Pub Date : 2022-10-05DOI: 10.53759/7669/jmc202202022
Geim Sllian, Toi Mazur
There has been an increase in the importance of software Security, Trust, and Privacy (STP). Product systems must be designed with trustworthy STP protection methods while still rendering the required benefits of applications to its consumers. As a result of this large skill gap, colleges and the software sector have found themselves in a state of supply- and-demand conflict. STP-aware software development requires a new practice Capability Maturity Model (CMM) to address this issue. In order to help colleges progressively increase their students' capacity to apply what they have learned in the classroom, this contribution provides a model that consists of 4 levels: Awareness, Curriculum, Project, and Enterprise, for STP-aware software development. Software development that is STP-aware has been shown to be quite beneficial in the development of programming talent's practice capabilities for learners.
{"title":"A Capability Maturity Model for STP aware Software Development","authors":"Geim Sllian, Toi Mazur","doi":"10.53759/7669/jmc202202022","DOIUrl":"https://doi.org/10.53759/7669/jmc202202022","url":null,"abstract":"There has been an increase in the importance of software Security, Trust, and Privacy (STP). Product systems must be designed with trustworthy STP protection methods while still rendering the required benefits of applications to its consumers. As a result of this large skill gap, colleges and the software sector have found themselves in a state of supply- and-demand conflict. STP-aware software development requires a new practice Capability Maturity Model (CMM) to address this issue. In order to help colleges progressively increase their students' capacity to apply what they have learned in the classroom, this contribution provides a model that consists of 4 levels: Awareness, Curriculum, Project, and Enterprise, for STP-aware software development. Software development that is STP-aware has been shown to be quite beneficial in the development of programming talent's practice capabilities for learners.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83484981","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 : 2022-09-01DOI: 10.18178/ijmlc.2022.12.5.1097
Mahed Javed, L. Mihaylova, N. Bouaynaya
Abstract —This paper presents a novel framework for image classification which comprises a convolutional neural network (CNN) feature map extractor combined with a Gaussian process (GP) classifier. Learning within the CNN-GP involves forward propagating the predicted class labels, then followed by backpropagation of the maximum likelihood function of the GP with a regularization term added. The regularization term takes the form of one of the three loss functions: the Kullback-Leibler divergence, Wasserstein distance, and maximum correntropy. The training and testing are performed in mini batches of images. The forward step (before the regularization) involves replacing the original images in the mini batch with their close neighboring images and then providing these to the CNN-GP to get the new predictive labels. The network performance is evaluated on MNIST, Fashion-MNIST, CIFAR10, and CIFAR100 datasets. Precision-recall and receiver operating characteristics curves are used to evaluate the performance of the GP classifier. The proposed CNN-GP performance is validated with different levels of noise, motion blur, and adversarial attacks. Results are explained using uncertainty analysis and further tests on quantifying the impact on uncertainty with attack strength are carried out. The results show that the testing accuracy improves for networks that backpropagate the maximum likelihood with regularized losses when compared with methods that do not. Moreover, a comparison with a state-of-art CNN Monte Carlo dropout method is presented. The outperformance of the CNN-GP framework with respect to reliability and computational efficiency is
{"title":"Robustness Analysis of Gaussian Process Convolutional Neural Network with Uncertainty Quantification","authors":"Mahed Javed, L. Mihaylova, N. Bouaynaya","doi":"10.18178/ijmlc.2022.12.5.1097","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1097","url":null,"abstract":" Abstract —This paper presents a novel framework for image classification which comprises a convolutional neural network (CNN) feature map extractor combined with a Gaussian process (GP) classifier. Learning within the CNN-GP involves forward propagating the predicted class labels, then followed by backpropagation of the maximum likelihood function of the GP with a regularization term added. The regularization term takes the form of one of the three loss functions: the Kullback-Leibler divergence, Wasserstein distance, and maximum correntropy. The training and testing are performed in mini batches of images. The forward step (before the regularization) involves replacing the original images in the mini batch with their close neighboring images and then providing these to the CNN-GP to get the new predictive labels. The network performance is evaluated on MNIST, Fashion-MNIST, CIFAR10, and CIFAR100 datasets. Precision-recall and receiver operating characteristics curves are used to evaluate the performance of the GP classifier. The proposed CNN-GP performance is validated with different levels of noise, motion blur, and adversarial attacks. Results are explained using uncertainty analysis and further tests on quantifying the impact on uncertainty with attack strength are carried out. The results show that the testing accuracy improves for networks that backpropagate the maximum likelihood with regularized losses when compared with methods that do not. Moreover, a comparison with a state-of-art CNN Monte Carlo dropout method is presented. The outperformance of the CNN-GP framework with respect to reliability and computational efficiency is","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42243897","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 : 2022-09-01DOI: 10.18178/ijmlc.2022.12.5.1110
{"title":"Rotated Grid Search for Hyperparameter Optimization","authors":"","doi":"10.18178/ijmlc.2022.12.5.1110","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1110","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43695297","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 : 2022-09-01DOI: 10.18178/ijmlc.2022.12.5.1106
Ye, In Chang, Siang, Jia Du, Chin, Ting Lin
An erasable itemset is the low profit itemset in the product database. The previous algorithms for mining erasable itemsets ignore the weight of each component of the product and mine erasable itemsets by concerning the product profit only in static product databases. But, when we consider the weight of each component, previous algorithms for mining weighted erasable itemsets would violate the anti-monotone property. That is, the subset X of an erasable pattern Y may not be an erasable pattern. The IWEI algorithm uses the static overestimated factor of itemsets profits to satisfy the “anti-monotone property” of weighted erasable itemset and constructs the IWEI-Tree and OP-List data structure for the dynamic database. However, the IWEI-Tree has to be reconstructed, when reading the whole product database is finished. It will take long time to complete the mining of the whole tree, if the database is frequently updated. The IWEI algorithm generates the too low static value of the overestimated factor to prune candidates. To solve those problems, in this paper, we propose the Inverted-Product-List algorithm (InvP-List) and with the local estimated factor to identify weighted erasable itemsets candidates from the Candidate-List which is generated from InvP-List. We propose the appropriate estimated factor to reduce the number of candidates which is called LMAW. LMAW is a local estimated factor which is used to check whether the itemset is a weighted erasable itemset or not. Our InvP-List algorithm also requires only one database scan. Moreover, our proposed algorithm concerning the local estimated factor creates few numbers of candidates than the IWEI algorithm. From the performance study, we show that our InvP-List algorithm is more efficient than the IWEI algorithm both in the real and the synthetic datasets.
{"title":"Mining Weighted Erasable Itemsets Over the Incremental Database Based on the InvP-List","authors":"Ye, In Chang, Siang, Jia Du, Chin, Ting Lin","doi":"10.18178/ijmlc.2022.12.5.1106","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1106","url":null,"abstract":"An erasable itemset is the low profit itemset in the product database. The previous algorithms for mining erasable itemsets ignore the weight of each component of the product and mine erasable itemsets by concerning the product profit only in static product databases. But, when we consider the weight of each component, previous algorithms for mining weighted erasable itemsets would violate the anti-monotone property. That is, the subset X of an erasable pattern Y may not be an erasable pattern. The IWEI algorithm uses the static overestimated factor of itemsets profits to satisfy the “anti-monotone property” of weighted erasable itemset and constructs the IWEI-Tree and OP-List data structure for the dynamic database. However, the IWEI-Tree has to be reconstructed, when reading the whole product database is finished. It will take long time to complete the mining of the whole tree, if the database is frequently updated. The IWEI algorithm generates the too low static value of the overestimated factor to prune candidates. To solve those problems, in this paper, we propose the Inverted-Product-List algorithm (InvP-List) and with the local estimated factor to identify weighted erasable itemsets candidates from the Candidate-List which is generated from InvP-List. We propose the appropriate estimated factor to reduce the number of candidates which is called LMAW. LMAW is a local estimated factor which is used to check whether the itemset is a weighted erasable itemset or not. Our InvP-List algorithm also requires only one database scan. Moreover, our proposed algorithm concerning the local estimated factor creates few numbers of candidates than the IWEI algorithm. From the performance study, we show that our InvP-List algorithm is more efficient than the IWEI algorithm both in the real and the synthetic datasets.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47439723","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 : 2022-09-01DOI: 10.18178/ijmlc.2022.12.5.1098
S. Tangjitsitcharoen, W. Laiwatthanapaisan
Abstract —An intelligent machine and manufacturing system has a significant role in the near future, especially when the circumstance of manufacturing industries are seriously competitive. New technologies are continuously being developed to serve future manufacturing. CNC turning machine is widely utilized in various advanced manufacturing industries. Straightness is a critical parameter in CNC turning process, which affects the workpiece assembly directly. However, control of straightness of the workpieces during in-process turning is difficult to be measured. Moreover, CNC turning machine cannot be adjusted real-time without stopping the operation. Hence, the aim of this research is to develop the straightness prediction model in the CNC turning process under various cutting conditions for carbon steel and aluminum workpieces in order to improve in-process monitoring and control of straightness. The cutting forces ratio has been adopted to estimate straightness. The Daubechies wavelet transform is utilized to decompose the dynamic cutting forces to remove the noise signals for better prediction. The straightness is calculated by employing the two-layer feed forward neural network, which is trained with the Levenberg-Marquardt back-propagation algorithm. As a result, the in-process straightness could be predicted well with greater accuracy and reliability using the proposed straightness
{"title":"Straightness Prediction in CNC Turning Process for Carbon Steel and Aluminum Workpieces Applying Artificial Neural Networks","authors":"S. Tangjitsitcharoen, W. Laiwatthanapaisan","doi":"10.18178/ijmlc.2022.12.5.1098","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1098","url":null,"abstract":" Abstract —An intelligent machine and manufacturing system has a significant role in the near future, especially when the circumstance of manufacturing industries are seriously competitive. New technologies are continuously being developed to serve future manufacturing. CNC turning machine is widely utilized in various advanced manufacturing industries. Straightness is a critical parameter in CNC turning process, which affects the workpiece assembly directly. However, control of straightness of the workpieces during in-process turning is difficult to be measured. Moreover, CNC turning machine cannot be adjusted real-time without stopping the operation. Hence, the aim of this research is to develop the straightness prediction model in the CNC turning process under various cutting conditions for carbon steel and aluminum workpieces in order to improve in-process monitoring and control of straightness. The cutting forces ratio has been adopted to estimate straightness. The Daubechies wavelet transform is utilized to decompose the dynamic cutting forces to remove the noise signals for better prediction. The straightness is calculated by employing the two-layer feed forward neural network, which is trained with the Levenberg-Marquardt back-propagation algorithm. As a result, the in-process straightness could be predicted well with greater accuracy and reliability using the proposed straightness","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47397241","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 : 2022-09-01DOI: 10.18178/ijmlc.2022.12.5.1111
{"title":"Structure Level Pruning of Efficient Convolutional Neural Networks with Sparse Group LASSO","authors":"","doi":"10.18178/ijmlc.2022.12.5.1111","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1111","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42267927","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 : 2022-09-01DOI: 10.18178/ijmlc.2022.12.5.1105
{"title":"A Machine Learning Approach for the Classification of Lower Back Pain in the Human Body","authors":"","doi":"10.18178/ijmlc.2022.12.5.1105","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1105","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44835280","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 : 2022-09-01DOI: 10.18178/ijmlc.2022.12.5.1109
{"title":"A New Approach to Neural Network Design for Fast Convergence via Feed-forward Loop","authors":"","doi":"10.18178/ijmlc.2022.12.5.1109","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1109","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42193611","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 : 2022-09-01DOI: 10.18178/ijmlc.2022.12.5.1107
{"title":"Lifespan Prediction for Lung and Bronchus Cancer Patients via Machine Learning Techniques","authors":"","doi":"10.18178/ijmlc.2022.12.5.1107","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1107","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47615132","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 : 2022-09-01DOI: 10.18178/ijmlc.2022.12.5.1100
S. Iqbal, Jerin Ishrat Natasha
Abstract —Universities are reputable institutions for higher education and therefore it is crucial that the students have satisfactory grades. Quite often it is seen that during the first few semesters many students dropout from the universities or have to struggle in order to complete the courses. One way to address the issue is early grade prediction using Machine Learning techniques, for the courses taken by the students so that the students in need can be provided special assistance by the instructors. Machine Learning Algorithms such as Linear Regression, Decision Tree Regression, Gaussian Naïve Bayes, Decision Tree Classifier have been applied on the data set to predict students’ results and to compare their accuracy. The evaluated profile data have been collected from the students of 10th semester or above of the Computer Science department, BRAC University, Dhaka, Bangladesh. The Decision Tree Classifier technique has been found to perform the best in predicting the grade, closely followed by Decision Tree Regression and Linear Regression has performed the
{"title":"Early Grade Prediction Using Profile Data","authors":"S. Iqbal, Jerin Ishrat Natasha","doi":"10.18178/ijmlc.2022.12.5.1100","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1100","url":null,"abstract":" Abstract —Universities are reputable institutions for higher education and therefore it is crucial that the students have satisfactory grades. Quite often it is seen that during the first few semesters many students dropout from the universities or have to struggle in order to complete the courses. One way to address the issue is early grade prediction using Machine Learning techniques, for the courses taken by the students so that the students in need can be provided special assistance by the instructors. Machine Learning Algorithms such as Linear Regression, Decision Tree Regression, Gaussian Naïve Bayes, Decision Tree Classifier have been applied on the data set to predict students’ results and to compare their accuracy. The evaluated profile data have been collected from the students of 10th semester or above of the Computer Science department, BRAC University, Dhaka, Bangladesh. The Decision Tree Classifier technique has been found to perform the best in predicting the grade, closely followed by Decision Tree Regression and Linear Regression has performed the","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46751804","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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