{"title":"The Importance of High-Bandwidth Low-Latency Network Systems in the Modern Age","authors":"","doi":"10.14738/tecs.111.13967","DOIUrl":"https://doi.org/10.14738/tecs.111.13967","url":null,"abstract":"","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125894079","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":"Evaluating the Influence of Passive Design Strategies on Cooling Energy Demand in Local Adobe, Stone and Concrete Dwellings in Wadi Hadramout, Yemen","authors":"","doi":"10.14738/tecs.113.14815","DOIUrl":"https://doi.org/10.14738/tecs.113.14815","url":null,"abstract":"","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131319074","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":"Standardization of Criteria across Multiple Evaluators to Detect Objects","authors":"","doi":"10.14738/tecs.111.13876","DOIUrl":"https://doi.org/10.14738/tecs.111.13876","url":null,"abstract":"","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122786700","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":"Scrutinizing UML Teaching and Learning Modeling Tools","authors":"","doi":"10.14738/tecs.111.13820","DOIUrl":"https://doi.org/10.14738/tecs.111.13820","url":null,"abstract":"","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123787168","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 : 2022-12-28DOI: 10.14738/tmlai.106.13645
Salmah Nansamba, Hadi Harb
Solar photovoltaic (PV) systems are one of the fastest growing renewable energy technologies and plenty of research has been and continues to be carried out in this domain. Maximization of solar PV power plant production, efficiency and return on investment can only be achieved by having adequate and effective maintenance systems in place. Of the various maintenance schemes, predictive maintenance is popular for its effectiveness and minimization of resource wastage. Maintenance activities are scheduled based on the real time condition of the system with priority being given to the system components with the highest likelihood of failure. A good predictive maintenance system is based on the premise of being able to anticipate faults before they occur. In this study therefore, a fault prediction tool for a solar plant in Uganda is proposed. The hybrid tool is developed using both feed forward and long short term memory neural networks for power prediction, in conjunction with a mean chart statistical process control tool for final fault prediction. Results from the study demonstrate that the feed forward and long short term memory neural network modules of the proposed tool attain mean absolute errors of 4.2% and 6.9% respectively for power production predictions. The fault prediction capability of the tool is tested under both normal and abnormal operating conditions. Results show that the tool satisfactorily discriminates against the fault and non-fault conditions thereby achieving successful solar PV system fault prediction.
{"title":"Developing a Neural Network Based Fault Prediction Tool for a Solar Power Plant in Uganda","authors":"Salmah Nansamba, Hadi Harb","doi":"10.14738/tmlai.106.13645","DOIUrl":"https://doi.org/10.14738/tmlai.106.13645","url":null,"abstract":"Solar photovoltaic (PV) systems are one of the fastest growing renewable energy technologies and plenty of research has been and continues to be carried out in this domain. Maximization of solar PV power plant production, efficiency and return on investment can only be achieved by having adequate and effective maintenance systems in place. Of the various maintenance schemes, predictive maintenance is popular for its effectiveness and minimization of resource wastage. Maintenance activities are scheduled based on the real time condition of the system with priority being given to the system components with the highest likelihood of failure. A good predictive maintenance system is based on the premise of being able to anticipate faults before they occur. In this study therefore, a fault prediction tool for a solar plant in Uganda is proposed. The hybrid tool is developed using both feed forward and long short term memory neural networks for power prediction, in conjunction with a mean chart statistical process control tool for final fault prediction. Results from the study demonstrate that the feed forward and long short term memory neural network modules of the proposed tool attain mean absolute errors of 4.2% and 6.9% respectively for power production predictions. The fault prediction capability of the tool is tested under both normal and abnormal operating conditions. Results show that the tool satisfactorily discriminates against the fault and non-fault conditions thereby achieving successful solar PV system fault prediction.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114094148","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 : 2022-12-07DOI: 10.14738/tmlai.106.13515
O. Gal, Rafi Michaeli, Y. Doytsher
In this paper, we present the current state-of-the-art models for Automatic Speech Recognition due to a self-supervised training implemented on Hebrew language. The motivation behind using self-supervised learning is that even though we wouldn't probably get the accuracy rates as if we choose a supervised learning, we still can achieve amazing results with relatively low amount of data. This way of training allows us to train a model on unlabeled data (or to use a pre-trained model, which is always more accessible. It’s goal in the first unsupervised phase is to learn some good representations from raw audio samples, which can be useful for speech recognition tasks, without using any label data. Then, the model can be fine-tuned on a particular dataset for a specific purpose. It means that our involvement really occurs in the last layers of the model. This kind of training proved to be very powerful. We present complete framework from model to practice with simulations and training model and present an impressive result on Hebrew.
{"title":"Self-Supervised Learning in Hebrew–Model to Practice Framework","authors":"O. Gal, Rafi Michaeli, Y. Doytsher","doi":"10.14738/tmlai.106.13515","DOIUrl":"https://doi.org/10.14738/tmlai.106.13515","url":null,"abstract":"In this paper, we present the current state-of-the-art models for Automatic Speech Recognition due to a self-supervised training implemented on Hebrew language. The motivation behind using self-supervised learning is that even though we wouldn't probably get the accuracy rates as if we choose a supervised learning, we still can achieve amazing results with relatively low amount of data. This way of training allows us to train a model on unlabeled data (or to use a pre-trained model, which is always more accessible. It’s goal in the first unsupervised phase is to learn some good representations from raw audio samples, which can be useful for speech recognition tasks, without using any label data. Then, the model can be fine-tuned on a particular dataset for a specific purpose. It means that our involvement really occurs in the last layers of the model. This kind of training proved to be very powerful. We present complete framework from model to practice with simulations and training model and present an impressive result on Hebrew.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126312545","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 : 2022-12-02DOI: 10.14738/tmlai.106.13492
Kodai Kitagawa
Ball velocity of pitching is an important factor in baseball players. Commonly, ball velocity measurement requires specific devices such as radar gun. On the other hand, Gomaz et al. developed the accurate ball velocity measurement using two inertial sensors on pelvis and trunk. Recently, smartphone installed inertial sensor is popular device in daily life. Therefore, if ball velocity can be measured by only a single inertial sensor, baseball players can measure own ball velocity by only smartphone in daily life and various situations. Thus, the objective of this study is to propose and evaluate the ball velocity prediction method using the only a single inertial sensor. The proposed method predicts ball velocity using by a single inertial sensor and machine learning technique. Five machine learning algorithms (linear regression, support vector machine, gaussian process, artificial neural network, and M5P) predicted ball velocity by data of single inertial sensor, body height, and body weight. In this study, Gomaz et al.’s public data for ball velocity and inertial data during pitching of baseball players were used for this investigation. Sensor placement was either sternum or pelvis. Accuracy of prediction was evaluated by root mean square error (RMSE) between actual and predicted value via leave-one-out cross-validation. The results showed that greatest algorithm (M5P) could accurately predict ball velocity by only single inertial sensor and body parameters (RMSE < 2.0 mph). These results suggest that ball velocity can be measured by only single inertial sensor such as smartphone.
{"title":"Comparison of Machine Learning Algorithms for Ball Velocity Prediction in Baseball Pitcher using a Single Inertial Sensor","authors":"Kodai Kitagawa","doi":"10.14738/tmlai.106.13492","DOIUrl":"https://doi.org/10.14738/tmlai.106.13492","url":null,"abstract":"Ball velocity of pitching is an important factor in baseball players. Commonly, ball velocity measurement requires specific devices such as radar gun. On the other hand, Gomaz et al. developed the accurate ball velocity measurement using two inertial sensors on pelvis and trunk. Recently, smartphone installed inertial sensor is popular device in daily life. Therefore, if ball velocity can be measured by only a single inertial sensor, baseball players can measure own ball velocity by only smartphone in daily life and various situations. Thus, the objective of this study is to propose and evaluate the ball velocity prediction method using the only a single inertial sensor. The proposed method predicts ball velocity using by a single inertial sensor and machine learning technique. Five machine learning algorithms (linear regression, support vector machine, gaussian process, artificial neural network, and M5P) predicted ball velocity by data of single inertial sensor, body height, and body weight. In this study, Gomaz et al.’s public data for ball velocity and inertial data during pitching of baseball players were used for this investigation. Sensor placement was either sternum or pelvis. Accuracy of prediction was evaluated by root mean square error (RMSE) between actual and predicted value via leave-one-out cross-validation. The results showed that greatest algorithm (M5P) could accurately predict ball velocity by only single inertial sensor and body parameters (RMSE < 2.0 mph). These results suggest that ball velocity can be measured by only single inertial sensor such as smartphone.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116477774","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 : 2022-12-02DOI: 10.14738/tmlai.106.13419
S. Jason
The reduction of power consumption, which can lower the operation costs of Cloud providers, lengthen the useful life of a machine, as well as lessen the environmental effect caused by power consumption, is one of the critical concerns for large-scale Cloud applications. To satisfy the needs of various clients, Virtual Machines (VMs) as resources (Infrastructure as a Service (IaaS)) can be dynamically allocated in cloud data centers. In this research, we study the energy-efficient scheduling of real-time VMs by taking set processing intervals into account, with the providers' goal of lowering power consumption. Finding the best solutions is an NP-complete problem when virtual machines (VMs) share arbitrary amounts of a physical machine's (PM) total capacity, as demonstrated in numerous open-source resources. Our strategy treats the issue as a modified interval partitioning problem and takes into account configurations with dividable capacities to make the problem formulation easier and assist save energy. There are presented both exact and approximate solutions. The proposed systems consume 8–30% less power than the existing algorithms, according to simulation data.
{"title":"Real-time Virtual Machine Energy-Efficient Allocation in Cloud Data Centers Using Interval-packing Methods","authors":"S. Jason","doi":"10.14738/tmlai.106.13419","DOIUrl":"https://doi.org/10.14738/tmlai.106.13419","url":null,"abstract":"The reduction of power consumption, which can lower the operation costs of Cloud providers, lengthen the useful life of a machine, as well as lessen the environmental effect caused by power consumption, is one of the critical concerns for large-scale Cloud applications. To satisfy the needs of various clients, Virtual Machines (VMs) as resources (Infrastructure as a Service (IaaS)) can be dynamically allocated in cloud data centers. In this research, we study the energy-efficient scheduling of real-time VMs by taking set processing intervals into account, with the providers' goal of lowering power consumption. Finding the best solutions is an NP-complete problem when virtual machines (VMs) share arbitrary amounts of a physical machine's (PM) total capacity, as demonstrated in numerous open-source resources. Our strategy treats the issue as a modified interval partitioning problem and takes into account configurations with dividable capacities to make the problem formulation easier and assist save energy. There are presented both exact and approximate solutions. The proposed systems consume 8–30% less power than the existing algorithms, according to simulation data.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127848581","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 : 2022-11-14DOI: 10.14738/tmlai.106.13359
Abderrahman Lakbir, Amale Laaraussi, A. Bouayad
The objective of this paper is to examine how entrepreneurial orientation (EO) manifests itself in the context of agricultural SMEs integrated into the value chain in the Fez-Meknes region. More specifically, we seek to know to what extent the dimensions of EO are expressed in agricultural SMEs in the Fez-Meknes region. To do this, we used a qualitative study of 15 agricultural SMEs, using the Nvivo software for data analysis. This study revealed that the dimensions of EO in agricultural SMEs in the Fes-Meknes region do not differ from those reported in the literature. The three dimensions of EO were demonstrated by the agricultural SMEs in the region in a manner similar to that reported in the literature. The SMEs interviewed demonstrated innovation, risk taking, and proactivity.
{"title":"The Entrepreneurial Orientation of Agricultural SMEs in the Fez-Meknes Region: A Qualitative Study","authors":"Abderrahman Lakbir, Amale Laaraussi, A. Bouayad","doi":"10.14738/tmlai.106.13359","DOIUrl":"https://doi.org/10.14738/tmlai.106.13359","url":null,"abstract":"The objective of this paper is to examine how entrepreneurial orientation (EO) manifests itself in the context of agricultural SMEs integrated into the value chain in the Fez-Meknes region. More specifically, we seek to know to what extent the dimensions of EO are expressed in agricultural SMEs in the Fez-Meknes region. To do this, we used a qualitative study of 15 agricultural SMEs, using the Nvivo software for data analysis. This study revealed that the dimensions of EO in agricultural SMEs in the Fes-Meknes region do not differ from those reported in the literature. The three dimensions of EO were demonstrated by the agricultural SMEs in the region in a manner similar to that reported in the literature. The SMEs interviewed demonstrated innovation, risk taking, and proactivity.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133940442","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 : 2022-10-18DOI: 10.14738/tmlai.105.13168
R. Balan, E. Massawe
This paper describes a selection procedure for an Optimum Sampling Plan, offering maximum consumer protection in terms of AOQL and MAPD. The greatest lower bound (glb) property of AOQL for a fixed MAPD is used to design the plan offering highest precision on outgoing quality for the lot. Tables for optimum sampling plans corresponding to specified MAPD and g l b of AOQL are listed along with AQL. Empirical relation to determine AOQL for given acceptance number and MAPD is determined. Also an approximated acceptance number function in terms of (MAPD, AOQL) is developed. Lower and Upper bounds of AOQL for some parametric sampling plans are listed.
{"title":"Optimum Sampling Plan on Quality Indices AOQL and MAPD","authors":"R. Balan, E. Massawe","doi":"10.14738/tmlai.105.13168","DOIUrl":"https://doi.org/10.14738/tmlai.105.13168","url":null,"abstract":"This paper describes a selection procedure for an Optimum Sampling Plan, offering maximum consumer protection in terms of AOQL and MAPD. The greatest lower bound (glb) property of AOQL for a fixed MAPD is used to design the plan offering highest precision on outgoing quality for the lot. Tables for optimum sampling plans corresponding to specified MAPD and g l b of AOQL are listed along with AQL. Empirical relation to determine AOQL for given acceptance number and MAPD is determined. Also an approximated acceptance number function in terms of (MAPD, AOQL) is developed. Lower and Upper bounds of AOQL for some parametric sampling plans are listed.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134548915","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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