Pub Date : 2023-01-01DOI: 10.18178/ijml.2023.13.1.1125
{"title":"Application of Classification Methods in Forecasting broadband internet subscribers leaving the network on Broadband Internet Subscribers Leaving the Network","authors":"","doi":"10.18178/ijml.2023.13.1.1125","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.1.1125","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79309318","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.2023.13.1.1123
{"title":"Effect of Drop and Rebuilt Operator for Solving the Biobjective Obnoxious p-algorithm for dealing with a special case related to p-Median Problem","authors":"","doi":"10.18178/ijml.2023.13.1.1123","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.1.1123","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88150387","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.2023.13.4.1146
Yanjing Ding, Weiwei Yue, Qinghua Li
Early screening of cervical lesions is of great significance in pathological diagnosis. Owing to the complexity of cell morphological changes and the limitations of medical images, accurate segmentation of cervical cells is still a challenging task. In this paper, an isomorphic multi-branch modulation deformable convolution residual model is proposed to extract features for enhancing the segmentation of small cells and overlapping cytoplasmic boundaries. Then the regional feature extraction, boundary box recognition, and adding a single pixel-level mask at the last level are integrated and optimized based on the cascade regional convolution neural network (Cascade R-CNN) to complete the segmentation of cervical cells for getting better accuracy. The proposed framework was evaluated on the ISBI2014 cervical cell segmentation competition public dataset. Experimental results show that the average accuracy of the network model in cervical cell segmentation is 81.1%, and the accuracy of small targets is 77%. To some extent, it can assist pathologists in screening cervical cancer in the early phase.
{"title":"Automated Segmentation of Cervical Cell Images Using IMBMDCR-Net","authors":"Yanjing Ding, Weiwei Yue, Qinghua Li","doi":"10.18178/ijml.2023.13.4.1146","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.4.1146","url":null,"abstract":"Early screening of cervical lesions is of great significance in pathological diagnosis. Owing to the complexity of cell morphological changes and the limitations of medical images, accurate segmentation of cervical cells is still a challenging task. In this paper, an isomorphic multi-branch modulation deformable convolution residual model is proposed to extract features for enhancing the segmentation of small cells and overlapping cytoplasmic boundaries. Then the regional feature extraction, boundary box recognition, and adding a single pixel-level mask at the last level are integrated and optimized based on the cascade regional convolution neural network (Cascade R-CNN) to complete the segmentation of cervical cells for getting better accuracy. The proposed framework was evaluated on the ISBI2014 cervical cell segmentation competition public dataset. Experimental results show that the average accuracy of the network model in cervical cell segmentation is 81.1%, and the accuracy of small targets is 77%. To some extent, it can assist pathologists in screening cervical cancer in the early phase.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"25 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":"136207213","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.2023.13.4.1141
Fady Tawfik, Yi Gu
In the field of medical images diagnoses, doctors need a valuable second opinion when diagnosing thoracic diseases in chest X-rays. Existing methods of interpreting chest X-ray images classify them into a list of findings without specifying their locations on the images, resulting in uninterpretable results. Convolutional Neural Network (CNN) is a popular model for thoracic diseases diagnoses, which is a deep learning technique that has shown high accuracy in image classification and feature detection. In this work, an advanced CNN model is proposed to identify 14 findings in chest X-rays. For each test image, the intended CNN model should predict a bounding box and class for all findings. The classes range from 0 to 13, with each number corresponding to a specific disease in the dataset. The results have demonstrated that the proposed model outperforms the CapsNet model with an accuracy of 94% in X-ray images classification and labeling.
{"title":"An Advanced Convolutional Neural Network for Detecting Chest X-ray Abnormalities","authors":"Fady Tawfik, Yi Gu","doi":"10.18178/ijml.2023.13.4.1141","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.4.1141","url":null,"abstract":"In the field of medical images diagnoses, doctors need a valuable second opinion when diagnosing thoracic diseases in chest X-rays. Existing methods of interpreting chest X-ray images classify them into a list of findings without specifying their locations on the images, resulting in uninterpretable results. Convolutional Neural Network (CNN) is a popular model for thoracic diseases diagnoses, which is a deep learning technique that has shown high accuracy in image classification and feature detection. In this work, an advanced CNN model is proposed to identify 14 findings in chest X-rays. For each test image, the intended CNN model should predict a bounding box and class for all findings. The classes range from 0 to 13, with each number corresponding to a specific disease in the dataset. The results have demonstrated that the proposed model outperforms the CapsNet model with an accuracy of 94% in X-ray images classification and labeling.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"32 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":"136207360","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.2023.13.4.1147
C Cynthia, Debayani Ghosh, Gopal Krishna Kamath
Machine learning techniques are widely used to protect cyberspace against malicious attacks. In this paper, we propose a machine learning-based intrusion detection system to alleviate Distributed Denial-of-Service (DDoS) attacks, which is one of the most prevalent attacks that disrupt the normal traffic of the targeted network. The model prediction is interpreted using the SHapley Additive exPlanations (SHAP) technique, which also provides the most essential features with the highest Shapley values. For the proposed model, the CICIDS2017 dataset from Kaggle is used for training the classification algorithms. The top features selected by the SHAP technique are used for training a Conditional Tabular Generative Adversarial Networks (CTGAN) for synthetic data generation. The CTGAN-generated data are then used to train prediction models such as Support Vector Classifier (SVC), Random Forest (RF), and Naïve Bayes (NB). The performance of the model is characterized using a confusion matrix. The experiment results prove that the attack detection rate is significantly improved after applying the SHAP feature selection technique.
{"title":"Detection of DDoS Attacks Using SHAP-Based Feature Reduction","authors":"C Cynthia, Debayani Ghosh, Gopal Krishna Kamath","doi":"10.18178/ijml.2023.13.4.1147","DOIUrl":"https://doi.org/10.18178/ijml.2023.13.4.1147","url":null,"abstract":"Machine learning techniques are widely used to protect cyberspace against malicious attacks. In this paper, we propose a machine learning-based intrusion detection system to alleviate Distributed Denial-of-Service (DDoS) attacks, which is one of the most prevalent attacks that disrupt the normal traffic of the targeted network. The model prediction is interpreted using the SHapley Additive exPlanations (SHAP) technique, which also provides the most essential features with the highest Shapley values. For the proposed model, the CICIDS2017 dataset from Kaggle is used for training the classification algorithms. The top features selected by the SHAP technique are used for training a Conditional Tabular Generative Adversarial Networks (CTGAN) for synthetic data generation. The CTGAN-generated data are then used to train prediction models such as Support Vector Classifier (SVC), Random Forest (RF), and Naïve Bayes (NB). The performance of the model is characterized using a confusion matrix. The experiment results prove that the attack detection rate is significantly improved after applying the SHAP feature selection technique.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"86 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":"136208740","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 : 2022-11-01DOI: 10.18178/ijmlc.2022.12.6.1114
{"title":"Realizing Blood Glucose Prediction by Convolutional Recurrent Neural Networks with Residual Blocks","authors":"","doi":"10.18178/ijmlc.2022.12.6.1114","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.6.1114","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44980172","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 : 2022-11-01DOI: 10.18178/ijmlc.2022.12.6.1115
{"title":"Recurrent Neural Network and Convolutional Network for Diabetes Blood Glucose Prediction","authors":"","doi":"10.18178/ijmlc.2022.12.6.1115","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.6.1115","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48498501","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 : 2022-11-01DOI: 10.18178/ijmlc.2022.12.6.1118
{"title":"A Deep Regression Network with Key-joints Localization for Accurate Hand Pose Estimation","authors":"","doi":"10.18178/ijmlc.2022.12.6.1118","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.6.1118","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47950602","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 : 2022-11-01DOI: 10.18178/ijmlc.2022.12.6.1119
{"title":"Support for Visually Impaired Persons' Understanding of Proximity Space and Action Recognition Based on Pointing","authors":"","doi":"10.18178/ijmlc.2022.12.6.1119","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.6.1119","url":null,"abstract":"","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46949381","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 : 2022-11-01DOI: 10.18178/ijmlc.2022.12.6.1120
T. J. Anande, M. Leeson
—Generating network traffic flows remains a critical aspect of developing cyber and network security systems. In this survey, we first consider the history of network traffic generation methods and identify the weaknesses of these. We then proceed to introduce more recent approaches based on machine learning (ML) models. In particular, we focus on Generative Adversarial Network (GAN) models, which have developed from their initial form to encompass many variants in today’s ML landscape. The use of GANs for generating traffic flows that have appeared in the literature are then presented. For each instance, we present the architecture, training methods, generated results, identified limitations and prospects for further research. We thus demonstrate that GANs are key to future developments in network traffic generation and secure cyber and network systems. loss and flow duration . Flow-level
{"title":"Generative Adversarial Networks (GANs): A Survey on Network Traffic Generation","authors":"T. J. Anande, M. Leeson","doi":"10.18178/ijmlc.2022.12.6.1120","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.6.1120","url":null,"abstract":"—Generating network traffic flows remains a critical aspect of developing cyber and network security systems. In this survey, we first consider the history of network traffic generation methods and identify the weaknesses of these. We then proceed to introduce more recent approaches based on machine learning (ML) models. In particular, we focus on Generative Adversarial Network (GAN) models, which have developed from their initial form to encompass many variants in today’s ML landscape. The use of GANs for generating traffic flows that have appeared in the literature are then presented. For each instance, we present the architecture, training methods, generated results, identified limitations and prospects for further research. We thus demonstrate that GANs are key to future developments in network traffic generation and secure cyber and network systems. loss and flow duration . Flow-level","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67504484","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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