Article
Computer Science, Artificial Intelligence
M. Tanveer, A. Tiwari, R. Choudhary, M. A. Ganaie
Summary: This study proposes a novel large scale pinball twin support vector machine (LPTWSVM) to address the limitations of the twin support vector machines (TWSVMs), using a unique pinball loss function and improving model performance by eliminating matrix inversion calculation and minimizing structural risk.
Article
Computer Science, Interdisciplinary Applications
Chuanhua Xu, Menad Nait Amar, Mohammed Abdelfetah Ghriga, Hocine Ouaer, Xiliang Zhang, Mahdi Hasanipanah
Summary: This study presents an evolving support vector regression (SVR) model using Grey Wolf optimization (GWO) for predicting the shear strength (SS) and uniaxial compressive strength (UCS) of rocks. The experimental results show that the proposed SVR-GWO model outperforms other models in the prediction of these parameters.
ENGINEERING WITH COMPUTERS
(2022)
Article
Thermodynamics
Mingqiang Lin, Chenhao Yan, Jinhao Meng, Wei Wang, Ji Wu
Summary: Accurate state of health estimation is crucial for lithium-ion batteries management. This paper proposes a novel method using simulated annealing algorithm and support vector regression, which extracts health factors from DTC curves and constructs a model to estimate SOH with optimized hyperparameters. Experimental results show the superiority of the proposed method in accuracy and real-time performance compared to other models.
Article
Engineering, Civil
S. Saravanan, K. Gajalakshmi
Summary: This study applies various soft computing techniques to analyze and predict the strength of aluminum-stainless steel explosive clads. Experimental data is used to train models, resulting in accurate predictions.
ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Huajun Wang, Yu-Ting Zhao, Naihua Xiu, Nai-Yang Deng
Summary: This paper investigates the general forms and characteristics of nonparallel support vector machines (NSVMs) and categorizes them into two types. It reveals the advantages and defects of different types and points out the inconsistency problems. Based on this observation, a novel max-min distance-based NSVM is proposed with desired consistency. The proposed NSVM has the consistency of training and test and the consistency of metric, and it assigns each sample an ascertained loss.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoming Wang, Shitong Wang, Zengxi Huang, Yajun Du
Summary: This paper introduces a novel method called sparse support vector machine guided by radius-margin bound (RMB-SSVM) to efficiently condense the basis vectors in support vector machines. By selecting basis vectors and learning corresponding coefficients with a criterion related to SVM's generalization ability, the RMB-SSVM model can yield better performance.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Sambhav Jain, Reshma Rastogi
Summary: This paper proposes Parametric non-parallel support vector machines for binary pattern classification. The model brings noise resilience and sparsity by intelligently redesigning the Support vector machine optimization. The experimental results validate its scalability for large scale problems.
Article
Computer Science, Information Systems
Sebastian Maldonado, Julio Lopez, Carla Vairetti
Summary: The predictive performance of classification methods relies heavily on the nature of the environment and dataset shift issue. A novel Fuzzy Support Vector Machine strategy is proposed in this paper to improve performance by redefining the loss function and applying aggregation operators to deal with dataset shift. Our methods outperform traditional classifiers in terms of out-of-time prediction using simulated and real-world dataset for credit scoring.
INFORMATION SCIENCES
(2021)
Article
Construction & Building Technology
Linlin Zhou, Deju Zhu, Md Zillur Rahman, Shuaicheng Guo, Wenbo Ma, Guangyan Feng, Yong Yi, Caijun Shi
Summary: This study evaluates the influences of coral coarse aggregate (CCA) and cement contents on the durability performance of basalt fiber-reinforced polymer (BFRP) bars embedded in seawater sea sand coral aggregate concrete (SSCC). The results show that the use of CCA leads to higher permeability of SSCC, causing lower interlaminar shear strength of BFRP bars compared to those embedded in seawater sea sand concrete (SSC). Increasing cement content accelerates the deterioration of BFRP bars. The findings provide valuable insights for the application of BFRP bars in marine environments.
CONSTRUCTION AND BUILDING MATERIALS
(2023)
Article
Computer Science, Information Systems
Esra Celik, Deniz Dal
Summary: This study introduces a simulated annealing-based metaheuristic for cluster-based task scheduling, implemented in both serial and parallel versions in C++. The effectiveness of the method is demonstrated through twelve benchmarks from the Braun dataset, with both versions outperforming the best latency values reported in the literature within 90 seconds. Various techniques and considerations, such as random number generation, data structures, and compiler effects, are analyzed to improve the quality of scheduling solutions and decrease program execution time.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Mahmud Esad Yigit, Gulay Oke Gunel, Mustafa Emre Aydemir, Tayfun Gunel
Summary: This paper uses Soft Computing techniques to shorten the design time of antenna structures and achieve a desired performance. A Support Vector Machine regression model is employed to design a microstrip antenna with targeted characteristics. Comparisons with literature and an optimization tool verify the accuracy and efficiency of this approach.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Civil
Ilyas Saribas, Caglar Goksu, Ergun Binbir, Alper Ilki
Summary: This paper experimentally investigated the shear-flexure interaction on seismic performance of reinforced concrete columns constructed with recycled aggregate concrete, finding that shear deformations have a detrimental effect on seismic performance of columns. However, columns made of natural or recycled aggregate concrete exhibited similar seismic performances under different interactions of shear and flexure, with reduced deformation capability observed when the ratio of transverse reinforcement is decreased. The study also discussed the possibility of reducing the required ratio of minimum transverse reinforcement per technical documents for columns located in low to moderate seismicity regions.
ENGINEERING STRUCTURES
(2021)
Article
Green & Sustainable Science & Technology
Ehsan Momeni, Fereydoon Omidinasab, Ahmad Dalvand, Vahid Goodarzimehr, Abas Eskandari
Summary: This study aimed to develop an intelligent model for predicting the flexural strength of recycled reinforced concrete (RRC) beams. The results showed the feasibility of the PSO-ANN and ICA-ANN models in evaluating the flexural performance of RRC beams, with the PSO-ANN model outperforming the other models in terms of prediction accuracy.
Article
Computer Science, Artificial Intelligence
Fadime Demirtas, Erkan Tanyildizi
Summary: In recent years, the positive effect of quantum techniques on machine learning methods, especially in dealing with big data, has been studied. This study applied Quantum Support Vector Machine steps to the breast cancer dataset, examined different feature maps and hyper-parameter tuning methods, and compared the performances and running costs of Linear, Non-linear, and Quantum support vector machines.
ADVANCES IN ELECTRICAL AND COMPUTER ENGINEERING
(2022)
Article
Computer Science, Information Systems
Hasitha Muthumala Waidyasooriya, Masanori Hariyama
Summary: Quantum annealing is a method to solve combinatorial optimization problems by utilizing quantum fluctuations, but the processing time increases exponentially with the number of variables. This article introduces a highly-parallel accelerator implemented on FPGA, which achieves significant speed-up compared to single-core CPU implementation.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2021)
Article
Agronomy
Isa Esfandiarpour-Borujeni, Seyed Javad Hosseinifard, Hossein Shirani, Maryam Zeinadini, Ali Asghar Besalatpour
COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS
(2018)
Article
Meteorology & Atmospheric Sciences
Iman Fazeli Farsani, M. R. Farzaneh, A. A. Besalatpour, M. H. Salehi, M. Faramarzi
THEORETICAL AND APPLIED CLIMATOLOGY
(2019)
Article
Environmental Sciences
Yousef Hassanzadeh, Amirhosein Aghakhani Afshar, Mohsen Pourreza-Bilondi, Hadi Memarian, Ali Asghar Besalatpour
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2019)
Article
Agronomy
Zanyar Feizi, Shamsollah Ayoubi, Mohammad Reza Mosaddeghi, Ali Asghar Besalatpour, Mojtaba Zeraatpisheh, Jesus Rodrigo-Comino
ARCHIVES OF AGRONOMY AND SOIL SCIENCE
(2019)
Article
Meteorology & Atmospheric Sciences
Bahareh Aghasi, Ahmad Jalalian, Hossein Khademi, Ali Asghar Besalatpour
Article
Soil Science
Mohammad Hossein Hemmat Jou, Davood Namdar Khojasteh, Ali Asghar Besalatpour
CANADIAN JOURNAL OF SOIL SCIENCE
(2019)
Article
Agronomy
Fatemeh Hojjatnooghi, Hossein Shirani, Ebrahim Pazira, Ali Asghar Besalatpour, Ali Mohammadi Torkashvand
COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS
(2019)
Article
Geosciences, Multidisciplinary
I Kouchami-Sardoo, H. Shirani, I Esfandiarpour-Boroujeni, A. A. Besalatpour, M. A. Hajabbasi
Article
Environmental Sciences
I. Kouchami Sardo, A. A. Besalatpour, H. Bashari, H. Shirani, O. Yildiz
LAND DEGRADATION & DEVELOPMENT
(2020)
Article
Environmental Sciences
Sajjad Hazrati, Mohsen Farahbakhsh, Ghasem Heydarpoor, Ali Asghar Besalatpour
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
(2020)
Article
Agronomy
Shamsollah Ayoubi, Ameneh Mohammadi, Mohammad Reza Abdi, Farideh Abbaszadeh Afshar, Lin Wang, Mojtaba Zeraatpisheh
Summary: This study examined soil redistribution and soil quality changes induced by land degradation and orchard plantation in a semi-arid region in central Iran. The results showed that converting abandoned drylands to apple orchards improved soil quality and reduced soil loss.
Article
Chemistry, Analytical
Sanaz Saidi, Shamsollah Ayoubi, Mehran Shirvani, Kamran Azizi, Mojtaba Zeraatpisheh
Summary: This study aimed to predict the cation exchange capacity (CEC) of soil in the west of Iran by combining topographic features, remote sensing data, and other environmental variables using machine learning models. Soil samples were collected and analyzed in the laboratory, with clay types identified as the main factor affecting CEC. Random forest (RF) was identified as the best model for predicting CEC in the training dataset, while the Cubist model (Cu) performed well in the validation dataset. The RF model was then used to generate a CEC map, showing the spatial distribution of CEC and identifying important variables influencing its variability in the study area.
Article
Geochemistry & Geophysics
Shamsollah Ayoubi, Anashia Milikian, Mohammad Reza Mosaddeghi, Mojtaba Zeraatpisheh, Shuai Zhao
Summary: Soil characteristics, especially clay content and clay type, have significant impacts on splash erosion. In this study, splash erosion decreased and shear strength increased with increased clay content.
Article
Environmental Sciences
Salman Naimi, Shamsollah Ayoubi, Mojtaba Zeraatpisheh, Jose Alexandre Melo Dematte
Summary: This study utilized machine learning algorithms combined with multiple data sources to predict soil salinity, achieving high accuracy in spatial prediction.
Proceedings Paper
Agriculture, Multidisciplinary
Neda Abbasi, Christian Opp, Lars Ribbe, Oscar Manuel Baez-Villanueva, Ali Asghar Besalatpour
2020 MEDITERRANEAN AND MIDDLE-EAST GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (M2GARSS)
(2020)