Mendel最新文献

{"title":"A New Integral Function Algorithm for Global Optimization and Its Application to the Data Clustering Problem","authors":"Ridwan Pandiya, Atina Ahdika, Siti Khomsah, Rima Dias Ramadhani","doi":"10.13164/mendel.2023.2.162","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.162","url":null,"abstract":"The filled function method is an approach to finding global minimum points of multidimensional unconstrained global optimization problems. The conventional parametric filled functions have computational weaknesses when they are employed in some benchmark optimization functions. This paper proposes a new integral function algorithm based on the auxiliary function approach. The proposed method can successfully be used to find the global minimum point of a function of several variables. Some testing global optimization problems have been used to show the ability of this recommended method. The integral function algorithm is then implemented to solve the center-based data clustering problem. The results show that the proposed algorithm can solve the problem successfully.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"68 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139169307","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}

{"title":"Detecting Outliers Using Modified Recursive PCA Algorithm For Dynamic Streaming Data","authors":"Yasi Dani, Agus Yodi Gunawan, M. L. Khodra, S. Indratno","doi":"10.13164/mendel.2023.2.237","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.237","url":null,"abstract":"Outlier analysis has been widely studied and has produced many methods. However, there is still rare a method to detect outliers for dynamically streaming batch data (online learning). In the present research, a novel online algorithm to detect outliers in such dataset is proposed. Data points are proceeded by applying a modified recursive PCA to predict sequentially parameters of the model; eigenvalues and eigenvectors of the statistical detection model are recursively updated using approximate values by perturbation methods. More specifically, the recursive eigenstructure is obtained from the derivation of the covariance matrix using the first-order perturbation technique. The Mahalanobis distance is then used as an outlier score. Our algorithm performances are evaluated using some metrics, namely accuration, precision, recall, F1-score, AUC-PR, and the execution time. Results show that the proposed online outlier detection is computationally efficient in time and the algorithm's performance effectiveness is comparable to that of the offline outlier detection algorithm via classical PCA.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"129 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138953685","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}

{"title":"Diagonal Partitioning Strategy Using Bisection of Rectangles and a Novel Sampling Scheme","authors":"Nabila Guessoum, L. Chiter","doi":"10.13164/mendel.2023.2.131","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.131","url":null,"abstract":"In this paper, we consider a global optimization problem where the objective function is assumed to be Lipschitz-continuous with an unknown Lipschitz constant. Building upon the recently introduced BIRECT (BIsection of RECTangles) algorithm, we propose a new diagonal partitioning and sampling scheme. Our framework, named BIRECT-V (V for vertices), combines bisection with the sampling of two points. In the initial hyper-rectangle, these points are located at 1/3 and 1 along the main diagonal. Unlike most DIRECT-type algorithms, where evaluating the objective function at vertices is not suitable for bisection, our strategy, when combined with bisection, provides more comprehensive information about the objective function. However, the creation of new sampling points may coincide with existing ones at shared vertices, resulting in additional evaluations of the objective function and increasing the number of function evaluations per iteration. To overcome this issue, we propose modifying the original optimization domain to obtain a good approximation of the global solution. Experimental investigations demonstrate that this modification positively impacts the performance of the BIRECT-V algorithm. Our proposal shows promise as a global optimization algorithm compared to the original BIRECT and two popular DIRECT-type algorithms on a set of test problems. It particularly excels at high-dimensional problems","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138956172","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}

{"title":"Stock and Structured Warrant Portfolio Optimization Using Black-Litterman Model and Binomial Method","authors":"Cornelius Francis Jayadi, Novriana Sumarti","doi":"10.13164/mendel.2023.2.220","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.220","url":null,"abstract":"In recent years, the number of Indonesian investors has rapidly increased during the COVID-19 pandemic which happened all around the world. There have been a massive number of influencers in social media who were promoting investment. Although stocks and warrants are interesting choices, mutual funds still become the main ones for beginners. Therefore, this research focuses on the development of a stock portfolio model using the Black-Litterman method which involves the investor’s views towards the stock returns. The research refers to one of the largest equity funds in Indonesia, that is Sucorinvest Equity Fund, by using the top ten of its stocks that are majority in the fund (as of April 28, 2023). Furthermore, this research also constructs a structured warrant portfolio, but it is separated from the initially constructed stock portfolio. Structured warrants could be an appropriate choice for low-budget investors. It was newly introduced in Indonesia in September 2022 so it is interesting to be observed. Based on the results and the implemented assumptions, the return obtained from the stock portfolio is superior to the observed fund’s return. Meanwhile, call structured warrant portfolio using the existing product in the market yields a negative return, because the exercise price and warrant offered price were too high. Thus, structured warrants could be considered overpriced at the moment, so the chance of obtaining profit is extremely small. Due to its similar properties to call and put options, we propose the warrant pricing and use it in simulations, so in the future, structured warrants may become an attractive instrument for the investors.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"119 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138953829","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}

{"title":"Optimized Fixed-Time Synergetic Controller via a modified Salp Swarm Algorithm for Acute and Chronic HBV Transmission System","authors":"Saadi Achour, Khalil Mokhtari, Abdelaziz Rahmoune, Fares Yazid","doi":"10.13164/mendel.2023.2.191","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.191","url":null,"abstract":"In this paper, we propose a Salp Swarm Algorithm (SSA) Optimized Fixed-Time Synergetic Control (FTSC) strategy for the spread of hepatitis B infection. The utilization of the SSA optimization algorithm for optimizing the Synergetic Control (SC) fraction parameters presents a non-trivial challenge due to the restriction that only odd numbers can be used for the fractional power. Therefore, an enhanced and adapted version of the SSA algorithm is proposed to effectively address this specific scenario. Our strategic approach centers on the reduction of susceptible, acutely infected, and chronically infected individuals by employing control parameters like isolation, treatment, and vaccination. The objective is to drive these target state variables to their smallest values in a fixed-time, thereby effectively controlling the epidemic. We support our proposal with numerical simulations to demonstrate the feasibility and effectiveness of the control strategy. A comparison is conducted between FTSC and SC in scenarios with and without optimization. The results indicated that FTSC holds a distinct advantage, consistently demonstrating significant progress, with up to 30% reduction in the total convergence time to zero, outperforming SC in each case.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"89 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138954445","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}

{"title":"Predicting Football Match Outcomes with Machine Learning Approaches","authors":"Bing Shen Choi, Lee Kien Foo, Sook-Ling Chua","doi":"10.13164/mendel.2023.2.229","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.229","url":null,"abstract":"The increasing use of data-driven approaches has led to the development of models to predict football match outcomes. However, predicting match outcomes accurately remains a challenge due to the sport's inherent unpredictability. In this study, we have investigated the usage of different machine learning models in predicting the outcome of English Premier League matches. We assessed the performance of random forest, logistic regression, linear support vector classifier and extreme gradient boosting models for binary and multiclass classification. These models are trained with datasets obtained using different sampling techniques. The result showed that the models performed better when trained with dataset obtained using a balanced sampling technique for binary classification. Additionally, the models' predictions were evaluated by conducting simulation on football betting profits based on the 2022-2023 EPL season. The model achieved the highest accuracy is the binary class random forest, but the model provided the highest football betting profit is the binary class logistic regression.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"29 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138955160","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}

{"title":"Experiential Marketing Tourism and Hospitality Tours Generation Hybrid Model","authors":"Luis Alfaro, Claudia Rivera, Jorge Luna-Urquizo, Antonio Arroyo-Paz, Lucy Delgado, Elisa Castañeda","doi":"10.13164/mendel.2023.2.273","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.273","url":null,"abstract":"The emergence of immersive technologies presents unprecedented opportunities for both users and organizations. This paper explores the future of digital marketing as a new ecosystem wherein innovative marketing strategies enable organizations to communicate with their customer base in ways previously unattainable, reshaping traditional marketing concepts into novel and unimaginable actions. This study proposes an experiential marketing tourism and hospitality tours generation hybrid model. The model focuses on generating virtual tours based on 360° VR videos, specifically designed for hotel environments, their surroundings, and tourist zones. The immersive environment proposal includes the design of user interface prototypes and incorporates the automated division of 360° videos using convolutional neural networks. Subsequently, personalized tours are composed based on user profiles, utilizing a Case-Based Reasoning (CBR). Functionality tests for the video division and labeling component, as well as the composition of tours according to user profiles recommended by the CBR, yielded satisfactory results. The application of this system has the potential to positively influence reservation intentions and enhance brand image. Immersive experiences have the capability to trigger effects in affective, attitudinal/behavioral, and cognitive dimensions.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"30 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138955702","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}

{"title":"Modern Tendency to Practice-Oriented Learning","authors":"ZuoYuan Liu, Akmatali Alimbekov, Sergey Glushkov, Lyazzat Ramazanova","doi":"10.13164/mendel.2023.2.155","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.155","url":null,"abstract":"Today, technology is changing quickly and apparently affects all parts of life. Compared to a few years ago, many things have changed, including thoughts, habits, social activities, and ways of life. Thus, this study determined the impact of virtual reality technologies as practice-oriented learning stimuli on the development of information competence and academic performance of future primary school teachers. One hundred eighteen students from the Pedagogy Faculty of the M. Utemisov West Kazakhstan University and 105 students from the Kyrgyz National University majoring in the same field were divided into two groups for the research. Respondents in the experimental group took virtual reality courses, and their progress was evaluated by contrasting their grades before and after the programme. Based on the preliminary analysis of the student's academic performance, it should be noted that most of them performed mediocrely. However, observations by tutors and teachers revealed that when classes were taught using virtual reality platforms such as EyeJack and CoSpaces Edu, students in the experimental group were more willing to participate in tasks and seminars. Furthermore, according to the results of Content Module 2, students in the experimental group performed significantly better than students in the control group in terms of their overall academic performance (p=4.187). The article's practical significance comes from considering how virtual reality technologies might enhance Kazakhstan's and Kyrgyzstan's educational systems.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"114 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138958446","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}

{"title":"Initial Coin Offering Prediction Comparison Using Ridge Regression, Artificial Neural Network, Random Forest Regression, and Hybrid ANN-Ridge","authors":"Toai Kim Tran, R. Šenkeřík, Hahn Thi Xuan Vo, Huan Minh Vo, Adam Ulrich, Marek Musil, I. Zelinka","doi":"10.13164/mendel.2023.2.283","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.283","url":null,"abstract":"\u0000 \u0000 \u0000Can machine learning take a prediction to win an investment in ICO (Initial Coin Offering)? In this research work, our objective is to answer this question. Four popular and lower computational demanding approaches including Ridge regression (RR), Artificial neural network (ANN), Random forest regression (RFR), and a hybrid ANN-Ridge regression are compared in terms of accuracy metrics to predict ICO value after six months. We use a dataset collected from 109 ICOs that were obtained from the cryptocurrency websites after data preprocessing. The dataset consists of 12 fields covering the main factors that affect the value of an ICO. One-hot encoding technique is applied to convert the alphanumeric form into a binary format to perform better predictions; thus, the dataset has been expanded to 128 columns and 109 rows. Input data (variables) and ICO value are non-linear dependent. The Artificial neural network algorithm offers a bio-inspired mathematical model to solve the complex non-linear relationship between input variables and ICO value. The linear regression model has problems with overfitting and multicollinearity that make the ICO prediction inaccurate. On the contrary, the Ridge regression algorithm overcomes the correlation problem that independent variables are highly correlated to the output value when dealing with ICO data. Random forest regression does avoid overfitting by growing a large decision tree to minimize the prediction error. Hybrid ANN-Ridge regression leverages the strengths of both algorithms to improve prediction accuracy. By combining ANN’s ability to capture complex non-linear relationships with the regularization capabilities of Ridge regression, the hybrid can potentially provide better predictive performance compared to using either algorithm individually. After the training process with the cross-validation technique and the parameter fitting process, we obtained several models but selected three of the best in each algorithm based on metrics of RMSE (Root Mean Square Error), R2 (R-squared), and MAE (Mean Absolute Error). The validation results show that the presented Ridge regression approach has an accuracy of at most 99% of the actual value. The Artificial neural network predicts the ICO value with an accuracy of up to 98% of the actual value after six months. Additionally, the Random forest regression and the hybrid ANN-Ridge regression improve the predictive accuracy to 98% actual value. \u0000 \u0000 \u0000","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"54 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138954516","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}

{"title":"Quantitative Strategic Planning Matrix as a Superior Strategic Management Tools and Techniques in Evaluating Decision Alternatives","authors":"Elvis Elezaj, Bekë Kuqi","doi":"10.13164/mendel.2023.2.090","DOIUrl":"https://doi.org/10.13164/mendel.2023.2.090","url":null,"abstract":"Evaluation of alternatives to making decisions still remains as the most difficult field for every manager. Considering that uncertainty, stress, emotions and many other factors still remain insurmountable during decision-making in the work of managers. The research will bring a contemporary approach to the evaluation of alternatives through the multi-stage method by conducting a series of exhibitions for an effective decision. Model will create a logical structure derivation of exhibitions by revealing options and paths toward strategic leadership. The research used mixed methods of data collection to create a more meaningful and integrative research design. The sample was elongated over a survey of 250 organizations. The research brings to the surface a clear analysis of the following path as a guide and practically used to gain differentiating advantages towards the long-term through Strategic Attractiveness Position in Industry (ST-API). From this analysis structure, a clearness leadership orientation is created for managers, a recommendation for strategic leadership, revealing a group of strategies to undertake depending on the ST-API dimension IFE (Internal Factor Evaluation) or ST-API dimension EFE (External Factor Evaluation) through crafting \"Option's\" since the organizations are concentrated in the vicinity of the corner (nook) in quad IV, conclusively in \"growth and build\". Occurrated in this axle, organizations are advised to orient their actions towards the \"develop products\" in order to go towards longevity and leaderism in the industry.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"54 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138957150","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}