Pub Date : 2023-12-20DOI: 10.13164/mendel.2023.2.255
Mahamed G. H. Omran, Andries Engelbrecht
This paper is a brief guide aimed at evaluating the time complexity of metaheuristic algorithms both mathematically and empirically. Starting with the mathematical foundational principles of time complexity analysis, key notations and fundamental concepts necessary for computing the time efficiency of a metaheuristic are introduced. The paper then applies these principles on three well-known metaheuristics, i.e. differential evolution, harmony search and the firefly algorithm. A procedure for the empirical analysis of metaheuristics' time efficiency is then presented. The procedure is then used to empirically analyze the computational cost of the three aforementioned metaheuristics. The pros and cons of the two approaches, i.e. mathematical and empirical analysis, are discussed.
{"title":"Time Complexity of Population-Based Metaheuristics","authors":"Mahamed G. H. Omran, Andries Engelbrecht","doi":"10.13164/mendel.2023.2.255","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.255","url":null,"abstract":"This paper is a brief guide aimed at evaluating the time complexity of metaheuristic algorithms both mathematically and empirically. Starting with the mathematical foundational principles of time complexity analysis, key notations and fundamental concepts necessary for computing the time efficiency of a metaheuristic are introduced. The paper then applies these principles on three well-known metaheuristics, i.e. differential evolution, harmony search and the firefly algorithm. A procedure for the empirical analysis of metaheuristics' time efficiency is then presented. The procedure is then used to empirically analyze the computational cost of the three aforementioned metaheuristics. The pros and cons of the two approaches, i.e. mathematical and empirical analysis, are discussed.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138994499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-20DOI: 10.13164/mendel.2023.2.202
Rim Amami, Rim Amami, Chiraz Trabelsi, Sherin Hassan Mabrouk, Hassan A. Khalil
Voice recognition systems have become increasingly important in recent years due to the growing need for more efficient and intuitive human-machine interfaces. The use of Hybrid LSTM networks and deep learning has been very successful in improving speech detection systems. The aim of this paper is to develop a novel approach for the detection of voice pathologies using a hybrid deep learning model that combines the Bidirectional Long Short-Term Memory (BiLSTM) and the Convolutional Neural Network (CNN) architectures. The proposed model uses a combination of temporal and spectral features extracted from speech signals to detect the different types of voice pathologies. The performance of the proposed detection model is evaluated on a publicly available dataset of speech signals from individuals with various voice pathologies(MEEI database). The experimental results showed that the hybrid BiLSTM-CNN model outperforms several classifiers by achieving an accuracy of 98.86%. The proposed model has the potential to assist health care professionals in the accurate diagnosis and treatment of voice pathologies, and improving the quality of life for affected individuals.
{"title":"A Robust Voice Pathology Detection System Based on the Combined BiLSTM–CNN Architecture","authors":"Rim Amami, Rim Amami, Chiraz Trabelsi, Sherin Hassan Mabrouk, Hassan A. Khalil","doi":"10.13164/mendel.2023.2.202","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.202","url":null,"abstract":"Voice recognition systems have become increasingly important in recent years due to the growing need for more efficient and intuitive human-machine interfaces. The use of Hybrid LSTM networks and deep learning has been very successful in improving speech detection systems. The aim of this paper is to develop a novel approach for the detection of voice pathologies using a hybrid deep learning model that combines the Bidirectional Long Short-Term Memory (BiLSTM) and the Convolutional Neural Network (CNN) architectures. The proposed model uses a combination of temporal and spectral features extracted from speech signals to detect the different types of voice pathologies. The performance of the proposed detection model is evaluated on a publicly available dataset of speech signals from individuals with various voice pathologies(MEEI database). The experimental results showed that the hybrid BiLSTM-CNN model outperforms several classifiers by achieving an accuracy of 98.86%. The proposed model has the potential to assist health care professionals in the accurate diagnosis and treatment of voice pathologies, and improving the quality of life for affected individuals.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"125 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138994537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-07DOI: 10.13164/mendel.2023.2.245
Trang Hoang, Bao Quoc Bui, Hoang Trong Nguyen, Phuc That Bao Ton
The proposed genetic algorithm (GA) and particle swarm optimization (PSO) applied for the optimal design of a one-stage operational amplifier circuit with a current mirror load are studied in this work. The sizes of transistors are optimized using the proposed GA and PSO for improved areas and performance parameters of the circuit. A number of performance parameters are collected from the data set created by GA and PSO to optimize the size of transistors and other design parameters. The Spectre simulator is chosen for the simulation of circuit parameters to obtain necessary for the GA and PSO algorithm. Post-optimization results justify that the proposed GA and PSO methods are competitive with differential evolution regarding convergence speed, design specifications, and the optimal CMOS one-stage operational amplifier circuit parameters.
本文研究了应用遗传算法（GA）和粒子群优化（PSO）对带电流镜负载的单级运算放大器电路进行优化设计的问题。利用所提出的 GA 和 PSO 优化了晶体管的尺寸，以改善电路的面积和性能参数。从 GA 和 PSO 创建的数据集中收集了大量性能参数，以优化晶体管的尺寸和其他设计参数。选择 Spectre 仿真器对电路参数进行仿真，以获得 GA 和 PSO 算法所需的参数。优化后的结果证明，在收敛速度、设计规格和最佳 CMOS 单级运算放大器电路参数方面，所提出的 GA 和 PSO 方法与差分进化法相比具有竞争力。
{"title":"Evolutionary Optimization Techniques in Analog Integrated Circuit Designs","authors":"Trang Hoang, Bao Quoc Bui, Hoang Trong Nguyen, Phuc That Bao Ton","doi":"10.13164/mendel.2023.2.245","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.245","url":null,"abstract":"The proposed genetic algorithm (GA) and particle swarm optimization (PSO) applied for the optimal design of a one-stage operational amplifier circuit with a current mirror load are studied in this work. The sizes of transistors are optimized using the proposed GA and PSO for improved areas and performance parameters of the circuit. A number of performance parameters are collected from the data set created by GA and PSO to optimize the size of transistors and other design parameters. The Spectre simulator is chosen for the simulation of circuit parameters to obtain necessary for the GA and PSO algorithm. Post-optimization results justify that the proposed GA and PSO methods are competitive with differential evolution regarding convergence speed, design specifications, and the optimal CMOS one-stage operational amplifier circuit parameters.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"23 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138983707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.13164/mendel.2023.1.045
P. Bujok, M. Lacko, Patrik Kolenovsky
In this paper, the performance of the Differential Evolution algorithm is evaluated when solving real-world problems. A Set of 13 engineering optimisation problems was selected from the fields of mechanics and industry to illustrate the usability of the Differential Evolution algorithm. Twelve variants of the standard Differential Evolution with various settings of the control parameters are compared with 19 state-of-the-art adaptive variants of this algorithm. The results are analysed statistically to achieve significant differences. Three variants of adaptive Differential Evolution provided better results compared to other algorithms. Some adaptive variants of Differential Evolution perform significantly worse than the original Differential Evolution with the fixed setting of the control parameters.
{"title":"Differential Evolution and Engineering Problems","authors":"P. Bujok, M. Lacko, Patrik Kolenovsky","doi":"10.13164/mendel.2023.1.045","DOIUrl":"https://doi.org/10.13164/mendel.2023.1.045","url":null,"abstract":"In this paper, the performance of the Differential Evolution algorithm is evaluated when solving real-world problems. A Set of 13 engineering optimisation problems was selected from the fields of mechanics and industry to illustrate the usability of the Differential Evolution algorithm. Twelve variants of the standard Differential Evolution with various settings of the control parameters are compared with 19 state-of-the-art adaptive variants of this algorithm. The results are analysed statistically to achieve significant differences. Three variants of adaptive Differential Evolution provided better results compared to other algorithms. Some adaptive variants of Differential Evolution perform significantly worse than the original Differential Evolution with the fixed setting of the control parameters.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"19 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72615393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.13164/mendel.2023.1.025
Kurt Pace Debono, Maria Kontorinaki, Monique Sciortino
In light of the recent severe Supply Chain (SC) disruptions that have occurred across multiple industries around the globe, three essential and linked themes have emerged in SC management: the well-being of employees, SC sustainability, and competition between SCs for limited resources. In this paper, we create a game-theoretic SC network model that incorporates together non-cooperative SC competition, employee productivity and engagement, and green investing. Each competing firm within the network seeks to maximise its profit by determining an optimal flow of products and allocation of green investments across the SC according to a predetermined budget. A carbon tax on emissions and consumer sustainability preferences are also included in the model. The model is solved using a Variational Inequality reformulation. The illustrative numerical examples presented in this paper have been inspired by the Maltese dairy industry and demonstrate the applicability of the model to real-world problems. The results highlight the significance of the employee engagement factor in enabling firms to adopt and realise more sustainable SC practices.
{"title":"A Game Theoretic Competitive Supply Chain Network Model with Green Investments and Labour","authors":"Kurt Pace Debono, Maria Kontorinaki, Monique Sciortino","doi":"10.13164/mendel.2023.1.025","DOIUrl":"https://doi.org/10.13164/mendel.2023.1.025","url":null,"abstract":"In light of the recent severe Supply Chain (SC) disruptions that have occurred across multiple industries around the globe, three essential and linked themes have emerged in SC management: the well-being of employees, SC sustainability, and competition between SCs for limited resources. In this paper, we create a game-theoretic SC network model that incorporates together non-cooperative SC competition, employee productivity and engagement, and green investing. Each competing firm within the network seeks to maximise its profit by determining an optimal flow of products and allocation of green investments across the SC according to a predetermined budget. A carbon tax on emissions and consumer sustainability preferences are also included in the model. The model is solved using a Variational Inequality reformulation. The illustrative numerical examples presented in this paper have been inspired by the Maltese dairy industry and demonstrate the applicability of the model to real-world problems. The results highlight the significance of the employee engagement factor in enabling firms to adopt and realise more sustainable SC practices. \u0000 ","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83713249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.13164/mendel.2023.1.062
Tran Thi Thanh Thuy, L. D. Thuan, Nguyen Hong Duc, H. T. Minh
Current security challenges are made more difficult by the complexity and difficulty of spotting cyberattacks due to the Internet of Things explosive growth in connected devices and apps. Therefore, various sophisticated attack detection techniques have been created to address these issues in recent years. Due to their effectiveness and scalability, machine learning-based intrusion detection systems (IDSs) have increased. However, several factors, such as the characteristics of the training dataset and the training model, affect how well these AI-based systems identify attacks. In particular, the heuristic algorithms (GA, PSO, CSO, FA) optimized by the logistic regression (LR) approach employ it to pick critical features of a dataset and deal with data imbalance problems. This study offers an intrusion detection system (IDS) based on a deep neural network and heuristic algorithms combined with LR to boost the accuracy of attack detections. Our proposed model has a high attack detection rate of up to 99% when testing on the IoT-23 dataset.
{"title":"A Study on Heuristic Algorithms Combined With LR on a DNN-Based IDS Model to Detect IoT Attacks","authors":"Tran Thi Thanh Thuy, L. D. Thuan, Nguyen Hong Duc, H. T. Minh","doi":"10.13164/mendel.2023.1.062","DOIUrl":"https://doi.org/10.13164/mendel.2023.1.062","url":null,"abstract":"Current security challenges are made more difficult by the complexity and difficulty of spotting cyberattacks due to the Internet of Things explosive growth in connected devices and apps. Therefore, various sophisticated attack detection techniques have been created to address these issues in recent years. Due to their effectiveness and scalability, machine learning-based intrusion detection systems (IDSs) have increased. However, several factors, such as the characteristics of the training dataset and the training model, affect how well these AI-based systems identify attacks. In particular, the heuristic algorithms (GA, PSO, CSO, FA) optimized by the logistic regression (LR) approach employ it to pick critical features of a dataset and deal with data imbalance problems. This study offers an intrusion detection system (IDS) based on a deep neural network and heuristic algorithms combined with LR to boost the accuracy of attack detections. Our proposed model has a high attack detection rate of up to 99% when testing on the IoT-23 dataset.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79575080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.13164/mendel.2023.1.015
Indra Rusyadi Adiwijaya, S. Indratno, M. Siallagan, Agus Widodo, Eka Gandara
The Indonesian government provides incentives to facilitate community development through various funding programs to improve the economy and restore the national economy. However, there were many obstacles in determining the proper target beneficiaries. This study aims to assist decision-makers in determining targeted and accountable beneficiary candidates. In this study, a hybrid Analytical Hierarchy Process (AHP) method with Simple Additive Weighting (SAW) was used and integrated with machine learning modeling using Logistic Regression (LR). The AHP approach is used to determine the weight of each criterion, and the SAW method is used to sort out each available alternative with the help of an expert team's assessment. Instead, the LR method is used for the predictive analysis and classification of the resulting data.
{"title":"Integration of the Hybrid Decision Support System and Machine Learning Algorithm to Determine Government Assistance Recipients: A Case Study in the Indonesian Funding Program","authors":"Indra Rusyadi Adiwijaya, S. Indratno, M. Siallagan, Agus Widodo, Eka Gandara","doi":"10.13164/mendel.2023.1.015","DOIUrl":"https://doi.org/10.13164/mendel.2023.1.015","url":null,"abstract":"The Indonesian government provides incentives to facilitate community development through various funding programs to improve the economy and restore the national economy. However, there were many obstacles in determining the proper target beneficiaries. This study aims to assist decision-makers in determining targeted and accountable beneficiary candidates. In this study, a hybrid Analytical Hierarchy Process (AHP) method with Simple Additive Weighting (SAW) was used and integrated with machine learning modeling using Logistic Regression (LR). The AHP approach is used to determine the weight of each criterion, and the SAW method is used to sort out each available alternative with the help of an expert team's assessment. Instead, the LR method is used for the predictive analysis and classification of the resulting data.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87546070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.13164/mendel.2023.1.007
Indalecio Mendoza Uribe
In this work, the supervised machine learning technique was applied to develop a predictive model of the phase of the El Niño-Southern Oscillation (ENSO) phenomenon. Regression trees were specifically used by means of the Scikit-Learn library of the Python programming language. Data from the period 1950-2022 were used as training and test. The performance of the predictive model was validated using three continuous type error measurement metrics: Mean Absolute Error, Maximum Error and Root Mean Square Root. The results indicate that with a greater number of training data the model improves its performance, with a tendency to decrease the error in forecasts. Which starts for the year 1953 with errors of 0.77, 1.41 and 0.75 for MAE, ME and RMSE respectively, ending for the year 2022 with errors of 0.28, 0.72 and 0.13 for the same metrics. It is concluded that, based on the results, the developed model is consistent and reliable for ENSO phase forecasts in a 12-month window.
{"title":"Predictive Model of the ENSO Phenomenon Based on Regression Trees","authors":"Indalecio Mendoza Uribe","doi":"10.13164/mendel.2023.1.007","DOIUrl":"https://doi.org/10.13164/mendel.2023.1.007","url":null,"abstract":"In this work, the supervised machine learning technique was applied to develop a predictive model of the phase of the El Niño-Southern Oscillation (ENSO) phenomenon. Regression trees were specifically used by means of the Scikit-Learn library of the Python programming language. Data from the period 1950-2022 were used as training and test. The performance of the predictive model was validated using three continuous type error measurement metrics: Mean Absolute Error, Maximum Error and Root Mean Square Root. The results indicate that with a greater number of training data the model improves its performance, with a tendency to decrease the error in forecasts. Which starts for the year 1953 with errors of 0.77, 1.41 and 0.75 for MAE, ME and RMSE respectively, ending for the year 2022 with errors of 0.28, 0.72 and 0.13 for the same metrics. It is concluded that, based on the results, the developed model is consistent and reliable for ENSO phase forecasts in a 12-month window.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"262 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83393349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.13164/mendel.2023.1.001
B. Fieri, Derwin Suhartono
Offensive language is one of the problems that have become increasingly severe along with the rise of the internet and social media usage. This language can be used to attack a person or specific groups. Automatic moderation, such as the usage of machine learning, can help detect and filter this particular language for someone who needs it. This study focuses on improving the performance of the soft voting classifier to detect offensive language by experimenting with the combinations of the soft voting estimators. The model was applied to a Twitter dataset that was augmented using several augmentation techniques. The features were extracted using Term Frequency-Inverse Document Frequency, sentiment analysis, and GloVe embedding. In this study, there were two types of soft voting models: machine learning-based, with the estimators of Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, and AdaBoost as the best combination, and deep learning-based, with the best estimator combination of Convolutional Neural Network, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The results of this study show that the soft voting classifier was better in performance compared to classic machine learning and deep learning models on both original and augmented datasets.
{"title":"Offensive Language Detection Using Soft Voting Ensemble Model","authors":"B. Fieri, Derwin Suhartono","doi":"10.13164/mendel.2023.1.001","DOIUrl":"https://doi.org/10.13164/mendel.2023.1.001","url":null,"abstract":"Offensive language is one of the problems that have become increasingly severe along with the rise of the internet and social media usage. This language can be used to attack a person or specific groups. Automatic moderation, such as the usage of machine learning, can help detect and filter this particular language for someone who needs it. This study focuses on improving the performance of the soft voting classifier to detect offensive language by experimenting with the combinations of the soft voting estimators. The model was applied to a Twitter dataset that was augmented using several augmentation techniques. The features were extracted using Term Frequency-Inverse Document Frequency, sentiment analysis, and GloVe embedding. In this study, there were two types of soft voting models: machine learning-based, with the estimators of Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, and AdaBoost as the best combination, and deep learning-based, with the best estimator combination of Convolutional Neural Network, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The results of this study show that the soft voting classifier was better in performance compared to classic machine learning and deep learning models on both original and augmented datasets.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87933557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.13164/mendel.2023.1.037
Jefita Resti Sari, Atina Ahdika
Badminton is one of the most popular sports in the world, especially in Asia. It has a parent organization called Badminton World Federation (BWF). Discussion about player strategies in winning various championships held by BWF is an interesting topic to discuss. This paper aims to analyze the hitting patterns of badminton players by paying attention to the sequence of types of strokes made by the players, including lobs, netting, smashes, drives, and dropshots. Sequential pattern discovery using the equivalent class algorithm (SPADE) is the appropriate method to identify these problems because it can determine the rules and probabilities of player's hitting patterns based on the order of the types of strokes. In this paper, we analyze the stroke pattern of the two top-ranked badminton players in the men's singles sector at the Malaysia Open 2022 championship, where Viktor Axelsen and Kento Momota met in the final. Based on the results of these research, we analyze the strategies and recommended hitting patterns from the information on the two players' patterns. The results of this study, in general, can be used as information for players to understand and analyze the opponent's performance or strategy before competing.
{"title":"Modeling the Badminton Stroke Pattern Through the Sequential Pattern Discovery Using Equivalent Classes (SPADE) Algorithm","authors":"Jefita Resti Sari, Atina Ahdika","doi":"10.13164/mendel.2023.1.037","DOIUrl":"https://doi.org/10.13164/mendel.2023.1.037","url":null,"abstract":"Badminton is one of the most popular sports in the world, especially in Asia. It has a parent organization called Badminton World Federation (BWF). Discussion about player strategies in winning various championships held by BWF is an interesting topic to discuss. This paper aims to analyze the hitting patterns of badminton players by paying attention to the sequence of types of strokes made by the players, including lobs, netting, smashes, drives, and dropshots. Sequential pattern discovery using the equivalent class algorithm (SPADE) is the appropriate method to identify these problems because it can determine the rules and probabilities of player's hitting patterns based on the order of the types of strokes. In this paper, we analyze the stroke pattern of the two top-ranked badminton players in the men's singles sector at the Malaysia Open 2022 championship, where Viktor Axelsen and Kento Momota met in the final. Based on the results of these research, we analyze the strategies and recommended hitting patterns from the information on the two players' patterns. The results of this study, in general, can be used as information for players to understand and analyze the opponent's performance or strategy before competing.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83397646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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