Egyptian Informatics Journal最新文献_第9页

Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI) 自动驾驶汽车拥堵模型：基于lstm的透明预测模型与可解释人工智能（EAI）

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2024-12-01 DOI: 10.1016/j.eij.2024.100582

Muhammad Waqas , Sagheer Abbas , Umer Farooq , Muhammad Adnan Khan , Munir Ahmad , Nasir Mahmood

{"title":"Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI)","authors":"Muhammad Waqas , Sagheer Abbas , Umer Farooq , Muhammad Adnan Khan , Munir Ahmad , 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}

引用次数: 0

IFC: Editorial 国际金融公司: 编辑

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2024-12-01 DOI: 10.1016/S1110-8665(24)00150-6

引用次数: 0

Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare 优化机器学习增强自动化心电图分析在心血管保健

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2024-12-01 DOI: 10.1016/j.eij.2024.100578

Keyi Tang , Shuyuan Ma , Xiaohui Sun , Dongfang Guo

{"title":"Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare","authors":"Keyi Tang , Shuyuan Ma , Xiaohui Sun , 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}

引用次数: 0

PERS: Personalized environment recommendation system based on vital signs PERS：基于生命体征的个性化环境推荐系统

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2024-12-01 DOI: 10.1016/j.eij.2024.100580

A. Pravin Renold

引用次数: 0

Evolutionary approach for composing a thoroughly optimized ensemble of regression neural networks 组成彻底优化的回归神经网络集合的进化方法

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2024-12-01 DOI: 10.1016/j.eij.2024.100581

Lazar Krstic, Milos Ivanovic, Visnja Simic, Boban Stojanovic

{"title":"Evolutionary approach for composing a thoroughly optimized ensemble of regression neural networks","authors":"Lazar Krstic, Milos Ivanovic, Visnja Simic, 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}

引用次数: 0

Improving healthy food recommender systems through heterogeneous hypergraph learning 通过异构超图学习改进健康食品推荐系统

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2024-11-27 DOI: 10.1016/j.eij.2024.100570

Jing Wang , Jincheng Zhou , Muammer Aksoy , Nidhi Sharma , Md Arafatur Rahman , Jasni Mohamad Zain , Mohammed J.F. Alenazi , Aliyeh Aminzadeh

{"title":"Improving healthy food recommender systems through heterogeneous hypergraph learning","authors":"Jing Wang , Jincheng Zhou , Muammer Aksoy , Nidhi Sharma , Md Arafatur Rahman , Jasni Mohamad Zain , Mohammed J.F. Alenazi , 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}

引用次数: 0

Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning 利用深度学习建立基于 GDD 特征近似的高效脑肿瘤分类和生存分析模型

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2024-11-26 DOI: 10.1016/j.eij.2024.100577

M. Vimala , SatheeshKumar Palanisamy , Sghaier Guizani , Habib Hamam

{"title":"Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning","authors":"M. Vimala , SatheeshKumar Palanisamy , Sghaier Guizani , 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}

引用次数: 0

The best angle correction of basketball shooting based on the fusion of time series features and dual CNN 基于时间序列特征和双 CNN 融合的篮球投篮最佳角度校正

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2024-11-25 DOI: 10.1016/j.eij.2024.100579

Meicai Xiao

{"title":"The best angle correction of basketball shooting based on the fusion of time series features and dual CNN","authors":"Meicai Xiao","doi":"10.1016/j.eij.2024.100579","DOIUrl":"10.1016/j.eij.2024.100579","url":null,"abstract":"<div><div>The best shooting angle correction of basketball based on intelligent image analysis is an important branch of the development of intelligent sports. However, the current method is limited by the variability of the shape base, ignoring dynamic features and visual information, and there are some problems in the process of feature extraction and correction of related actions. This paper proposes a method to correct the best shooting angle of basketball based on the fusion of time series characteristics and dual CNN. Segmenting the shooting video, taking the video frame as the input of the key node extraction network of the shooting action, obtaining the video frame with the sequence information of the bone points, extracting the continuous T-frame video stack from it, and inputting it into the spatial context feature extraction network in the shooting posture prediction model based on dual stream CNN (MobileNet V3 network with multi-channel attention mechanism fusion module), extract the space context features of shooting posture; The superimposed optical flow graph of continuous video frames containing sequence information of bone points is input into the time convolution network (combined with Bi-LSTM network of multi-channel attention mechanism fusion module), extract the skeleton temporal sequence features during the shooting movement, using the spatial context features and skeleton temporal sequence features extracted from the feature fusion module, and realizing the prediction of shooting posture through Softmax according to the fusion results, calculate the shooting release speed under this attitude, solve the shooting release angle, and complete the correction of the best shooting release angle by comparing with the set conditions. The experimental results show that this method can achieve the best shooting angle correction, and the training learning rate is 0.2 × 10–3, training loss is about 0.05; MPJPE and MPJVE indicators are the lowest, and Top-1 indicators are the highest; The shooting percentage is about 95 %.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100579"},"PeriodicalIF":5.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706572","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}

引用次数: 0

Multi-factor evaluation of clustering methods for e-commerce application 电子商务应用聚类方法的多因素评估

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2024-11-20 DOI: 10.1016/j.eij.2024.100562

Adam Wasilewski , Krzysztof Juszczyszyn , Vera Suryani

{"title":"Multi-factor evaluation of clustering methods for e-commerce application","authors":"Adam Wasilewski , Krzysztof Juszczyszyn , Vera Suryani","doi":"10.1016/j.eij.2024.100562","DOIUrl":"10.1016/j.eij.2024.100562","url":null,"abstract":"<div><div>This research aimed to investigate the application of Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making to select the optimal clustering for e-commerce customer segmentation. In this context, clustering as an unsupervised machine learning method offered a way to overcome the limitations of traditional grouping, particularly by providing the ability to capture the diverse needs of consumers. A total of five different clustering methods were considered based on the behavioral data of e-commerce customers. Even though the analyzed algorithms were well-known and widely used, the comprehensive and multidirectional comparison was not trivial. Selected approaches were evaluated on the basis of twelve indicators (decision criteria), divided into four characteristics that take into account both the out-of-context aspects of clustering and the requirements arising from the context of using the clustering results. The results showed consistent outcomes from both analyzed Multi-Criteria Decision Methods, with some notable differences. The methods obtained the same ranking of the top three clustering algorithms (K-median - BIRCH - K-means). However, the TOPSIS and VIKOR sensitivity analysis recommended K-means in 87% of the cases and 60% of the variants verified, respectively. The parameterization of the decision factors had a significant impact on the final ranking of clustering options. This research demonstrated the practical application of the decision methods in selecting the best clustering for multivariate user interfaces to improve personalization in e-commerce.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100562"},"PeriodicalIF":5.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706571","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}

引用次数: 0

A study of Isual perceptual target monitoring in graphic design based on Multi-Task structured learning and interaction mapping 基于多任务结构化学习和交互映射的平面设计感知目标监控研究

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2024-11-19 DOI: 10.1016/j.eij.2024.100576

Jingchao Liu , Yang Zhang , Jing Wang

{"title":"A study of Isual perceptual target monitoring in graphic design based on Multi-Task structured learning and interaction mapping","authors":"Jingchao Liu , Yang Zhang , Jing Wang","doi":"10.1016/j.eij.2024.100576","DOIUrl":"10.1016/j.eij.2024.100576","url":null,"abstract":"<div><div>In graphic design, many materials come from images and videos, but the current visual target analysis still suffers from the disadvantages of poor results and not being able to understand the semantic information required by graphic design. In order to solve the above problems, this study builds a visual perception target monitoring network model by combining multi-task structured learning and interaction mapping detection methods, and based on the combined detection method. The study first analyses the target detection effect of the combined detection method, and the results show that compared with other methods, the ROC curve area of the method used in this paper is larger and the accuracy is higher, up to 96.45 %, and the maximum accuracy of the detection method is 90.00 %. Then the target tracking effect of the combined detection method is analysed, and the average success rate of the proposed method in multi-target tracking is maximum 99.69 %. Finally, the model’s effectiveness in target classification and identification is analysed, and the results show that the classification error rate of the network model based on the detection method is 4.99 %, which is lower than other models. From the above results, it can be seen that the visual perception target monitoring network model based on multi-task structured learning and interaction mapping detection method proposed in the study can achieve visual target perception and has certain application value in graphic design.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100576"},"PeriodicalIF":5.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706570","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}

引用次数: 0