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Amidst rapid technological and healthcare advancements, biomedical data classification using machine learning (ML) is pivotal for revolutionizing medical diagnosis, treatment, and research by organizing vast healthcarerelated data. Despite efforts to apply single ML models on clean datasets, satisfactory classification accuracy can still be elusive. In such cases, MLbased ensembles offer a promising solution. This paper explores cascaded ensembles as highly accurate methods. Existing cascade classifiers often partition large datasets into equal unique parts, limiting accuracy due to insufficient amount of useful information processed by weak classifiers of all levels of the cascade ensemble. To address this, we propose an improved cascaded ensemble scheme using a different data sampling approach. Our method forms larger subsamples at each cascade level, enhancing accuracy, and generalization properties during biomedical data analysis. Experimental comparisons demonstrate substantial increases in classification accuracy and generalization properties of the improved cascade ensemble. [ABSTRACT FROM AUTHOR]

Cyber Defense is becoming a major issue for every organization to keep business continuity intact. The presented paper explores the effectiveness of a meta-heuristic optimization algorithm-Artificial Bees Colony Algorithm (ABC) as an Nature Inspired Cyber Security mechanism to achieve adaptive defense. It experiments on the Denial- Of-Service attack scenarios which involves limiting the traffic flow for each node. Businesses today have adapted their service distribution models to include the use of the Internet, allowing them to effectively manage and interact with their customer data. This shift has created an increased reliance on online services to store vast amounts of confidential customer data, meaning any disruption or outage of these services could be disastrous for the business, leaving them without the knowledge to serve their customers. Adversaries can exploit such an event to gain unauthorized access to the confidential data of the customers. The proposed algorithm utilizes an Adaptive Defense approach to continuously select nodes that could present characteristics of a probable malicious entity. For any changes in network parameters, the cluster of nodes is selected in the prepared solution set as a probable malicious node and the traffic rate with the ratio of packet delivery is managed with respect to the properties of normal nodes to deliver a disaster recovery plan for potential businesses. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), considered an intriguing technology with substantial potential for tackling many societal concerns, has been developing into a significant component of the future. The foundation of IoT is the capacity to manipulate and track material objects over the Internet. The IoT network infrastructure is more vulnerable to attackers/hackers as additional features are accessible online. The complexity of cyberattacks has grown to pose a bigger threat to public and private sector organizations. They undermine Internet businesses, tarnish company branding, and restrict access to data and amenities. Enterprises and academics are contemplating using machine learning (ML) and deep learning (DL) for cyberattack avoidance because ML and DL show immense potential in several domains. Several DL teachings are implemented to extract various patterns from many annotated datasets. DL can be a helpful tool for detecting cyberattacks. Early network data segregation and detection thus become more essential than ever for mitigating cyberattacks. Numerous deep-learning model variants, including deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), are implemented in the study to detect cyberattacks on an assortment of network traffic streams. The Canadian Institute for Cybersecurity's CICDIoT2023 dataset is utilized to test the efficacy of the proposed approach. The proposed method includes data preprocessing, robust scalar and label encoding techniques for categorical variables, and model prediction using deep learning models. The experimental results demonstrate that the RNN model achieved the highest accuracy of 96.56%. The test results indicate that the proposed approach is efficient compared to other methods for identifying cyberattacks in a realistic IoT environment. [ABSTRACT FROM AUTHOR]

One of the tasks of robotics is to develop a robot's ability to perform specific actions for as long as possible without human assistance. One such step is to open different types of doors. This task is essential for any operation that involves moving a robot from one room to another. This paper proposes a versatile and computationally efficient algorithm for an autonomous mobile robot opening different types of doors, using machine learning methods. The latter include the YOLOv5 object detection model, the RANSAC iterative method for estimating the mathematical model parameters, and the DBSCAN clustering algorithm. Alternative clustering methods are also compared. The proposed algorithm was explored and tested in simulation and on a real robot manufactured by SOMATIC version Dalek. The percentage of successful doors opened out of the total number of attempts was used as an accuracy metric. The proposed algorithm reached an accuracy of 95% in 100 attempts. The result of testing the door-handle detection algorithm on simulated data was an error of 1.98 mm in 10,000 samples. That is, the average distance from the door handle found by the detector to the real one was 1.98 mm. The proposed algorithm has shown high accuracy and the ability to be applied in real time for opening different types of doors. [ABSTRACT FROM AUTHOR]

This paper presents a solution for small businesses to gather statistics on the presence of their customers in and around their institutions, using Bluetooth technology. The solution includes a model of a mobile system based on Bluetooth technology that can provide information about the detectable devices' relative position to create statistics on users' presence in a specific timeframe. The research conducted shows that Bluetooth technology can be efficiently used to log the user's relative position. This paper proposes a two-component system, which includes a mobile application and a cloud-based database with a simple online query interface. The prototype was implemented for the Android operating system and confirms the feasibility of the proposed solution. The developed prototype application was designed as a foundation for further commercial development. The proposed solution provides small businesses with an accessible way of collecting data on the presence of people without having to buy specialized equipment. [ABSTRACT FROM AUTHOR]

The network intrusion detection system (NIDs) is a significant research milestone in information security. NIDs can scan and analyze the network to detect an attack or anomaly, which may be a continuing intrusion or perhaps an intrusion that has just occurred. During the pandemic, cybercriminals realized that home networks lurked with vulnerabilities due to a lack of security and computational limitations. A fundamental difficulty in NIDs is providing an effective, robust, lightweight, and rapid framework to perform real-time intrusion detection. This research proposes an efficient, functional cybersecurity approach based on machine/deep learning algorithms to detect anomalies using lightweight network-based IDs. A lightweight, real-time, network-based anomaly detection system can be used to secure connected IoT devices. The UNSW-NB15 dataset is used to evaluate the proposed approach DeepNet and compare results alongside other state-of-the-art existing techniques. For the classification of network-based anomalies, the proposed model achieves 99.16% accuracy by using all features and 99.14% accuracy after feature reduction. The experimental results show that the network anomalies depend exceptionally on features selected after selection. [ABSTRACT FROM AUTHOR]

The article's main contribution is the description of the process of the security subsystem countering the impact of typed cyber-physical attacks as a model of end states in continuous time. The input parameters of the model are the flow intensities of typed cyber-physical attacks, the flow intensities of possible cyber-immune reactions, and the set of probabilities of neutralization of cyber-physical attacks. The set of admissible states of the info-communication system is described taking into account possible variants of the development of the modeled process. The initial parameters of the model are the probabilities of the studied system in the appropriate states at a particular moment. The dynamics of the info-communication system's life cycle are embodied in the form of a matrix of transient probabilities. The mentioned matrix connects the initial parameters in the form of a system of Chapman's equations. The article presents a computationally efficient concept based on Gershgorin's theorems to solve such a system of equations with given initiating values. Based on the presented scientific results, the article proposes the concept of calculating the time to failure as an indicator of the reliability of the info-communication system operating under the probable impact of typical cyber-physical attacks. The adequacy of the model and concepts presented in the article is proved by comparing a statically representative amount of empirical and simulated data. We emphasize that the main contribution of the research is the description of the process of the security subsystem countering the impact of typed cyber-physical attacks as a model of end states in continuous time. Based on the created model, the concept of computationally efficient solution of Chapman's equation system based on Gershgorin's theorems and calculating time to failure as an indicator of the reliability of the info-communication system operating under the probable impact of typed cyber-physical attacks are formalized. These models and concepts are the highlights of the research. [ABSTRACT FROM AUTHOR]

The article examines the subject-system interaction session, where the system is understood as a base station, and the subject is understood as a mobile communication device. The peculiarity of the study is taking into account the phenomenon relevant to modern communication infrastructures, which is that the base station supports the division of information traffic into a subspace of guaranteed personalized traffic and a subspace of general-purpose traffic. The study considers a highly critical empirical emergency when the general-purpose traffic subspace may cease to be available at any time. The presented mathematical apparatus describes the impact of such an emergency on the active communication sessions supported by the system in receiving new incoming requests of increasing intensity. To characterize this emergency situation, expressions adapted for practical application are presented to calculate such qualitative parameters as the probability of stability, the probability of failure, and unavailability. [ABSTRACT FROM AUTHOR]

The advent of additive technologies has provided a significant breakthrough in the production of medical implants. It has reduced costs, increased productivity and accuracy of the implant manufacturing process. However, there are problems associated with assessing defects in the microstructure, mechanical and technological properties of alloys, both during their production by powder metallurgy and in the process of 3D printing. Thus traditional research methods of alloys properties demand considerable human, material, and time resources. At the same time, artificial intelligence tools create opportunities for intelligent evaluation of the conformity for the microstructure, phase composition, and properties of titanium powder’s alloys. It provides new possibilities for the efficient production of biocompatible implants for various functional purposes. However, the accuracy of the methods and models used should be as high as possible. In this paper we designed a hybrid PNN-SVM (Probabilistic Neural Network-Support Vector Machine) high-precision approach for the intelligent evaluation of alloy properties for additive manufacturing of biomedical implants. We have proposed a new approach for extending the dimensionality of input data space by the outputs of the summation layer of the modified PNN topology. Subsequent classification based on the expanded dataset is performed using SVM. We conducted experimental modeling of the proposed approach using a data set on the properties of titanium alloys Ti-6Al-4V and Ti-Al-V-Zr. We have demonstrated a significant increase in the accuracy of the PNN-SVM scheme compared to the single classifiers that form it and other machine learning methods. [ABSTRACT FROM AUTHOR]

The functional safety assessment is one of the primary tasks both at the design stage and at the stage of operation of critical infrastructure at all levels. The article's main contribution is the information technology of calculating the author's metrics of functional safety for estimating the instance of the model of the cyber-physical system operation. The calculation of metric criteria analytically summarizes the results of expert evaluation of the system in VPR-metrics and the results of statistical processing of information on the system's operation presented in the parametric space Markov model of this process. The advantages of the proposed approach are the following: the need to process orders of magnitude less empirical data to obtain objective estimates of the investigated system; taking into account the configuration scheme and architecture of the security subsystem of the investigated system when calculating the metric; completeness, compactness, and simplicity of interpretation of evaluation results; the ability to assess the achievability of the limit values of the metric criteria based on the model of operation of the investigated system. The paper demonstrates the application of the proposed technology to assess the functional safety of the model of a real cyber-physical system. [ABSTRACT FROM AUTHOR]

The paper deals with factorial experimental design models decoding. For the ease of calculation of the experimental mathematical models, it is convenient first to code the independent variables. When selecting independent variables, it is necessary to take into account the range covered by each. A wide range of choices of different variables is presented in this paper. After calculating the regression model, its variables must be returned to their original values for the model to be easy recognized and represented. In the paper, the procedures of simple first order models, with interactions and with second order models, are presented, which could be a very complicated process. Models without and with the mutual influence of independent variables differ. The encoding and decoding procedure on a model with two independent first-order parameters is presented in details. Also, the procedure of model decoding is presented in the experimental surface roughness parameters models' determination, in the face milling machining process, using the first and second order model central compositional experimental design. The simple calculation procedure is recommended in the case study. Also, a large number of examples using mathematical models obtained on the basis of the presented methodology are presented throughout the paper. [ABSTRACT FROM AUTHOR]

Inappropriate soil management practices and specific climatic conditions in semi-arid region cause loss of soil organic matter (SOM), decline soil fertility, and trigger soil erosion processes and desertification. A two-year field study was carried out to investigate the effects of tillage intensity and fertilizer regime treatments on the productivity of sunflower (Helianthus annus L.) and soil physicochemical properties in the semi-arid highland region in northwest Iran (37°31′ north (N), 46°53′ east (E)). Five fertilizer treatments were included under conventional (CT) or reduced tillage systems (RT): F1, no fertilizer application; F2, 20 t∙ha−1 farmyard manure (FYM); F3, 40 t∙ha−1 FYM; F4: 20 t∙ha−1 FYM + 50% of the recommended dose of nitrogen–phosphorus–potassium (NPK) chemical fertilizer; F5: full dose of the recommended chemical NPK fertilizer. Results showed that utilization of FYM decreased bulk density (BD); its effects were more evident under the highest SOM content for the F2 and F3 treatments, whereas application of mineral fertilizer had no significant effect upon SOM content, and elevated levels of FYM preserved higher organic carbon contents in topsoil. The highest N, P, and K contents of the soil were obtained with FYM plus inorganic fertilizer application in the RT system. Hydraulic conductivity and soil moisture content were significantly improved by RT and FYM application. The highest root growth was observed for F3 and F4 under the RT system. The effect of fertilizer and tillage treatments was more pronounced during the second year. Traits related to growth and seed quality such as achene oil content, leaf area, and harvest index were enhanced by chemical fertilization in the CT system. The highest achene yield and oil percentage were recorded for plants grown with F3 and F4. The best option for enhancing sunflower productivity and quality in semi-arid, high-altitude environments is the application of organic fertilizers amended with reduced amounts of chemical fertilizers. [ABSTRACT FROM AUTHOR]

Featured Application: Development of an adaptive neuro-fuzzy inference systems (ANFIS) for the recognition and prediction of the cutting temperature in hard turning that can meet industrial requirements. The machining of hard materials with the most economical process is a challenge that is the aim of production systems. Increasing demands of the market require a hard processing hardened steel in order to avoid finishing grinding. This research considers the turning of hardened steel without cooling with two types of tools: cubic boron nitride (CBN) and hard metal (HM) inserts. To estimate the influence of machining conditions on cutting temperature, a central composition design with three factors on five levels was used. The development of advanced models allows one to meet the accelerated demands in terms of productivity, product quality, and reduced production costs. Based on experimental data, three input regimes (cutting speed, feed, and depth of cut), and one attributive factor (tool material) were used as input variables, while cutting temperature was used as the output of the adaptive neuro-fuzzy inference systems (ANFIS). The model was trained, tested, and validated with a combined input/output data set. The obtained ANFIS model could be applied with high precision to determine the cutting temperature in machining of hardened steel. From an economic point of view, the obtained model can directly affect the cost of processing because cutting temperature and tool life are directly relieved. [ABSTRACT FROM AUTHOR]

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The purpose of this paper is creating analyses for understanding the personality characteristics related to emotional intelligence and how can this define the development program of personal characteristics in the processes of education for Industry 4.0. The main research goal is to measure dimensions of emotional intelligence in the student population based on a quantitative survey (Emotional Competence Inventory) through dimensions of emotional intelligence: self-regulation, self-awareness, and attitude towards changes. Since the student population was the research target, a group of 338 engineering students was selected. The group was characterized by highly diversified geographic origin, having previously completed school and achieved success. The results show that there are statistically significant differences between all three dimensions of emotional intelligence. Namely, self-regulation and attitude towards changes (both directly and indirectly through self-regulation) are positively affected by self-awareness, while attitude towards changes is positively affected by self-regulation. Developing student emotional potential is one of the most important actuators of business for Industry 4.0, especially in countries with low educational attainment and low social and economic indicators. [ABSTRACT FROM AUTHOR]

The study was conducted by applying machine learning and data mining methods to treatment personalization. This allows individual patient characteristics to be investigated. The personalization method was built on the clustering method and associative rules. It was suggested to determine the average distance between instances in order to find the optimal performance metrics. The formalization of the medical data preprocessing stage was proposed in order to find personalized solutions based on current standards and pharmaceutical protocols. The patient data model was built using time-dependent and time-independent parameters. Personalized treatment is usually based on the decision tree method. This approach requires significant computation time and cannot be parallelized. Therefore, it was proposed to group people by conditions and to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. The novelty of the paper is the new clustering method, which was built from an ensemble of cluster algorithms, and the usage of the new distance measure with Hopkins metrics, which were 0.13 less than for the k-means method. The Dunn index was 0.03 higher than for the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm. The next stage was the mining of associative rules provided separately for each cluster. This allows a personalized approach to treatment to be created for each patient based on long-term monitoring. The correctness level of the proposed medical decisions is 86%, which was approved by experts. [ABSTRACT FROM AUTHOR]

The concept of sustainable tourism development is imposed as an inevitable way of improving the tourism industry as a whole. This study tries to offer an adequate inclusion of sustainable factors in overall tourism development efficiency results. Through the detection and estimation of potential sources of efficiency, the paper will do the efficiency benchmarking of tourism services on the level of countries as destinations. In order to complete the task, data collection was focused on 27 EU countries and five Western Balkan countries over the period from 2011 to 2017. This paper utilized an output-oriented data envelopment analysis (DEA) procedure to estimate efficiency scores for each country, and a panel data Tobit regression model to emphasize the (in)significance of each individual tourism development indicator. The results in the first stage show relatively high-efficiency scores, particularly in the case of EU 15 countries and with room for improvement in the case of the others. The second stage reveals positive and significant effects on relative tourism efficiency by the sustainability of tourism development, the share of GDP, tourist arrivals and inbound receipts, as well as visa requirements and rate of use. Policymakers should gradually take control of the mentioned variables to protect the interests of all relevant stakeholders involved in the tourism development process. [ABSTRACT FROM AUTHOR]

By using the balanced scorecard model, a company is able to identify its advantages, as well as its deficiencies, and thus improve its business. The introduction of innovations and the implementation of innovative activities in companies are key for gaining a competitive advantage. There is no ideal model that would measure the non-financial, non-tangible perspectives of a company (such as customer perspective, the perspective of research and innovation, and the perspective of internal processes). The main goal of this paper is researching the applicability of the balanced scorecard model in small- and medium-sized companies as the basis for a model for assessing innovative activities in the Republic of Slovakia and the Republic of Serbia. First, a hypothetical model was created based on theoretical data from world scientific articles. Then, the structural equation model (SEM model) was created, based on the conducted research in 223 companies and the obtained results. [ABSTRACT FROM AUTHOR]

An approach to use Operational Intelligence with mathematical modeling and Machine Learning to solve industrial technology projects problems are very crucial for today's IT (information technology) processes and operations, taking into account the exponential growth of information and the growing trend of Big Data-based projects. Monitoring and managing high-load data projects require new approaches to infrastructure, risk management, and data-driven decision support. Key difficulties that might arise when performing IT Operations are high error rates, unplanned downtimes, poor infrastructure KPIs and metrics. The methods used in the study include machine learning models, data preprocessing, missing data imputation, SRE (site reliability engineering) indicators computation, quantitative research, and a qualitative study of data project demands. A requirements analysis for the implementation of an Operational Intelligence solution with Machine learning capabilities has been conducted and represented in the study. A model based on machine learning algorithms for transaction status code and output predictions, in order to execute system load testing, risks identification and, to avoid downtimes, is developed. Metrics and indicators for determining infrastructure load are given in the paper to obtain Operational intelligence and Site reliability insights. It turned out that data mining among the set of Operational Big Data simplifies the task of getting an understanding of what is happening with requests within the data acquisition pipeline and helps identify errors before a user faces them. Transaction tracing in a distributed environment has been enhanced using machine learning and mathematical modelling. Additionally, a step-by-step algorithm for applying the application monitoring solution in a data-based project, especially when it is dealing with Big Data is described and proposed within the study. [ABSTRACT FROM AUTHOR]

Considering the benefits of the organic production system, it is recognized as one of the main drivers of future economic development. However, the imbalance between demand and supply at the local market level represents one of the serious obstacles that prevents its future growth. Therefore, this article examines the key factors related to the main elements of the offer that have the strongest impact on consumer preferences and acceptance of organic food products. In that sense, organic product, price, distribution channel, and promotion are considered the main elements of the offer and are analyzed in this paper from the consumer preferences perspective. Further, this article provides insight into some of the sensory properties of the offer that are important to consumers. Finally, it gives recommendations for optimization of the offer on the organic food market based on the analysis of the influence of each of those elements (product, price, distribution, and promotion) on consumer acceptance of organic products and making purchasing decisions. The data were collected using a questionnaire, and analyzed using the structural equation model (SEM). The results revealed that price and promotion have the strongest impact on consumer acceptance and buying decisions. Further analysis revealed that attitudes towards organic food products, price/quality ratio, distribution barriers, and modern media as a promotion instrument are the factors that have the most significant impact on consumer perception and attitudes towards the available market offer. These findings can help producers and other decision makers to better understand what creates added value of the organic food products in consumers' mind and therefore make an offer that is in line with their expectations and preferences, which is recognized as one of the main prerequisites for the acceptance and purchase of organic food products. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the impact of sponsorship perceptions and attitudes of sport team fans on their purchasing decision, as well as to investigate the significance of sport sponsorship in promoting socially responsible companies and other sustainable practices in society. This paper gives insight in possibilities and effects of promoting sustainable activities and socially responsible companies, through sport sponsorship, by targeting and educating sport team fans as a very important consumer segment. Data were collected from anonymous respondents who are fans of different sport teams. Attitudes were measured by asking the respondents to rank the statements based on a 5-point Likert scale. In order to analyze the data, structural equation modelling was conducted using R package. Results show that the attitudes towards sponsorship, as well as sponsorship perceptions, have a strong positive impact on the purchasing decisions of consumers, regarding products of sponsors, which enables the creation of a loyal base of fans as consumers. Results also reveal that sport sponsorship can be an effective way of promoting socially responsible companies and sustainable practices, giving an opportunity to create an educated and highly dedicated consumer base. In this way the positive effects of sports and sponsorship are spreading beyond the stadiums and arenas. These findings can help companies that are considering investing in this form of marketing, in adequately selecting and managing the campaign, but also can be very useful for official institutions in their efforts to foster sustainable practices in society. Although the fact that the research was conducted only in Montenegro can be stated as a limitation, it should be noted that this type of research has been done for the first time in this area, which gives special value to the research and increases the importance of its contribution to the existing base of scientific papers in this field. [ABSTRACT FROM AUTHOR]

An introduction to the special issue of the journal is presented in which the editors discuss papers within the issue on topics including enhancing process management using the technology Cloud, value creation from big data and strategies to support agile organizations.

Experimental research of cutting force components during dry face milling operations are presented in the paper. The study was provided when milling of ductile cast iron alloyed with copper and its austempered ductile iron after the proper austempering process. In the study, virtual instrumentation designed for cutting forces components monitoring was used. During the research, orthogonal cutting forces components versus time were monitored and relationship of cutting forces components versus speed, feed and depth of cut were determined by artificial neural network and response surface methodology. An analysis was made regarding the consistency of the measured cutting forces and the values obtained from the model supported by an artificial neural network for the investigated interval of the cutting regime. Based on the results, an analysis of the feasibility of the application of austempered ductile iron in the industrial sector with the aspect of machinability as well as the application of the models based on artificial intelligence, was given. At the end of the presentation, the influence of the aforementioned cutting regimes on cutting force components is presented as well. [ABSTRACT FROM AUTHOR]