Egyptian Informatics Journal最新文献

{"title":"An immune plasma algorithm with Q-learning based pandemic management for path planning of unmanned aerial vehicles","authors":"Selcuk Aslan ,&nbsp;Sercan Demirci","doi":"10.1016/j.eij.2024.100468","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100468","url":null,"abstract":"<div><p>The countries have experienced the tremendous potential of unmanned aerial vehicles and their military counterparts in recent years. For further improving the task performances of these autonomous vehicles, their flight paths should be determined or calculated optimally by taking into account enemy weapon systems, fuel or battery usage and some limitations about the turning, climbing or diving angles. Immune Plasma algorithm (IP algorithm or IPA) is the first intelligent optimization technique modeling the details of an infection treatment method called convalescent or immune plasma gained popularity again with the coronavirus disease and showed its promising performance for various engineering problems. In this study, Q-learning that is a reinforcement learning algorithm was integrated into the workflow of the IPA for managing some pandemic measures including lockdown, partial opening and full opening. Moreover, the treatment schema was completely changed in order to improve the search efficiency and remove the requirement of algorithm specific control parameters. The newly introduced IPA variant also named Q-learning IPA (Q-LIPA) was tested with the purpose of planning paths and a set of detailed experiments was carried out over twelve test cases of three different battlefield scenarios. The paths found by Q-LIPA were compared with the paths of well-known intelligent optimization techniques and their modifications. Comparative studies indicated that both Q-learning based pandemic measure management and specialized treatment schema positively contribute to the solving performance and help Q-LIPA to outperform its rivals for the majority of the test cases.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000318/pdfft?md5=0939bfc4e31d6a5d61c6cd59893d42ff&pid=1-s2.0-S1110866524000318-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140345351","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":"Predict student learning styles and suitable assessment methods using click stream","authors":"Ahmed Rashad Sayed ,&nbsp;Mohamed Helmy Khafagy ,&nbsp;Mostafa Ali ,&nbsp;Marwa Hussien Mohamed","doi":"10.1016/j.eij.2024.100469","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100469","url":null,"abstract":"<div><p>Adaptive learning, which aims to give each learner engaging, effective learning experiences, is one method of offering modified education. Adaptive learning seeks to consider the student's unique characteristics by personalizing the learning course materials and evaluation procedures. To determine the student's preferred learning strategies, we first ascertain their attributes utilizing VAK learning styles. In this study, we developed an integrated model to classify learners based on their learning activity clicks by combining machine learning algorithms like K-Nearest Neighbor (KNN), random forest (RF), and support vector machine (SVM) and Logistic regression (LR) with semantic association, which is used to help us map learning activity with VAK learning style. This enables us to classify learners, determine their preferred methods of learning, and offer the most suitable; as a result, we were able to group pupils according to their learning styles and provide the best evaluation technique or strategies. To assess the effectiveness of the suggested model, several tests were executed on the actual dataset (Open University Learning Analytics Dataset, or OULAD). According to studies, using a Random Forest algorithm, the suggested model can predict which evaluation strategy or strategies will be most effective for each student and can classify individuals with the highest degree of accuracy—98%.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111086652400032X/pdfft?md5=b233fa14d33e247573dbe47da804417b&pid=1-s2.0-S111086652400032X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140339006","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":"Intelligent vineyard blade density measurement method incorporating a lightweight vision transformer","authors":"Shan Ke ,&nbsp;Guowei Dai ,&nbsp;Hui Pan ,&nbsp;Bowen Jin","doi":"10.1016/j.eij.2024.100456","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100456","url":null,"abstract":"<div><p>Under the new demand model of Agriculture 4.0, automated spraying is a very complex task in precision agriculture, which needs to be combined with a computerized vision perception system to distinguish the plant leaf density and execute the spraying operation in real-time accordingly. Aiming at the accurate determination of grape leaf density, an image leaf density determination method based on the lightweight Vision Transformer (ViT) architecture is proposed, which designs a fusion data augmentation method containing a dual augmentation spatial extension and weather data augmentation method, where the former adopts the pixel augmentation and spatial augmentation for the original image processing, and the latter realizes the data augmentation from the empirical point of view adapted to the agricultural operation environment, and fuses the two in order to expand the sample capacity of the grape leaf density image, which then enhances the model's generalization ability and robustness. The lightweight ViT model has self-attention that can automatically and efficiently extract high-frequency local feature representations and use the two-branch structure to mix high-frequency and low-frequency information to form grapevine-leaf density features in the region of interest. The semantic analysis of the feature extraction layer is parsed using t-SNE and histogram methods, which improves the transparency of the model from the multidimensional with frequency domain distribution space. The experimental results show that the fusion data augmentation method can effectively improve the model recognition accuracy, and the accuracy of comparing the included data augmentation methods is improved by 0.55 % and 3.46 %, respectively. The accuracy of recognizing all four types of grape leaf densities exceeded 94 %, and the MCC reached 90.39 %. In addition, the proposed lightweight ViT improves the accuracy by at least 0.34 % with FLOPs of only 0.6 G compared to the popular MobileViT. The proposed method of this work has high recognition speed and accuracy, which can provide practical technical support for plant protection spraying robots and improve the profitability of growers based on the reduction of pesticide residues.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000197/pdfft?md5=52d8334c13dafd8abe1906870a9c1190&pid=1-s2.0-S1110866524000197-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140181240","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 shallow 3D convolutional neural network for violence detection in videos","authors":"Naz Dündar ,&nbsp;Ali Seydi Keçeli ,&nbsp;Aydın Kaya ,&nbsp;Hayri Sever","doi":"10.1016/j.eij.2024.100455","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100455","url":null,"abstract":"<div><p>With the recent worldwide statistical rise in the amount of public violence, automated violence detection in surveillance cameras has become a matter of high importance. This work introduces an end-to-end, trainable 3D Convolutional Neural Network (3D CNN) for detecting violence in video footage. The proposed network is inherently capable of processing both spatial and temporal information, thereby obviating the need for additional models that would introduce higher computational requirements and complexity. This work has two main contributions: 1) developing a lightweight 3D CNN suitable for inference on edge devices as mobile systems, and 2) a comprehensive explanation of all components comprising a CNN model, thereby enhances model interpretability. Experiments were conducted to assess the performance of the proposed model using a consolidated dataset combining four benchmark datasets. The results of the experiments support the asserted contributions, which are discussed in detail.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000185/pdfft?md5=26f705021bf8a72c4c1ae1b9cb9a844a&pid=1-s2.0-S1110866524000185-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140188023","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":"An intelligent optimization strategy for nurse-patient scheduling in the Internet of Medical Things applications","authors":"Hassan Harb ,&nbsp;Aline Abboud ,&nbsp;Ameer Sardar Kwekha Rashid ,&nbsp;Ghina Saad ,&nbsp;Abdelhafid Abouaissa ,&nbsp;Lhassane Idoughmar ,&nbsp;Mouhammad AlAkkoumi","doi":"10.1016/j.eij.2024.100451","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100451","url":null,"abstract":"<div><p>In the last years, the world has witnessed a potential increasing in the patient number resulted from the increasing number of aged persons along with the emergence of new virus and diseases. This imposes a high pressure on hospitals that suffer from a shortage of medical staff, personal equipment and adequate interventions to overcome such a challenge. Particularly, nurse scheduling is becoming a crucial operation to hospitals in order to efficiently handing the patents and increasing the performance of health system. In this paper, we present an efficient Nurse-Patient Scheduling (NPS) mechanism that is based on the patient classification according to the severity levels of their vital signs. The main goal of NPS is to balance the workload of nurses and it consists of three phases: patient monitoring, patient classification, and nurse scheduling. The first phase aims to periodically monitor the patients using a configurable window time and collect their vital signals through a set of biomedical sensors. The second phase allows for the extraction of prospective features among the collected data then to classify them according to a set of predefined criteria such as patient criticality level, patient age, and the allocated treatment time of each patient. In the last phase, we propose a novel scheduling algorithm that combines both genetic and particle swarm optimization methods in order to find the best scheduling assignment of nurses over patients. We performed simulations based on real health data and we demonstrated the performance of NPS mechanism in terms of obtaining optimal of nurses to patients according to the predefined criteria.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000148/pdfft?md5=436421cf0c52aa3117303c49bbd3541e&pid=1-s2.0-S1110866524000148-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140000050","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":"Dual subpopulation artificial bee colony algorithm based on individual gradation","authors":"Zhaolu Guo ,&nbsp;Hongjin Li ,&nbsp;Kangshun Li","doi":"10.1016/j.eij.2024.100452","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100452","url":null,"abstract":"<div><p>To boost the search performance of Artificial Bee Colony (ABC) algorithm for handling some complicated optimization problems, a dual subpopulation ABC based on individual gradation (DPGABC) is presented. In DPGABC, the whole population is segmented into two subpopulations with different gradations. Then, the subpopulations respectively utilize the strategies with different characteristics as the candidate strategies. So the individuals can exploit the benefits of various strategies to optimize the search performance. Meanwhile, the dual subpopulation mechanism can maintain good population diversity while achieving good convergence performance. In addition, a knowledge-driven parameter update mechanism is designed to improve the convergence performance. The CEC2014 test set is applied for relevant experiments to validate the performance of DPGABC. From the results, DPGABC performs well on most functions.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111086652400015X/pdfft?md5=1d97602897374278d036e61c9da410dc&pid=1-s2.0-S111086652400015X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140041927","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":"Design and development of an effective classifier for medical images based on machine learning and image segmentation","authors":"Firas H. Almukhtar ,&nbsp;Shahab Wahhab Kareem ,&nbsp;Farah Sami Khoshaba","doi":"10.1016/j.eij.2024.100454","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100454","url":null,"abstract":"<div><p>Recently, there has been an increase in the death rate due to encephaloma tumours affecting all age groups. Because of their intricate designs and the interference they cause in diagnostic imaging, these tumours are notoriously difficult to spot. Early and accurate detection of tumours is crucial because it allows for identifying and predicting malignant regions using medical imaging. Using segmentation and relegation techniques, medical scans can aid clinicians in making an early diagnosis and potentially save time. On the other hand, the identification of tumours may be a laborious and extended process for professional doctors owing to the complex nature of tumour formations and the presence of noise in the data produced by Magnetic Resonance Imaging (MRI) since it is pretty imperative to locate and determine the site of the tumour as quickly as feasible. This research proposes a method for detecting brain cancers from MRI scans based on machine learning. It uses the Support Vector Machine, K Nearest Neighbor, and Nave Bayes algorithms for image preprocessing, picture segmentation, feature extraction, and classification. According to the findings, the SVM algorithm accomplished the best level of accuracy, which is 89 %.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000173/pdfft?md5=7ec1f9523c5b29d5a2ff04ad6e4b018b&pid=1-s2.0-S1110866524000173-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140112955","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)00022-7","DOIUrl":"https://doi.org/10.1016/S1110-8665(24)00022-7","url":null,"abstract":"","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000227/pdfft?md5=add510994915db71f509f8fd73671d07&pid=1-s2.0-S1110866524000227-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140187881","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":"Enhancing DDoS attack detection in IoT using PCA","authors":"Sanjit Kumar Dash ,&nbsp;Sweta Dash ,&nbsp;Satyajit Mahapatra ,&nbsp;Sachi Nandan Mohanty ,&nbsp;M. Ijaz Khan ,&nbsp;Mohamed Medani ,&nbsp;Sherzod Abdullaev ,&nbsp;Manish Gupta","doi":"10.1016/j.eij.2024.100450","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100450","url":null,"abstract":"<div><p>Internet of Things (IoT) security and reliability rely on the capacity to identify distributed denial-of-service (DDoS) assaults in IoT networks. This research presents a comprehensive study on DDoS attack detection using the NSL-KDD dataset. The dataset contains a diverse set of network traffic data. This paper proposes two approaches, one utilizing Principal Component Analysis (PCA) and another without PCA, to compare their performance. Robust scaling and encoding techniques are applied as preprocessing steps. The experiment outcomes demonstrate a noteworthy improvement in the accuracy of DDoS attack detection in IoT devices by integrating PCA and Robust Scaler. Notably, the Random Forest and KNN classifiers demonstrate exceptional performance with an accuracy of 99.87 % and 99.14 %, respectively, while Naïve Bayes shows a lower accuracy of 87.14 %. The findings from this experiment contribute valuable insights into enhancing the security of IoT devices against DDoS attacks. The proposed approach showcases the importance of appropriate preprocessing techniques in achieving robust intrusion detection systems for IoT environments.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000136/pdfft?md5=79ba5dd7dc26e95a91245bb192dd085a&pid=1-s2.0-S1110866524000136-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139727194","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":"An efficient 32-bit color image encryption technique using multiple chaotic maps and advanced ciphers","authors":"Mohammed Es-sabry ,&nbsp;Nabil El Akkad ,&nbsp;Lahbib Khrissi ,&nbsp;Khalid Satori ,&nbsp;Walid El-Shafai ,&nbsp;Torki Altameem ,&nbsp;Rajkumar Singh Rathore","doi":"10.1016/j.eij.2024.100449","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100449","url":null,"abstract":"<div><p>In this study, we introduce a refined approach to encrypting 32-bit color images, leveraging the potential of four 1D chaotic maps – the Logistic map, Tent map, Chebyshev map, and Sine map. These chaotic maps intricately populate the four matrices within our encryption system, assigning exclusive integers ranging from 0 to 255. Our proposed methodology employs 16 × 16 matrices to represent the four channels (red, green, blue, and alpha) of a 32-bit color image, strategically utilizing specific grids for channel encryption. The top-left and bottom-right grids facilitate the encryption of the red and alpha channels, respectively, while the top-right and bottom-left grids are employed for encrypting the green and blue channels. The algorithm initiates by extracting decimal values from each pixel in the source image, mapping them to their corresponding positions in the matrices. A subsequent right circular shift operation on each pixel, determined by its row and column coordinates, is performed to prevent the encryption of areas with uniform color. To enhance security further, we employ the Four-square cipher method to encrypt the decimal values of the pixels. In the confusion stage, we apply the Arnold Cat Map transformation to strategically rearrange the position of all pixels, introducing an additional layer of complexity. Rigorous assessments using various security criteria were conducted to evaluate our algorithm's performance against common attacks, yielding consistently excellent results. Our method demonstrated superior outcomes, including a 25 % to 44 % increase in resistance to common attacks compared to existing methods.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000124/pdfft?md5=46ed048013dd5672d6cf2e032bce3552&pid=1-s2.0-S1110866524000124-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139719651","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}