik Sonia Umaira Ahmad Sial Faisal Bukhari Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

Carcinoma, particularly breast cancer, poses a formidable challenge, especially in Pakistan where it stands as the most prevalent cancer among women. This research investigates the complex landscape of breast cancer in Pakistan, utilizing a dataset from local hospitals. Through comprehensive statistical analyses and advanced machine learning techniques, including XGBoost and RandomForest, the study uncovers significant associations between socio-economic and psychological factors and the timing of breast cancer diagnosis among Pakistani women. Factors such as embarrassment, fear of treatment, and lack of awareness emerge as crucial determinants. The findings provide valuable insights for tailored interventions and contribute to the global fight against breast cancer.

关键词： Hospitals Statistical analysis Psychology Machine learning Forestry Market research Breast cancer Timing Decision trees Socioeconomics

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Twitter-Based Early Depression Detection Through Machine Learning 4th

Twitter-Based Early Depression Detection Through Machine Lea...

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4th International Conference on Advances in Computational science and Engineering, ICACSE 2023

作者： Ting, Kee Hui Majid, Mazlina Abdul Ibrahim, Ashraf Osman Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Centre for Artificial Intelligence & Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia

ISBN: (纸本)9789819729760

Depression, a prevalent mental health disorder, significantly affects individuals’ quality of life, emphasizing the critical need for early detection and intervention to ensure successful treatment. However, conventional methods for early depression detection have limitations that can impact accuracy and effectiveness. The proposed research advocates integrating technology to overcome these challenges, explicitly leveraging social media and machine learning. The focus is on detecting early signs of depression in Twitter users. A dataset of tweet texts is gathered and employed in machine learning classifiers, including Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). The research aims to identify the most effective algorithm for depression detection by comparing testing results and determining the classifier with the highest accuracy level. This contributes to achieving Sustainable Development Goal 3 (SDG3) by enhancing individuals’ quality of life. The result proved that the Support Vector Machine (Linear Support Vector Classifier) is best performed with the highest accuracy value of 90.57% among the three tested classification models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Digital Twin System for Smart Campuses

Digital Twin System for Smart Campuses

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2024 IEEE International Conference on Control science and Systems Engineering, ICCSSE 2024

作者： Wang, Huijie Cai, Yuanyuan Yue, Fabu Luo, Hao Abdipoor, Sina Goh, Say Leng School of Big Data BaoShan University Yunnan BaoShan678000 China Universiti Putra Malaysia Department of Computer Science 43400 Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Labuan87000 Malaysia

ISBN: (纸本)9798331517199

In an era where educational institutions are increasingly embracing technology to improve efficiency and enhance learning environments, the concept of smart campuses has emerged as a crucial development. The traditional approach of monitoring campus environments through video footage and manual inspections is becoming outdated as technology advances. To empower smarter campus management and enhance the educational experience, this study presents a systematic approach for smart campuses built on the foundation of digital twins. This technology creates a virtual replica of the physical campus, enabling real-time monitoring of various aspects like security, teaching, maintenance, environment, assets, and energy usage. We simulate real-world campus behavior by constructing this digital counterpart. The resulting digital twin image can be visualized through various user-friendly interfaces, including virtual reality and 3D models, empowering managers to make informed decisions for a more efficient and secure campus environment. Implementing a digital twin system in a smart campus setting demonstrates significant improvements in operational efficiency, security management, and environmental sustainability. By leveraging the power of digital twins, educational institutions can transition towards smarter, more responsive, and sustainable campus environments, ultimately enriching the educational experience for students and staff. © 2024 IEEE.

关键词： 3d modeling campus management digital twin real-time monitoring

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The preliminary design of outbreak detection model based on inspired immune system 3

The preliminary design of outbreak detection model based on ...

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2013 3rd World Congress on Information and Communication Technologies, WICT 2013

作者： Mohsin, Mohamad Farhan Mohamad Bakar, Azuraliza Abu Hamdan, Abdul Razak School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Malaysia Data Mining and Optimization Research Group C Centre for Artificial Intelligence Technology Faculty of Science and Information Technology Selangor Malaysia

ISBN: (纸本)9781479932306

The aim of outbreak detection model is to obtain high detection rate but at the same time maintaining the low false alarm rate. However, because of the weakness of early outbreak signal that behaves under uncertainties, it causes imbalance result between detection rate and false alarm rate. In this study, an early review of danger theory, an approach of artificial immune system is performed to seek the possibility it can be applied in outbreak detection. To investigate that, a detection model based on danger theory is developed and the model is tested with dengue outbreak dataset. The model is then evaluated in term of detection rate, specificity, false alarm rate, and accuracy. The preliminary result indicates that the proposed model has ability to generate good detection result with a balance between detection rate and false alarm rate. © 2013 IEEE.

关键词： Immune system

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