Pub Date : 2023-01-05DOI: 10.53759/7669/jmc202303002
Vandan Gupta
The objective of this paper to investigate analysis of creep in the Functionally Graded Material disc with variable thickness made of aluminum alloy-based metal matrix composite containing silicon carbide particles in presence of thermal gradients in the radial direction. It has been concluded that distributions of the stress and strain rate in an anisotropic disc got affected from the thermal gradients. Thus, the presence of thermal gradients in rotating disc plays a significant role in developing the creep response.
{"title":"Investigate of Creep Response in Functionally Graded Material Rotating Disc with variable Thickness","authors":"Vandan Gupta","doi":"10.53759/7669/jmc202303002","DOIUrl":"https://doi.org/10.53759/7669/jmc202303002","url":null,"abstract":"The objective of this paper to investigate analysis of creep in the Functionally Graded Material disc with variable thickness made of aluminum alloy-based metal matrix composite containing silicon carbide particles in presence of thermal gradients in the radial direction. It has been concluded that distributions of the stress and strain rate in an anisotropic disc got affected from the thermal gradients. Thus, the presence of thermal gradients in rotating disc plays a significant role in developing the creep response.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"86 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73282849","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}
Hard disk drive manufacturing is complicated and involves several steps of assembling and testing. Poor yield in one step can result in fail product of the whole lot. Accurate yield prediction is thus important to product monitoring and management. This paper presents a novel idea of data preparation and modeling to predict yield in the process of hard disk drive production. Data balancing technique based on clustering and re-sampling is introduced to make the proportion of the pass and fail products comparable. Then, we propose a strategy to aggregate manufacturing data to be in a reasonable group size and efficient for the subsequent step of yield predictive model creation. Experimental results reveal that grouping data into a constant size of 10,000 records can lead to the more accurate yield prediction as compared to the intuitive idea of weekly grouping.
{"title":"Data Balancing and Aggregation Strategy to Predict Yield in Hard Disk Drive Manufacturing","authors":"Nittaya Kerdprasop, Anusara Hirunyawanakul, Paradee Chuaybamroong, Kittisak Kerdprasop","doi":"10.18178/ijml.2023.13.4.1148","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.4.1148","url":null,"abstract":"Hard disk drive manufacturing is complicated and involves several steps of assembling and testing. Poor yield in one step can result in fail product of the whole lot. Accurate yield prediction is thus important to product monitoring and management. This paper presents a novel idea of data preparation and modeling to predict yield in the process of hard disk drive production. Data balancing technique based on clustering and re-sampling is introduced to make the proportion of the pass and fail products comparable. Then, we propose a strategy to aggregate manufacturing data to be in a reasonable group size and efficient for the subsequent step of yield predictive model creation. Experimental results reveal that grouping data into a constant size of 10,000 records can lead to the more accurate yield prediction as compared to the intuitive idea of weekly grouping.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136207421","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 : 2023-01-01DOI: 10.18178/ijml.2023.13.4.1143
Berny Carrera, Kwanho Kim
A smart city should ideally be environmentally friendly and sustainable, and energy management is one technique to monitor sustainable use. Similarly, this notion might be applied in an urban form, such as the sort of city in which a university would be located. This research analyzes the possibility for a university to enhance energy management by permitting the adoption of a variety of intelligent technologies that increase the energy sustainability of a city's infrastructure and the effectiveness of its operations. In the first module of the proposed system, we place significant emphasis on the data capabilities necessary to create energy statistics for each of its various buildings. In the second phase of the technique, we employ the collected data to conduct a data analysis of the energy behavior inside micro-cities, from which we derive characteristics. In the third module, we develop baseline regressors to assess the varying degrees of efficacy of the proposed model. Last, we describe a way for developing an energy prediction model using a deep learning regression model to solve the problem of short-term energy consumption forecasting. The performance metric results show that the suggested deep learning model increases performance prediction compared to other traditional regression techniques. The proposed model has superior RMSE, MAE and R squared results compared to alternative regression models.
{"title":"Sustainable Smart University: A Short-Term Deep Learning Framework for Energy Consumption Forecast","authors":"Berny Carrera, Kwanho Kim","doi":"10.18178/ijml.2023.13.4.1143","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.4.1143","url":null,"abstract":"A smart city should ideally be environmentally friendly and sustainable, and energy management is one technique to monitor sustainable use. Similarly, this notion might be applied in an urban form, such as the sort of city in which a university would be located. This research analyzes the possibility for a university to enhance energy management by permitting the adoption of a variety of intelligent technologies that increase the energy sustainability of a city's infrastructure and the effectiveness of its operations. In the first module of the proposed system, we place significant emphasis on the data capabilities necessary to create energy statistics for each of its various buildings. In the second phase of the technique, we employ the collected data to conduct a data analysis of the energy behavior inside micro-cities, from which we derive characteristics. In the third module, we develop baseline regressors to assess the varying degrees of efficacy of the proposed model. Last, we describe a way for developing an energy prediction model using a deep learning regression model to solve the problem of short-term energy consumption forecasting. The performance metric results show that the suggested deep learning model increases performance prediction compared to other traditional regression techniques. The proposed model has superior RMSE, MAE and R squared results compared to alternative regression models.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136208517","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 : 2023-01-01DOI: 10.18178/ijml.2023.13.1.1124
{"title":"Relaxed Training Procedure for a Binary Neural Network","authors":"","doi":"10.18178/ijml.2023.13.1.1124","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.1.1124","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87545701","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 : 2023-01-01DOI: 10.18178/ijml.2023.13.1.1128
{"title":"Optimal Operation of Geothermal Power Plant by Artificial Neural Network","authors":"","doi":"10.18178/ijml.2023.13.1.1128","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.1.1128","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73519811","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 : 2023-01-01DOI: 10.18178/ijml.2023.13.1.1127
{"title":"Conversational AI – Virtual Assistant & Chatbot at Sika Ltd.: A Case Study from the Chemical Industry","authors":"","doi":"10.18178/ijml.2023.13.1.1127","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.1.1127","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86664395","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 : 2023-01-01DOI: 10.18178/ijml.2023.13.4.1144
I-Cheng Chang, Shen-De Chen, Tai-Yi Yu
With the rapid development of network technology and the digital economy, the wave of the era of artificial intelligence has swept the world. Facing the era of big data and artificial intelligence, data-oriented technologies are undoubtedly served as the practical research trend. Therefore, the precise analysis provided by big data and artificial intelligence can provide effective and accurate knowledge and decision-making references for all sectors. In order to effectively and appropriately evaluate the potential risk to soil and groundwater for gas station industry, this study focuses on the potential risk factors affecting soil and groundwater pollution. In the past, our team has evaluated the risk factors affecting the remediation cost of soil and groundwater pollution for possible potential pollution sources such as gas stations, this study proceeds with the existing industrial database for in-depth discussion, uses machine learning technology to evaluate the key factors of pollution risk for soil and groundwater, and compares the differences, applicability and relative importance of the three machine learning techniques (such as neural networks, random forests and support vector machine). The performance indicators reveal that the random forest algorithm is better than support vector machine and artificial neural network. The relative importance of parameters of different machine learning models is not consistent, and the first five dominant parameters are location, number of gas monitoring wells, age of gas station, numbers of gasoline oil nozzle, and number of fuel dispenser for random forest model.
{"title":"Establishment of Risk Prediction Model for Soil and Groundwater Pollution of Gas Station with Machine Learning Techniques","authors":"I-Cheng Chang, Shen-De Chen, Tai-Yi Yu","doi":"10.18178/ijml.2023.13.4.1144","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.4.1144","url":null,"abstract":"With the rapid development of network technology and the digital economy, the wave of the era of artificial intelligence has swept the world. Facing the era of big data and artificial intelligence, data-oriented technologies are undoubtedly served as the practical research trend. Therefore, the precise analysis provided by big data and artificial intelligence can provide effective and accurate knowledge and decision-making references for all sectors. In order to effectively and appropriately evaluate the potential risk to soil and groundwater for gas station industry, this study focuses on the potential risk factors affecting soil and groundwater pollution. In the past, our team has evaluated the risk factors affecting the remediation cost of soil and groundwater pollution for possible potential pollution sources such as gas stations, this study proceeds with the existing industrial database for in-depth discussion, uses machine learning technology to evaluate the key factors of pollution risk for soil and groundwater, and compares the differences, applicability and relative importance of the three machine learning techniques (such as neural networks, random forests and support vector machine). The performance indicators reveal that the random forest algorithm is better than support vector machine and artificial neural network. The relative importance of parameters of different machine learning models is not consistent, and the first five dominant parameters are location, number of gas monitoring wells, age of gas station, numbers of gasoline oil nozzle, and number of fuel dispenser for random forest model.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136208291","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 : 2023-01-01DOI: 10.18178/ijml.2023.13.4.1145
Patcharasiri Fuangfoo, Krung Sinapiromsaran
Classification is one important area in machine learning that labels the class of an instance via a classifier from known-class historical data. One of the popular classifiers is k-NN, which stands for “k-nearest neighbor” and requires a global parameter k to proceed. This global parameter may not be suitable for all instances. Naturally, each instance may situate on different regions of clusters such as an interior instance placed inside a cluster, a border instance placed on the outskirts, an outer instance placed faraway from any cluster, which requires a different number of neighbors. To automatically assign a different number of neighbors to each instance, the concept of scoring from the anomaly detection research is desired. The Mass-ratio-variance Outlier Factor, MOF, is selected as the scoring scheme for the number of neighbors of each instance. MOF gives the highest score to an instance placed very far from any cluster and the lowest score to an instance surrounded by other instances. This leads to the proposed classifier called the conglomerate nearest neighbor classifier, which does not require any parameter assigning the appropriate number of neighbors to each instance ordered by MOF. Experimental results show that this classifier exhibits similar accuracy to the k-nearest neighbor algorithm with the best k over the synthesized datasets. Six UCI datasets, the QSAR dataset, the German dataset, the Cancer dataset, the Wholesale dataset, the Haberman dataset, and the Glass3 dataset are used in the experiment. This method outperforms two UCI datasets, Wholesale and Glass3, and displays similar performance with respect to these six UCI datasets.
{"title":"Parameter-Free Conglomerate nearest Neighbor Classifier Using Mass-Ratio-Variance Outlier Factors","authors":"Patcharasiri Fuangfoo, Krung Sinapiromsaran","doi":"10.18178/ijml.2023.13.4.1145","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.4.1145","url":null,"abstract":"Classification is one important area in machine learning that labels the class of an instance via a classifier from known-class historical data. One of the popular classifiers is k-NN, which stands for “k-nearest neighbor” and requires a global parameter k to proceed. This global parameter may not be suitable for all instances. Naturally, each instance may situate on different regions of clusters such as an interior instance placed inside a cluster, a border instance placed on the outskirts, an outer instance placed faraway from any cluster, which requires a different number of neighbors. To automatically assign a different number of neighbors to each instance, the concept of scoring from the anomaly detection research is desired. The Mass-ratio-variance Outlier Factor, MOF, is selected as the scoring scheme for the number of neighbors of each instance. MOF gives the highest score to an instance placed very far from any cluster and the lowest score to an instance surrounded by other instances. This leads to the proposed classifier called the conglomerate nearest neighbor classifier, which does not require any parameter assigning the appropriate number of neighbors to each instance ordered by MOF. Experimental results show that this classifier exhibits similar accuracy to the k-nearest neighbor algorithm with the best k over the synthesized datasets. Six UCI datasets, the QSAR dataset, the German dataset, the Cancer dataset, the Wholesale dataset, the Haberman dataset, and the Glass3 dataset are used in the experiment. This method outperforms two UCI datasets, Wholesale and Glass3, and displays similar performance with respect to these six UCI datasets.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136207999","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}
This paper present the study results of predicting energy consumption in the steel industry using modeling methods based on machine learning and deep learning techniques. Machine learning algorithms used in this work include artificial neural network (ANN), k-nearest neighbors (kNN), random forest (RF), and gradient boosting (GB). Deep learning technique is long short-term memory (LSTM). Linear regression, which is the statistical-based learning algorithm, is also applied to be the baseline of this comparative study. The modeling results reveal that among the statistical-based and machine learning-based techniques, GB and RF are the best two models to predict energy consumption, whereas ANN shows the predictive performance comparable to the linear regression model. Nevertheless, LSTM outperforms both statistical-based and machine learning-based algorithms in predicting industrial energy consumption.
{"title":"Deep Learning and Machine Learning Models to Predict Energy Consumption in Steel Industry","authors":"Kittisak Kerdprasop, Nittaya Kerdprasop, Paradee Chuaybamroong","doi":"10.18178/ijml.2023.13.4.1142","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.4.1142","url":null,"abstract":"This paper present the study results of predicting energy consumption in the steel industry using modeling methods based on machine learning and deep learning techniques. Machine learning algorithms used in this work include artificial neural network (ANN), k-nearest neighbors (kNN), random forest (RF), and gradient boosting (GB). Deep learning technique is long short-term memory (LSTM). Linear regression, which is the statistical-based learning algorithm, is also applied to be the baseline of this comparative study. The modeling results reveal that among the statistical-based and machine learning-based techniques, GB and RF are the best two models to predict energy consumption, whereas ANN shows the predictive performance comparable to the linear regression model. Nevertheless, LSTM outperforms both statistical-based and machine learning-based algorithms in predicting industrial energy consumption.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136208241","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 : 2023-01-01DOI: 10.18178/ijml.2023.13.1.1126
{"title":"Protection of Sensitive Data in a Multi-Cloud Database Based on Fragmentation, Encryption, and Hashing","authors":"","doi":"10.18178/ijml.2023.13.1.1126","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.1.1126","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"126 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76224107","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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