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2nd International Conference on Information Systems and Software Technologies, ICI2ST 2021

作者： Mosquera, Estefania Patricia Galvez Gaibor, Marco Oswaldo Santorum Escuela Politécnica Nacional Faculty of System Engineering Quito Ecuador Escuela Politécnica Nacional Department of Informatics and Computer Science Quito Ecuador

ISBN: (纸本)9780738143613

Organizational processes are constantly evolving due to different factors. To take care of this continuous transformation, these processes must be flexible enough, which implies their formalization and optimal design. To operate efficiently, the organizations must identify and manage all their processes, therefore it is important to ensure their quality. There are companies in Ecuador devoted to providing consulting services for management of processes;to that end they use methodologies, techniques, and good practices, in order to provide optimal services to meet the expectations of their customers. However, this not always *** objective of this work is to present a proposal of a model to guide the quality in the design of processes, which allows expert companies to perform a design following good practices and to evaluate the quality from syntactic, semantic, and pragmatic perspectives. © 2021 IEEE.

关键词： Design

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Attack Analysis of Face Recognition Authentication Systems Using Fast Gradient Sign Method

arXiv

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arXiv 2022年

作者： Musa, Arbena Vishi, Kamer Rexha, Blerim Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Serbia Department of Informatics University of Oslo Oslo Norway

Biometric authentication methods, representing the"something you are" scheme, are considered the most secure approach for gaining access to protected resources. Recent attacks using Machine Learning techniques demand a serious systematic reevaluation of biometric authentication. This paper analyzes and presents the Fast Gradient Sign Method (FGSM) attack using face recognition for biometric authentication. Machine Learning techniques have been used to train and test the model, which can classify and identify different people’s faces and which will be used as a target for carrying out the attack. Furthermore, the case study will analyze the implementation of the FGSM and the level of performance reduction that the model will have by applying this method in attacking. The test results were performed with the change of parameters both in terms of training and attacking the model, thus showing the efficiency of applying the FGSM. © 2022, CC BY-NC-ND.

关键词： Face recognition

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Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home

Machine Learning Regression Approach for Estimating Energy C...

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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Husin, Nor Syafiqah I.M. Mostafa, Salama A. Jaber, Mustafa Musa Gunasekaran, Saraswathy Shamini Al-Shakarchi, Ali H. Abdulsattar, Nejood Faisal Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Al-turath University Department of Medical Instruments Engineering Techniques Baghdad Iraq Al-farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq Universiti Tenaga Nasional College of Computing & Informatics Department of Computing Selangor Malaysia Ninevah University College of Information Technology Mosul Iraq Imam Ja'afar Al-Sadiq University College of Information Technology Department of Computer Technical Engineering Al-Muthanna Iraq

ISBN: (纸本)9798350341881

This paper attempts to use machine learning algorithms to estimate the energy consumption of appliances in a smart home environment. This work aims to promote awareness among smart home systems and users about their appliances' energy consumption and guide them toward energy-saving practices. To achieve this, three machine learning algorithms, namely Decision Forest (DF), Boosted Decision Tree (BDT), and Linear Regression (LR), were chosen for regression tasks to estimate the energy consumption of several appliances accurately. The time-series datasets, namely appliance energy prediction datasets, are used for training and testing the algorithms. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which comprises six processing phases, was employed in this work. The test is performed by utilizing 10-fold cross-validation. The results obtained assess the models' performance in predicting the appliances' energy consumption. The experimental results indicate that the three models exhibit varying degrees of accuracy in predicting energy consumption, as measured by their respective R-squared values. Among the three models, the random forest model exhibited superior performance by achieving the highest R2 values of 0.62 and 0.54 during the training and testing phases, respectively. © 2023 IEEE.

关键词： Energy utilization

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Enterprise Architecture Trends in the Digital Transformation Era

Enterprise Architecture Trends in the Digital Transformation...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Irfan Mahendra Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Nusa Mandiri University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia

Enterprise Architecture (EA) is a solution to build alignment between business strategy and information technology in dealing with digital transformation that causes fundamental changes for companies and businesses. The purpose of this study is to examine the trends and progress of EA in responding to the challenges of the digital transformation era, which is carried out through SLRs using the PRISMA method. According to the research findings, implementing EA in businesses serves the following purposes: establishing clear objectives and strategies for the development of business and technology, achieving harmony between business and information technology, time and cost efficiency, and effectiveness in business development processes and information technology. Then, it was concluded that there are nine components of EA, namely, vision, mission, and strategic objectives; business architecture; data & information architecture; information system architecture; infrastructure and network architecture; security architecture; human resources architecture; governance, legal, and compliance; and standards & regulations.

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GRAPH THEORETICAL MODELS AND ALGORITHMS OF PORTFOLIO COMPRESSION

arXiv

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arXiv 2022年

作者： Hanics, Mihály Péter Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Computer Science and Information Theory Hungary

Matching problems under preferences gained significant attention not just in mathematical sciences, but in other fields too since the paper published by Gale and Shapley in 1962-ben on the college admission being modelled as a matching problem with preferences, and Al Roth's work on matching problems in practice, such as modelling kidney exchanges as matching markets. For their work on matching markets, Roth and Shapley received the 2012 Nobel Memorial Prize in Economics. Today, this interdisciplinary field extended to other parts of mathematics, for example, stability of flows is examined, and focuses on the real-life application of this field. One such application is financial clearing, or portfolio compression. Market participants (banks, organizations, companies, financial agents) sign contracts, creating liabilities between each other, which increases the systemic risk. Large, dense markets commonly can be compressed by reducing obligations without lowering the net notional of each participant (an example is if liabilities make a cycle between agents, then it is possible to reduce each of them altogether without any net notional changing), and our target is to eliminate as much excess notional as possible in practice (excess is defined as the difference between gross and net notional). A limiting factor which may reduce the effectiveness of the compression can be the preferences and priorities of compression participants, who may individually define conditions for the compression, which must be considered when designing the clearing process, otherwise a participant may bail out, resulting in the designed clearing process to be impossible to execute. These markets can be well-represented with edge-weighted graphs. In this paper, I examine cases when preferences and conditions of participants on behalf of clearing are given, e.g., in what order would they pay back their liabilities (a key factor can be rate of interest) and I show a clearing algorithm for these p

关键词： Graph theory

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Industrial Internet of Things Intrusion Detection Method Using Machine Learning and Optimization Techniques

引用

Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Gaber, Tarek Awotunde, Joseph B. Folorunso, Sakinat O. Ajagbe, Sunday A. Eldesouky, Esraa Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Mathematical Science Olabisi Onabanjo University Ago-Iwoye120107 Nigeria Department of Computer and Industrial Production Engineering First Technical University Ibadan200255 Nigeria Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Department of Computer Science Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt

The emergence of the Internet of Things (IoT) has witnessed immense growth globally with the use of various devices found in home, transportation, healthcare, and industry. The deployment and implementation of the IoT paradigm in industrial settings lead to the architectural changes of Industrial Automation and Control Systems (IACS) plus the countless connectivity of industrial systems. This resulted in what is referred to as the Industrial Internet of Things (IIoT), which removes the barrier of connecting IACS to isolated conventional ICT platforms. In recent times, the IoT has started hacking our personal lives and not only our world, thus creating a platform for impending IoT cyberattacks. The widespread use of the IoT has created a rich platform for possible IoT cyberattacks. Machine learning (ML) algorithms have been driven solutions to secure wireless communication in IIoT-based systems, and their use in solving various cybersecurity challenges. Therefore, this paper proposes a novel intrusion detection model based on the Particle Swarm Optimization (PSO) and Bat algorithm (BA) for feature selection, and the Random Forest (RF) classifier for the classification of malicious behaviors in IIoT-based network traffic. An IIoT-based cybersecurity dataset, WUSTL-IIOT-2021 Dataset, was used to evaluate the performance of the proposed model using accuracy, recall, precision, and F1-score. The results of the two feature selection were compared to identify the most promising one. The results were compared with other recent state-of-the-art ML and multiobjective algorithms, and the results showed better performance. The RF along with BA classifier had proved to be the best classifier. © 2023 Tarek Gaber et al.

关键词： Internet of things

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Comparison of Biocode Based Machine Learning and Segmentation Model for Automated Prawn Size Prediction for Real Prawn Farm 5

Comparison of Biocode Based Machine Learning and Segmentatio...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Zariful, Muhammad Wafiq Abd Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Fakhrurroja, Hanif Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Basri, Aida Maryam Universiti Technologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei National Research and Innovation Agency Research Center for Smart Mechatronics Bandung Indonesia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Universiti Technologi Brunei School of Applied Science and Matematics Bandar Seri Begawan Brunei

ISBN: (纸本)9798350374407

The aquaculture industry has witnessed a significant surge in technological integration over the past decade, leveraging advancements in the Internet of Things (IoT) and Artificial Intelligence (AI) to enhance production efficiency. Monitoring the growth of aquatic cultures, such as fishes and prawns, plays a pivotal role in optimizing production. However, traditional methods, notably in prawn aquaculture in Asia, still heavily rely on stress-inducing techniques like netting and manual measurements. This paper proposes a novel approach using YOLO-based detection algorithm with biocode reference point based on the ArUco design to predict prawn size based on the length and are dimension to analysed the improvement in predicting the size. The propose solution is compared extensively using statistical tests against segmentation model with and without fine-tuning. Initial results show that both YOLOv8 segmentation and YOLOv8 with Segment Anything Model (SAM) give similar results with the actual mean length of 15.7 cm and predicted mean lengths are 20.5 cm and 20.8 cm respectively. However, after fine-tuning the parameter, the accuracy of predicted results improved significantly with 15.8 cm for YOLOv8 segmentation and 16 cm for YOLOv8 with SAM. The mean absolute error of 0.93 cm for YOLOv8 segmentation and 1.10 cm for YOLOv8 with SAM, and the width of 0.48 cm and 0.78 cm respectively. © 2024 IEEE.

关键词： Deep learning

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Clustering the Academician Knowledge Qualification: K-Means for Performance Measurement Analysis

Clustering the Academician Knowledge Qualification: K-Means ...

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Student Conference on Research and Development (SCOReD)

作者： Novi Yanti Sarjon Defit Okfalisa Department of Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Riau Indonesia Department of Information Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Sumatera Barat Indonesia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

The assessment of an academic’s performance is not solely based on the tangible results achieved. Nonetheless, the formation aspect of the Knowledge Management (KM) process, which arises in the pursuit of outcomes, must be acknowledged. This emphasizes the significance of KM process development for academics in achieving their essential responsibilities including teaching and learning, research and publishing, and community service. Consequently, it is essential to develop a KM model for assessing the accomplishments of academics in higher education. An evaluation of academic performance in higher education was conducted to identify the success factors of knowledge management, by considering five key elements viz, knowledge creation, knowledge acquisition, knowledge storage, knowledge sharing, and knowledge application. Accordingly, sixty-five sub-variables were identified as success model factors for evaluating academic knowledge management performance. A K-Means clustering method was utilized to assess the KM qualifications of academics at an Islamic public institution in Indonesia (University X) as the primary study. Herein, a 5-point Likert scale questionnaire was administered to 500 academics and subsequently categorized into three performance levels such as low, medium, and high performance. As a result, 140 academicians were categorized as low KM performance, 139 were clustered as medium qualification, and 221 academicians were positioned in high performance. Furthermore, a recommendation analysis is presented to elucidate the accomplishments of academic credentials for each cluster and propose corrective actions for enhancement. Therefore, the scholars at University X might enhance their knowledge management practices to get a superior quality university.

关键词： Measurement Technological innovation Publishing Knowledge acquisition Education Knowledge management Filling Space exploration Research and development Qualifications

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Comparative Study of Undersampling Techniques in Text Classification

Comparative Study of Undersampling Techniques in Text Classi...

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International Conference on informatics and Computing (ICIC)

作者： Usman Ependi Ade Putra Nahdatul Akma Ahmad Albertus Dwiyoga Widiantoro Muhammad Arifin Faiz Zulkifli Informatics Department Faculty Science Technology Universitas Bina Darma Palembang Indonesia Computerized Accounting Department Faculty of Vocational Universitas Bina Darma Palembang Indonesia College of Computing Informatics and Mathematics Universiti Teknologi MARA Perak Malaysia Information Systems Department Faculty of Computer Science Soegijapranata Catholic University Semarang Indonesia Information Systems Department Faculty of Engineering Muria Kudus University Kudus Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Imbalanced datasets pose critical challenges in text classification, often leading to biased predictions and reduced model performance. This study compares three undersampling techniques: Random UnderSampler, Near Miss, and Tomek Links, to evaluate their impact on five machine learning classifiers: Logistic Regression, Support Vector Machine, Random Forest, Naive Bayes, and Neural Network. The results demonstrate that the Random Forest classifier achieves the highest performance with precision of $\mathrm{9 7 \%}$, recall of $\mathrm{9 9 \%}$, and F1-score of $\mathrm{9 8 \%}$ across all methods, showcasing its robustness against data imbalance. The Near Miss technique improves Neural Network performance to a recall of $83 \%$ and F1-score of $\mathrm{7 1 \%}$, while Tomek Links further boosts Neural Network precision to $\mathrm{6 8 \%}$, recall to $\mathrm{7 2 \%}$, and F1-score to $\mathrm{7 0 \%}$. In contrast, simpler models like Naive Bayes exhibit limited improvements, with precision and recall scores around 47-51%. These findings underscore the importance of selecting appropriate undersampling strategies to enhance text classification outcomes and address data imbalance effectively.

关键词： Support vector machines Logistic regression Text categorization Neural networks Predictive models Robustness Data models Bayes methods informatics Random forests

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A Dual-Band MIMO Monopole Antenna with Enhanced Isolation for Sub 6 GHz 5G Applications

A Dual-Band MIMO Monopole Antenna with Enhanced Isolation fo...

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2024 International Conference on Advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Hamrioui, Fatma Zohra Bouaza, Abdelmaoula Touhami, Rachida Yagoub, Mustapha C.E. Azrar, Arab Electronic Department 10 Rue des Frères OUDEK El Harrach Algiers16200 Algeria Usthb university Bp32 Instrumentation laboratory Electronics and Informatics faculty El Alia Bab Ezzouar Algiers Algeria University of Ottawa Element laboratory School of Electrical Engineering and Computer Science Ottawa Canada Institute of Electrical and Electronic Engineering University M'Hamed BOUGARA-Boumerdes Signals and Systems Laboratory Electronics Department Boumerdes Algeria

ISBN: (纸本)9798350353754

In this paper, a novel dual-band MIMO monopole antenna for 5G applications is presented. The proposed MIMO antenna is made up of two rings that are T-shaped, generating two distinguished bands: the N77 band and the 6 GHz 5G band. A Tshaped ground branch with a bottom slit is integrated into the ground plane to mitigate the mutual coupling at lower and higher operating bands by redistributing the current, thus improving the isolation enhancement. A reversed T-shaped grounded parasitic element with extra grounded parasitic elements on the partial ground is inserted on top of the substrate, between the MIMO elements. The grounded process is introduced by inserting two metallic vias in each branch of the parasitic element. These parasitic elements effectively reduce the coupling between the MIMO antennas by restricting the current to the respective antenna, thus improving the isolation at the higher band. To verify the design principle of the suggested MIMO antenna, this latter is manufactured and tested. It exhibits excellent impedance matching and high isolation characteristics, and a compact footprint of 29 mm × 42.5 mm. Thus, it is attractive for 5G MIMO systems. © 2024 IEEE.

关键词： Monopole antennas

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