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1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Panganai, Marvelous Alexander Nyandoro, Leslie Kudzai Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

Drowsiness and fatigue are the major reasons for triggering serious and severe road crashes in Zimbabwe and the whole world at large. The developments in technology in recent years brought support and backing to drivers using intelligent automobile systems. Several research studies used datasets of a single driver for training and testing. Some as well used mainly day time images for training and testing the models. Therefore, fatigue and drowsiness is a key possible field of study to prevent numerous number of sleep induced road crashes. In this paper, two methods of feature extraction were proposed which are the MLP (Multilayer - Perceptron), the CNN (Convolutional Neural Network). The PCA (Principal Component Analysis) method was used for dimensionality reduction. Based on these methods, five classifiers where used to detect drowsiness on the driver. The five classifiers used where the LDA, XGBoost, LR, Decision Tree and the K-Nearest neighbors. Experiments were done in order to examine the capacity and usefulness of the approaches contrasted with other techniques. Experimental outcomes demonstrate that the feature extraction technique of CNN provided high accuracy on the five classifiers. The KNN was the average best classifier with a 100% accuracy. Experimental results indicated that the PCA improved the classifiers. This study delivers important and significant answers in practice to curb motor vehicle crashes due to drowsiness. © 2022 IEEE.

关键词： Feature extraction

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Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

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Insurance Fraud Detection using Machine Learning 1

Insurance Fraud Detection using Machine Learning

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1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Tongesai, MacHinya Mbizo, Godfrey Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

Many insurance companies today deal with the issue of fraudulent insurance claims, which results in significant yearly financial loss. Since the losses are covered by raising policyholders' premium costs, these frauds have a negative impact on society. The traditional claim investigation procedure has also been blamed for producing unreliable conclusions because it is time-consuming and laborious. Therefore, using machine learning and the XGBoost method, we construct an automated fraud detection application framework in this study. Accurately identifying fraud claims in a shorter amount of time is the goal. Data analysis is utilized throughout the process to validate, sanitize, and extract the pertinent data. As a result, the insurance firm can retain its reputation outside by employing this structure and has a reliable relationship with clients that they can share. © 2022 IEEE.

关键词： Insurance

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Performance Evaluation of Intrusion Detection System with SVM 6

Performance Evaluation of Intrusion Detection System with SV...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Diaz, Ricky Aurelius Nurtanto Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia

ISBN: (纸本)9798331539603

Various machine learning techniques have been proposed to improve the effectiveness of Intrusion Detection Systems (IDS), where IDS is one of the important parts of the network that functions to maintain network security. With its various capabilities and reputation, Support Vector Machine (SVM) is often applied in various classification cases. However, it has constraints with accuracy and computing time when it comes to data that has large dimensions. Utilizing metaheuristic techniques is one possibility that researchers can use to solve this problem. To overcome this, in this study, we will evaluate the performance of SVM for the IDS case by utilizing the metaheuristic Grey Wolf Optimization (GWO) algorithm. From the experiments with three different iteration models, the best results of the combination of SVM and GWO were found in a model with 10 GWO iterations and 10 populations with an accuracy of 97.47% using 15 features out of 48 original features of the UKM-IDS20 dataset. © 2024 IEEE.

关键词： Network intrusion

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Using Goal-Conditioned Reinforcement Learning with Deep Imitation to Control Robot Arm in Flexible Flat Cable Assembly Task

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IEEE Transactions on Automation Science and engineering 2024年第4期21卷 6217-6228页

作者： Li, Jingchen Shi, Haobin Hwang, Kao-Shing Northwestern Polytechnical University School of Computer Science Xi'an710072 China National Sun Yat-sen University Department of Electrical Engineering Kaohsiung81164 Taiwan Kaohsiung Medical University Department of Healthcare Administration and Medical Informatics Kaohsiung80708 Taiwan

Leveraging reinforcement learning on high-precision decision-making in Robot Arm assembly scenes is a desired goal in the industrial community. However, tasks like Flexible Flat Cable (FFC) assembly, which require highly trained workers, pose significant challenges due to sparse rewards and limited learning conditions. In this work, we propose a goal-conditioned self-imitation reinforcement learning method for FFC assembly without relying on a specific end-effector, where both perception and behavior plannings are learned through reinforcement learning. We analyze the challenges faced by Robot Arm in high-precision assembly scenarios and balance the breadth and depth of exploration during training. Our end-to-end model consists of hindsight and self-imitation modules, allowing the Robot Arm to leverage futile exploration and optimize successful trajectories. Our method does not require rule-based or manual rewards, and it enables the Robot Arm to quickly find feasible solutions through experience relabeling, while unnecessary explorations are avoided. We train the FFC assembly policy in a simulation environment and transfer it to the real scenario by using domain adaptation. We explore various combinations of hindsight and self-imitation learning, and discuss the results comprehensively. Experimental findings demonstrate that our model achieves fast and advanced flexible flat cable assembly, surpassing other reinforcement learning-based methods. Note to Practitioners - The motivation of this article stems from the need to develop an efficient and accurate FFC assembly policy for 3C (computer, Communication, and Consumer Electronic) industry, promoting the development of intelligent manufacturing. Traditional control methods are incompetent to complete such a high-precision task with Robot Arm due to the difficult-to-model connectors, and existing reinforcement learning methods cannot converge with restricted epochs because of the difficult goals or trajectories. To

关键词： Manipulators

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Simulation of optimal methodologies for channel estimation in a MISO RIS-aided system 27

Simulation of optimal methodologies for channel estimation i...

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27th Pan-Hellenic Conference on Progress in Computing and informatics with International Participation, PCI 2023

作者： Drampalou, Stamatia Miridakis, Nikolaos Karkazis, Panagiotis Department of Informatics and Computer Engineering University of West Attica 250 Thivon Av. Athens12244 Greece

ISBN: (纸本)9798400716263

Extensive research in telecommunications and especially in wireless systems assisted by reconfigurable intelligent surfaces (RIS) has emerged at the forefront of cutting-edge wireless communications nowadays. RISs are composed of several arrays of passive elements, where their purpose is to receive the transmitted signal and send it to the corresponding receiver. One of the main disadvantages of RIS, due to the various processes performed in the RIS controller, is the channel estimation time. Therefore, research has focused on optimized channel estimation algorithms in multiple input- multiple output (MIMO) systems to reduce channel overhead and estimation time. In a previous publication, we presented the methodologies that have been used for channel estimation and our goal in this research is to simulate the proposed algorithms on a common system and common parameters. The presentation of the operation mode of the optimal channel estimation methodologies is implemented in a multiple input- single output (MISO) system and in a Base Station (BS) -RIS-user channel. Then we will present in detail the results of the comparison of the methods and mention future scenarios that can be tested. © 2023 Owner/Author.

关键词： Channel estimation

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Improving Open Weather Prediction Data Accuracy Using Machine Learning Techniques 14

Improving Open Weather Prediction Data Accuracy Using Machin...

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Tsipis, Evangelos Banti, Konstantina Louta, Malamati Dimokas, Nikos University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Western Macedonia Department of Informatics Kastoria Greece

ISBN: (纸本)9798350318067

Weather prediction methods have evolved significantly over the past fifty years, including advances in numerical weather prediction, high-performance computing, mesoscale modeling, assimilation of observations from new sources, and ensemble prediction systems. In recent years, machine learning techniques, including Long Short- Term Memory (LSTM) models, have shown promise in improving the accuracy of weather predictions. In this paper, we aim at investigating the use of LSTM models to improve the accuracy of open weather datasets. Our work was conducted as part of the AUGEIAS research project, which aims at capitalizing on research results in the field of Internet of Things (IoT) and Low Power Wide Area Networks (LPWAN). More specifically, the project target is to create a smart ecosystem, utilizing machine learning techniques that will enable and optimize the use of treated wastewater reuse in agriculture. Our research indicates that, within the context of the AUGEIAS project, single-step LSTM models outperform multistep models in enhancing weather prediction accuracy. © 2023 IEEE.

关键词： Internet of things

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Mobile Application for Agribusiness Crops Transport Vehicle Reservation with First Come First Served Algorithm 3

Mobile Application for Agribusiness Crops Transport Vehicle ...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Sasmito, Ginanjar Wiro Wiyono, Slamet Irwansyah, Edy Suhartono, Derwin Politeknik Harapan Bersama Informatics Engineering Department Tegal Indonesia Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9781665494007

In Indonesia, Agribusiness is the most important and biggest economic sector in national economic scale. The vital role of agribusiness in national economy has a significant influence in future economic development. In managing agribusiness system, one of the issues encountered is crops transport issue. No integration of crops transport vehicle data causes a problematic agribusiness crops transport process, even more so for the agribusiness doer who has no transport vehicle or during harvest that naturally needs a lot of crops transport vehicles. Therefore, a mobile-based computer application used to reserve agribusiness crops transport vehicles that can be accessed in real time is needed. The application was developed within 25 days using scrum framework. The reservation system adopted FCFS Algorithm implemented in the mobile application, so it optimally serves transportation reservation with priority of first come first served by the service provider's system. © 2023 IEEE.

关键词： Crops

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3rd IEEE International Conference on Software engineering and Artificial Intelligence, SEAI 2023

作者： Senthilvelan, Prasanth Li, Jialong Tei, Kenji Waseda University Department of Computer Science and Communication Engineering Tokyo Japan National Institute of Informatics Tokyo Japan

ISBN: (纸本)9798350337952

In this paper, we present an efficient design of a shield called Parameterized Shield which allows for easy modification of its requirement parameters in response to the changes in the environment instead of manually making many shields during the development time and changing the whole shield during runtime. We propose to generate one proper shield that has many requirement parameters where even when there is any complex situation, there is not any need for extra effort from engineers to develop additional requirements and prepare the shield but only the parameters need to be changed when there is a change in the environment which avoids choosing a whole new shield every time an environment change occurs during runtime. And similarity-based adaptation will be used to choose the right parameters for the shield according to the environment with the help of the MAPE-K loop framework and reinforcement learning and to give the appropriate policy for the agent to act according to that environment. We will demonstrate using a cleaning robot as an example. © 2023 IEEE.

关键词： Reinforcement learning

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A Review of Fintech Management and Security Development 10

A Review of Fintech Management and Security Development

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Ritonga, Ali Akbar Zuhri Harahap, Syaiful Sooai, Adri Gabriel Nugroho, Aryo Samsir Prasetyo Adi, Puput Dani Universitas Labuhanbatu Faculty of Science and Technology Department of Informatics Management North Sumatera Labuhanbatu Indonesia Universitas Labuhanbatu Faculty of Science and Technology Department of Information System North Sumatera Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Universitas Narotama Faculty of Computer Science Department of Information System Surabaya Indonesia Universitas Labuhanbatu Faculty of Computer Science Department of Informatics Engineering North Sumatera Labuhanbatu Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in this case often experience many losses, one of the biggest losses is the existence of hackers and theft of money through the Internet platform or Internet Server. This research reviews how the ins and outs of the flow of funds located on servers or online banks. And ways to overcome hackers and bypass data. Comprehensively reviewed in this research review. This research discusses Cryptocurrency Analysis, Bitcoin, and Ethereum Comparison which are some of the methods used in online-based Finance, which is without currency but has the same value even more than currency if the conversion process is carried out at the bank. In this research, it is comprehensively discussed how methods are carried out in the process of securing data, Cryptocurrency, Bitcoin, and Ethereum are comprehensively discussed to get a lot of data and comparisons and the right steps to deal with crime via the internet. The predicted use of IoT in the Fintech sector is 140 billion connected devices, IoT Loans 110 billion connected devices, and IoT insurance with an estimated 90 billion connected devices, in the approaching 2026. © 2024 IEEE.

关键词： Ethereum

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