Egyptian Informatics Journal最新文献

{"title":"An information dissemination strategy in social networks based on graph and content analysis","authors":"Jing Huang","doi":"10.1016/j.eij.2024.100563","DOIUrl":"10.1016/j.eij.2024.100563","url":null,"abstract":"<div><div>Social networking platforms like Facebook, Twitter, Instagram, and LinkedIn have revolutionized communication, but there’s growing concern about invalid information, misinformation, and disinformation. Malicious actors exploit these platforms for economic, political, or ideological purposes, impacting public trust, democratic processes, and individual decision-making. Research is being conducted to develop tools to distinguish genuine and invalid information. Twitter, with its vast user base, has become a focal point for studying information diffusion patterns and identifying potential sources of misinformation. A novel method is proposed for identifying information dissemination paths based on node centrality criteria, analyzing the network structure and characteristics of Twitter users to uncover influential nodes that play a crucial role in spreading information across the network. The study explores the potential of deep learning and ensemble learning techniques in content development to improve the accuracy of information classification. Examining the performance of the proposed hybrid model in classifying misinformation showed that in terms of average accuracy, f-measure, and AUC, it achieved 98.6 %, 0.9858, and 0.9862 respectively, which are at least 1.6 %, 1.62 % and 1.5 % higher than the compared method. Additionally, the proposed model could recognize the leader nodes in information dissemination by the highest accuracy of 86% which is competitive with the metaheuristic-based approaches such as FFO and GWO. By leveraging advanced computational techniques and data analysis, we can strive towards a more informed and trustworthy digital environment, where users can navigate through the sea of information with confidence and make well-informed decisions.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100563"},"PeriodicalIF":5.0,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174381","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":"High-accuracy lung disease classification via logistic regression and advanced feature extraction techniques","authors":"Swapandeep Kaur ,&nbsp;Sheifali Gupta ,&nbsp;Deepali Gupta ,&nbsp;Sapna Juneja ,&nbsp;Ali Nauman ,&nbsp;Mudassir Khan ,&nbsp;Izhar Husain ,&nbsp;Asharul Islam ,&nbsp;Saurav Mallik","doi":"10.1016/j.eij.2024.100596","DOIUrl":"10.1016/j.eij.2024.100596","url":null,"abstract":"<div><div>Lung disease diagnosis through medical imaging integrated with machine learning has seen significant advancements. This study investigates the optimization of lung disease classification by exploring various preprocessing, feature extraction, and machine-learning classifier combinations. Our methodology begins with an input dataset of lung X-ray images, which undergoes preprocessing steps such as image sharpening and histogram equalization to enhance image quality. Subsequently, feature extraction techniques, including Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and Local Binary Patterns (LBP), are applied to the pre-processed images. We evaluate the effectiveness of several machine learning classifiers—Naive Bayes (NB), K Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM)—on both original and pre-processed images to determine the optimal classifier. Following the selection of the best-performing classifier, i.e., Logistic Regression, we further optimize the classification process by applying combinations of the feature extraction techniques (SIFT + HOG, SIFT + LBP, HOG + LBP, SIFT + HOG + LBP). The SIFT + HOG + LBP feature extraction, in combination with Logistic Regression, performed the best on the original images, obtaining an accuracy of 97.12 %, precision of 97.97 %, recall of 97.55 %, and F1-score of 97.76 %. Our study presents the comparative performance of each preprocessing and feature extraction technique, both individually and in combination, in bringing about an improvement in the accuracy of lung disease detection. The study concludes with the identification of the most effective preprocessing and feature extraction combination, coupled with the best machine learning classifier, providing a robust framework for enhanced lung disease diagnosis.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100596"},"PeriodicalIF":5.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175669","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":"Towards precision medicine in Glioblastoma: Unraveling MGMT methylation status in glioblastoma using adaptive sparse autoencoders","authors":"Sumaiya Fazal ,&nbsp;Hafeez Ur Rehman ,&nbsp;Moutaz Alazab","doi":"10.1016/j.eij.2024.100583","DOIUrl":"10.1016/j.eij.2024.100583","url":null,"abstract":"<div><div>Glioblastoma is a type of cancer known for its fast growth, invasive behavior, and resistance to different treatments. It accounts for more than 50% of all malignant brain tumors. Due to its complexity, it is crucial to customize treatment plans according to each patient’s needs. The methylation status of O-6-methylguanine-DNA methyltransferase (MGMT) is a crucial biomarker (Butler et al., 2020) for predicting patient response to treatment plans and plays a critical role in the treatment response and prognosis of patients with glioblastomas, which is an aggressive form of brain cancer. It is difficult to predict the MGMT methylation status using existing methods due to the heterogeneous contrast appearance, substantial variability within lesions, and irregular enhancement patterns associated with the MGMT signature in MR images. In this work, we propose a novel method ADAptive sParse auToencoders (ADAPT) to determine the methylation status of MGMT promoter gene from the signatures present in the MRI images. Our approach involves learning features by repeatedly producing artificial MRI images, followed by a tailored sparse autoencoder that incorporates an adaptive sparse penalty to predict the MGMT methylation status. To achieve this, we designed an autoencoder neural network to generate synthetic MRI slices which extract and learn features from numerous MR sequences. We then designed sparsity restrictions by modeling an adaptive sparse penalty along with employing transfer learning to predict the methylation status of the MRI slice. The behavior of adaptive sparse penalty adjusts itself and the learning parameters in accordance with changes in the data (i.e., contrast variations, tumoural location variation etc., in MR images). The proposed technique achieves significant improvements in MRI image synthesis, preserving the brain tissue, fat, and individual tumor structures in all MRI modality images. On top of that the adaptive sparse penalty parameter resulted in the highest scores for the prediction of MGMT methylation status. Based on the cross-validation results of the ADAPT framework, the adaptive sparse penalty model performed exceptionally well in predicting the MGMT methylation status in MRI images. It achieved an overall accuracy score of 0.95, a specificity of 0.93, and a sensitivity of 0.94, demonstrating a promising potential for enhancing the interpretation of MRI data in determining MGMT methylation status, thereby contributing to improved diagnostic accuracy, as well as constructing refined personalized treatment plans.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100583"},"PeriodicalIF":5.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174379","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":"Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI)","authors":"Muhammad Waqas ,&nbsp;Sagheer Abbas ,&nbsp;Umer Farooq ,&nbsp;Muhammad Adnan Khan ,&nbsp;Munir Ahmad ,&nbsp;Nasir Mahmood","doi":"10.1016/j.eij.2024.100582","DOIUrl":"10.1016/j.eij.2024.100582","url":null,"abstract":"<div><div>Urban traffic congestion presents a range of vital difficulties requiring precise prediction models in order to facilitate traffic management for Autonomous Vehicles. This work introduces a novel framework that regulates a Long Short-Term Memory (LSTM) system with methods provided by Explainable Artificial Intelligence (XAI) to explain traffic congestion behavioural modes. For enhanced accuracy and transparency, the integration of EAI methodologies with LSTM based models is addressed as a novel approach towards congestion prediction, while significant research has been done previously using Machine Learning that compared previous proposed based model congestion monitoring improvement through Federated Learning Waqas et al. [18]. This wok proposes the enhances ML focused on Long Short-Term Memory with EAI (LSTM-EAI) model for Smart City environments that require accurate traffic congestion rate forecast to improve the urban mobility. The proposed model provides better interpretability that help stakeholders to understand how the input plays an important role in the condition of traffic jams. The results show that the LSTM-EAI model is 5 % better than previous methods for both the accuracy and reliability of congestion prediction, and may become a practical and effective solution for the urban traffic problem.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100582"},"PeriodicalIF":5.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748309","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":"IFC: Editorial","authors":"","doi":"10.1016/S1110-8665(24)00150-6","DOIUrl":"10.1016/S1110-8665(24)00150-6","url":null,"abstract":"","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100587"},"PeriodicalIF":5.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181856","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":"Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare","authors":"Keyi Tang ,&nbsp;Shuyuan Ma ,&nbsp;Xiaohui Sun ,&nbsp;Dongfang Guo","doi":"10.1016/j.eij.2024.100578","DOIUrl":"10.1016/j.eij.2024.100578","url":null,"abstract":"<div><div>The rapid advancement of connected health technology, exemplified by wearable devices like the Apple Watch, has revolutionized healthcare by enhancing the diagnosis, monitoring, and treatment of various conditions, particularly heart-related issues. However, these devices generate vast amounts of ECG data that require interpretation, underscoring the need for reliable automated ECG analysis methods. This study explores the use of machine learning and deep learning algorithms, including Support Vector Classifier (SVC), RandomForest, XGBoost, and LinearSVC, for ECG classification, aiming to improve accuracy and diagnostic capabilities. While traditional methods rely on heuristic features and shallow architectures, this research focuses on leveraging deep learning architectures to automatically extract relevant features from ECG signals. The proposed approach demonstrates promising results in accurately categorizing heartbeats, offering a potential solution to the limitations of current classification methods. By optimizing classification models with metaheuristic algorithms, such as JADE, the study achieves significant performance improvements, highlighting the effectiveness of integrating advanced optimization techniques into ECG analysis processes. Ultimately, the findings underscore the potential of machine learning and deep learning algorithms in advancing automated ECG analysis for improved cardiovascular healthcare.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100578"},"PeriodicalIF":5.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748310","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":"PERS: Personalized environment recommendation system based on vital signs","authors":"A. Pravin Renold","doi":"10.1016/j.eij.2024.100580","DOIUrl":"10.1016/j.eij.2024.100580","url":null,"abstract":"<div><div>The integration of the Internet of Things (IoT) in healthcare has facilitated real-time monitoring of vital signs and environmental conditions. However, existing systems often lack personalized recommendations that consider the interplay between these factors. This work introduces the Personalized Environment Recommendation System (PERS), which leverages a portable device to continuously collect data on key health metrics, including pulse rate and body temperature, alongside environmental parameters. Utilizing Artificial Neural Networks, PERS analyzes the data to generate tailored health recommendations for users. Experimental results demonstrate an accuracy of 98.7%, highlighting the system’s effectiveness in enhancing patient care and supporting informed health decisions. The findings suggest that PERS can significantly improve health monitoring by providing actionable insights based on individual health profiles and environmental contexts.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100580"},"PeriodicalIF":5.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748308","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":"Evolutionary approach for composing a thoroughly optimized ensemble of regression neural networks","authors":"Lazar Krstic,&nbsp;Milos Ivanovic,&nbsp;Visnja Simic,&nbsp;Boban Stojanovic","doi":"10.1016/j.eij.2024.100581","DOIUrl":"10.1016/j.eij.2024.100581","url":null,"abstract":"<div><div>The paper presents the GeNNsem (<strong>Ge</strong>netic algorithm A<strong>NN</strong>s en<strong>sem</strong>ble) software framework for the simultaneous optimization of individual neural networks and building their optimal ensemble. The proposed framework employs a genetic algorithm to search for suitable architectures and hyperparameters of the individual neural networks to maximize the weighted sum of accuracy and diversity in their predictions. The optimal ensemble consists of networks with low errors but diverse predictions, resulting in a more generalized model. The scalability of the proposed framework is ensured by utilizing micro-services and Kubernetes batching orchestration. GeNNsem has been evaluated on two regression benchmark problems and compared with related machine learning techniques. The proposed approach exhibited supremacy over other ensemble approaches and individual neural networks in all common regression modeling metrics. Real-world use-case experiments in the domain of hydro-informatics have further demonstrated the main advantages of GeNNsem: requires the least training sessions for individual models when optimizing an ensemble; networks in an ensemble are generally simple due to the regularization provided by a trivial initial population and custom genetic operators; execution times are reduced by two orders of magnitude as a result of parallelization.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100581"},"PeriodicalIF":5.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181857","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":"Improving healthy food recommender systems through heterogeneous hypergraph learning","authors":"Jing Wang ,&nbsp;Jincheng Zhou ,&nbsp;Muammer Aksoy ,&nbsp;Nidhi Sharma ,&nbsp;Md Arafatur Rahman ,&nbsp;Jasni Mohamad Zain ,&nbsp;Mohammed J.F. Alenazi ,&nbsp;Aliyeh Aminzadeh","doi":"10.1016/j.eij.2024.100570","DOIUrl":"10.1016/j.eij.2024.100570","url":null,"abstract":"<div><div>Recommender systems in health-conscious recipe suggestions have evolved rapidly, particularly with the integration of both homogeneous and heterogeneous graphs. However, incorporating IoT devices into healthcare, such as wearable fitness trackers and smart nutrition scales, presents new challenges. These devices generate vast amounts of dynamic, personalized data, which traditional Graph Neural Network (GNN) models — limited to simple pairwise connections — fail to capture effectively. For example, IoT sensors tracking daily nutrient intake require complex, multi-faceted analysis that traditional methods struggle to handle. To overcome these limitations, researchers have employed hypergraphs, which capture higher-order relationships among nodes, such as user–food and ingredient interactions. Traditional methods using static weights in the Laplacian hypergraph, inspired by homogeneous graph techniques, often fail to account for users’ evolving health interests. Our study introduces a novel approach for recommending healthy foods by leveraging user–food and food-ingredient hyperedges, integrating both convolution and attention-based hypergraph mechanisms to dynamically adjust weights based on user similarities. Unlike previous methods, we convert the heterogeneous hypergraph into a homogeneous space, using a unified loss function to generate recommendations that adapt to individual users’ changing dietary preferences. The model is evaluated on five metrics — AUC, NDCG, Precision, Recall, and F1-score — and shows superior performance compared to existing models on two real-world food datasets, Allrecipes and Food.com. Our results demonstrate significant improvements in recommendation accuracy and personalization, showcasing the system’s effectiveness in integrating IoT data for more responsive, health-focused food suggestions.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100570"},"PeriodicalIF":5.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722590","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":"Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning","authors":"M. Vimala ,&nbsp;SatheeshKumar Palanisamy ,&nbsp;Sghaier Guizani ,&nbsp;Habib Hamam","doi":"10.1016/j.eij.2024.100577","DOIUrl":"10.1016/j.eij.2024.100577","url":null,"abstract":"<div><div>The problem of brain tumor classification (BTC) has been approached with several methods and uses different features obtained from MRI brain scans. However, they suffer from achieving higher performance in BTC and produce poor performance with a higher false ratio. A convolutional neural network (CNN) based on BTC and a survival analysis model based on GDD (growth distribution depth) are presented. Initially, an adaptive median filter (AMF) is used to preprocess the MRI images in order to lower the amount of noise in the images. Secondly, in order to calculate the GDD value, the texture, shape, and gradient characteristics are extracted. Third, CNN is used to train the retrieved features based on the labels that were found. In the classification, the GDD features extracted are used to measure TSF (Tumor Support Factor) in each of them. The neurons of the network measure the value of tumor weight (TW) to perform classification. Additionally, the technique evaluates a patient’s survival and calculates the survival rate based on the TSF value of the growth characteristic. The multi-layer perceptron allows the computation of TW and supports the efficient performance of classification. The proposed method improves tumor classification performance by up to 97%.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100577"},"PeriodicalIF":5.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722589","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}