Article
Engineering, Multidisciplinary
Elsayed Badr, Sultan Almotairi, Mustafa Abdul Salam, Hagar Ahmed
Summary: The study presents a novel approach to improve breast cancer diagnosis accuracy, including enhancing support vector machine performance, introducing new scaling techniques, and utilizing parallel techniques to enhance efficiency.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Geosciences, Multidisciplinary
Peng He, Wenjing Wu
Summary: A novel prediction model, LGWO-SVR, is proposed for forecasting dam displacements, which combines support vector regression and Levy flight-based grey wolf optimizer. The model is validated with multiple-arch dam as a case study and compared with other algorithms. Results show that the LGWO-SVR model has high accuracy, stability, and prediction rate, making it suitable for dam engineering applications.
FRONTIERS IN EARTH SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhuo Wang, Pengjian Shang, Xuegeng Mao
Summary: In this paper, the cumulative residual Tsallis singular entropy (CRTSE) is introduced to measure the complex characteristics of nonlinear signals. The effectiveness and robustness of CRTSE are verified through simulation experiments. The proposed CRTSE-GWOSVM model based on grey wolf optimized support vector machine (GWOSVM) can effectively and accurately identify complex systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Construction & Building Technology
Hongfang Lu, Saleh Behbahani, Xin Ma, Tom Iseley
Summary: A hybrid model combining multi-objective grey wolf optimizer and support vector machine was proposed to predict composite material properties in six datasets. The results showed that the model performed well in property prediction, and the prediction accuracy was closely related to the amount of data in the training set.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Computer Science, Artificial Intelligence
Fereshteh Jeyafzam, Babak Vaziri, Mohsen Yaghoubi Suraki, Ali Asghar Rahmani Hosseinabadi, Adam Slowik
Summary: In medical science, collecting and classifying data from various diseases pose challenges, especially in diagnosing conditions like diabetes which can lead to severe complications. Machine learning methods, such as support vector machine, can be used to predict diabetes complications by adjusting parameters through optimization algorithms.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Meng Liu, Kaiping Luo, Junhuan Zhang, Shengli Chen
Summary: The study shows that the hybrid algorithm combining grey wolf optimizer and support vector machine can help achieve stable excess returns, improve the predictive performance of the support vector regression machine, and achieve better profitability and reliability in the Chinese A-share market.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Haiqing Yang, Zhihui Wang, Kanglei Song
Summary: A new hybrid intelligence technique was introduced to predict the performance of the full-face tunnel boring machine (TBM). By measuring and considering the important parameters, a predictive model was established and evaluated. The results showed that the model had high accuracy in predicting the TBM performance.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Interdisciplinary Applications
Jian Zhou, Shuai Huang, Mingzheng Wang, Yingui Qiu
Summary: This study proposes two support vector machine models for predicting soil liquefaction potential, optimized by genetic algorithm and grey wolf optimizer. The results show that the GWO-SVM model achieved the highest classification accuracy on three data sets and outperformed the GA-SVM model.
ENGINEERING WITH COMPUTERS
(2022)
Article
Social Sciences, Interdisciplinary
Xin Ma, Yanqiao Deng, Hong Yuan
Summary: Natural gas is crucial in China's energy system reconstruction, and accurate forecasting of its supply and consumption indicators is important for decision-making by the government and energy companies. This study proposes a Grey Wavelet Support Vector Regressor model that combines the grey system model and support vector regression model for handling the complex features of Chinese natural gas datasets. The proposed model outperforms other models in out-of-sample forecasting, demonstrating its high potential in predicting natural gas supply and consumption in China.
Article
Acoustics
Ismail Shahin, Osama Ahmad Alomari, Ali Bou Nassif, Imad Afyouni, Ibrahim Abaker Hashem, Ashraf Elnagar
Summary: Nowadays, the analysis and interpretation of emotions in human speech communication have gained significant attention in the field of human-computer interaction. Various speech recognition systems have been proposed to recognize the emotional states of speakers through their speech recordings. Feature extraction is a critical step in building an emotion recognition system, but not all extracted features are relevant for classifying emotions accurately. This study introduces an intelligent feature selection method called GWO-KNN, which uses a bio-inspired optimization algorithm and a K-nearest neighbor classifier to enhance the classification performance of emotion recognition systems by identifying the most relevant subset of features. The proposed method outperforms classical methods and recent state-of-the-art approaches on three different databases.
Article
Energy & Fuels
Shuang Li, Kun Xu, Guangzhe Xue, Jiao Liu, Zhengquan Xu
Summary: An improved grey wolf optimized support vector regression model for predicting coal spontaneous combustion temperature is proposed in this study, considering the characteristics of prediction data samples and the timeliness of applicable models. The effectiveness of the improved grey wolf optimizer algorithm is verified by numerical experiments, showing stronger global search ability, faster convergence speed, and better stability. The proposed prediction model has significant advantages in accuracy and stability, providing better decision reference for predicting and warning coal spontaneous combustion fires in coal mines.
Article
Engineering, Chemical
Hossein Mashhadimoslem, Vahid Kermani, Kourosh Zanganeh, Ahmed Shafeen, Ali Elkamel
Summary: This study proposes a new optimizer linked to the machine learning (ML) model to predict the adsorption performance in different adsorbents. It aims to provide a unified framework for predicting adsorption phenomena under different process conditions. The ML approach linked to the grey wolf optimizer algorithm (GWO) is used to predict the adsorbed amount of O-2 and N-2 on carbon-based adsorbents.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaobing Yu, WangYing Xu, ChenLiang Li
Summary: Grey wolf optimizer is a novel swarm intelligent algorithm with superior optimization capacity. However, it is easy to trap into local optimum when solving complex and multimodal functions. The proposed opposition-based learning grey wolf optimizer incorporates a jumping rate to help the algorithm jump out of local optimum, and dynamically adjusts the coefficient to balance exploration and exploitation.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Environmental Sciences
Li Li, Zhongxu Zhang, Dongsheng Zhao, Yue Qiang, Bo Ni, Hengbin Wu, Shengchao Hu, Hanjie Lin
Summary: This study proposes an improved prediction model to estimate the scale of debris flows, and validates its effectiveness using data from Beichuan County. The results demonstrate strong predictive capabilities and improved accuracy in predicting the scale of debris flows. Additionally, the study identifies key factors that influence the scale of debris flows in Beichuan County.
Article
Computer Science, Artificial Intelligence
Kemal Tutuncu, Mehmet Akif Sahman, Ekrem Tusat
Summary: Modeling and optimization based on natural phenomena and physical earth observations are crucial, with the integration of machine learning and metaheuristic algorithms showing promising results in determining the most suitable local geoid model with fewer reference points. Among the eight hybrid approaches tested in the study, the combination of GWO and ELM outperformed others in achieving better results with a reduced number of reference points.
APPLIED SOFT COMPUTING
(2021)
Article
Metallurgy & Metallurgical Engineering
Agus Dwi Anggono, Marischa Elveny, Walid Kamal Abdelbasset, Aleksandr Mikhailovich Petrov, Kirill Aleksandrovich Ershov, Yu Zhu, Akhat Yunusov, Supat Chupradit, Yasser Fakri Mustafa, Aravindhan Surendar
Summary: Using nanoindentation technique, the creep deformation behavior of Zr55Co25Al15Ni5 bulk metallic glass was studied. The Maxwell-Voigt model was applied to describe the deformation and relaxation kinetics near the glass transition. The study found that at higher temperatures and loading rates, the serrated behavior indicating shear events disappeared, and creep deformation could be divided into two distinct characteristic relaxation times. Creep deformation at higher temperatures tends to have higher relaxation times corresponding to the viscoplastic behavior of the material.
TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS
(2022)
Article
Green & Sustainable Science & Technology
Xuesong Zhang, Farag M. A. Altalbawy, Tahani A. S. Gasmalla, Ali Hussein Demin Al-Khafaji, Amin Iraji, Rahmad B. Y. Syah, Moncef L. Nehdi
Summary: This research compared various machine learning models to forecast the uniaxial compressive strength (UCS) of rocks. The support vector machine with radial basis function outperformed all other methods and achieved high accuracy (R-2 = 0.99, PI = 1.92). The models showed excellent accuracy (R-2 > 90%) in estimating UCS, with a small average difference of +0.28% compared to the measured values.
Article
Green & Sustainable Science & Technology
Habib Satria, Rahmad B. Y. Syah, Moncef L. Nehdi, Monjee K. Almustafa, Abdelrahman Omer Idris Adam
Summary: This article proposes an effective evolutionary hybrid optimization method, CNGPS, based on the northern goshawk optimization algorithm (NGO) and pattern search (PS), for identifying unknown parameters in photovoltaic (PV) models. The effectiveness of the CNGPS algorithm is verified through mathematical test functions and compared with conventional NGO and other optimization methods. The CNGPS algorithm demonstrates better performance and lower error in parameter extraction for PV models.
Article
Computer Science, Information Systems
Sutrisno Sutrisno, Nurul Khairina, Rahmad B. Y. Syah, Ehsan Eftekhari-Zadeh, Saba Amiri
Summary: Despite the impact of the Coronavirus pandemic on people's physical and psychological well-being, it has also affected the psychological conditions of many employees, particularly in organizations and privately owned businesses facing pandemic-related restrictions. This study aimed to analyze the relationship between demographic variables, resilience, Coronavirus, and burnout in start-ups using an RBF neural network. The study employed a quantitative research method with a sample population of start-up managers and employees. Standard surveys and specially designed questionnaires were used to collect data, and their validity and reliability were confirmed. The designed network structure had ten neurons in the input layer, forty neurons in the hidden layer, and one neuron in the output layer. The training and test data were divided into 70% and 30% respectively. The results showed that the designed network was able to accurately classify all the data, and the method presented in this research can greatly contribute to the sustainability of companies.
Article
Thermodynamics
Gholamreza Boroumandfar, Alimorad Khajehzadeh, Mahdiyeh Eslami, Rahmad B. Y. Syah
Summary: In this paper, robust planning of a hybrid photovoltaic/wind/battery storage system in the distribution network is performed. The study aims to minimize power losses cost and purchasing power cost from both the hybrid system and upstream network, by considering uncertainties in network demand and renewable generation. The proposed methodology, based on information gap decision theory, uses the flow direction algorithm to determine the optimal installation location and capacity of the hybrid system components, as well as the uncertainty radius of the uncertain parameters. The results show that the planning approach significantly reduces system costs and provides a robust hybrid system against forecasting errors caused by uncertainties.
Article
Computer Science, Theory & Methods
Marischa Elveny, Mahyuddin K. M. Nasution, Rahmad B. Y. Syah
Summary: Accurate and efficient business analytical predictions are crucial for decision making in today's competitive landscape. By using data analysis, statistical methods, and predictive modeling, businesses can extract insights and make informed decisions. Optimizing business analytics predictions can lead to improved operations, reduced costs, and increased profits.
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Rahmad B. Y. Syah, Aryan Veisi, Zainal Arifin Hasibuan, Mustafa A. Al-Fayoumi, Mohammad Sh. Daoud, Ehsan Eftekhari-Zadeh
Summary: Accurately determining phase fractions in two-phase flows is crucial in industries related to petroleum and petrochemical production and processing. Among various sensor types, the capacitance-based sensor is recognized as one of the most precise and widely used. This study utilized COMSOL Multiphysics software to simulate and compare different electrode configurations for measuring oil-air two-phase flow in an annular pattern. Results demonstrated that the proposed arrow-shaped capacitance-based sensor had 21% higher sensitivity compared to existing sensor designs, indicating its superior performance and potential for high-sensitivity applications.
Proceedings Paper
Computer Science, Artificial Intelligence
Mahyuddin K. M. Nasution, Raditya Macy Widyatamaka Nasution, Rahmad Syah, Marischa Elveny
Summary: This paper describes the human effort to address the challenges in scientific development. The limitations of biology have led to collaboration with other fields, particularly technology, resulting in the emergence of biotechnology. Another technology, computer science, is also relevant, especially in the field of data science. These fields have the potential to drive scientific and efficient studies in biotechnology, although the business sector is still in its early stages.
DATA SCIENCE AND ALGORITHMS IN SYSTEMS, 2022, VOL 2
(2023)
Article
Computer Science, Theory & Methods
Rizki Muliono, Mayang Septania Iranita, Rahmad B. Y. Syah
Summary: This study categorizes different types of Batak ulos cloth using Convolutional Neural Network (CNN) and Modular Neural Network (MNN) methods for image recognition and classification. 80% of the data was used for training, 20% for testing. The achieved accuracy is 97.83%, loss value is 0.0793, val loss is 2.1885, and val accuracy is 0.7429.
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2023)
Correction
Energy & Fuels
Rahmad Syah, John William Grimaldo Guerrero, Andrey Leonidovich Poltarykhin, Wanich Suksatan, Surendar Aravindhan, Dmitry O. Bokov, Walid Kamal Abdelbasset, Samaher Al-Janabi, Ayad F. Alkaim, Dmitriy Yu. Tumanov
Article
Construction & Building Technology
Xinzhe Yuan, Mohammad Ali Karbasforoushha, Rahmad B. Y. Syah, Mohammad Khajehzadeh, Suraparb Keawsawasvong, Moncef L. L. Nehdi
Summary: Mathematical optimization is applied to minimize energy usage in the design of low-energy buildings. A hybrid technique, called POSCO, combining the pelican optimization algorithm (POA) and the single candidate optimizer (SCO), is proposed for building energy optimization challenges. POSCO benefits from both the local search power of SCO and the global search capabilities of POA. The effectiveness of POSCO is verified through mathematical test functions and it outperforms conventional POA and other optimization techniques in finding the global solution for various test functions.
Article
Computer Science, Theory & Methods
Muhammad Khahfi Zuhanda, Noriszura Ismail, Rezzy Eko Caraka, Rahmad Syah, Prana Ugiana Gio
Summary: This study analyzes the Traveling Salesman Problem in Medan City, Indonesia, using four heuristic algorithms and finds that hybrid methods show promise in generating superior solutions.
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Zulhery Noer, Marischa Elveny, Abduladheem Turki Jalil, A. Heri Iswanto, Samaher Al-Janabi, Ayad F. Alkaim, Gulnara Mullagulova, Natalia Nikolaeva, Rustem Adamovich Shichiyakh
Summary: This research investigates the scheduling problem for harvesting agricultural products, aiming to minimize the maximum completion time of agricultural land. The results show that the proposed mathematical model is only capable of solving small and medium-sized problems.
FOUNDATIONS OF COMPUTING AND DECISION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Rahmad Syah, Marischa Elveny, Enni Soerjati, John William Grimaldo Guerrero, Rawya Read Jowad, Wanich Suksatan, Surendar Aravindhan, Olga Yuryevna Voronkova, Dinesh Mavaluru
Summary: A location-allocation problem model is proposed in this paper to reduce waiting time and unemployment probability. The accurate solution of the epsilon constraint method is used for solving, and sensitivity analysis is performed.
FOUNDATIONS OF COMPUTING AND DECISION SCIENCES
(2022)
