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

Advanced deep learning for masked individual surveillance 针对蒙面个人监控的高级深度学习

International Journal of Cognitive Computing in Engineering Pub Date : 2024-01-01 DOI: 10.1016/j.ijcce.2024.07.003

Mohamed Elhoseny , Ahmed Hassan , Marwa H. Shehata , Mohammed Kayed

{"title":"Advanced deep learning for masked individual surveillance","authors":"Mohamed Elhoseny , Ahmed Hassan , Marwa H. Shehata , Mohammed Kayed","doi":"10.1016/j.ijcce.2024.07.003","DOIUrl":"10.1016/j.ijcce.2024.07.003","url":null,"abstract":"<div><p>During Covid-19 pandemic, face masks have become a ubiquitous protective measure. This poses new challenges for surveillance systems that heavily rely on facial recognition. To address this critical issue, we present a novel enhanced surveillance system that leverages deep learning techniques to tackle two crucial tasks simultaneously: anomaly detection of masked individuals' activities and masked face completion for accurate recognition. For anomaly detection, we employ a custom-designed deep neural network capable of processing real-time video streams. Finding a dataset of anomaly events of masked individuals is a big challenge for us. We handle this challenge using efficient techniques such as Dlib library and other image processing techniques. The network is trained on a diverse dataset encompassing normal and abnormal activities of masked individuals, enabling it to identify suspicious behaviors effectively. The surveillance cameras will exchange information, using a suitable network protocol, about detected anomalies and share relevant image data to aid in decision-making and choose the best images for further processing. In the context of masked face completion, we present a novel architecture called CCGAN network that is a combination of convolutional neural network (CNN) and conditioned generative adversarial network (CGAN) to generate the hidden parts of the face in a form that is accurate and close to the original face shape, as shown in our results. We conduct extensive experiments on publicly available datasets, demonstrating superior performance in both anomaly detection and masked face completion tasks. We have achieved 90% accuracy for anomaly detection of masked people.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 406-415"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266630742400024X/pdfft?md5=23b1010f10e33b54d442633da88beab1&pid=1-s2.0-S266630742400024X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141710925","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

Analyzing emotions in online classes: Unveiling insights through topic modeling, statistical analysis, and random walk techniques 分析在线课堂中的情绪：通过主题建模、统计分析和随机漫步技术揭示洞察力

International Journal of Cognitive Computing in Engineering Pub Date : 2024-01-01 DOI: 10.1016/j.ijcce.2024.05.003

Benyoussef Abdellaoui , Ahmed Remaida , Zineb Sabri , Mohammed Abdellaoui , Abderrahim El Hafidy , Younes El Bouzekri El Idrissi , Aniss Moumen

{"title":"Analyzing emotions in online classes: Unveiling insights through topic modeling, statistical analysis, and random walk techniques","authors":"Benyoussef Abdellaoui , Ahmed Remaida , Zineb Sabri , Mohammed Abdellaoui , Abderrahim El Hafidy , Younes El Bouzekri El Idrissi , Aniss Moumen","doi":"10.1016/j.ijcce.2024.05.003","DOIUrl":"https://doi.org/10.1016/j.ijcce.2024.05.003","url":null,"abstract":"<div><p>High dropout rates globally perpetuate educational disparities with various underlying causes. Despite numerous strategies to address this issue, more attention should be given to understanding and addressing student emotions during classes. This lack of focus adversely affects learner engagement and retention rates. While previous studies on online learning have primarily emphasized the effectiveness of technology, infrastructure, cognition, motivation, and economic benefits, there is still a gap in understanding the emotional aspects of distance learning. First, this study addresses this gap by employing thematic modeling and utilizing non-negative matrix factorization (NMF) for emotion recognition through students’ deep learning techniques and facial emotion recognition (FER). Second, statistical analysis of these findings further augments the depth of the study. Finally, the research proposes a mathematical model based on the random walk of emotional state transitions. The findings of this study underscore the importance of considering emotions in distance learning environments and their significant impact on student’s academic performance and satisfaction. By acknowledging and addressing these emotional factors, educators can enhance learner engagement, promote positive emotions, mitigate negative emotions during online learning, and ultimately improve the effectiveness of online courses.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 221-236"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000159/pdfft?md5=86e4fad31b74370addbe96b8a3e7de72&pid=1-s2.0-S2666307424000159-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141249839","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

Supportive particle swarm optimization with time-conscious scheduling (SPSO-TCS) algorithm in cloud computing for optimized load balancing 云计算中的支持性粒子群优化与时间意识调度（SPSO-TCS）算法，用于优化负载平衡

International Journal of Cognitive Computing in Engineering Pub Date : 2024-01-01 DOI: 10.1016/j.ijcce.2024.05.002

M. Menaka (Research Scholar), K.S. Sendhil Kumar (Associate Professor)

{"title":"Supportive particle swarm optimization with time-conscious scheduling (SPSO-TCS) algorithm in cloud computing for optimized load balancing","authors":"M. Menaka (Research Scholar), K.S. Sendhil Kumar (Associate Professor)","doi":"10.1016/j.ijcce.2024.05.002","DOIUrl":"10.1016/j.ijcce.2024.05.002","url":null,"abstract":"<div><p>Task scheduling for virtual machines (VMs) has shown to be essential for the effective development of cloud computing at the lowest cost and fastest turnaround time. A number of research gaps about job schedule optimization are included in the current paper. A thorough analysis of the data generated by this activity is essential to resolving the resource allocation mechanism of the cloud architecture. To fully utilize virtual machines with a similar weight distribution, a strategy-oriented mixed support and load balancing structure has been developed in this work. To minimize make-span time and accomplish initial load balancing, the SPSO-TCS technique combines Time-Conscious Scheduling with Supportive Particle Swarm Optimization. Finding the optimal make span time minimization for each virtual environment is the aim of this stage. Its main objective is to discover the sequence of activities with the least computation time and to reduce the time required to finish each operation. Utilizing the hybrid idea leads to a decrease in makespan and the use of the least amount of energy.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 192-198"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000160/pdfft?md5=640fe5c8a4af09bb04b94695fa88c4ba&pid=1-s2.0-S2666307424000160-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141034806","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

Image cyberbullying detection and recognition using transfer deep machine learning 利用传输深度机器学习进行图像网络欺凌检测和识别

International Journal of Cognitive Computing in Engineering Pub Date : 2024-01-01 DOI: 10.1016/j.ijcce.2023.11.002

Ammar Almomani , Khalid Nahar , Mohammad Alauthman , Mohammed Azmi Al-Betar , Qussai Yaseen , Brij B. Gupta

{"title":"Image cyberbullying detection and recognition using transfer deep machine learning","authors":"Ammar Almomani , Khalid Nahar , Mohammad Alauthman , Mohammed Azmi Al-Betar , Qussai Yaseen , Brij B. Gupta","doi":"10.1016/j.ijcce.2023.11.002","DOIUrl":"10.1016/j.ijcce.2023.11.002","url":null,"abstract":"<div><p>Cyberbullying detection on social media platforms is increasingly important, necessitating robust computational methods. Current approaches, while promising, have not fully leveraged the combined strengths of deep learning and traditional machine learning for enhanced performance. Moreover, online content complexity requires models that can capture nuanced contexts beyond text, which many current methods lack. This research proposes a novel hybrid approach using deep learning models as feature extractors and machine learning classifiers to improve cyberbullying detection. Extracting features using pre-trained deep learning models like InceptionV3, ResNet50, and VGG16, then feeding them into classifiers like Logistic Regression and Support Vector Machines, enhances understanding of the complex contexts where cyberbullying occurs. Experiments on an image dataset showed that combining deep learning and machine learning achieved higher accuracy than using either approach alone. This novel framework bridges the gap in existing literature and contributes to broader efforts to combat cyberbullying through more nuanced, context-aware detection methods. The hybrid technique demonstrates the potential of blending deep learning's representation learning strengths with machine learning's sample efficiency and interpretability.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 14-26"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307423000360/pdfft?md5=f0516ed3a8944953ff70302625dee76b&pid=1-s2.0-S2666307423000360-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139023122","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

Adversarial learning for Mirai botnet detection based on long short-term memory and XGBoost 基于长短期记忆和 XGBoost 的 Mirai 僵尸网络检测对抗学习

International Journal of Cognitive Computing in Engineering Pub Date : 2024-01-01 DOI: 10.1016/j.ijcce.2024.02.004

Vajratiya Vajrobol , Brij B. Gupta , Akshat Gaurav , Huan-Ming Chuang

{"title":"Adversarial learning for Mirai botnet detection based on long short-term memory and XGBoost","authors":"Vajratiya Vajrobol , Brij B. Gupta , Akshat Gaurav , Huan-Ming Chuang","doi":"10.1016/j.ijcce.2024.02.004","DOIUrl":"10.1016/j.ijcce.2024.02.004","url":null,"abstract":"<div><p>In today's world, where digital threats are on the rise, one particularly concerning threat is the Mirai botnet. This malware is designed to infect and command a collection of Internet of Things (IoT) devices. The use of Mirai attacks has intensified in recent times, thus threatening the smooth operation of numerous devices that are connected to a network. Such attacks carry adverse consequences that include interference with services or the leakage of confidential information. To fight this growing threat, smart and flexible detection techniques are required to counter the new methods cyber attackers use. The aim of this research is to develop a resilient defense against Mirai botnet attacks. The Long Short Term Memory term (LSTM) and XGBoost combined have the best performance of 97.7% accuracy score. With this combination, the aim is to strengthen our cyber defenses against sophisticated and dynamically operating Mirai botnets to further enhance the security of our digital world.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 153-160"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000081/pdfft?md5=a342579382a3b571e70a19deb7fea9bd&pid=1-s2.0-S2666307424000081-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140091462","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

Data-driven strategies for digital native market segmentation using clustering 利用聚类进行数字原生市场细分的数据驱动战略

International Journal of Cognitive Computing in Engineering Pub Date : 2024-01-01 DOI: 10.1016/j.ijcce.2024.04.002

Md Ashraf Uddin , Md. Alamin Talukder , Md. Redwan Ahmed , Ansam Khraisat , Ammar Alazab , Md. Manowarul Islam , Sunil Aryal , Ferdaus Anam Jibon

{"title":"Data-driven strategies for digital native market segmentation using clustering","authors":"Md Ashraf Uddin , Md. Alamin Talukder , Md. Redwan Ahmed , Ansam Khraisat , Ammar Alazab , Md. Manowarul Islam , Sunil Aryal , Ferdaus Anam Jibon","doi":"10.1016/j.ijcce.2024.04.002","DOIUrl":"10.1016/j.ijcce.2024.04.002","url":null,"abstract":"<div><p>The rapid growth of internet users and social networking sites presents significant challenges for entrepreneurs and marketers. Understanding the evolving behavioral and psychological patterns across consumer demographics is crucial for adapting business models effectively. Particularly, the emergence of new firms targeting adolescents and future generations underscores the importance of comprehending online consumer behavior and communication dynamics. To tackle these challenges, we introduce a Machine Learning-based Digital Native Market Segmentation designed to cater specifically to the interests of digital natives. Leveraging an open-access prototype dataset from social networking sites (SNS), our study employs a variety of clustering techniques, including Kmeans, MiniBatch Kmeans, AGNES, and Fuzzy C-means, to uncover hidden interests of teenage consumers from SNS data. Through rigorous evaluation of these clustering approaches by default parameters, we identify the optimal number of clusters and group consumers with similar tastes effectively. Our findings provide actionable insights into business impact and critical patterns driving future marketing growth. In our experiment, we systematically evaluate various clustering techniques, and notably, the Kmeans cluster outperforms others, demonstrating strong segmentation ability in the digital market. Specifically, it achieves silhouette scores of 63.90% and 58.06% for 2 and 3 clusters, respectively, highlighting its effectiveness in segmenting the digital market.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 178-191"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000135/pdfft?md5=ec289b65f0b3a99e015c8fec612c23f6&pid=1-s2.0-S2666307424000135-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141047443","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

COVINet: A hybrid model for classification of COVID and Non-COVID pneumonia in CT and X-Ray imagery COVINet:一种用于CT和x线图像中COVID和非COVID肺炎分类的混合模型

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.03.005

Vasu Mittal, Akhil Kumar

{"title":"COVINet: A hybrid model for classification of COVID and Non-COVID pneumonia in CT and X-Ray imagery","authors":"Vasu Mittal, Akhil Kumar","doi":"10.1016/j.ijcce.2023.03.005","DOIUrl":"https://doi.org/10.1016/j.ijcce.2023.03.005","url":null,"abstract":"<div><p>The COVID-19 pandemic has resulted in a significant increase in the number of pneumonia cases, including those caused by the Coronavirus. To detect COVID pneumonia, RT-PCR is used as the primary detection tool for COVID-19 pneumonia but chest imaging, including CT scans and X-Ray imagery, can also be used as a secondary important tool for the diagnosis of pneumonia, including COVID pneumonia. However, the interpretation of chest imaging in COVID-19 pneumonia can be challenging, as the signs of the disease on imaging may be subtle and may overlap with normal pneumonia. In this paper, we propose a hybrid model with the name COVINet which uses ResNet-101 as the feature extractor and classical K-Nearest Neighbors as the classifier that led us to give automated results for detecting COVID pneumonia in X-Rays and CT imagery. The proposed hybrid model achieved a classification accuracy of 98.6%. The model's precision, recall, and F1-Score values were also impressive, ranging from 98-99%. To back and support the proposed model, several CNN-based feature extractors and classical machine learning classifiers have been exploited. The outcome with exploited combinations suggests that our model can significantly enhance the accuracy and precision of detecting COVID-19 pneumonia on chest imaging, and this holds the potential of being a valuable resource for early identification and diagnosis of the illness by radiologists and medical practitioners.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 149-159"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49741910","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

Region based medical image encryption using advanced zigzag transform and 2D logistic sine map (2DLSM) 基于先进之字形变换和二维逻辑正弦映射(2DLSM)的区域医学图像加密

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2023.10.001

Prabhavathi K , Anandaraju M B , Vinayakumar Ravi

{"title":"Region based medical image encryption using advanced zigzag transform and 2D logistic sine map (2DLSM)","authors":"Prabhavathi K , Anandaraju M B , Vinayakumar Ravi","doi":"10.1016/j.ijcce.2023.10.001","DOIUrl":"https://doi.org/10.1016/j.ijcce.2023.10.001","url":null,"abstract":"<div><p>A large number of medical images are generated for diagnostic purposes, disease monitoring, research and education, quality control in health services, and so on. The secure transmission and storage of them demand a significant effort. Most of the available encryption schemes are designed for non-medical images, whereas medical images need a higher level of security and robust authentication. Additionally, in certain cases, only a specific part of the image, which may be separated into the region of interest and the region of background, medical images can be divided into these two regions. A region-based medical image encryption using a 2D logistic sine map (2DLSM) and an advanced zig zag transform is used to secure medical images. First, the Region of Interest (ROI) is extracted from the original medical image using basic morphological techniques, including edge detection, dilation, and erosion. Secondly, the ROI is encrypted using a complex zigzag transform and a 2D logistic sine map (2DLSM). Advanced zigzag transform that crosses in both directions while beginning at random points to jumble the image. This new zigzag transform method is more complex than existing zigzag transform techniques because the number of sequence types is equal to the number of pixels in the plaintext image. The confused image is diffused using a random sequence created using the 2D logistic sine map approach after numerous iterations of an advanced zigzag transformation. In order to save time and computational resources, the background region pixels are eliminated during encryption. Experiments and security analyses show that the suggested approach is strong in defending against diverse assaults and can effectively secure ROI of different types and sizes of medical photos.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"4 ","pages":"Pages 349-362"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49741953","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

Granular-based state estimation for nonlinear fractional control systems and its circuit cognitive application 非线性分数阶控制系统基于粒度的状态估计及其电路认知应用

International Journal of Cognitive Computing in Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.ijcce.2022.12.001

Tao Zhan

引用次数: 6

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