International Journal on Artificial Intelligence Tools最新文献_第7页

Image Enhancement Approach for the Underwater Images Using the Optimized Color Balancing Model 基于优化颜色平衡模型的水下图像增强方法

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2023-06-01 DOI: 10.1142/s0218213023500501

S. R. Lyernisha, C. Seldev Christopher, S. R. Fernisha

引用次数: 0

Epileptic Seizure Detection in EEG Signal Using Optimized Convolutional Neural Network with Selected Feature Set 基于特征集优化卷积神经网络的脑电信号癫痫发作检测

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2023-06-01 DOI: 10.1142/s0218213023500458

N. Fatma, P. Singh, M. K. Siddiqui

引用次数: 0

Neural Network-based Tool for Survivability Assessment of K-variant Systems 基于神经网络的k变系统生存能力评估工具

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2023-05-24 DOI: 10.1142/s0218213023500495

Berk Bekiroglu, B. Korel

{"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}

引用次数: 0

COVID-Attention: Efficient COVID19 Detection using Pre-trained Deep Models Based on Vision Transformers and X-ray Images COVID注意力：使用基于视觉变换器和X射线图像的预训练深度模型进行有效的COVID19检测

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2023-05-11 DOI: 10.1142/s021821302350046x

I. Haouli, Walid Hariri, H. Seridi-Bouchelaghem

引用次数: 1

To Improve the Scalability of an Edge-based Supply Chain Management Framework Utilizing High Priority Access Smart Contract and Blockchain Technology 利用高优先级访问智能合约和区块链技术提高基于边缘的供应链管理框架的可扩展性

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2023-05-04 DOI: 10.1142/s0218213023500471

P. Manivannan, Ö. Özer, U. Harita, V. Ramasamy

引用次数: 0

Classification of Visually Evoked Potential EEG Using Hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron Classifier 基于混合锚定粒子群优化的尺度共轭梯度多层感知器视觉诱发电位脑电分类

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2023-05-01 DOI: 10.1142/s021821302340016x

Ravichander Janapati, Vishwas Dalal, Usha Desai, Rakesh Sengupta, S. Kulkarni, D. Hemanth

{"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}

引用次数: 0

Breast Masses Segmentation: A Framework of Skip Dilated Semantic Network and Machine Learning 乳房肿块分割:跳跃扩展语义网络和机器学习框架

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2023-05-01 DOI: 10.1142/s0218213023400122

Saliha Zahoor, U. Shoaib, M. I. Lali

{"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}

引用次数: 0

Editorial: Special Issue on Emerging Techniques in Trusted and Reliable Machine Learning 社论:关于可信和可靠机器学习中的新兴技术的特刊

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2023-05-01 DOI: 10.1142/s0218213023020025

Muhammad Attique Khan, I. Hatzilygeroudis

引用次数: 0

Winners of Nikolaos Bourbakis Award for 2022 2022年Nikolaos Bourbakis奖得主

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2023-05-01 DOI: 10.1142/s0218213023820018

引用次数: 0

Extracting Pseudocode from Digital Block Diagram in Technical Documents 从技术文档中的数字框图中提取伪代码

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2023-04-20 DOI: 10.1142/s0218213023500434

N. Gkorgkolis, N. Bourbakis

引用次数: 0