rss_2.0Cybernetics and Information Technologies FeedSciendo RSS Feed for Cybernetics and Information Technologies and Information Technologies Feed 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 DenseNet Model with Fusion of Channel and Spatial Attention for Facial Expression Recognition<abstract><title style='display:none'>Abstract</title> <p>Facial Expression Recognition (FER) is a fundamental component of human communication with numerous potential applications. Convolutional neural networks, particularly those employing advanced architectures like Densely connected Networks (DenseNets), have demonstrated remarkable success in FER. Additionally, attention mechanisms have been harnessed to enhance feature extraction by focusing on critical image regions. This can induce more efficient models for image classification. This study introduces an efficient DenseNet model that utilizes a fusion of channel and spatial attention for FER, which capitalizes on the respective strengths to enhance feature extraction while also reducing model complexity in terms of parameters. The model is evaluated across five popular datasets: JAFFE, CK+, OuluCASIA, KDEF, and RAF-DB. The results indicate an accuracy of at least 99.94% for four lab-controlled datasets, which surpasses the accuracy of all other compared methods. Furthermore, the model demonstrates an accuracy of 83.18% with training from scratch on the real-world RAF-DB dataset.</p> </abstract>ARTICLEtrue Comprehensive Approach for Monitoring Student Satisfaction in Blended Learning Courses<abstract> <title style='display:none'>Abstract</title> <p>Due to the great importance of student satisfaction with educational services, many HEIs conduct annual surveys. Analyzing the results of such surveys, tracking trends, and comparing the evaluation results to help governing bodies make data-driven decisions to take measures to improve the quality of courses is time-consuming and requires a lot of manual work. As a solution to the problem, this paper proposes a comprehensive approach to monitoring student satisfaction with the quality of blended learning courses. The developed software tool analyzes results and enables users with different roles to generate reports with aggregated results at different levels, allowing them to make informed decisions and take measures to ensure a higher quality of courses. The generated reports during the pilot experiments proved the tool’s applicability. This tool can be implemented in any HEI, regardless of the software systems used.</p> </abstract>ARTICLEtrue and Temporal Variations on Air Quality Prediction Using Deep Learning Techniques<abstract> <title style='display:none'>Abstract</title> <p>Air Pollution is constantly causing a severe effect on the environment and public health. Prediction of air quality is widespread and has become a challenging issue owing to the enormous environmental data with time-space nonlinearity and multi-dimensional feature interaction. There is a need to bring out the spatial and temporal factors that are influencing the prediction. The present study concentrates on the correlation prediction of spatial and temporal relations. A Deep learning technique has been proposed for forecasting the accurate prediction. The proposed Bi_ST model is evaluated for 17 cities in India and China. The predicted results are evaluated with the performance metrics of RMSE, MAE, and MAPE. Experimental results demonstrate that our method Bi_ST accredits more accurate forecasts than all baseline RNN and LSTM models by reducing the error rate. The accuracy of the model obtained is 94%.</p> </abstract>ARTICLEtrue A Denial-of-Service Resistant Trust Model for VANET<abstract> <title style='display:none'>Abstract</title> <p>The Denial of Service (DoS) attack threatens the availability of key components of Vehicular Ad-hoc Network (VANET). Various centralized and decentralized trust-based approaches have been proposed to secure the VANET from DoS attack. The centralized approach is less efficient because the attack on the central trust manager leads to the overall failure of services. In comparison, the cluster-based decentralized approach faces overhead because of frequent changes in cluster members due to the high speed of the vehicles. Therefore, we have proposed a cluster-based Denial-of-Service Resistant Trust model (DoSRT). It improves decentralized trust management using speed deviation-based clustering and detects DoS attack based on the frequency of messages sent. Through performance evaluation, we have found that DoSRT improves precision, recall, accuracy, and F-Score by around 19%, 16%, 20%, and 17% in the presence of 30% DoS attackers.</p> </abstract>ARTICLEtrue Tolerance of Cloud Infrastructure with Machine Learning<abstract> <title style='display:none'>Abstract</title> <p>Enhancing the fault tolerance of cloud systems and accurately forecasting cloud performance are pivotal concerns in cloud computing research. This research addresses critical concerns in cloud computing by enhancing fault tolerance and forecasting cloud performance using machine learning models. Leveraging the Google trace dataset with 10000 cloud environment records encompassing diverse metrics, we systematically have employed machine learning algorithms, including linear regression, decision trees, and gradient boosting, to construct predictive models. These models have outperformed baseline methods, with C5.0 and XGBoost showing exceptional accuracy, precision, and reliability in forecasting cloud behavior. Feature importance analysis has identified the ten most influential factors affecting cloud system performance. This work significantly advances cloud optimization and reliability, enabling proactive monitoring, early performance issue detection, and improved fault tolerance. Future research can further refine these predictive models, enhancing cloud resource management and ultimately improving service delivery in cloud computing.</p> </abstract>ARTICLEtrue Models and Strategy Approaches Dealing with Economic Crises, Natural Disasters, and Pandemics – An Overview<abstract> <title style='display:none'>Abstract</title> <p>The occurrence of large-scale crises is a great challenge for people. In such cases, many levels of public life are affected and recovery takes time and considerable resources. Therefore, approaches and tools for predicting and preventing crises, as well as models and methods for crisis management and crisis overcoming, are necessary. In this review, we present approaches, models, and methods that support decision-making in relation to the prevention and resolution of large-scale crises. We divide crises into three types: natural disasters, pandemics, and economic crises. For each type of crisis situation, the types of applied tasks that are solved and the corresponding models and methods that are used to support decision-makers in overcoming the crises are discussed. Conclusions are drawn on the state of the art in this area and some directions for future work are outlined.</p> </abstract>ARTICLEtrue Cryptographic Encryption and Decryption Using Advanced Encryption Standard and Data Encryption Standard<abstract> <title style='display:none'>Abstract</title> <p>This research proposes an efficient hybridized approach for symmetrical encryption of image files in bitmap formats. Due to the heavy use of lightweight encryption in fields such as military and corporate workplaces, intruders try to intercept communication through illegal means and gain access to classified information. This can result in heavy losses if the leaked image data is misused. The proposed enhances the security and efficiency of one of the most used standard symmetric algorithms, Advanced Encryption Standard (AES). In the proposed method, the AES architecture has been modified using a less intensive algorithm, Data Encryption Standard (DES). DES carries a sub-process of permuting data columns rather than the AES’s mixing feature. The proposed algorithm is analyzed using a set of 16 bitmap images of varying memory sizes and resolutions. The effectiveness of the algorithm is evaluated solely in terms of perceptual invisibility as per the main objective of the research.</p> </abstract>ARTICLEtrue Least Angle Regression Based LASSO Feature Selection and Swish Activation Function Model for Startup Survival Rate<abstract> <title style='display:none'>Abstract</title> <p>A startup is a recently established business venture led by entrepreneurs, to create and offer new products or services. The discovery of promising startups is a challenging task for creditors, policymakers, and investors. Therefore, the startup survival rate prediction is required to be developed for the success/failure of startup companies. In this paper, the feature selection using the Convex Least Angle Regression Least Absolute Shrinkage and Selection Operator (CLAR-LASSO) is proposed to improve the classification of startup survival rate prediction. The Swish Activation Function based Long Short-Term Memory (SAFLSTM) is developed for classifying the survival rate of startups. Further, the Local Interpretable Model-agnostic Explanations (LIME) model interprets the predicted classification to the user. Existing research such as Hyper Parameter Tuning (HPT)-Logistic regression, HPT-Support Vector Machine (SVM), HPT-XGBoost, and SAFLSTM are used to compare the CLAR-LASSO. The accuracy of the CLAR-LASSO is 95.67% which is high when compared to the HPT-Logistic regression, HPT-SVM, HPT-XGBoost, and SAFLSTM.</p> </abstract>ARTICLEtrue Novel Self-Exploration Scheme for Learning Optimal Policies against Dynamic Jamming Attacks in Cognitive Radio Networks<abstract> <title style='display:none'>Abstract</title> <p>Cognitive Radio Networks (CRNs) present a compelling possibility to enable secondary users to take advantage of unused frequency bands in constrained spectrum resources. However, the network is vulnerable to a wide range of jamming attacks, which adversely affect its performance. Several countermeasures proposed in the literature require prior knowledge of the communication network and jamming strategy that are computationally intensive. These solutions may not be suitable for many real-world critical applications of the Internet of Things (IoT). Therefore, a novel self-exploration approach based on deep reinforcement learning is proposed to learn an optimal policy against dynamic attacks in CRN-based IoT applications. This method reduces computational complexity, without prior knowledge of the communication network or jamming strategy. A simulation of the proposed scheme eliminates interference effectively, consumes less power, and has a better Signal-to-Noise Ratio (SNR) than other algorithms. A platform-agnostic and efficient anti-jamming solution is provided to improve CRN’s performance when jamming occurs.</p> </abstract>ARTICLEtrue the Performance and Characteristics of Single Linkage and Complete Linkage Hierarchical Clustering Methods for IoT Sensor Networks<abstract> <title style='display:none'>Abstract</title> <p>The research explores applying hierarchical clustering methods, namely single linkage and complete linkage, in IoT Sensor Networks (ISNs). ISNs are distributed systems comprising numerous sensor nodes that collect data from the environment and communicate with each other to transmit the data to a base station. Hierarchical clustering is a technique that groups nodes into clusters based on proximity and similarity. This paper implements and compares the performance of single linkage and complete linkage methods in terms of cluster size, network lifetime, and cluster quality. The study’s findings provide guidance for ISN researchers and designers in selecting the appropriate clustering method that meets their specific requirements.</p> </abstract>ARTICLEtrue