Egyptian Informatics Journal最新文献

{"title":"An improved multiobjective evolutionary algorithm for time-dependent vehicle routing problem with time windows","authors":"Jia-ke Li ,&nbsp;Jun-qing Li ,&nbsp;Ying Xu","doi":"10.1016/j.eij.2024.100574","DOIUrl":"10.1016/j.eij.2024.100574","url":null,"abstract":"<div><div>Time-dependent vehicle routing problem with time windows (TDVRPTW) is a pivotal problem in logistics domain. In this study, a special case of TDVRPTW with temporal-spatial distance (TDVRPTW-TSD) is investigated, which objectives are to minimize the total travel time and maximize customer satisfaction while satisfying the vehicle capacity. To address it, an improved multiobjective evolutionary algorithm (IMOEA) is developed. In the proposed algorithm, a hybrid initialization strategy with two efficient heuristics considering temporal-spatial distance is designed to generate high-quality and diverse initial solutions. Then, two crossover operators are devised to broaden the exploration space. Moreover, an efficient local search heuristic combing the adaptive large neighborhood search (ALNS) and the variable neighborhood descent (VND) is developed to improve the exploration capability. Finally, detailed comparisons with several state-of-the-art algorithms are tested on a set of instances, which verify the efficiency and effectiveness of the proposed IMOEA.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100574"},"PeriodicalIF":5.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658097","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":"Distributed hybrid flowshop scheduling with consistent sublots under delivery time windows: A penalty lot-assisted iterated greedy algorithm","authors":"Jinli Liu ,&nbsp;Yuyan Han ,&nbsp;Yuting Wang ,&nbsp;Yiping Liu ,&nbsp;Biao Zhang","doi":"10.1016/j.eij.2024.100566","DOIUrl":"10.1016/j.eij.2024.100566","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Integrating the delivery time windows into the distributed hybrid flow shop scheduling contributes to ensuring the timely delivery of products and enhancing customer satisfaction. In view of this, this study focuses on distributed hybrid flowshop scheduling with consistent sublots under the delivery time windows constraint, denoted as &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;DH&lt;/mi&gt;&lt;msub&gt;&lt;mi&gt;F&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/msub&gt;&lt;mrow&gt;&lt;mo&gt;|&lt;/mo&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;lot&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;cs&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;|&lt;/mo&gt;&lt;/mrow&gt;&lt;mi&gt;ε&lt;/mi&gt;&lt;mrow&gt;&lt;mfenced&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mi&gt;D&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;/mrow&gt;&lt;/mfenced&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. However, there exist some challenges of problem model modeling and algorithmic design for the problem to be addressed. Therefore, we first construct a mixed integer linear programming (MILP) model tailored to &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;DH&lt;/mi&gt;&lt;msub&gt;&lt;mi&gt;F&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/msub&gt;&lt;mrow&gt;&lt;mo&gt;|&lt;/mo&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;lot&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;cs&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;|&lt;/mo&gt;&lt;/mrow&gt;&lt;mi&gt;ε&lt;/mi&gt;&lt;mrow&gt;&lt;mfenced&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mi&gt;D&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;/mrow&gt;&lt;/mfenced&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; with the aim of minimizing the total weighted earliness and tardiness (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;TWET&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;). Additionally, we introduce a penalty lot-assisted iterated greedy (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;_&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;mi&gt;G&lt;/mi&gt;&lt;mi&gt;_&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) and idle time insertion to coincide better with delivery time windows, in which a delivery-time-based multi-rule NEH, an adaptive insertion-based reconstruction based on the changing of the delivery status, a trilaminar penalty lot-assisted local search, and an elitist list-based acceptance criterion are designed to save convergence time and reduce the late deliveries attempts. Lastly, we also introduce a completely new method to generate delivery time windows and create 400 distinct instances. Based on the average results from five runs of 400 instances, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;_&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;mi&gt;G&lt;/mi&gt;&lt;mi&gt;_&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; demonstrates improvements of 59.0 %, 72.3 %, 76.9 %, and 25.5 % compared to &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;HIGT&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;DABC&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;CVND&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;mi&gt;G&lt;/mi&gt;&lt;mi&gt;_&lt;/mi&gt;&lt;mi&gt;M&lt;/mi&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, respectively. When considering the minimum values from each instance, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;_&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;mi&gt;G&lt;/mi&gt;&lt;mi&gt;_&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; exhibits enhancements of 59.4 %, 71.8 %, 74.9 %, and 25.4 % over &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;HIGT&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;DABC&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;CVND&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;mi&gt;G&lt;/","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100566"},"PeriodicalIF":5.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658095","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":"Cyber epidemic spread forecasting based on the entropy-extremal dynamic interpretation of the SIR model","authors":"Viacheslav Kovtun ,&nbsp;Krzysztof Grochla ,&nbsp;Mohammed Al-Maitah ,&nbsp;Saad Aldosary ,&nbsp;Tetiana Gryshchuk","doi":"10.1016/j.eij.2024.100572","DOIUrl":"10.1016/j.eij.2024.100572","url":null,"abstract":"<div><div>The spread of a cyber epidemic at an early stage is an uncertain process characterized by a small amount of statistically unreliable data. Nonlinear dynamic models, most commonly the SIR model, are widely used to describe such processes. The description of the studied process obtained using this model is sensitive to the initial conditions set and the quality of tuning the controlled parameters based on the results of operational observations, which are inherently uncertain. This article proposes a transition to a stochastic interpretation of the controlled parameters of the SIR model and the introduction of additional stochastic parameters that represent the variability of operational data measurements. The process of estimating the probability density functions of these parameters and noises is implemented as a strict optimization problem. The resulting mathematical apparatus is generalized in the form of two versions of the entropy-extremal adaptation of the SIR model, which are applied to forecast the spread of a cyber epidemic. The first version is focused on estimating the SIR model parameters based on operational data. In contrast, the second version focuses on stochastic modelling of the transmission rate indicator and its impact on forecasting the studied process. The forecasting result represents the average trajectory from the set of trajectories obtained using the authors’ models, which characterize the dynamics of compartment <em>I</em>. The experimental part of the article compares the classical Least Squares method with the authors’ entropy-extremal approach for estimating the SIR model parameters based on etalon data on the spread of the most threatening categories of malware cyber epidemics. The empirical results are characterized by a significant reduction in the Mean Absolute Percentage Error regarding the etalon data over the prediction interval, which proves the adequacy of the proposed approach.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100572"},"PeriodicalIF":5.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658096","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":"BACAD: AI-based framework for detecting vertical broken access control attacks","authors":"Ahmed Anas ,&nbsp;Ayman A. Alhelbawy ,&nbsp;Salwa El Gamal ,&nbsp;Basheer Youssef","doi":"10.1016/j.eij.2024.100571","DOIUrl":"10.1016/j.eij.2024.100571","url":null,"abstract":"<div><div>Vertical Broken Access Control (VBAC) vulnerability is one of the most commonly identified issues in web applications, posing significant risks. Consequently, addressing this pervasive threat is crucial for ensuring system confidentiality and integrity. Broken access control attack detector (BACAD) is a novel framework that leverages advanced AI techniques to neutralize VBAC exploits and attacks in real-time using a dynamic and practical technique. The detection process consists of two steps. The first step is user role classification using an advanced artificial intelligence (AI) model created in a learning phase. The learning phase includes BACAD initial configuration and application user roles traffic generation used for AI model training. The AI model at the core of BACAD analyzes web requests and responses utilizing a robust feature extraction, and dynamic hyperparameter tuning to ensure optimal performance across diverse scenarios. The second step is the decision step, which determines whether the incoming request–response pair is benign or an attack by validating it vs the BACAD session information set. The evaluation against a spectrum of real-world and demonstration web applications highlights remarkable efficiency in detecting VBAC exploits, providing robust application protection against different sets of VBAC attacks. Furthermore, it shows that BACAD addresses the VBAC problem by presenting an applicable, dynamic, flexible, and technology-independent solution to counter VBAC vulnerability risks. Thus, BACAD contributes significantly to the ongoing efforts aimed at enhancing web application security.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100571"},"PeriodicalIF":5.0,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658094","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":"MRI-based brain tumor ensemble classification using two stage score level fusion and CNN models","authors":"Oussama Bouguerra,&nbsp;Bilal Attallah,&nbsp;Youcef Brik","doi":"10.1016/j.eij.2024.100565","DOIUrl":"10.1016/j.eij.2024.100565","url":null,"abstract":"<div><div>This paper proposes a novel two-stage approach to improve brain tumor classification accuracy using the Br35H MRI Scan Dataset. The first stage employs advanced image enhancement algorithms, GFPGAN and Real-ESRGAN, to enhance the image dataset’s quality, sharpness, and resolution. Nine deep learning models are then trained and tested on the enhanced dataset, experimenting with five optimizers. In the second stage, ensemble learning algorithms like weighted sum, fuzzy rank, and majority vote are used to combine the scores from the trained models, enhancing prediction results. The top 2, 3, 4, and 5 classifiers are selected for ensemble learning at each rating level. The system’s performance is evaluated using accuracy, recall, precision, and F1-score. It achieves 100% accuracy when using the GFPGAN-enhanced dataset and combining the top 5 classifiers through ensemble learning, outperforming current methodologies in brain tumor classification. These compelling results underscore the potential of our approach in providing highly accurate and effective brain tumor classification.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100565"},"PeriodicalIF":5.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658093","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":"HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network","authors":"D. Santhakumar ,&nbsp;K. Dhana Shree ,&nbsp;M. Buvanesvari ,&nbsp;A. Saran Kumar ,&nbsp;Ayodeji Olalekan Salau","doi":"10.1016/j.eij.2024.100573","DOIUrl":"10.1016/j.eij.2024.100573","url":null,"abstract":"<div><div>Diabetes mellitus, also known as diabetes, is a medical condition marked by high blood sugar levels and impacts a large population worldwide. Treating diabetes is not feasible. It can be managed. Hence, it is crucial to promptly identify a diagnosis of diabetes. This study explores the effects of diabetes on the heart, focusing on heart rate variability (HRV) signals, which can offer valuable information about the existence and seriousness of diabetes through the evaluation of diabetes-related heart problems. Extracting crucial data from the irregular and nonlinear HRV signal can be quite challenging. Studying cardiac diagnostics involves a thorough analysis of electrocardiogram (ECG) signals. Traditional electrocardiogram recordings utilize twelve channels, each capturing a complex combination of activities originating from different regions of the heart. Examining ECG signals recorded on the body’s surface may not be an effective method for studying and diagnosing diabetic issues. The study introduces a research proposal utilizing a high-density resolution electrocardiogram (ECG) system with a minimum of 64 channels and multi-view convolutional neural network classification (HD-MVCNN) to address the mentioned challenges. This framework may help identify the hypoglycaemia effects on brain regions, leading to decreased complexity and increased theta and delta power during scalp electrocardiogram procedures. The convolutional architectural model primarily contributes to enhancement and optimization through its Stochastic Gradient Descent (SGD) along with convolutional layers and according to results, the HD-MVCNN demonstrated better stability and accuracy in comparison to traditional classification models. Thus, HD-MVCNN shows promise as a powerful method for classifying features in diabetes clinical data.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100573"},"PeriodicalIF":5.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593126","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 hybrid encryption algorithm based approach for secure privacy protection of big data in hospitals","authors":"Wei Li ,&nbsp;Qian Huang","doi":"10.1016/j.eij.2024.100569","DOIUrl":"10.1016/j.eij.2024.100569","url":null,"abstract":"<div><div>Aiming at the hidden danger of information security caused by the lack of medical big data information security firewall, this paper proposes a security privacy protection method for hospital big data based on hybrid encryption algorithm. First, collect hospital big data including hospital medical business system, mobile wearable devices and big health data; Secondly, use byte changes to compress hospital big data to achieve safe transmission of hospital big data; Then, the hospital sender uses the AES session key to encrypt the hospital big data and the ECC public key to encrypt the AES session key, uses SHA-1 to calculate the hash value of the medical big data, and uses the ECC public key to sign the hash value; The hospital receiver uses the ECC private key to verify the signature, and decrypts the AES session key using the ECC private key. After the AES session key decrypts, the hospital big data, the hospital big data security privacy protection is completed. The experimental results show that the method is superior to conventional ECC algorithm or RSA and AES hybrid encryption algorithm in terms of encryption and decryption time and security strength. The average correlation coefficient of encrypted hospital big data is only 0.0576, and the RL curve value is low and gentle. The encrypted data has good scrambling effect and low privacy leakage probability, which ensures the confidentiality and integrity of medical data in the transmission process.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100569"},"PeriodicalIF":5.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586849","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 new probabilistic linguistic decision-making process based on PL-BWM and improved three-way TODIM methods","authors":"Jie Chen ,&nbsp;Chuancun Yin","doi":"10.1016/j.eij.2024.100567","DOIUrl":"10.1016/j.eij.2024.100567","url":null,"abstract":"<div><div>Probabilistic linguistic term sets (PLTSs) provide a flexible tool to express linguistic preferences, which allow decision-makers to label linguistic information with different probabilities. In this paper, a method based on a PLTS is proposed to address multi-criteria decision-making problems (MCDM). We develop the theory of PLTSs and put forward a novel best–worst method (BWM), termed PL-BWM, based on PLTS. Our method fully reflects the preference information of decision-makers and accurately provides the importance level of the criteria. The combined weight of the criteria is obtained by merging PL-BWM-based subjective weights and similarity minimization-based objective weights. Upon introducing a three-way decision system to improve the TODIM method, a novel three-way TODIM method is proposed and showcased on an optimal new energy vehicle selection problem. The effectiveness and accuracy of the proposed method are verified by sensitivity analysis and comparative analysis. Our approach paves the way for new developments in solving MCDM problems and for novel applications in otherwise difficult ranking problems.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100567"},"PeriodicalIF":5.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578487","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":"Interval valued inventory model with different payment strategies for green products under interval valued Grey Wolf optimizer Algorithm fitness function","authors":"Subhash Chandra Das ,&nbsp;Md. Al-Amin Khan ,&nbsp;Ali Akbar Shaikh ,&nbsp;Adel Fahad Alrasheedi","doi":"10.1016/j.eij.2024.100561","DOIUrl":"10.1016/j.eij.2024.100561","url":null,"abstract":"<div><div>Numerous studies have explored pricing and lot-sizing strategies for various payment methods, but most have focused primarily on the buyer’s perspective. This study, however, approaches these strategies from a different perspective, incorporating key and relevant factors often overlooked. The volume of sales increases when a seller accepts a buyer’s credit. However, it reduces sales volume when a seller requests a buyer make a payment in advance. To boost sales and profitability, a vendor occasionally provides a price reduction in exchange for a down payment. Demanding a down payment from a customer earns interest and carries without any risk of default. When a vendor offers customers the option to pay with credit, a higher delay payment period facility plan may boost sales volume, but it also increases the risk of default. To maximize profit per unit of time, the vendor aims to simultaneously determine the optimal selling price, replenishment schedule, and payment method. This is achieved by comparing and calculating the vendor’s profit per time unit for credit, cash, and advance payment options. This is done by comparing and calculating the seller’s profit for each piece of time for credit, cash, and advance payments. The following managerial impacts are highlighted by means of numerical analyses: (1) A particular payment type, among the three available options, yields the seller’s highest profit under certain conditions. (2) It is vitally crucial for a vendor to provide a price reduction if an advance payment is required. (3) Advance payment results in higher profit than delayed payment if sales volume does not significantly fall while switching from credit to advance payments, or vice versa. To solve the optimization problem, a popular metaheuristic algorithm (viz., Grey Wolf Optimizer) is used and finally performed a post optimality analysis for making a fruitful conclusion.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100561"},"PeriodicalIF":5.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571498","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 SDN to enhance security in IoT networks","authors":"Safi Ibrahim ,&nbsp;Aya M. Youssef ,&nbsp;Mahmoud Shoman ,&nbsp;Sanaa Taha","doi":"10.1016/j.eij.2024.100564","DOIUrl":"10.1016/j.eij.2024.100564","url":null,"abstract":"<div><div>Software-defined networking (SDN) is a revolutionary technology that has revolutionised network management by providing flexibility and adaptability. As the popularity of SDN increases, it is crucial to address security vulnerabilities in these dynamic networks. This paper proposes a framework for enhancing security in SDN by utilising three separate Deep Learning models, namely Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). This framework is utilised for the InSDN dataset, a huge dataset specifically created for SDN security research. The dataset consists of a total of 343,939 instances, encompassing both normal and attack traffic. The regular data yields a sum of 68,424, whereas the attack traffic comprises 275,515 occurrences. This study employs multiclassification algorithms to precisely detect and categorise diverse security threats in SDN. The InSDN dataset faces issues related to class imbalance, which are addressed by using the Synthetic Minority Over-sampling Technique (SMOTE). The SMOTE technique is utilised to create artificial instances of the underrepresented class, hence achieving a more equitable distribution of security hazards within the dataset. This strategy improves the efficacy of multiclassification techniques, ultimately resulting in greater accuracy in the identification and classification of different security threats in SDN environments. The initial DNN model exhibited satisfactory performance, with an accuracy of 87%. The second CNN model demonstrated strong and consistent performance, with an accuracy rate of 99%. In addition, an LSTM model attained a 90% accuracy rate.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"28 ","pages":"Article 100564"},"PeriodicalIF":5.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553747","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}