Egyptian Informatics Journal最新文献

{"title":"Basketball technical action recognition based on a combination of capsule neural network and augmented red panda optimizer","authors":"Nu Sha","doi":"10.1016/j.eij.2024.100603","DOIUrl":"10.1016/j.eij.2024.100603","url":null,"abstract":"<div><div>Basketball is a group sport that needs precise identification of the players’ practical actions in different shooting movements for effective training and performance enhancement. This subjective nature of training assessments that most of the time rely only on coaches’ observations, highlights the need for objective analysis tools. The subjective and non-objective nature of present educational calculations that are often based on the observations and experiences of coaches and coaches, highlights the requirement for objective and data-driven analysis tools. Such tools can help trainers make more precise and unbiased calculations of student performance and make better instructional choices. This study presents a new model to identify the basketball technical actions based on combination of the CapsNets or Capsule Neural Networks with an ARPO or augmented variant of Red Panda Optimizer. The study conducts the tasks presented by changing lighting settings and complicated human movements in basketball. By means of the suggested CapsNets/ARPO model, the network’s capability can be improved in distinguishing the dynamic targets. The CapsNet/ARPO system reaches advanced performance in the recognition of shooting actions in basketball, with an accuracy of 92.6% and outperforming existing approaches. Its modular design and user-friendly interface make it easily integrable, and a case study with a professional team indicates significant improvements in player performance (15.6% increase in shooting accuracy) and reduced implementation time (30%) to demonstrate its potential to improve basketball analytics and coaching.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100603"},"PeriodicalIF":5.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A file archival integrity check method based on the BiLSTM + CNN model and deep learning","authors":"Jinxun Li,&nbsp;Tingjun Wang,&nbsp;Chao Ma,&nbsp;Yunxuan Lin,&nbsp;Qing Yan","doi":"10.1016/j.eij.2024.100597","DOIUrl":"10.1016/j.eij.2024.100597","url":null,"abstract":"<div><div>Validating and integrity-checking archives ensures that files are authentic, trustworthy, and usable. In the age of digital technology, historical records must be genuine. Researching in archives raises ethical issues while having little to do with individuals. Traditional archive integrity solutions have scaling issues, real-time monitoring issues, and missed opportunities. An updated Archive File Integrity Check Method (AFICM) may solve these issues, and the paper explains it. Deep learning allows the combination of a Bidirectional Long-Short Term Memory (Bi-LSTM) with adaptive gating and an adaptive Temporal Convolutional Neural Network (TCNN) with multi-scale temporal attention. This method protects archived material against manipulation, which is crucial. The recommended method extracts complex sequential patterns and variants using adaptive TCNN trained on file data. Next, it analyzes these features using a Bi-LSTM network and attenuation method. It allows it to highlight significant temporal correlations while downplaying irrelevant data selectively. The hybrid model outperforms checksums in accuracy and dependability. It uses adaptive TCNNs for time-related feature extraction and attenuated Bi-LSTM for refinement. The F1 score, recall, accuracy, precision, and AU-ROC are critical measures for model evaluation. The AICM performed well overall, with 97.32% precision and 98.95% accuracy. This integrity check method outperforms others with an F1 score of 97.58, an AU-ROC of 0.983, and a recall rate of 98.18%. The findings set a new standard for archiving system integrity testing by showing the model’s dependability and security in several use scenarios.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100597"},"PeriodicalIF":5.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bayesian regularization intelligent computing scheme for the fractional dengue virus model","authors":"Manoj Gupta ,&nbsp;Pattarasinee Bhattarakosol","doi":"10.1016/j.eij.2024.100606","DOIUrl":"10.1016/j.eij.2024.100606","url":null,"abstract":"<div><div>This research’s goal is to investigate the numerical assessments of a fractional order dengue viral model (FO-DVM) by using the artificial intelligence procedure of Bayesian regularization neural networks (BRNNs). The FO derivatives present more precise results as compared to integer order for solving the DVM. The dynamics of the mathematical DVM form is considered into five classes. The computing stochastic BRNNs approach is presented for three variations with the selection of the data as testing 13%, authentication 11% and training 76% together with sixteen hidden neurons. The result’s comparison is accessible in the form of overlapping, which is based on the BRNNs approach and reference Adam solutions. However, minor absolute error around 10^-05 to 10^-07 enhances the worth of the proposed solver. The BRNNs approach is used to minimize the mean square error for the mathematical FO-DVM. The obtained measurements of error histograms values, and regression coefficient calculated as 1 are presented to verify the efficiency of stochastic BRNNs approach.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100606"},"PeriodicalIF":5.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced breast cancer detection and classification via CAMR-Gabor filters and LSTM: A deep Learning-Based method","authors":"Vinit Kumar ,&nbsp;Chandrashekhara K T ,&nbsp;Naga Padmaja Jagini ,&nbsp;K Varada Rajkumar ,&nbsp;Rakesh Kumar Godi ,&nbsp;Praveen Tumuluru","doi":"10.1016/j.eij.2024.100602","DOIUrl":"10.1016/j.eij.2024.100602","url":null,"abstract":"<div><div>Breast cancer detection and classification are crucial for early diagnosis and effective treatment planning. This work proposed Modified Context-Aware Multiresolution Gabor Filters-Based Breast Cancer Classification (CAMR- GF-BCC) for identifying and categorizing breast cancer. Initially, the mammographic images are preprocessed through normalization and Gaussian filtering to enhance image quality and suppress noise. Subsequently, the processed images are segmented using the DeepLabv3 + model, which effectively delineates the regions of interest. Post-segmentation, the images are masked to isolate the significant features for analysis. Arithmetic features and CAMR-GF are then performed from these masked images, capturing essential characteristics pertinent to breast cancer detection. These features serve as inputs to a Long Short-Term Memory (LSTM). It is utilized in the work of categorization, leveraging its capability to handle sequential data and capture complex patterns. The proposed method is rigorously evaluated using standard performance metrics, showing its effectiveness in precisely identifying and categorizing breast cancer. This CAMR-GF-BCC work has 99.48 accuracy, 99.64 sensitivity, 99.14 specificity, 99.01 precision, and 98.16 F1-score. The results indicate a promising improvement in diagnostic accuracy, potentially aiding in timely and precise breast cancer treatment decisions.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100602"},"PeriodicalIF":5.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced colon cancer detection: Integrating context-aware multi-image fusion (Camif) in a multi-stage framework","authors":"M.V.R. Vittal","doi":"10.1016/j.eij.2025.100609","DOIUrl":"10.1016/j.eij.2025.100609","url":null,"abstract":"<div><div>Colon cancer begins in the large intestine, often evolving from benign polyps into malignant cancer. Early detection through screening is vital for effective treatment and better survival rates. Risk factors include age, genetics, diet, and lifestyle, with symptoms like changes in bowel habits and blood in the stool, though early stages may be asymptomatic. This work proposed a comprehensive multi classes detection and classification of colon cancer. In this work we used publicly available Curated Colon Dataset to diagnose conditions such as esophagitis, ulcerative colitis, polyps, and normal cases. The proposed approach uses advanced deep learning models to integrate pre-processing, segmentation, and classification. The process begins with pre-processing, which involves resizing, contrast enhancement, noise reduction, and normalization of pixel values. This work proposes a Context-Aware Multi-Image Fusion (CA-MIF) technique in the preprocessing phase to improve the visibility of blood vessels and tissue texture, enhancing diagnostic accuracy. The processed images are then input to a U-Net++ model for segmentation, generating masks highlighting regions of interest, including the colon and affected areas. Post-segmentation, image enhancement techniques further refine the quality and clarity of the images. Enhanced images are then classified using the ResNet-50 model, trained to categorize images into four distinct classes: esophagitis, ulcerative colitis, polyps, and normal. In the classification phase, cancerous classes (ulcerative colitis and polyps) undergo additional segmentation using DeepLabv3+. Model 1 (DeepLabv3+) is applied to ulcerative colitis, generating detailed masks to analyze affected regions, while Model 2 (DeepLabv3+) is used for polyps. For the U-Net++ and DeepLabv3+ models, evaluation measures are segmentation accuracy, precision, recall, and F1 score; for the ResNet-50 model, these metrics are classification accuracy, precision, recall, and F1 score. When it comes to detecting and differentiating between malignant and non-cancerous illnesses, the framework achieves great accuracy., demonstrating its effectiveness and potential for clinical applications in medical image analysis. The results indicate the proposed method’s high efficacy, achieving an F1 score of 99.31. It also demonstrated strong performance metrics with a specificity of 99.91, sensitivity of 99.10, accuracy of 98.18, and a Dice coefficient of 99.82, highlighting its robust capability in accurately detecting colon cancer.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100609"},"PeriodicalIF":5.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing urban public sports facilities for recognition and optimization using intelligent image processing","authors":"Zhongqian Zhang","doi":"10.1016/j.eij.2024.100604","DOIUrl":"10.1016/j.eij.2024.100604","url":null,"abstract":"<div><div>Quality of urban public sports facilities has an implication for increasing sports satisfaction levels in individuals and for developing a better way of life in cities. The current study aims to assess and improve urban public sports services through intelligent image processing techniques for assessing sports facilities. The method incorporates an optimized Residual-Shuffle Network modified by a boosted variant of Spring Search Algorithm (BSSA) for efficient image recognition along with metaheuristics and super-efficiency data envelopment analysis (SE-DEA) model. The images captured systematically using photographic equipment identify such key information as facility usage, viewer demographics, and activity levels by deep learning algorithms. Sports facilities’ effectiveness evaluation for improvement and optimization has been done using metaheuristics and SE-DEA model. The model has been verified with other modern methods, including Faster R-CNN and Convolutional Neural Network (CNN). The results indicate that the SE-DEA model with an accuracy of 94.76% in recognizing sports facilities, outperforming advanced comparative models like Faster R-CNN (74.21%) and CNN (60.54%). The mean execution time of SE-DEA is 5.6 s, which is slower than Faster R-CNN (4.13 s) but faster than CNN (10.98 s). Also, the SE-DEA model provides a significant reduction in costs, with a public service fee of 1200 (compared to 3200 for traditional public services) and a facility maintenance cost of 1000 (compared to 2500 for traditional public services).</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100604"},"PeriodicalIF":5.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating feature extraction by SIFT methods for prostate cancer early detection","authors":"Shadan Mohammed Jihad ,&nbsp;Ali Aalsaud ,&nbsp;Firas H. Almukhtar ,&nbsp;Shahab Kareem ,&nbsp;Raghad Zuhair Yousif","doi":"10.1016/j.eij.2024.100607","DOIUrl":"10.1016/j.eij.2024.100607","url":null,"abstract":"<div><div>Globally, for this leading type of cancer among males, early detection is indispensable for increasing treatment success rates and prognoses of the patients. This research study, therefore, seeks to explore the effectiveness of the SIFT method in improving feature extraction toward the accurate detection of incipient prostate cancer. The robust SIFT relates to tasks of object recognition within computer vision, in the recognition of prostatic regions where grey-level distributions differ remarkably between benign and malignant tissues. The adopted methodology was based on the comparative analysis and benchmarking of the performance of feature extraction based on SIFT against traditional image processing techniques with a generic representation on a number of metrics: sensitivity, specificity, and overall diagnostic accuracy. A dataset consisting of annotated prostate MRI images was utilized to train and validate the model. According to the results so far revealed, the SIFT model can isolate and recognize key features across different scales and angles far better than the cue given by any of the conventional methods currently in use, therefore indicating a much more accurate and reliable cue to early-stage prostate cancer.</div><div>Besides, the model developed on SIFT was found to have significantly improved the rate of detection for early-stage prostate tumors, which usually go undetected in conventional methods of imaging. This study, therefore, highlights the potential for use in the early detection of prostate cancer with advanced feature extraction methods, such as SIFT, and points toward a very promising direction of further research on applying computer vision techniques to problems in medical diagnostic applications. It would, therefore, suggest further experimentations to optimize these methodologies in clinical settings, otherwise which may revolutionize clinical diagnostics for prostate cancer and early intervention strategies.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100607"},"PeriodicalIF":5.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spam detection using hybrid model on fusion of spammer behavior and linguistics features","authors":"Amna Iqbal ,&nbsp;Muhammad Younas ,&nbsp;Saman Iftikhar ,&nbsp;Fakeeha Fatima ,&nbsp;Rabia Saleem","doi":"10.1016/j.eij.2024.100605","DOIUrl":"10.1016/j.eij.2024.100605","url":null,"abstract":"<div><div>E-commerce sites, forums, and blogs have become popular platforms for people to share their views. Reviews have emerged as a crucial source of information for potential customers, influencing their purchasing decisions. Similarly for gaining profit or fame, Spam reviews are deliberately written with the intention of defaming businesses or individuals. This act is known as review spamming. Spam review detection is rapidly answered by various ML techniques. Review of spamming is more challenging task in multilingual communities. Spammer behavior features and linguistic features often exhibit complex relationships that influence the nature of spam reviews. The unified representation of features is another challenging task in spam detection. Various deep learning approaches have been proposed for review spamming, including different neural networks (Convolutional Neural Network, CNN). These methods are specialized in extracting the features but lack to capture feature dependencies effectively with other features. Spam Review Detection using the Fusion Gradient Boosting (GB) Model and Support Vector Machine (SVM) (Hybrid-BoostSVM) is proposed with fusion of spammer behavior features and linguistic features to automatically detect and classify the spam reviews. Fusion enables the proposed model to automatically learn the interactions between the features during the training process, allowing it to capture complex relationships and make predictions based on both types of features. It apparently shows the promising result by obtaining <strong>94.6 %</strong> accuracy.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100605"},"PeriodicalIF":5.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The quality of service assessment of eMBB and mMTC traffic in a clustered 5G ecosystem of a smart factory","authors":"Viacheslav Kovtun ,&nbsp;Oksana Kovtun ,&nbsp;Krzysztof Grochla ,&nbsp;Oleh Yasniy","doi":"10.1016/j.eij.2024.100598","DOIUrl":"10.1016/j.eij.2024.100598","url":null,"abstract":"<div><div>Industry 4.0 demands seamless integration of smart factories with the Industrial Internet of Things (IIoT), reliant on robust communication infrastructure. Leveraging 5G’s support for enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC), and Network Slicing (NS), this study explores quality of service assurance within a clustered 5G ecosystem of a smart factory. The article proposes two new multi-parameter scenarios for allocating the entire channel pool between different types of requests (mMTC, eMBB) for the model of an integrated open 5G cluster. The scenario with isolation does not allow channel reassignment between requests of different types or originating zones (handover or from the coverage area of the target 5G cluster), while the virtual scenario allows such reassignment. It is shown that using these scenarios, the stationary distribution of probabilities of states of the corresponding two-dimensional Markov chains has a multiplicative form. An information technology has been developed to calculate QoS indicators for different types of requests using these channel allocation scenarios. Studies have been conducted on the effectiveness of using one or another access strategy depending on the loads on the 5G cluster. Based on the proposed mathematical apparatus, the information technology enables finding the optimal scenario for channel allocation, as well as calculating the parameters’ values for such a scenario.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100598"},"PeriodicalIF":5.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph Sample and Aggregate-Attention network optimized for automatic translation of five line stanzas of Tang poems to poetic language","authors":"Haiyan Yang ,&nbsp;Yuping Fu","doi":"10.1016/j.eij.2024.100575","DOIUrl":"10.1016/j.eij.2024.100575","url":null,"abstract":"<div><div>Tang poems, also known as Tang poetry is a significant genre of classical Chinese poetry that flourished during the Tang dynasty, which spanned from 7th to the 9th century. These poems are celebrated for their artistic elegance, rich imagery, and profound emotional expressions. Tang poetry covers a wide range of themes, including nature, love, politics, society, and personal reflections. The Tang dynasty’s poetic legacy has left an indelible mark on Chinese literature and has had a lasting influence on poetry throughout the world. The Tang dynasty saw the propagation of Buddhism in China, and this spiritual influence is evident in many Tang poems. Poets often blended Buddhist concepts and imagery into their verses, adding a layer of depth and universality. In this manuscript, Graph Sample and Aggregate-Attention Network optimized for automatic translation of five line stanzas of tang poems to poetic language (GSAAN-AT-FLS-TPPL) is proposed. First, the data is collected from Poem Comprehensive Dataset (PCD). Then the collected data is given to preprocessing using Modified Fractional Order Unscented Kalman Filter for identifying the errors. Then the data is trained using GSAAN and Pelican Optimization algorithm for getting accurate results. The proposed GSAAN-AT-FLS-TPPL is performed in Python and its efficacy is analyzed under some metrics, such as Accuracy, Computational time, Recall, Mean Square Error and Power Dissipation. The simulation outcomes proves that the proposed technique attains 25.34%, 22.39% and 28.45 % higher precision, 24.98%, 18%, 29.1% lower computational time compared with the existing methods.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100575"},"PeriodicalIF":5.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}