rss_2.0Sciendo RSS Feed for Cybernetics and Information Technologieshttps://sciendo.com/journal/CAIThttps://www.sciendo.comhttps://sciendo-parsed.s3.eu-central-1.amazonaws.com/656df4aada317b51c3d3bbae/cover-image.jpghttps://sciendo.com/journal/CAIT140216https://sciendo.com/article/10.2478/cait-2023-0043<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>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00432023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0045<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>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00452023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0042<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>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00422023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0034<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>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00342023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0033<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>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00332023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0036<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>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00362023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0039<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>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00392023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0040<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>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00402023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0041<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>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00412023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0037<abstract> <title style='display:none'>Abstract</title> <p>Effective spam filtering plays a crucial role in enhancing user experience by sparing them from unwanted messages. This imperative underscores the importance of safeguarding email systems, prompting scholars across diverse fields to delve deeper into this subject. The primary objective of this research is to mitigate the disruptive effects of spam on email usage by introducing improved security measures compared to existing methods. This goal can be accomplished through the development of a novel spam filtering technique designed to prevent spam from infiltrating users’ inboxes. Consequently, a hybrid filtering approach that combines an information gain philter and a Wrapper Grey Wolf Optimizer feature selection algorithm with a Naive Bayes Classifier, is proposed, denoted as GWO-NBC. This research is rigorously tested using the WEKA software and the SPAMBASE dataset. Thorough performance evaluations demonstrated that the proposed approach surpasses existing solutions in terms of both security and accuracy.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00372023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0035<abstract> <title style='display:none'>Abstract</title> <p>One of the methods most frequently used to recommend films is collaborative filtering. We examine the potential of collaborative filtering in our paper’s discussion of product suggestions. In addition to utilizing collaborative filtering in a new application, the proposed system will present a better technique that focuses especially on resolving the cold start issue. The suggested system will compute similarity using the Pearson Correlation Coefficient (PCC). Collaborative filtering that uses PCC suffers from the cold start problem or a lack of information on new users to generate useful recommendations. The proposed system solves the issue of cold start by gauging each new user by certain arbitrary parameters and recommending based on the choices of other users in that demographic. The proposed system also solves the issue of users’ reluctance to provide ratings by implementing a keyword-based perception system that will aid users in finding the right product for them.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00352023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0046<abstract> <title style='display:none'>Abstract</title> <p>Transmission of information is an essential component in an IoT device for sending, receiving, and collecting data. The Smart devices in IoT architecture are designed as physical devices linked with computing resources that can connect and communicate with another smart device through any medium and protocol. Communication among various smart devices is a challenging task to exchange information and to guarantee the information reaches the destination entirely in real-time in the same order as sent without any data loss. Thus, this article proposes the novel Bat-based Deep Belief Neural framework (BDBN) method for the air pollution monitoring scheme. The reliability of the proposed system has been tested under the error condition in the transport layer and is validated with the conventional methods in terms of Accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson correlation coefficient (r), Coefficient of determination (R²) and Error rate.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00462023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0038<abstract> <title style='display:none'>Abstract</title> <p>A novel proposed Binary Volleyball Premier League algorithm (BVPL) has shown some promising results in a Parkinson’s Disease (PD) dataset related to fitness and accuracy [<xref ref-type="bibr" rid="j_cait-2023-0038_ref_001">1</xref>]. This paper evaluates and provides an overview of the efficiency of BVPL in feature selection compared to various metaheuristic optimization algorithms and PD datasets. Moreover, an improved variant of BVPL is proposed that integrates the opposite-based solution to enlarge search domains and increase the possibility of getting rid of the local optima. The performance of BVPL is validated using the accuracy of the k-Nearest Neighbor Algorithm. The superiority of BVPL over the competing algorithms for each dataset is measured using statistical tests. The conclusive results indicate that the BVPL exhibits significant competitiveness compared to most metaheuristic algorithms, thereby establishing its potential for accurate prediction of PD. Overall, BVPL shows high potential to be employed in feature selection.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00382023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0044<abstract> <title style='display:none'>Abstract</title> <p>Predicting students’ academic performance is a critical research area, yet imbalanced educational datasets, characterized by unequal academic-level representation, present challenges for classifiers. While prior research has addressed the imbalance in binary-class datasets, this study focuses on multi-class datasets. A comparison of ten resampling methods (SMOTE, Adasyn, Distance SMOTE, BorderLineSMOTE, KmeansSMOTE, SVMSMOTE, LN SMOTE, MWSMOTE, Safe Level SMOTE, and SMOTETomek) is conducted alongside nine classification models: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), Logistic Regression (LR), Extra Tree (ET), Random Forest (RT), Extreme Gradient Boosting (XGB), and Ada Boost (AdaB). Following a rigorous evaluation, including hyperparameter tuning and 10 fold cross-validations, KNN with SmoteTomek attains the highest accuracy of 83.7%, as demonstrated through an ablation study. These results emphasize SMOTETomek’s effectiveness in mitigating class imbalance in educational datasets and highlight KNN’s potential as an educational data mining classifier.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00442023-11-30T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0025<abstract> <title style='display:none'>Abstract</title> <p>Wireless networks have become essential in daily life, with a growing number of base stations and connected devices. However, increasing traffic and energy consumption pose challenges. This research proposes a Dual Step Hybrid Mechanism (DSHM) for energy optimization, incorporating MIMO technologies. The first step introduces an optimal algorithm that iteratively updates the probability distribution to achieve the best solution. The second step focuses on reducing energy consumption while maximizing energy efficiency, using specific techniques and strategies to minimize usage without compromising energy maximization. The proposed approach is evaluated using parameter settings, including block length, path loss, hardware impairments, and bandwidth. The research investigates the impact of hardware impairments on energy efficiency and analyzes performance under different SINR constraints. The study also examines energy efficiency in active user density and base station density, highlighting the superior energy efficiency achieved by MIMO configurations.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00252023-09-28T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0032<abstract> <title style='display:none'>Abstract</title> <p>This study presents design of traffic light system with feedback control that considers a crossroad in an urban area. Two types of controllers are designed – fuzzy and analytical, which have been tested separately on Aimsun platform through a simulation. The aim of the study is to compare the performance of both controllers in terms of increasing traffic flow and decreasing queue length. The controllers manage the duration of the green light according to the traffic flow. Two different formal models are designed, tested, and compared. They have produced adequate solutions in terms of developing controllers for modeling and simulation of transportation tasks.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00322023-09-28T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0031<abstract> <title style='display:none'>Abstract</title> <p>Wireless Sensor Networks (WSN) aggregate data from multiple sensors and transfer it to a central node. Sensor nodes should use as little energy as possible to aggregate data. This work has focused on optimal clustering and cluster head node selection to save energy. HyperGraphs (HGC) and cluster head selection based on distance and energy consumption are unique approaches to spectral clustering. GRA computes a relational matrix to select the cluster head. The network’s Moving Agent (MA) may use Hypergraphed Particle Swarm Optimization (HGPSO) to collect data from cluster heads. Compared to the clustering algorithm without agent movement, the HGC-GRA-HGPSO approach has increased residual energy by 5.59% and packets by 2.44%. It also has improved residual energy by 2.45% compared to Grey Wolf Optimizer-based Clustering (GWO-C).</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00312023-09-28T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0028<abstract> <title style='display:none'>Abstract</title> <p>The article proposes an original interdisciplinary approach to the design and construction of cyber-physical robot systems for mechanical processing. From a methodological aspect, the goal is the unification of modeling/synthesis and simulation software of a robot system for mechanical processing operating under the conditions of digital robust control. The system includes industrial robots; modules for implementing technological operations and transport systems. Adherence to the principle of modular construction, reconfiguration, and multi-operation ensures high flexibility and quick response when readjusting the system, and the optimization criteria – minimizing idle moves of the robot, leads to a reduction in the work cycle. The robust control, simultaneously in the instrumental, configurational, and system direction, is a counteraction in the mode of uncertainty, both to external signal disturbances and to possible constantly acting, system reparametrizing factors. This creates prerequisites for maintaining and implementing in online mode both the technological and geometric parameters of the details processed.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00282023-09-28T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0030<abstract> <title style='display:none'>Abstract</title> <p>Throughout the years neural networks have been based on the perceptron model of the artificial neuron. Attempts to stray from it are few to none. The perceptron simply works and that has discouraged research around other neuron models. New discoveries highlight the importance of dendrites in the neuron, but the perceptron model does not include them. This brings us to the goal of the paper which is to present and test different models of artificial neurons that utilize dendrites to create an artificial neuron that better represents the biological neuron. The authors propose two models. One is made with the purpose of testing the idea of the dendritic neuron. The distinguishing feature of the second model is that it implements activation functions after its dendrites. Results from the second model suggest that it performs as well as or even better than the perceptron model.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00302023-09-28T00:00:00.000+00:00https://sciendo.com/article/10.2478/cait-2023-0026<abstract> <title style='display:none'>Abstract</title> <p>Each person’s unique traits hold valuable insights into their consumer behavior, allowing scholars and industry experts to develop innovative marketing strategies, personalized solutions, and enhanced user experiences. This study presents a conceptual framework that explores the connection between fundamental personality dimensions and users’ online shopping styles. By employing the TIPI test, a reliable and validated alternative to the Five-Factor model, individual consumer profiles are established. The results reveal a significant relationship between key personality traits and specific online shopping functionalities. To accurately forecast customers’ needs, expectations, and preferences on the Internet, we propose the implementation of two Machine Learning models, namely Decision Trees and Random Forest. According to the applied evaluation metrics, both models demonstrate fine predictions of consumer behavior based on their personality.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/cait-2023-00262023-09-28T00:00:00.000+00:00en-us-1