Wasit Journal of Computer and Mathematics Science最新文献

A proposed CLCOA Technique Based on CLAHE using Cat Optimized Algorithm for Plants Images Enhancement 基于 CLAHE 的拟议 CLCOA 技术，使用猫优化算法增强植物图像

Wasit Journal of Computer and Mathematics Science Pub Date : 2024-04-21 DOI: 10.31185/wjcms.202

Ahmed Naser

{"title":"A proposed CLCOA Technique Based on CLAHE using Cat Optimized Algorithm for Plants Images Enhancement","authors":"Ahmed Naser","doi":"10.31185/wjcms.202","DOIUrl":"https://doi.org/10.31185/wjcms.202","url":null,"abstract":"Image Enhancement is one of the mainly significant with complex techniques in image study. The purpose of image enhancement is to advance the optical presence of an image, or to support a “improved convert representation for future mechanized image processing. Various images similar medical images, satellite images, natural with even real life photographs which have a lowly contrast and noise. This study presents a new enhancement technique based on standard contrast limited adaptive histogram equalization (CLAHE) technique for image enhancement which its name CLCOA. The suggested technique depends on augmentation of swarm intelligence via using Cat Swarm Optimization algorithm (CSO). The swarm intelligence is used to obtain the optimal structure of CLAHE technique. Tomato plant images have used and applied as dataset because of its important and influence in our life. For fair analysis of two techniques, Absolute Mean Brightness Error (AMBE), peak signal-to-noise ratio (PSNR), entropy and Contrast Gain of fundus images are analyzed by using MATLAB. The results show that performance of the proposed technique reveals the efficiently and robustness when compared results of standard technique.\u0000 ","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"119 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140678475","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

Teeth and Technology: The Responsibility of Artificial Intelligence Techniques in the Dental Field- A Literature Review 牙齿与技术：人工智能技术在牙科领域的责任--文献综述

Wasit Journal of Computer and Mathematics Science Pub Date : 2024-04-21 DOI: 10.31185/wjcms.240

Maad M. Mijwil

{"title":"Teeth and Technology: The Responsibility of Artificial Intelligence Techniques in the Dental Field- A Literature Review","authors":"Maad M. Mijwil","doi":"10.31185/wjcms.240","DOIUrl":"https://doi.org/10.31185/wjcms.240","url":null,"abstract":"With the significant growth of modern technology and its integration into many different industries, especially in the healthcare sector, artificial intelligence is one of the critical methods contributing to the development of medical fields, including dentistry. It possesses important and influential techniques that contribute to improving the results of patient care, diagnosis, treatment planning, and tracking the spread of diseases. These techniques play a major role in assisting dentists in diagnosing patients with high efficiency and accuracy. In this review, artificial intelligence techniques in developing the field of dentistry will be reviewed by highlighting the most important literature in which these techniques are involved. A search was conducted in Web of Science, Scopus, and PubMed databases from 2018 to 2023, where many articles were found (n=432), and articles that did not meet the selection criteria were excluded, resulting in thirty included. These articles involve artificial intelligence techniques in six areas: periodontal, dental implantology, forensic dentistry, oral medicine and pathology, orthodontics, and diagnostics/dentistry. In addition, this review presents matters related to artificial intelligence in dentistry, including data security, ethical concerns, and developing dentists' skills. This article finds that deep learning methods are widely utilized in the growth of dentistry, as the results show the accuracy of the results obtained, which is equivalent to the accuracy of professionals, and that it contributes to reducing human errors and revolutionizing the improvement of patient outcomes.","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"112 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140678179","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

Enhancing Spam Detection: A Crow-Optimized FFNN with LSTM for Email Security 增强垃圾邮件检测：用于电子邮件安全的乌鸦优化 FFNN 与 LSTM

Wasit Journal of Computer and Mathematics Science Pub Date : 2024-04-01 DOI: 10.31185/wjcms.199

Saif Alsudani, Hussein Nasrawi, Muntadher Shattawi, Adel Ghazikhani

{"title":"Enhancing Spam Detection: A Crow-Optimized FFNN with LSTM for Email Security","authors":"Saif Alsudani, Hussein Nasrawi, Muntadher Shattawi, Adel Ghazikhani","doi":"10.31185/wjcms.199","DOIUrl":"https://doi.org/10.31185/wjcms.199","url":null,"abstract":"Email security is paramount in today's digital landscape, as the proliferation of spam emails poses a significant threat to individuals and organizations alike. To combat this menace, this study introduces a novel approach that marries the power of Crow Search Optimization (CSO) with a Feedforward Neural Network (FFNN) and Long Short-Term Memory (LSTM) architecture to bolster spam detection. The proposed Crow-Optimized FFNN with LSTM (C-FFNN-LSTM) leverages CSO to fine-tune the neural network's parameters, optimizing its ability to distinguish between legitimate emails and spam. The CSO algorithm mimics the collaborative behavior of crows, thereby enhancing the model's convergence and robustness. Experimental results showcase the effectiveness of the C-FFNN-LSTM approach, achieving remarkable accuracy rates and reducing false positives. This innovation not only enhances email security but also offers a promising avenue for refining spam detection algorithms across various domains. In an era of ever-evolving cyber threats, the C-FFNN-LSTM framework stands as a beacon of improved email security, safeguarding digital communication channels. \u0000In our methodology, we attained an outstanding accuracy level of 99.1% during the testing phase.","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"39 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140766982","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

Non-Compatible Action Graph and Its Adjacency Matrix for The Non-abelian Tensor Product for Groups of Prime Power Order 素幂级数群的非标注张量积的非兼容作用图及其邻接矩阵

Wasit Journal of Computer and Mathematics Science Pub Date : 2024-04-01 DOI: 10.31185/wjcms.204

Mohd Shahoodh

引用次数: 0

Unsupervised Classification of Landsat-8 Satellite Imagery-Based on ISO Clustering 基于 ISO 聚类的 Landsat-8 卫星图像无监督分类

Wasit Journal of Computer and Mathematics Science Pub Date : 2024-04-01 DOI: 10.31185/wjcms.212

Ehsan Ali Al-Zubaidi, Riyad Al Khafaji

引用次数: 0

Optimization of Association Rule Using Ant Colony Optimization (ACO) Approach 基于蚁群算法的关联规则优化

Wasit Journal of Computer and Mathematics Science Pub Date : 2023-10-11 DOI: 10.31185/wjcms.190

Roni La’biran

引用次数: 0

Maximizing Signal Quality for One Dimensional Cells In Mobile Communications 移动通信中一维小区信号质量最大化

Wasit Journal of Computer and Mathematics Science Pub Date : 2023-09-30 DOI: 10.31185/wjcms.160

Adheed Sallomi, None Sazan Kamiran Hasan

引用次数: 0

On triple g transformation and its properties 关于三重g变换及其性质

Wasit Journal of Computer and Mathematics Science Pub Date : 2023-09-30 DOI: 10.31185/wjcms.177

Ahmed Mahdi

引用次数: 0

Enhancing Intrusion Detection with LSTM Recurrent Neural Network Optimized by Emperor Penguin Algorithm 帝企鹅算法优化LSTM递归神经网络增强入侵检测

Wasit Journal of Computer and Mathematics Science Pub Date : 2023-09-30 DOI: 10.31185/wjcms.166

Saif Alsudani, None Adel Ghazikhani

{"title":"Enhancing Intrusion Detection with LSTM Recurrent Neural Network Optimized by Emperor Penguin Algorithm","authors":"Saif Alsudani, None Adel Ghazikhani","doi":"10.31185/wjcms.166","DOIUrl":"https://doi.org/10.31185/wjcms.166","url":null,"abstract":"Intrusion detection systems (IDS) have been developed to identify and classify these attacks in order to prevent them from occurring. However, the accuracy and efficiency of these systems are still not satisfactory. In previous research, most of the methods used were based on ordinary neural networks, which had low accuracy. Therefore, this thesis, with the aim of presenting a new approach to intrusion detection and improving its accuracy and efficiency, uses long-term memory (LSTM) optimized with the Penguin optimization algorithm (EPO). In the proposed approach, first, the features were pre-processed by normalization, cleaning, and formatting in number format. In the next step, the linear discriminant analysis (LDA) method was used to reduce the dimensions of the processed features, and after that, the EPO algorithm was used to optimize the size of the hidden unit of the LSTM network. Finally, the optimized network was evaluated using the NSL-KDD dataset, which is a widely used benchmark dataset in the field of intrusion detection. The results obtained for the training and test datasets were 99.4 and 98.8%, respectively. These results show that the proposed approach can accurately identify and classify network intrusions and outperform many existing approaches. Keywords: Intrusion Detection Systems, Penguin Meta-Heuristic Algorithm, Long-Term Memory Neural Network, Linear Detection Analysis.","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135039089","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

Fraud Detection and Identification in Credit Card Based on Machine Learning Techniques 基于机器学习技术的信用卡欺诈检测与识别

Wasit Journal of Computer and Mathematics Science Pub Date : 2023-09-30 DOI: 10.31185/wjcms.185

Omega John Unogwu, None Youssef Filali

{"title":"Fraud Detection and Identification in Credit Card Based on Machine Learning Techniques","authors":"Omega John Unogwu, None Youssef Filali","doi":"10.31185/wjcms.185","DOIUrl":"https://doi.org/10.31185/wjcms.185","url":null,"abstract":"Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135039167","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