International Journal of Cognitive Computing in Engineering最新文献_第10页

An ensemble machine learning based bank loan approval predictions system with a smart application 基于智能应用程序的集成机器学习的银行贷款审批预测系统

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.09.001

Nazim Uddin , Md. Khabir Uddin Ahamed , Md Ashraf Uddin , Md. Manwarul Islam , Md. Alamin Talukder , Sunil Aryal

{"title":"An ensemble machine learning based bank loan approval predictions system with a smart application","authors":"Nazim Uddin , Md. Khabir Uddin Ahamed , Md Ashraf Uddin , Md. Manwarul Islam , Md. Alamin Talukder , Sunil Aryal","doi":"10.1016/j.ijcce.2023.09.001","DOIUrl":"https://doi.org/10.1016/j.ijcce.2023.09.001","url":null,"abstract":"<div><p>Banks rely heavily on loans as a primary source of revenue; however, distinguishing deserving applicants who will reliably repay loans presents an ongoing challenge. Conventional selection processes often struggle to identify the most suitable candidates from a pool of loan applicants. In response to this challenge, we present an innovative machine learning (ML) based loan prediction system designed to identify qualified loan applicants autonomously. This comprehensive study encompasses data preprocessing, effective data balancing using SMOTE, and the implementation of diverse ML models, including Logistic Regression, Decision Tree, Random Forest, Extra Trees, Support Vector Machine, K-Nearest Neighbors, Gaussian Naive Bayes, AdaBoost, Gradient Boosting, and advanced deep learning models such as deep neural networks, recurrent neural networks, and long short-term memory models. The model's performance is rigorously assessed in terms of accuracy, recall, and F1_score. Our experimental analysis reveals that the Extra Trees outperforms its counterparts. Furthermore, we successfully predict bank loan defaulters through an ensemble voting model, which includes the top three ML models, achieving a remarkable 0.62% increase in accuracy compared to the Extra Trees. To facilitate user interaction, we have developed a user-friendly desktop-based application. Notably, our findings demonstrate that the voting-based ensemble model surpasses both individual ML models, including Extra Trees, and existing state-of-the-art approaches, achieving an impressive accuracy of 87.26%. This innovative system has the potential to significantly streamline and enhance the efficiency of bank loan approval processes, ultimately benefiting both financial institutions and loan applicants alike.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 327-339"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49741950","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}

引用次数: 0

Ontology-based video retrieval using modified classification technique by learning in smart surveillance applications 基于本体的基于学习改进分类技术的视频检索在智能监控中的应用

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.02.003

B. Sathiyaprasad

{"title":"Ontology-based video retrieval using modified classification technique by learning in smart surveillance applications","authors":"B. Sathiyaprasad","doi":"10.1016/j.ijcce.2023.02.003","DOIUrl":"https://doi.org/10.1016/j.ijcce.2023.02.003","url":null,"abstract":"<div><p>With the rapid advancements in communication and multimedia computing technology, multimedia information, particularly video data, has recently become disproportionately accessible. Video is widely used in a variety of applications, making efficient management and retrieval of the expanding volume of video data critical. To manage such tasks, include automated recognition of the image queries in video retrieval with a reduced degree of system memory usage, spot detection, maintenance time, identification of exact duplicate videos, and so on. This research determination demonstrates the Modified R-Ratio with Viola-Jones Classification Method (MRVJCM) with the relevance of developing techniques and algorithms for automatic recognition of image queries. The R-Ratio Viola–Jones framework has two distinguishable feature maintenance processes, such as Feature Selection and Integration and Feature Cascading. These two distinct features are applied to the motion features (texture, emotions, elements, and shape) within the three features. The proposed techniques and their relevant algorithms are used to retrieve the most accurate videos and to assure the mathematical operation of video retrieval in the operation of the comparable protected system. As a result, the proposed MRVJCM achieves 98% of accuracy, 93% of precision, 92% of recall, and 42% of RMSE.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 55-64"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49742115","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}

引用次数: 1

Research on mixed decision implications based on formal concept analysis 基于形式概念分析的混合决策含义研究

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.02.007

Xingguo Ren , Deyu Li , Yanhui Zhai

引用次数: 4

EDFA: Ensemble deep CNN for assessing student's cognitive state in adaptive online learning environments EDFA:用于自适应在线学习环境中评估学生认知状态的集成深度CNN

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.11.001

Swadha Gupta, Parteek Kumar, RajKumar Tekchandani

{"title":"EDFA: Ensemble deep CNN for assessing student's cognitive state in adaptive online learning environments","authors":"Swadha Gupta, Parteek Kumar, RajKumar Tekchandani","doi":"10.1016/j.ijcce.2023.11.001","DOIUrl":"10.1016/j.ijcce.2023.11.001","url":null,"abstract":"<div><p>Ensuring student engagement is crucial for effective learning outcomes in any classroom setting, including e-learning environments. However, the absence of immediate supervision in online classes makes monitoring and maintaining student attentiveness difficult. To address this challenge, this study proposes a cognitive state detection system that continuously monitors the facial emotion of the learner in an adaptive learning environment. The algorithm is proposed to detect cognitive states such as attentiveness and inattentiveness. The system has been implemented on four separate databases and evaluated using three ensemble models: FT-EDFA, FC-EDFA, and OT-EDFA. The ensemble models have been created by applying transfer learning to two popular pre-trained models, VGG19 and ResNet50, which can learn useful features from facial images for emotion recognition tasks. Combining the features learned by both models, the ensemble approach can achieve better performance in recognising facial emotions. The proposed system can provide continuous feedback to instructors, enabling them to adjust their teaching methods to maintain student engagement and interest. The study has achieved promising results, surpassing the performance of existing methods with recognition rates of 93.11%, 92.34%, and 91.12% on the newly created dataset. By detecting cognitive states in online learners, the proposed system can help instructors understand how engaged and interested their students are during classes. Overall, facial emotion recognition can be useful for improving the quality of e-learning platforms and enhancing student learning outcomes.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 373-387"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307423000359/pdfft?md5=4132d21c0e89460ee78a8801e1f811a5&pid=1-s2.0-S2666307423000359-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135714503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human-like evaluation by facial attractiveness intelligent machine 人脸吸引力智能机的类人评价

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.04.001

Mohammad Karimi Moridani , Nahal Jamiee , Shaghayegh Saghafi

{"title":"Human-like evaluation by facial attractiveness intelligent machine","authors":"Mohammad Karimi Moridani , Nahal Jamiee , Shaghayegh Saghafi","doi":"10.1016/j.ijcce.2023.04.001","DOIUrl":"https://doi.org/10.1016/j.ijcce.2023.04.001","url":null,"abstract":"<div><p>Facial attractiveness is an important factor in social interactions and has been widely studied in psychology and neuroscience. This paper presents a novel approach to the problem of predicting facial attractiveness using machine learning and computer vision techniques. Our main objective is to investigate whether an intelligent machine can learn and accurately predict facial attractiveness based on objective rules in facial features.</p><p>To achieve this, we collected datasets of facial images and corresponding attractiveness rankings for women. We then utilized various machine learning methods, including k-nearest neighbors (KNN) and support vector regression (SVR), to train a predictor model that learned from these datasets to provide a human-like assessment of facial attractiveness. The model used facial feature parameters, such as symmetry and proportion, as input to determine the attractiveness ranking as output.</p><p>We evaluated the performance of our trained predictor model using several metrics, including the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). The best performance was achieved using the KNN algorithm during the testing phase, with R2=0.9902, RMSE=0.0056, and MAPE=0.0856. It indicated a significant improvement in the accuracy of facial attractiveness prediction compared to previous studies.</p><p>Our results demonstrate that an intelligent machine can learn and predict facial attractiveness based on objective rules in facial features, providing a promising approach for ranking facial attractiveness. In comparison to previous studies in this area, our approach shows significant improvement in accuracy, with a correlation coefficient higher than that of human ratings. This work has significant implications for the fields of psychology, neuroscience, and computer science, as it provides a new perspective on the concept of facial attractiveness and its quantification using machine learning.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 160-169"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49741750","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}

引用次数: 2

Continuous word level sign language recognition using an expert system based on machine learning 基于机器学习的专家系统的连续字级手语识别

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.04.002

R Sreemathy, MP Turuk, S Chaudhary, K Lavate, A Ushire, S Khurana

{"title":"Continuous word level sign language recognition using an expert system based on machine learning","authors":"R Sreemathy, MP Turuk, S Chaudhary, K Lavate, A Ushire, S Khurana","doi":"10.1016/j.ijcce.2023.04.002","DOIUrl":"https://doi.org/10.1016/j.ijcce.2023.04.002","url":null,"abstract":"<div><p>The study of sign language recognition systems has been extensively explored using many image processing and artificial intelligence techniques for many years, but the main challenge is to bridge the communication gap between specially-abled people and the general public. This paper proposes a python-based system that classifies 80 words from sign language. Two different models have been proposed in this work: You Only Look Once version 4 (YOLOv4) and Support Vector Machine (SVM) with media-pipe. SVM utilizes the linear, polynomial and Radial Basis Function (RBF) kernels. The system does not need any additional pre-processing and image enhancement operations. The image dataset used in this work is self-created and consists of 80 static signs with a total of 676 images. The accuracy of SVM with media-pipe is 98.62% and the accuracy of YOLOv4 obtained is 98.8% which is higher than the existing state-of-the-art methods. An expert system is also proposed which utilizes both the above models to predict the hand gesture more accurately in real-time.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 170-178"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49741751","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}

引用次数: 2

Research on the standardization strategy of granular computing 颗粒计算标准化策略研究

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.09.004

Donghang Liu , Xuekui Shangguan , Keyu Wei , Chensi Wu , Xiaoying Zhao , Qifeng Sun , Yaoyu Zhang , Ruijun Bai

{"title":"Research on the standardization strategy of granular computing","authors":"Donghang Liu , Xuekui Shangguan , Keyu Wei , Chensi Wu , Xiaoying Zhao , Qifeng Sun , Yaoyu Zhang , Ruijun Bai","doi":"10.1016/j.ijcce.2023.09.004","DOIUrl":"https://doi.org/10.1016/j.ijcce.2023.09.004","url":null,"abstract":"<div><p>As intelligent systems continue to evolve, problems are becoming increasingly complex. The constant abundance of data puts a higher demand on the value of data utilization. Granular computing is a new computational paradigm for complex problem-solving. It takes structured thinking, structured problem-solving methods, and structured information processing patterns as its research objects and belongs to the scope of higher-level human cognitive mechanism research. The development and application of granular computing must be more standardized and unified. The granular computing standardization strategy is the most direct means to promote the regularization of granular computing. In this paper, we first sort out the main applications of granular computing in standards. According to the characteristics of granular computing, a framework of its standard system is proposed to provide a reference for the subsequent research of granular computing standards. The next direction of the granular computing standards strategy is discussed, and solutions are given.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 340-348"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49741951","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}

引用次数: 0

Periocular Region based Gender Identification using Transfer Learning 基于迁移学习的眼周区域性别识别

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.07.003

Aishwarya Kumar, K.R. Seeja

{"title":"Periocular Region based Gender Identification using Transfer Learning","authors":"Aishwarya Kumar, K.R. Seeja","doi":"10.1016/j.ijcce.2023.07.003","DOIUrl":"https://doi.org/10.1016/j.ijcce.2023.07.003","url":null,"abstract":"<div><p>COVID-19 broke out at the end of 2019 and is still affecting the lifestyle of the people. To protect ourselves from the deadly disease, wearing a face mask is recommended when coming in contact with others. The usage of face masks in our daily lives leads to the problem of occlusion for facial image-based gender identification systems. Gender Identification System is an application of computer vision used in biometrics, consumer identification, and security systems. In the situation of masked faces, the only visible part of the face is the area around the eye, i.e., Periocular Region. The motivation behind this research is to build a gender identification system from periocular images with pre-trained CNN models using the Transfer Learning approach. In the proposed methodology, the optimal periocular ROI is first extracted and passed to the different pre-trained CNN models (VGG16, VGG19, ResNet50, ResNet101, Inception V3, DenseNet121) for feature extraction. Then, the fully connected layers are added to the base models for classification. The proposed approach with VGG19, ResNet101, and ResNet50 as the base models outperform existing models with an average accuracy of 98.65%, 98.96%, and 98.99%, respectively, in different experiments on the benchmark UBIPr dataset.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 277-286"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49742015","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}

引用次数: 3

Brain-computer interfacing for flexion and extension of bio-inspired robot fingers 仿生机器人手指屈伸的脑机接口

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.02.006

H.M.K.K.M.B. Herath , W.R. de Mel , Mamta Mittal

{"title":"Brain-computer interfacing for flexion and extension of bio-inspired robot fingers","authors":"H.M.K.K.M.B. Herath , W.R. de Mel , Mamta Mittal","doi":"10.1016/j.ijcce.2023.02.006","DOIUrl":"https://doi.org/10.1016/j.ijcce.2023.02.006","url":null,"abstract":"<div><p>Brain-computer interface (BCI) technology is a topic of study with assistive robot systems that are expanding significantly nowadays. Several advancements have been made in the BCI sector to aid disabled people. However, most studies have used more electrodes, and most design structures are different from the anatomy of the human hand. To control robot fingers like real human hands with fewer electrodes is yet to be developed. This research aims to investigate the controllability of robot fingers using motor imagery. The proposed electroencephalogram (EEG) acquisition system comprises eight EEG electrodes attached to the primary motor cortex region of the human brain. The real human hand's anatomical behavior aided in the robot's development. Initially, the performance of the robot finger model was evaluated on the computer simulation. Finally, a robot model was developed, and flexion and extension movements were examined. According to the experiment's findings, finger flexion and extension control with eight EEG electrodes showed promising results with an accuracy of <em>90.0±1.43%</em> and a precision of <em>0.89</em>. Furthermore, we observed that as people age, the accuracy of robot control decreases.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 89-99"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49742118","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}

引用次数: 1

The Research on Relative Knowledge Distances and Their Cognitive Features 相对知识距离及其认知特征研究

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.03.004

Kanchao Lian , Tao Wang , Baoli Wang , Min Wang , Weihua Huang , Jie Yang

{"title":"The Research on Relative Knowledge Distances and Their Cognitive Features","authors":"Kanchao Lian , Tao Wang , Baoli Wang , Min Wang , Weihua Huang , Jie Yang","doi":"10.1016/j.ijcce.2023.03.004","DOIUrl":"https://doi.org/10.1016/j.ijcce.2023.03.004","url":null,"abstract":"<div><p>Considering that existing knowledge distances fail to include cognitive differences between knowledge and correlations among cognitive standpoints of intelligent agents, this paper first explores the notion of relative knowledge distances in light of relative cognitive principles. Then, under the context of precise and fuzzy settings, this paper depicts the transformation difficulty between any two knowledge given the condition of specific knowledge, and further proves the newly owned features due to the increase of relative knowledge distances and the refinement of conditional knowledge granularities, which can well reflect progressive features of humans’ multi-granularity cognition. Meanwhile, this paper analyzes the difference between absolute knowledge and relative knowledge distances in the structural features of hierarchical clustering. At last, to model and simulate humans’ conditional cognitive features, this paper designs a feature selection algorithm based on the proposed relative knowledge distances to demonstrate the effect of cognitive standpoints and paths on different cognition such as strength, hold and weakness.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 135-148"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49741401","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}

引用次数: 4