rss_2.0Cybernetics and Information Technologies FeedSciendo RSS Feed for Cybernetics and Information Technologies and Information Technologies Feed Novel Deep Transfer Learning-Based Approach for Face Pose Estimation<abstract> <title style='display:none'>Abstract</title> <p>An efficient face recognition system is essential for security and authentication-based applications. However, real-time face recognition systems have a few significant concerns, including face pose orientations. In the last decade, numerous solutions have been introduced to estimate distinct face pose orientations. Nevertheless, these solutions must be adequately addressed for the three main face pose orientations: Yaw, Pitch, and Roll. This paper proposed a novel deep transfer learning-based multitasking approach for solving three integrated tasks, i.e., face detection, landmarks detection, and face pose estimation. The face pose variation vulnerability has been intensely investigated here underlying three modules: image preprocessing, feature extraction module through deep transfer learning, and regression module for estimating the face poses. The experiments are performed on the well-known benchmark dataset Annotated Faces in the Wild (AFW). We evaluate the outcomes of the experiments to reveal that our proposed approach is superior to other recently available solutions.</p> </abstract>ARTICLEtrue Selection Using Hybrid Metaheuristic Algorithm for Email Spam Detection<abstract> <title style='display:none'>Abstract</title> <p>In the present study, Krill Herd (KH) is proposed as a Feature Selection tool to detect spam email problems. This works by assessing the accuracy and performance of classifiers and minimizing the number of features. Krill Herd is a relatively new technique based on the herding behavior of small crustaceans called krill. This technique has been combined with a local search algorithm called Tabu Search (TS) and has been successfully employed to identify spam emails. This method has also generated much better results than other hybrid algorithm optimization systems such as the hybrid Water Cycle Algorithm with Simulated Annealing (WCASA). To assess the effectiveness of KH algorithms, SVM classifiers, and seven benchmark email datasets were used. The findings indicate that KHTS is much more accurate in detecting spam mail (97.8%) than WCASA.</p> </abstract>ARTICLEtrue Decentralized Storage in Blockchain: A Hybrid Cryptographic Framework<abstract> <title style='display:none'>Abstract</title> <p>The evolution of decentralized storage, propelled by blockchain advancements, has revolutionized data management. This paper focuses on content security in the InterPlanetary File System (IPFS), a leading decentralized storage network lacking inherent content encryption. To address this vulnerability, we propose a novel hybrid cryptographic algorithm, merging AES 128-bit encryption with Elliptic Curve Cryptography (ECC) key generation. The algorithm includes ECC key pairs, random IV generation, and content/AES key encryption using ECC public keys. Benchmarking against standard AES 256-bit methods shows a significant 20% acceleration in encryption speed and a 16% increase in decryption efficiency, affirming practicality for enhancing IPFS content security. This research contributes to securing decentralized storage and provides a performance-driven solution. The promising results highlight the viability of the proposed approach, advancing understanding and mitigating security concerns in IPFS and similar systems.</p> </abstract>ARTICLEtrue Vulture Optimization-Based Decision Tree (AVO-DT): An Innovative Method for Malware Identification and Evaluation through the Application of Meta-Heuristic Optimization Algorithm<abstract> <title style='display:none'>Abstract</title> <p>Malware remains a big threat to cyber security, calling for machine learning-based malware detection. Malware variations exhibit common behavioral patterns indicative of their source and intended use to enhance the existing framework’s usefulness. Here we present a novel model, i.e., African Vulture Optimization-based Decision Tree (AVO-DT) to increase the overall optimization.</p> <p>The datasets from Android apps and malware software train the AVO-DT model. After training, the datasets are pre-processed by removing training errors. The DT algorithm is used by the developed AVO model to carry out the detection procedure and predict malware activity. To detect malware activities and improve accuracy, such an AVO-DT model technique employs both static and dynamic methodologies. The other measurements on Android applications might be either malicious or benign. Here we also developed malware prevention and detection systems to address ambiguous search spaces in multidimensionality difficulties and resolve optimization challenges.</p> </abstract>ARTICLEtrue of Blended Learning Implementation in HEIs: Tool for Monitoring the Use of e-Learning Management Systems<abstract> <title style='display:none'>Abstract</title> <p>Despite the wide acceptance of blended learning in Higher Education Institutions (HEIs) worldwide, the issue of monitoring its implementation has been little addressed in the literature. The paper presents the results of the first stage of the study for the development and implementation of tools for monitoring the degree of use of blended learning courses within the learning process in HEIs. The tool introduced here extracts data from the database of the e-learning environment and visualizes the results of the data analysis in dashboards that provide valuable insights to decision-making for improving the quality of blended learning implementation. The tool allows governing bodies to track trends in the user registration, development, and updating of blended learning courses, the number of learners, and the usability of the courses by users for a selected period. Based on the results of tool experimental testing, goals for its further development are set.</p> </abstract>ARTICLEtrue Improved Parallel Biobjective Hybrid Real-Coded Genetic Algorithm with Clustering-Based Selection<abstract> <title style='display:none'>Abstract</title> <p>This work presents an improved parallel biobjective hybrid real-coded genetic algorithm (MORCGA-MOPSO-II). The approach is based on the combined use of the parallel Multi-Objective Real-Coded Genetic Algorithm (MORCGA) and the Multi-Objective Particle Swarm Optimization (MOPSO). At the same time, clustering-based selection techniques are used to form subpopulations of parent individuals. Using well-known clustering algorithms (e.g., k-Means, hierarchical clustering, c-means, and DBSCAN) in combination with the proposed clustering-based mutation (the CL-mutation) directed toward the obtained cluster centers allows for improving the quality of the Pareto fronts’ approximations. The results of the MORCGA-MOPSO-II were compared with other well-known multi-objective evolutionary algorithms (e.g., SPEA2, NSGA-II, FCGA, MOSPO, etc.). Moreover, the MORCGA-MOPSO-II was integrated with the previously developed agent-based model of a goods exchange through the objective functions. As a result, the Pareto fronts have been obtained for the agent-based model of a goods exchange in different configurations of the initial distribution of agents.</p> </abstract>ARTICLEtrue Approach to Hopfield Network-Based Energy-Efficient RFID Network Planning<abstract> <title style='display:none'>Abstract</title> <p>Radio Frequency IDentification (RFID) Network Planning (RNP) is the problem of placing RFID readers in a working area where a tag is interrogated by at least one reader and at the same time satisfies some constraints such as minimum number of placed readers, minimal interference, and minimal outside coverage. The RNP optimization has been proven NP-hard; thus, natural-inspired approaches are often used to find the optimal solution. The paper proposes an energy-efficient RNP approach in which the positions of placed readers are optimized by a Hopfield network, and the energy efficiency is achieved through a placement area restriction technique. A mechanism of redundant reader elimination is also added to minimize the number of placed readers. Simulation results show that the Hopfield network-based energy-efficient RNP approach achieves the maximum tag coverage and energy efficiency by reducing interference, outside coverage, and the number of placed readers.</p> </abstract>ARTICLEtrue Application (dApp) Development and Implementation<abstract> <title style='display:none'>Abstract</title> <p>This paper focuses on the development and deployment of a dApp (decentralized Application) for Smart Crop Production Data exchange (SCPDx) that runs on Antelope blockchain/IPFS infrastructure. The paper emphasizes practical approaches to dApp design and deployment, analyses architectural patterns of dApps, and underlines the role of smart contracts in implementing complex functionality. The paper’s contribution is the detailed description of the main smart contracts and the practical knowledge provided on the architecture and implementation of dApps, emphasizing the challenges and solutions in the development process, especially in the context of smart contract implementation. Future developments of the application towards additional data types processing, and design of an interface for leveraging, testing, and evaluating the performance of open source Large Language Models (LLMs) on specific datasets are commented on.</p> </abstract>ARTICLEtrue Generalized Network Flow Accounting for Motivation<abstract> <title style='display:none'>Abstract</title> <p>In the present work, the maximal generalized network flow, often referred to as the profit-loss flow, is examined, considering the motivation in the decision-making systems built on this flow. The general description of the features of motivation as a psychological process actively involved in decision-making systems through the generalized network flow is given. A method is proposed to embed motivation in a generalized network flow through the motivation coefficients on different network sections (arcs). It is shown that the proposed method offers more possibilities than the classical network flow. It is proven that the initial resource in the source does not match the final resource in the consumer. This has been suggested to be due to the influence of motivation. The theoretical and experimental results convincingly prove the possibilities of considering motivation in decision-making systems when managing the transportation of resources in an arbitrary transport network.</p> </abstract>ARTICLEtrue Recommender System for Educational Planning<abstract> <title style='display:none'>Abstract</title> <p>Knowledge-based recommender systems have always had their privileged place among all Decision Support Systems (DSS), given their advantage on several points over other techniques. Our paper presents a framework implementing a hybrid form of Rule-Based Reasoning and Case-Based Reasoning (RBR-CBR), to address the rarely discussed domain of educational planning. The system has been tested and presented outstanding results with a high accuracy, which will benefit educational planners’ decision support. We have also developed a dedicated application for this project to visualize the results obtained.</p> </abstract>ARTICLEtrue Recoloring Deutan CVD Image from Block SVD Watermark<abstract><title style='display:none'>Abstract</title> <p>People with Color Vision Deficiency (CVD), which arises as a deformation of the M cones in the eye, cannot detect the color green in the image (deutan anomaly). In the first part of the paper, deutan anomalous is described. After that, the image recoloring algorithm, which enables Deutan CVD people to see a wider spectrum in images, is described. Then, the effect of the Recoloring algorithm on images with inserted watermark is analyzed. An experiment has been carried out, in which the effect of the Recoloring algorithm on the quality of extracted watermark and Recoloring image is studied. In addition, the robustness of the inserted watermark in relation to spatial transformations (rotation, scaling) and compression algorithms has been tested. By applying objective measures and visual inspection of the quality of extracted watermark and recoloring image, the optimal insertion factor α is determined. All results are presented in the form of pictures, tables and graphics.</p> </abstract>ARTICLEtrue Survey on Lightweight Cryptographic Algorithms in IoT<abstract> <title style='display:none'>Abstract</title> <p>The Internet of Things (IoT) will soon penetrate every aspect of human life. Several threats and vulnerabilities are present due to the different devices and protocols used in an IoT system. Conventional cryptographic primitives or algorithms cannot run efficiently and are unsuitable for resource-constrained devices in IoT. Hence, a recently developed area of cryptography, known as lightweight cryptography, has been introduced, and over the years, numerous lightweight algorithms have been suggested. This paper gives a comprehensive overview of the lightweight cryptography field and considers various popular lightweight cryptographic algorithms proposed and evaluated over the past years for analysis. Different taxonomies of the algorithms and other associated concepts were also provided, which helps new researchers gain a quick overview of the field. Finally, a set of 11 selected ultra-lightweight algorithms are analyzed based on the software implementations, and their evaluation is carried out using different metrics.</p> </abstract>ARTICLEtrue Machine Learning for Fraudulent Social Media Profile Detection<abstract><title style='display:none'>Abstract</title> <p>Fake social media profiles are responsible for various cyber-attacks, spreading fake news, identity theft, business and payment fraud, abuse, and more. This paper aims to explore the potential of Machine Learning in detecting fake social media profiles by employing various Machine Learning algorithms, including the Dummy Classifier, Support Vector Classifier (SVC), Support Vector Classifier (SVC) kernels, Random Forest classifier, Random Forest Regressor, Decision Tree Classifier, Decision Tree Regressor, MultiLayer Perceptron classifier (MLP), MultiLayer Perceptron (MLP) Regressor, Naïve Bayes classifier, and Logistic Regression. For a comprehensive evaluation of the performance and accuracy of different models in detecting fake social media profiles, it is essential to consider confusion matrices, sampling techniques, and various metric calculations. Additionally, incorporating extended computations such as root mean squared error, mean absolute error, mean squared error and cross-validation accuracy can further enhance the overall performance of the models.</p> </abstract>ARTICLEtrue Rapidly Exploring Random Tree Optimization (MRRTO): An Enhanced Algorithm for Robot Path Planning<abstract><title style='display:none'>Abstract</title> <p>With the advancement of the robotics world, many path-planning algorithms have been proposed. One of the important algorithms is the Rapidly Exploring Random Tree (RRT) but with the drawback of not guaranteeing the optimal path. This paper solves this problem by proposing a Memorized RRT Optimization Algorithm (MRRTO Algorithm) using memory as an optimization step. The algorithm obtains a single path from the start point, and another from the target point to store only the last visited new node. The method for computing the nearest node depends on the position, when a new node is added, the RRT function checks if there is another node closer to the new node rather than that is closer to the goal point. Simulation results with different environments show that the MRRTO outperforms the original RRT Algorithm, graph algorithms, and metaheuristic algorithms in terms of reducing time consumption, path length, and number of nodes used.</p> </abstract>ARTICLEtrue Temporal Constraints of Events in EBS at Runtime<abstract><title style='display:none'>Abstract</title> <p>As a kind of software system, the Event-Based Systems (EBS) respond to events rather than executing a predefined sequence of instructions. Events usually occur in real time, so it is crucial that they are processed in the correct order and within temporal constraints. The objective of this work is to propose an approach to check if events of EBS at runtime preserve the specification of temporal constraints. To form the approach by logic process, we have formalized the EBS model, through which, we have proved that the complexity of the checking algorithms is only polynomial. The approach has been implemented as a tool (VER) to check EBS at runtime automatically. The results of the proposed method are illustrated by checking a real-world Event Driven Architecture (EDA) application, an Intelligent transportation system.</p> </abstract>ARTICLEtrue Approaches for Heterogeneous Big Data: A Survey<abstract><title style='display:none'>Abstract</title> <p>Modern organizations are currently wrestling with strenuous challenges relating to the management of heterogeneous big data, which combines data from various sources and varies in type, format, and content. The heterogeneity of the data makes it difficult to analyze and integrate. This paper presents big data warehousing and federation as viable approaches for handling big data complexity. It discusses their respective advantages and disadvantages as strategies for integrating, managing, and analyzing heterogeneous big data. Data integration is crucial for organizations to manipulate organizational data. Organizations have to weigh the benefits and drawbacks of both data integration approaches to identify the one that responds to their organizational needs and objectives. This paper aw well presents an adequate analysis of these two data integration approaches and identifies challenges associated with the selection of either approach. Thorough understanding and awareness of the merits and demits of these two approaches are crucial for practitioners, researchers, and decision-makers to select the approach that enables them to handle complex data, boost their decision-making process, and best align with their needs and expectations.</p> </abstract>ARTICLEtrue Intrusion Detection with Explainable AI: A Transparent Approach to Network Security<abstract><title style='display:none'>Abstract</title> <p>An Intrusion Detection System (IDS) is essential to identify cyber-attacks and implement appropriate measures for each risk. The efficiency of the Machine Learning (ML) techniques is compromised in the presence of irrelevant features and class imbalance. In this research, an efficient data pre-processing strategy was proposed to enhance the model’s generalizability. The class dissimilarity is addressed using k-Means SMOTE. After this, we furnish a hybrid feature selection method that combines filters and wrappers. Further, a hyperparameter-tuned Light Gradient Boosting Machine (LGBM) is analyzed by varying the optimal feature subsets. The experiments used the datasets – UNSW-NB15 and CICIDS-2017, yielding an accuracy of 90.71% and 99.98%, respectively. As the transparency and generalizability of the model depend significantly on understanding each component of the prediction, we employed the eXplainable Artificial Intelligence (XAI) method, SHapley Additive exPlanation (SHAP), to improve the comprehension of forecasted results.</p> </abstract>ARTICLEtrue Review on State-of-Art Blockchain Schemes for Electronic Health Records Management<abstract><title style='display:none'>Abstract</title> <p>In today’s world, Electronic Health Records (EHR) are highly segregated and available only within the organization with which the patient is associated. If a patient has to visit another hospital there is no secure way for hospitals to communicate and share medical records. Hence, people are always asked to redo tests that have been done earlier in different hospitals. This leads to monetary, time, and resource loss. Even if the organizations are ready to share data, there are no secure methods for sharing without disturbing data privacy, integrity, and confidentiality. When health data are stored or transferred via unsecured means there are always possibilities for adversaries to initiate an attack and modify them. To overcome these hurdles and secure the storage and sharing of health records, blockchain, a very disruptive technology can be integrated with the healthcare system for EHR management. This paper surveys recent works on the distributed, decentralized systems for EHR storage in healthcare organizations.</p> </abstract>ARTICLEtrue Edge Detection Methods in Image Steganography for High Embedding Capacity<abstract><title style='display:none'>Abstract</title> <p>In this research, we propose two new image steganography techniques focusing on increasing image-embedding capacity. The methods will encrypt and hide secret information in the edge area. We utilized two hybrid methods for the edge detection of the images. The first method combines the Laplacian of Gaussian (LoG) with the wavelet transform algorithm and the second method mixes the LOG and Canny. The Combining was performed using addWeighted. The text message will be encrypted using the GIFT cipher method for further security and low computation. For the effectiveness evaluation of the proposed method, various evaluation metrics were used such as embedding capacity, PSNR, MSE, and SSIM. The obtained results indicate that the proposed method has a greater embedding capacity in comparison with other methods, while still maintaining high levels of imperceptibility in the cover image.</p> </abstract>ARTICLEtrue Factors for Conducting Software-Process Improvement in Web-Based Software Projects<abstract><title style='display:none'>Abstract</title> <p>Continuous Software Process Improvement (SPI) is essential for achieving and maintaining high-quality software products. Web-based software enterprises, comprising a substantial proportion of global businesses and forming a cornerstone of the world’s industrial economy, are actively pursuing SPI initiatives. While these companies recognize the critical role of process enhancement in achieving success, they face challenges in implementing SPI due to the distinctive characteristics of Web-based software projects. This study aims to identify, validate, and prioritize the sustainability success factors that positively influence SPI implementation efforts in Web-based software projects. Data have been meticulously gathered through a systematic literature review and quantitatively through a survey questionnaire. The findings of this research empower Web-based software enterprises to refine their management strategies for evaluating and bolstering SPI practices within the Web-based software projects domain.</p> </abstract>ARTICLEtrue