Pub Date : 2023-06-01DOI: 10.1142/s0218213023500501
S. R. Lyernisha, C. Seldev Christopher, S. R. Fernisha
{"title":"Image Enhancement Approach for the Underwater Images Using the Optimized Color Balancing Model","authors":"S. R. Lyernisha, C. Seldev Christopher, S. R. Fernisha","doi":"10.1142/s0218213023500501","DOIUrl":"https://doi.org/10.1142/s0218213023500501","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49472168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1142/s0218213023500458
N. Fatma, P. Singh, M. K. Siddiqui
{"title":"Epileptic Seizure Detection in EEG Signal Using Optimized Convolutional Neural Network with Selected Feature Set","authors":"N. Fatma, P. Singh, M. K. Siddiqui","doi":"10.1142/s0218213023500458","DOIUrl":"https://doi.org/10.1142/s0218213023500458","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43583114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1142/s0218213023500495
Berk Bekiroglu, B. Korel
The K-variant is a multi-variant architecture to enhance the security of the time-bounded mission and safety-critical systems. Variants in the K-variant architecture are generated by controlled source program transformations. Previous experimental studies showed that the K-variant architecture might improve the security of systems against memory exploitation attacks. In order to estimate the survivability of K-variant systems, simulation techniques are utilized. However, these techniques are slow and may not be practical for the design of K-variant systems. Therefore, fast and highly accurate estimations of the survivability of K-variant systems are necessary for developers. The neural networks may allow quick and accurate estimation of the survivability of K-variant systems. The developed neural network-based tool can make quick and precise estimations of the survivability of K-variant systems under different conditions. In this paper, the accuracy of the neural network-based tool is investigated in an experimental study. The neural network-based tool estimations are compared with a K-variant attack emulator in three programs for up to ten variant systems under four attack types and three attack durations. The experimental study demonstrates that the neural network-based tool makes fast and accurate estimations of the survivability of K-variant systems under all the conditions investigated.
{"title":"Neural Network-based Tool for Survivability Assessment of K-variant Systems","authors":"Berk Bekiroglu, B. Korel","doi":"10.1142/s0218213023500495","DOIUrl":"https://doi.org/10.1142/s0218213023500495","url":null,"abstract":"The K-variant is a multi-variant architecture to enhance the security of the time-bounded mission and safety-critical systems. Variants in the K-variant architecture are generated by controlled source program transformations. Previous experimental studies showed that the K-variant architecture might improve the security of systems against memory exploitation attacks. In order to estimate the survivability of K-variant systems, simulation techniques are utilized. However, these techniques are slow and may not be practical for the design of K-variant systems. Therefore, fast and highly accurate estimations of the survivability of K-variant systems are necessary for developers. The neural networks may allow quick and accurate estimation of the survivability of K-variant systems. The developed neural network-based tool can make quick and precise estimations of the survivability of K-variant systems under different conditions. In this paper, the accuracy of the neural network-based tool is investigated in an experimental study. The neural network-based tool estimations are compared with a K-variant attack emulator in three programs for up to ten variant systems under four attack types and three attack durations. The experimental study demonstrates that the neural network-based tool makes fast and accurate estimations of the survivability of K-variant systems under all the conditions investigated.","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89668666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1142/s021821302350046x
I. Haouli, Walid Hariri, H. Seridi-Bouchelaghem
{"title":"COVID-Attention: Efficient COVID19 Detection using Pre-trained Deep Models Based on Vision Transformers and X-ray Images","authors":"I. Haouli, Walid Hariri, H. Seridi-Bouchelaghem","doi":"10.1142/s021821302350046x","DOIUrl":"https://doi.org/10.1142/s021821302350046x","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48763258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.1142/s0218213023500471
P. Manivannan, Ö. Özer, U. Harita, V. Ramasamy
{"title":"To Improve the Scalability of an Edge-based Supply Chain Management Framework Utilizing High Priority Access Smart Contract and Blockchain Technology","authors":"P. Manivannan, Ö. Özer, U. Harita, V. Ramasamy","doi":"10.1142/s0218213023500471","DOIUrl":"https://doi.org/10.1142/s0218213023500471","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42944986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1142/s021821302340016x
Ravichander Janapati, Vishwas Dalal, Usha Desai, Rakesh Sengupta, S. Kulkarni, D. Hemanth
Brain-Computer Interface is an emerging field that focuses on transforming brain data into machine commands. EEG-based BCI is widely used due to the non-invasive nature of Electroencephalogram. Classification of EEG signals is one of the primary components in BCI applications. Steady-State Visually Evoked Potential (SSVEP) paradigms have gained importance because of lesser training time, higher precision, and improved information transfer rate compared to P300 and motor imagery paradigms. In this paper, a novel hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron classifier (APS-MLP) is proposed to improve the classification accuracy of SSVEP five classes viz. 6.66, 7.5, 8.57, 10 and 12 Hz, signals. Scaled Conjugate Gradient descent anchors the initial position of Particle Swarm Optimization. The best position, Pbest, of each particle initializes an SCG-MLP, the accuracy of APS-MLP is obtained by averaging the accuracies of each SCG-MLP. The proposed method is compared with standard classifiers namely, k-NN, SVM, LDA and MLP. In which, the proposed algorithm achieves improved training and testing accuracies of 88.69% and 95.4% respectively, which is 12–15% higher than the standard EEG-based BCI classifiers. The proposed algorithm is robust, with a Cohen’s kappa coefficient of 0.96, and will be used in applications such as motion control and improving the quality of life for people with disabilities.
{"title":"Classification of Visually Evoked Potential EEG Using Hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron Classifier","authors":"Ravichander Janapati, Vishwas Dalal, Usha Desai, Rakesh Sengupta, S. Kulkarni, D. Hemanth","doi":"10.1142/s021821302340016x","DOIUrl":"https://doi.org/10.1142/s021821302340016x","url":null,"abstract":"Brain-Computer Interface is an emerging field that focuses on transforming brain data into machine commands. EEG-based BCI is widely used due to the non-invasive nature of Electroencephalogram. Classification of EEG signals is one of the primary components in BCI applications. Steady-State Visually Evoked Potential (SSVEP) paradigms have gained importance because of lesser training time, higher precision, and improved information transfer rate compared to P300 and motor imagery paradigms. In this paper, a novel hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron classifier (APS-MLP) is proposed to improve the classification accuracy of SSVEP five classes viz. 6.66, 7.5, 8.57, 10 and 12 Hz, signals. Scaled Conjugate Gradient descent anchors the initial position of Particle Swarm Optimization. The best position, Pbest, of each particle initializes an SCG-MLP, the accuracy of APS-MLP is obtained by averaging the accuracies of each SCG-MLP. The proposed method is compared with standard classifiers namely, k-NN, SVM, LDA and MLP. In which, the proposed algorithm achieves improved training and testing accuracies of 88.69% and 95.4% respectively, which is 12–15% higher than the standard EEG-based BCI classifiers. The proposed algorithm is robust, with a Cohen’s kappa coefficient of 0.96, and will be used in applications such as motion control and improving the quality of life for people with disabilities.","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90642468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1142/s0218213023400122
Saliha Zahoor, U. Shoaib, M. I. Lali
Many medical specialists used Computer Aided Diagnostic (CAD) systems as a second opinion to detect breast masses. The poor visualization of mass images makes it difficult to identify precisely. To segment the lesions from the mammograms is a difficult task due to different shapes, sizes, and locations of the masses. The motivation of this study is to develop a method that can segment breast mass lesions from mammogram images. The objective is to perform the segmentation of the breast mass mammogram images more precisely at an early stage. Breast mass segmentation is always a basic requirement in computer-aided diagnosis systems. In this study segmentation of the masses abnormalities from the mammogram images is performed by using the Skipping Dilated semantic segmentation approach. The study uses class weights and Dilation factor using semantic Convolutional Neural Network (CNN). It overcomes the class misbalance in tumors and background class, that affect the mean Intersection over Union (MIOU), and weighted-IOU (WIOU) by using class weights. Secondly, dilation convolution magnifies the receptive field exposure that enriches the convolutional operation with context attentiveness. Two public datasets of mammography INbreast and CBIS-DDSM are used. The WIOU of Skipping Dilated Semantic CNN for INbreast is 98.51% and CBIS-DDSM is 94.82% achieved.
许多医学专家使用计算机辅助诊断(CAD)系统作为检测乳房肿块的第二意见。大量图像的可视化效果不佳，难以精确识别。由于肿块的形状、大小和位置不同，从乳房x光片上分割病变是一项困难的任务。本研究的动机是开发一种方法，可以分割乳房肿块病变从乳房x光图像。目的是在早期阶段对乳房肿块进行更精确的分割。乳腺肿块分割一直是计算机辅助诊断系统的基本要求。在本研究中，肿块异常从乳房x线图像的分割是通过使用跳跃扩张语义分割方法进行的。该研究使用语义卷积神经网络(CNN)使用类权重和扩张因子。利用类权重克服了肿瘤和背景类的类不平衡对平均交联(Intersection over Union, MIOU)和加权iou (weighted-IOU, WIOU)的影响。其次，扩张卷积放大了感受野的暴露，丰富了卷积运算的上下文注意性。使用了两个公开的乳腺x线摄影数据集和CBIS-DDSM。INbreast的跳过扩展语义CNN的WIOU为98.51%，CBIS-DDSM的WIOU为94.82%。
{"title":"Breast Masses Segmentation: A Framework of Skip Dilated Semantic Network and Machine Learning","authors":"Saliha Zahoor, U. Shoaib, M. I. Lali","doi":"10.1142/s0218213023400122","DOIUrl":"https://doi.org/10.1142/s0218213023400122","url":null,"abstract":"Many medical specialists used Computer Aided Diagnostic (CAD) systems as a second opinion to detect breast masses. The poor visualization of mass images makes it difficult to identify precisely. To segment the lesions from the mammograms is a difficult task due to different shapes, sizes, and locations of the masses. The motivation of this study is to develop a method that can segment breast mass lesions from mammogram images. The objective is to perform the segmentation of the breast mass mammogram images more precisely at an early stage. Breast mass segmentation is always a basic requirement in computer-aided diagnosis systems. In this study segmentation of the masses abnormalities from the mammogram images is performed by using the Skipping Dilated semantic segmentation approach. The study uses class weights and Dilation factor using semantic Convolutional Neural Network (CNN). It overcomes the class misbalance in tumors and background class, that affect the mean Intersection over Union (MIOU), and weighted-IOU (WIOU) by using class weights. Secondly, dilation convolution magnifies the receptive field exposure that enriches the convolutional operation with context attentiveness. Two public datasets of mammography INbreast and CBIS-DDSM are used. The WIOU of Skipping Dilated Semantic CNN for INbreast is 98.51% and CBIS-DDSM is 94.82% achieved.","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83981250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1142/s0218213023020025
Muhammad Attique Khan, I. Hatzilygeroudis
{"title":"Editorial: Special Issue on Emerging Techniques in Trusted and Reliable Machine Learning","authors":"Muhammad Attique Khan, I. Hatzilygeroudis","doi":"10.1142/s0218213023020025","DOIUrl":"https://doi.org/10.1142/s0218213023020025","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73848934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1142/s0218213023820018
{"title":"Winners of Nikolaos Bourbakis Award for 2022","authors":"","doi":"10.1142/s0218213023820018","DOIUrl":"https://doi.org/10.1142/s0218213023820018","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43527741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-20DOI: 10.1142/s0218213023500434
N. Gkorgkolis, N. Bourbakis
{"title":"Extracting Pseudocode from Digital Block Diagram in Technical Documents","authors":"N. Gkorgkolis, N. Bourbakis","doi":"10.1142/s0218213023500434","DOIUrl":"https://doi.org/10.1142/s0218213023500434","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43991875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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