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A New Membrane Algorithm Based on Quasi-Affine TRansformation Evolution for Optimization Problems 1

15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Wang, Xiaopeng Snášel, Václav Mirjalili, Seyedali Chu, Shu-Chuan Shehadeh, Hisham A. Bai, Zhonghai Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava70800 Czech Republic Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Brisbane4006 Australia College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Faculty of Computer Science and Informatics Amman Arab University Amman11953 Jordan

ISBN: (数字)9789819700684

ISBN: (纸本)9789819700677

This paper develops a new meta-heuristic algorithm, called Membrane Algorithm based on Quasi-Affine TRansformation Evolution (MA-QUATRE). The proposed algorithm combines the principles of QUATRE, the one-level membrane structure, and the rules of membrane systems. The different operations of QUATRE are for each inner elementary membrane and outer skin membrane, and the transformation/communication is like rules in membrane systems. For the effectiveness of MA-QUATRE, it is first evaluated on the CEC2022 benchmark and compared with five QUATRE-related algorithms. Further, the proposed algorithm is used to solve two engineering design problems (tension/compression spring and pressure vessel design) and compared with the original QUATRE and six state-of-the-art algorithms. Finally, experimental results show that the proposed MA-QUATRE is a competitive algorithm for optimization problems. The source codes of this algorithm can be found in https://***/matlabcentral/fileexchange/134372-ma-quatre and https://***/wangxper/***. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Membranes

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Image Processing Implementation to Classify Coffee Fruit Ripeness Using K-Nearest Neighbor (KNN) Algorithm 8

Image Processing Implementation to Classify Coffee Fruit Rip...

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8th International Conference on Information Management and technology, ICIMTech 2023

作者： Hussaini Dermawan, M. Farhan Witarsyah, Deden Syamsuar, Dedy Fakhrurroja, Hanif Luthfi, Ahmad Herdiansyah, Muhammad Izman Telkom University Faculty of Industrial Engineering Bandung Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Islamic University of Indonesia Faculty of Industrial Technology Department of Informatics Jogjakarta Indonesia Universitas Bina Darma Informatics Study Program Faculty of Science and Technology Palembang Indonesia

ISBN: (纸本)9798350326093

Coffee is one of the plantation crops that has long been a cultivated plant in Indonesia. The classification of coffee fruit maturity manually still has several weaknesses and requires a long process, has low accuracy and is inconsistent, this is because the determination is made subjectively by coffee farmers. As for the classification of coffee fruit maturity levels automatically, it can be faster with objective determination, therefore the use of image processing is relatively easier, faster, and based on a quantified descriptive assessment to determine coffee maturity. Image Processing is a method used to process or manipulate images in 2-dimensional form. In the classification process, there are many methods used to obtain classification of objects based on training data. One of the algorithms used for the classification process is K-Nearest Neighbor (KNN). KNN is a classification technique for objects based on training data that is the closest or has similar characteristics to the object. KNN includes supervised learning algorithms, where the results of the new query instance are classified based on the majority of the categories in K-Nearest Neighbors (K-NN). The finding indicated that class classification of ripe and unripe were 88,24 % and 100% respectively with 93,33% accuracy level. © 2023 IEEE.

关键词： Classification (of information)

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Numerical Estimation of the Internal Positivity of the Fractional Order Model of a Two-Dimensional Heat Transfer Process 27th

Numerical Estimation of the Internal Positivity of the F...

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27th International Conference on Automation, AUTOMATION 2023

作者： Oprzędkiewicz, Krzysztof Department of Automatic Control and Robotics Faculty of Electrical Engineering Automatic Control Informatics and Biomedical Engineering AGH University of Science and Technology al. A Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031258435

In the paper the numerical estimation of the internal positivity of the model of a two dimensional heat transfer process is addressed. The considered thermal process is described by the fractional order state equation, derived from parabolic heat equation with homogenous Neumann boundary conditions and distributed control and observation. Two numerical algorithms of the positivity testing are proposed and compared. The first one consists in the full examination of the whole area. The next one uses Monte Carlo method. From tests it can be concluded that the considered model can be internally positive only for its lower orders and heating and measurements located closely to the corner of the tested area. The use of the Monte Carlo method allows to significantly shorten the duration of computations. The presented results can be useful in measurements using a thermal camera. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fractional order system Monte Carlo method Positivity Thermal camera

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Modeling of Thermal Processes in a Microcontroller System with the Use of Hybrid, Fractional Order Transfer Functions 21st

Modeling of Thermal Processes in a Microcontroller System wi...

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21st Polish Control Conference, PCC 2023

作者： Oprzedkiewicz, Krzysztof Rosol, Maciej Mitkowski, Wojciech Department of Automatic Control and Robotics Faculty of Electrical Engineering Automatic Control Informatics and Biomedical Engineering AGH University of Science and Technology al. A Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031351693

In the paper the problem of modeling of thermal processes in microcontroller system is addressed. The proposed models allow to describe thermal processes during work of an evaluation system. The temperature in critical places of the system is measured using thermal camera. The proposed models have the form of hybrid transfer functions, containing both Integer Order (IO) and Fractional Order (FO) parts. The step responses of the proposed transfer functions are computed analytically. Results of experiments show that the proposed models are more accurate in the sense of Mean Square Error (MSE) cost function than typical transfer function models with delay. The proposed models can be applied to predict of overheating of microcotroller systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Microcontrollers

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Revolutionizing STEM Education: BeSAR Augmented Reality Application Sparks Passion for science in Rural Primary Schools 4th

Revolutionizing STEM Education: BeSAR Augmented Reality Appl...

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

作者： Tanalol, Siti Hasnah Baharum, Aslina Tahir, Asni Saudi, Azali A. Wahab, Shaliza Hayati Saad, Nordin F. Michael, Jurry Center of Preparatory for Science and Technology UMS Sabah Kota Kinabalu Malaysia School of Engineering and Technology Sunway University Selangor Bandar Sunway Malaysia Faculty of Computing and Informatics UMS Sabah Kota Kinabalu Malaysia Faculty of Humanities Arts and Heritage UMS Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789819729760

In 2020, it was found that only 20% of the 2 million high school students majored in STEM (science, technology, engineering and Mathematics), compared to the national target of 40% of students. This requirement is important for the country to guarantee the sustainability of its development and progress. STEM education provides students with the critical skills needed to succeed in a rapidly changing and increasingly technology-dependent world. With this motivation, the idea was born to help increase the motivation and interest of primary school students in learning science and STEM by using augmented reality (AR) technology. This project has produced an Integrated science Learning Module and a mobile AR Application, BeSAR: Learning science through AR, to support STEM teaching and learning outside the classroom. The selected science Learning Modules are topics based on the elements found in the SK Eco-science Park Kundasang, namely Aquaponics, Biocompost, Water Cycle and Food Web. Using the developed BeSAR application allows students to experience a more interactive and interesting learning experience. The BeSAR mobile application was developed using the prototyping methodology. Using the Kansei assessment method, after the students used the BeSAR application, the findings showed that the students could accept the use of AR technology in learning and feel that STEM subjects, especially science, are something fun. The Kansei evaluation method is used to analyze the emotions or feelings of users towards the interface design of mobile applications. This study implies that the use of AR in science learning can be used as one of the ways to increase interest in science among rural students. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Lesson Learned from Weathering Clay Shale Residual Interface Shear Strength Testing Method 5th

Lesson Learned from Weathering Clay Shale Residual Interface...

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5th International Conference on Rehabilitation and Maintenance in Civil engineering, ICRMCE 2021

作者： Sagitaningrum, Fathiyah Hakim Kamaruddin, Samira Albati Nazir, Ramli Soepandji, Budi Susilo Alatas, Idrus M. Razak Faculty of Technology and Informatics University of Technology Malaysia Kuala Lumpur54100 Malaysia Centre of Tropical Geoengineering University of Technology Malaysia Johor Bahru81310 Malaysia Civil Engineering Department Faculty of Engineering University of Indonesia Depok16424 Indonesia National Science and Technology Institute Jakarta12630 Indonesia

ISBN: (纸本)9789811693472

Clay shale has been a problematic soil due to its weathering characteristic. Weathering is the degradation of shear strength due to exposure to water and air. As the depth of soil increases, the weathering effect would decrease, leading to different weathering grades. On a slope, the different weathering grades would lead to an interface slope failure. Until now, laboratory methods on finding the interface shear strength between the weathering grades seldom been investigated. This research will propose a method and give an evaluation of the results. An interface direct shear test has been conducted until its residual state between an undisturbed clay shale and weathering clay shale. The weathering condition of the bottom clay shale is achieved by soaking and drying the bottom of the sample using a two-days wetting-drying cycle. Results showed scattered values with different behavior for the soaked and unsoaked conditions for increasing weathering days. The soaked condition resulted in an increasing cohesion, increasing average stress ratio, and decreasing friction angle. The unsoaked condition resulted in decreasing cohesion, increasing friction angle, and increasing average stress ratio. Previous research shows that, the residual shear stress should have decreased as the weathering days increases. The cause for the contrary results is due to soil swelling and the absence of a separator when weathering the bottom clay shale. In conclusion, the test conducted is not quite suitable to understand the interface shearing behavior of weathering clay shale. Thus, a modification is suggested for future research according to the identified causes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Shear stress

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IoT-Enabled LoRa Wireless Sensor Network for Real-Time Air Quality Monitoring with Geographic Information System Mapping

IoT-Enabled LoRa Wireless Sensor Network for Real-Time Air Q...

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2023 International Conference on informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Maulana, Nurhuda Matheus Edward, Ian Joseph Universitas Pembangunan Nasional Veteran Jakarta Faculty of Computer Science Jakarta12450 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia

ISBN: (纸本)9798350307306

The Internet of Things (IoT) can potentially change how humans interact with the environment. This technology is able to be applied in various fields, such as urban management, agriculture, environmental monitoring, health, and transportation. Conventional air quality monitoring systems are complex sensing equipment and they can measure various parameters of air pollutants. This system requires very high costs and maintenance. Therefore, a low-cost air quality measurement system is required and is able to measure the surrounding air pollutants. By using wireless sensor network technology as the acquisition of environmental monitoring sensor data. A system design was created in the form of geographic information capable of describing air quality in a particular area. The air parameters used are carbon monoxide, dust particulate, light intensity, temperature, and relative humidity. The wireless network uses the LoRa module connected to the gateway. The web server provides a database and geographic information system interface from the gateway through the MQTT broker. Data transmission using a radio frequency of 915 Mhz and able to send data sensors from various points. Outdoor testing at many buildings is capable of transmitting sensor data with a packet loss of 0% at a distance of fewer than 111 meters and the largest packet loss of 51.28% at a distance of 442 meters. © 2023 IEEE.

关键词： Internet of things

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Comparative Analysis of Word vs. Character Embedding for Machine Learning Based Detection of Malicious URLs and DGA-Generated Domains 15

Comparative Analysis of Word vs. Character Embedding for Mac...

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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Ayodele, Oludayo C. Yerima, Suleiman Y. School of Computer Science and Informatics De Montfort University Leicester United Kingdom British University in Dubai Faculty of Engineering and Information Technology Dubai United Arab Emirates

ISBN: (纸本)9798350324433

This study presents a comprehensive comparative analysis of the effectiveness of word-level and character-level embeddings in the context of machine learning-based detection of malicious URLs and DGA-generated domains. Utilizing distinct datasets comprising DGA-generated domains and Spam URLs, we systematically evaluate various machine learning models coupled with word-level and character-level tokenization techniques. Our findings indicate that character-level tokenization yields superior results in identifying DGA-generated domains, particularly due to the random character composition of these URLs. Conversely, both word-level and character-level embeddings exhibit comparable success rates in classifying Spam URLs, owing to the non-random nature of their URL structures. The study sheds light on the importance of tailoring tokenization strategies based on the unique characteristics of the data. We recommend character-level embeddings for detecting DGA-generated domains characterized by random characters. In contrast, the choice between word-level and character-level embeddings is less critical when dealing with Spam URLs, as both approaches yield effective results. © 2023 IEEE.

关键词： Embeddings

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A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors

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Annals of Data science 2023年第6期10卷 1607-1634页

作者： Islam, Uwaise Ibna Haque, Enamul Alsalman, Dheyaaldin Islam, Muhammad Nazrul Moni, Mohammad Ali Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh University of Waterloo WaterlooONN2L 3G1 Canada School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol that can ultimately lead a human to disastrous consequences. As patients with this behavior display a high value of relapse, the best intervention approach is to prevent it at the very beginning. In this paper, we propose a framework based on machine learning techniques to identify individual vulnerability towards substance abuse by analyzing socio-economic aspects. We have carefully assessed the commonly involved causes to form the questionnaire for collecting data from healthy people and patients suffering from substance abuse. Using Pearson’s chi-squared test of independence, feature importance is measured to eliminate less significant features using backward elimination. Popular machine learning classification algorithms (Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest neighbors, and Gaussian Naive Bayes) are used to build the predictive classifier. To identify the key risk-factors of individual substance abuse, we extract association rules from the significant features and subsequent factors. Experimental results on real data-set support the effectiveness of the proposed framework. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Predictive analytics

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