Balkan Journal of Applied Mathematics and Informatics
GOCE DELCEV UNIVERSITY - STIP, FACULTY OF COMPUTER SCIENCE
SHORTEST PATH OPTIMIZATION IN TRANSPORTATION NETWORKS USING DIJKSTRA’S ALGORITHM: A CASE STUDY OF THE MACEDONIAN ROAD NETWORK
Efficient route determination in transportation networks represents an important challenge in modern navigation systems, logistics planning, and traffic management. Models based on graph theory provide an appropriate mathematical framework for representing road infrastructure and analyzing optimal paths between different locations. This paper examines the application of Dijkstra’s algorithm for solving the shortest-path problem in transportation networks modeled as weighted graphs. In the proposed model, cities in the Republic of North Macedonia are represented as vertices, while road connections between them are modeled as edges whose weights correspond to real geographical distances expressed in kilometers. To demonstrate the practical applicability of the proposed approach, a software application implemented in the Java programming language was developed. The system enables users to select a starting and a destination location, after which the algorithm automatically computes the shortest route within the modeled transportation network. The developed model illustrates how graph algorithms can be applied in the analysis and optimization of real transportation systems. The results obtained from the analysis of the modeled road network indicate that the algorithm efficiently identifies optimal routes and supports decision-making in route planning. The study confirms that Dijkstra’s algorithm represents a reliable and computationally efficient method for solving shortest-path problems in road networks. The proposed approach has potential applications in navigation systems, transportation planning, and logistics optimization, highlighting the practical significance of graph algorithms in modern transportation infrastructures..
Dijkstra’s algorithm; transportation networks; shortest path; graph theory; route optimization
Copyright (c) 2026 Balkan Journal of Applied Mathematics and Informatics
MODELING, SIMULATION AND SCADA-BASED CONTROL OF AN AUTOMATED SHEET METAL TRANSPORT SYSTEM
This paper presents the modeling, simulation, and implementation of an automated sheet metal transport system with integrated SCADA control. The proposed system is designed to improve the efficiency and reliability of industrial laser processing by replacing manual handling with an automated solution consisting of transport carts, a pusher mechanism, and a transversal unit with vacuum handling. A programmable logic controller (PLC) implemented in the UniLogic environment is used to coordinate all subsystems, including encoders, inductive sensors, and electromechanical actuators. In addition, a mathematical model of the motion control system is developed and analyzed using MATLAB/Simulink. The control strategy is based on a cascaded structure with an inner loop for speed regulation and an outer loop for position control. The simulation results demonstrate stable system behavior, accurate positioning, and fast convergence to the target position with negligible steady-state error. Furthermore, the SCADA system enables real-time monitoring, data logging, and alarm management, enhancing operational efficiency. The proposed approach provides a scalable and reliable solution for industrial automation, combining control theory, simulation, and practical implementation.
SCADA systems, PLC control, industrial automation, MATLAB/Simulink simulation, motion control systems, cascaded control, sheet metal transport system
INTEGRATED SMART DC AC ENERGY METER SUPPORTED IN IoT NETWORK
Energy measurement and the processing data in industrial plants have become area in which the contribution of electronics is significant. Recent advances in electronic hardware and software solutions have been implemented for more efficient monitoring and management of industrial processes. These technologies allow process data to be measured, processed, stored, visualized and distributed within industrial systems. In contemporary measurement system, there is a strong tendency towards digital signal processing of measured quantities, although some solutions still incorporate elements of analog processing. For effective process management, it is essential that users have timely and accurate information about measurement data within their industrial facilities. Driven by the need for reliable measurement, visualization and storage of process parameters, modern electronic systems enable on-site processing and storage, as well as transmission of measurement data through GSM and IoT network. The application of advanced electronic systems is evident not only in power plants, but also in industries related to healthy and high- quality food production, particularly in agriculture. This tend is largely due to the increasing number of independent agriculture facilities that are often located in remote areas and require autonomous power supply along with long- distance transmission of process data. Motivated by these requirements, this paper presents the design of an electronic system that provides a solution for supplying electricity to such facilities. The proposed system provides a solution for measurment of two key components of energy systems, namely DC and AC energy. In addition to the measurement, the system provides visualization and storage of measurement data, as well as the capability to transmit the data to an IoT network. The measured DC and AC energy values are displayed locally on LCD screens and stored on an SD card, while also being remotely transmitted to the IoT network for visualization on a mobile device.
Agro industrial facility, Process parameters, controller, GSM, SIM, Data log file
This paper presents the mathematical theory of heat flow in the Earth’s crust, with emphasis on conductive heat transfer in homogeneous and isotropic media. Starting from Fourier’s law, the fundamental differential equations governing one-dimensional and three-dimensional heat flow are derived and extended to include temperature variation with time. The study incorporates the effects of physical parameters such as thermal conductivity, density, and specific heat, leading to the classical heat equation. Special attention is given to geothermal applications, including the interpretation of subsurface temperature distributions and their relation to geological structures. The influence of porosity and moisture content on thermal conductivity is also analyzed. Furthermore, periodic temperature variations caused by solar radiation, including diurnal and annual cycles, are mathematically modeled to evaluate temperature changes with depth. The presented theory provides a basis for understanding geothermal processes and their applications in geophysics, engineering geology, and subsurface exploration.
heat flow, Fourier’s law, heat equation, thermal conductivity, temperature distribution, geothermal gradient
EXPERIMENTAL AND COMPARATIVE ANALYSIS OF COMPRESSIVE BEHAVIOUR AND ENERGY ABSORPTION OF FDM PRINTED PLA AND PETG
This paper presents an experimental and comparative analysis of the compressive behaviour and energy absorption of FDM-printed PLA and PETG specimens. A total of five specimens were tested for each material under identical printing and loading conditions. The mechanical response was evaluated based on stress–displacement curves obtained from compression testing. The results indicate that PLA exhibits higher peak stress values, reflecting greater stiffness and load-bearing capacity, but also shows more pronounced variability and less stable post-peak behaviour. In contrast, PETG demonstrates lower peak stress but more gradual and stable deformation, indicating higher ductility and improved energy dissipation. Energy absorption was determined by calculating the area under the force–displacement curves using numerical integration based on the trapezoidal rule. The results show that PLA can achieve higher energy absorption under specific conditions, but with significant variability, whereas PETG provides more consistent and predictable performance. For clarity of comparison, representative specimens were selected and presented in detail, while the full experimental dataset was used for overall analysis. The findings highlight the influence of material type and internal structure on compressive behaviour and provide useful insight for material selection in FDM applications.
FDM, PLA, PETG, compression behaviour, energy absorption, additive manufacturing
MATHEMATICAL MODELLING AND PREDICTION ANALYSIS OF THE TENSILE STRENGTH OF DC01 SHEET STEEL BASED ON TESTING SPEED
This paper presents a regression-based mathematical modelling approach for analysing and predicting the tensile behaviour of DC01 sheet steel as a function of tensile testing speed. Experimental tensile tests were conducted at three different crosshead speeds (2, 5, and 10 mm/min), and the corresponding ultimate tensile strength (UTS) values were obtained from engineering stress–strain curves. Two specimens were tested at each speed level, and the mean values were used for regression analysis to ensure data consistency. A linear regression model was developed to describe the relationship between testing speed and tensile strength, showing a strong correlation between the variables with a coefficient of determination R² = 0.9968. In addition, a quadratic regression model was introduced to capture potential nonlinear behaviour and improve prediction capability. The developed models were further used for the prediction of tensile strength at intermediate testing speeds, demonstrating the applicability of regression-based approaches for estimating material behaviour beyond experimentally tested conditions. A response surface analysis was also performed, providing a graphical interpretation of the relationship between testing speed and tensile strength. The results indicate that tensile strength slightly increases with increasing testing speed, reflecting the strain rate sensitivity of the material. The proposed modelling approach enables efficient analysis and prediction of mechanical properties and can serve as a useful tool in material testing, optimisation, and engineering applications.
DC01 steel, Tensile testing, Mathematical modelling, Regression analysis, Prediction, Tensile strength.
DIGITAL TECHNOLOGIES AS A TOOL FOR THE DEVELOPMENT OF SUSTAINABLE TOURISM: A CASE STUDY OF KAJAK.MK
Digital technologies and digital marketing have significantly transformed the tourism industry, reshaping consumer behavior, improving user experience and enabling more personalized and accessible travel services. Platforms like Kajak.mk are reshaping the sector by offering personalized travel planning, integrating user reviews and promoting sustainable choices. The aim of this paper is to explore the role of digital technologies as a tool for the development of sustainable tourism, with a special focus on the kajak.mk platform and its impact on local tourism in North Macedonia. The methodology includes a website analysis and a comparative analysis between kajak.mk and leading global platforms, with the aim of identifying differences in technological development, functionality and user engagement. This enables the identification of potential areas for improvement and future development.
FIXED POINT THEOREM FOR A CLASS OF CHATTERJEA-TYPE MAPPINGS
In this paper, a new class of mappings in a complete metric space is introduced, which is in a sense analogous to Chatterjea-type mappings, just as the class of Koparde–Waghmode mappings ([7]) is analogous to Kannan-type mappings. Furthermore, a fixed point theorem and a common fixed point theorem are proved regarding this class of mappings.
Complete metric space, Mappings, Fix point, Common fix point
COMPARATIVE ANALYSIS OF INITIAL SOLUTION METHODS FOR THE TRANSPORTATION PROBLEM
This paper presents a comparative evaluation of four classical heuristics for generating initial feasible solutions to the transportation problem: the Northwest Corner Method (NWC), Least Cost Method (LCM), Double Preference Method (DPM), and Vogel’s Approximation Method (VAM). A balanced transportation instance is analysed to quantify the impact of each method on total transportation cost. Results demonstrate that cost-aware heuristics significantly outperform the Northwest Corner approach, yielding identical minimum initial costs in the studied case. The study highlights the importance of intelligent initialization for accelerating convergence toward optimal solutions in logistics and production systems.
Transportation problem, Operations Research, Initial feasible solution, Vogel’s Approximation Method, Logistics optimization
EXAMINING PARENTS’ ATTITUDES TOWARD VIDEO GAMES IN CHILDREN’S ENGLISH LANGUAGE LEARNING: AN ANOVA AND REGRESSION ANALYSIS
Abstract. This study presents the findings of a statistical analysis conducted using ANOVA within the professional IBM SPSS software on a representative sample of 45 parents (32 women, 13 men), aged 30 to over 45 years, of students aged 7–14 years from several primary schools in Štip. The research was based on survey responses regarding the role and significance of video games in children’s English language learning. Descriptive ANOVA results (F = 0.7036, p = 0.4062) indicate no statistically significant difference between male and female respondents in their views on whether playing video games can support foreign language learning. A correlation of approximately 0.599 was observed between children’s affinity for learning English through games and their vocabulary progress. Regression analysis further identified key predictors of vocabulary development: R-squared = 0.394, Affinity (β = 0.468, p < 0.001), and Education_Attitude (β = 0.212, p = 0.122). Visualization of predicted versus actual values showed that the model reasonably approximates the observed outcomes, as indicated by the ideal prediction line (red line).Extended regression analysis confirmed vocabulary progress, with R-squared = 0.505, Affinity (β = 0.273, p = 0.031), and Usefulness (β = 0.440, p = 0.007) emerging as significant predictors, while Education_Attitude and Allow_Gaming did not show significant effects. Additionally, analysis of the impact of video games on motivation, expressed via correlation (r = 0.509), revealed a moderately strong positive relationship between the motivational influence of video games and vocabulary development in children.
ANOVA test, video games, technology, children, English language
The integration of artificial intelligence (AI) into medical imaging has revolutionized diagnostic radiology, particularly in the analysis of radiographic (X-ray) images. This paper reviews recent advances in AI-based systems for X-ray interpretation, examining their technical implementation, clinical applications, and impact on diagnostic accuracy. Deep learning algorithms, primarily Convolutional Neural Networks (CNNs), have demonstrated diagnostic accuracy comparable to or exceeding that of experienced radiologists in detecting various pathologies including pneumonia, pulmonary nodules, consolidation, pneumothorax, and fractures. AI systems improve physician sensitivity from 75.7% to 85.6% when detecting abnormalities, reduce interpretation time by up to 27%, and provide consistent, objective analysis unaffected by fatigue or cognitive bias. However, challenges remain regarding validation across diverse populations, integration into clinical workflows, and regulatory oversight. This review synthesizes current evidence from 2021-2025, highlighting the transformative potential of AI as a decision-support tool that augments rather than replaces radiologist expertise. Future directions include hybrid human-AI systems, multimodal integration of clinical data, and development of transparent, interpretable models for clinical deployment.
artificial intelligence, deep learning, X-ray analysis, medical imaging, convolutional neural networks
SECURITY CHALLENGES AND SOLUTIONS IN ROBOTIC AND INTERNET OF ROBOTIC THINGS (IoRT) SYSTEMS: A SCOPING REVIEW
Robotic systems and the Internet of Robotic Things (IoRT) are rapidly expanding into domains such as healthcare, industry, logistics, and smart environments, bringing significant benefits but also exposing new security challenges. Because robots exchange data, interact with cloud and edge platforms, and operate autonomously, cyber incidents can cause information breaches and physical harm. This scoping review maps current research on IoRT security by analyzing studies published in the last five years. Using PRISMA methodology, the review identifies the main threats affecting IoRT systems-ranging from adversarial manipulation and data leakage to insecure wireless communication and vulnerabilities in learning-based components. The examined literature shows that most solutions focus on protecting data confidentiality, ensuring integrity of robotic behavior, and applying cryptographic or AI-driven defense mechanisms, while protocol-level security remains notably underexplored. Overall, the review reveals a growing yet fragmented field and highlights the need for more systematic evaluation of communication protocols and robust, trustworthy AI methods to secure increasingly autonomous robotic systems.
Copyright (c) 2025 Balkan Journal of Applied Mathematics and Informatics
A FHIR -CENTRIC APPROACH FOR INTEROPERABLE REMOTE PATIENT MONITORING
Interoperability of health information systems is one of the main challenges in modern clinical practice. Healthcare institutions often use different ICT solutions that do not communicate with each other, which leads to fragmented data, duplicate data, limited access to important patient information, and potential risks in making clinical decisions. The key problem lies in the absence of a universally adopted standard for the exchange of health information data. As a potential solution to this situation, the HL7 FHIR standard appears, developed to enable fast, easy, and secure exchange of health information, resulting in efficiency and consistency in patient care. This paper presents a practical implementation of the FHIR standard through the development of a specialized platform for blood pressure monitoring. The system architecture includes a mobile application intended for patients and a desktop-based application intended for doctors, which communicate via a RESTful API in accordance with the FHIR specification. The implementation demonstrates how the FHIR standard enables seamless interoperability, efficient exchange of structured data, and improved collaboration between patients and doctors, confirming its fundamental role in overcoming structural barriers in the context of modern e-health.
COMPARATIVE EVALUATION AND ANALYSIS OF DIFFERENT DEEPFAKE DETECTORS
Deepfakes, synthetic media generated using deep learning, pose significant risks to the integrity, security, and trust of information. Reliable detection is therefore critical, yet existing models often fail when exposed to realworld distortions such as compression, occlusion, and lighting variations. This paper presents a comparative evaluation of deepfake detection models, including XceptionNet, EfficientNet, MesoNet, and Vision Transformers, across multiple benchmark datasets such as FaceForensics++, DFDC, Celeb-DF, and Wild- Deepfake. Models are assessed not only under pristine conditions but also under controlled distortions that reflect realistic deployment environments. The results show that XceptionNet and the fine-tuned Vision Transformers achieve the strongest accuracy and robustness, maintaining competitive performance across domains, while MesoNet demonstrates computational efficiency but suffers from reduced reliability under challenging conditions. EfficientNet provides a balance between parameter efficiency and detection quality, but lags behind in cross-dataset generalization. The findings highlight clear trade-offs between robustness, efficiency, and deployment feasibility, emphasizing that lightweight models are best suited for edge scenarios, whereas more complex architectures remain preferable in cloud or high-resource environments. The study concludes with open challenges and future research directions, including the integration of multimodal cues, domain adaptation, and explainable detection frameworks, to improve resilience against increasingly sophisticated deepfake generation techniques.
Deepfake detection; Generative Adversarial Networks; Computer Vision; Robustness; XceptionNet; Vision Transformers; Benchmarking
METHODS OF EXTRACTION AND ANALYSIS OF PEOPLE'S SENTIMENTS FROM SOCIAL MEDIA
This study explores methods for extracting and analyzing people's sentiments from social media, utilizing advanced natural language processing and machine learning techniques. The goal is to recognize sentiments and based on them to categorize posts and comments as positive, negative, or neutral in order to understand users' attitudes and emotions. The algorithms used for sentiment classification in this research include Naive Bayes, SVM, Logistic Regression, and Random Forest. Additionally, the study analyzes and compares the performance of these algorithms in terms of accuracy, recall, and F1 score, providing a comprehensive overview of their effectiveness. It also emphasizes the importance of hyper-parameter tunning to improve the accuracy of classification algorithms. The results of this study can be used to assist social media platforms, researchers, and policymakers in developing strategies to manage and improve user experiences, as well as to make informed decisions based on user reactions.
NAVIGATING DIGITAL TRANSITION IN EDUCATION
Digitalization has rapidly increased across all industries, but education has particularly badly impacted. Digital tools and platforms are becoming more and more integrated into both traditional and virtual classrooms. As part of the ongoing trend toward digitalization in education, this essay looks at how technology is influencing administrative, instructional and learning practices at various educational institutions. The study looks into the different technological advancements and tools that are being incorporated into educational systems. These include data analytics, online collaboration tools, virtual classrooms and artificial intelligence in education. All of these have been instrumental in changing the way the people learn. When an emphasis on how technologies like artificial intelligence (AI), virtual reality and blockchain are changing teaching, learning and administration this study explores the effects of digitalization on education. The study distinguishes between well-planned digital education and emergency remote teaching and it emphasizes the quick transition to online learning, especially during the COVID- 19 pandemic. Despite the flexibility, engagement and inclusive learning settings that digital technologies provide issues including curriculum design, faculty development and digital divide still exist. In order to successfully incorporate technology into education, the study highlights the necessity of sustainable techniques. From an initial selection of 194 sources obtained from Google Scholar, Science Direct (Elsevier) PubMed and the IEEE Xplore digital library 26 papers were analyzed.
digtal transition, aritificial intelligence, online learning.
SOLUTION OF DIDO’S PROBLEM USING VARIATIONS
Isoperimetric problems are an important class of variational problems. They represent problems in which the optimization is subject to one or more constraints. The term “isoperimetric problems” likely does not make you think of optimization with constraints. However, they have been given this name due to their origin, a problem from antiquity. Given a fixed perimeter, the problem asked to find the geometric figure that encloses the largest possible area. The answer is, perhaps intuitively, the circle. The techniques developed in this section show how to use calculus of variations to answer this and other similar questions. We begin by presenting a variant of this problem.
DESIGN AND SIMULATION OF A SCADA - CONTROLLED GREENHOUSE FOR OPTIMIZED ROSE CULTIVATION
This study presents the design and simulation of a SCADA system for monitoring and automated control of environmental and operational parameters in a rose greenhouse. Development of the SCADA system within this project was supported by the Ignition platform – a modern, modular and industrially oriented software solution from Inductive Automation, which enables the creation of SCADA, HMI and IoT applications. Careful design considerations were applied to ensure that the virtual model accurately reflects actual operational conditions. By modifying the parameters, the system can be easily adapted to manage different types of plants. The application of SCADA technology in horticulture enables advanced resource optimization strategies, resulting in enhanced greenhouse production outcomes, operational performance and efficiency. By combining design and simulation, this study highlights the potential of virtual SCADA systems as tools for planning, analysis, and development of efficient solutions for process management in contemporary horticulture. While the simulation confirms that the design meets all specified requirements, real greenhouse implementation would require modifications or adjustments to the control logic to adapt to actual operational conditions.
Tag, alarm, temperature, soil moist, air humidity, script
CNC LATHE PROGRAMMING: DESIGN AND DEVELOPMENT OF PROGRAM CODE FOR SIMULATING LINEAR INTERPOLATION MOTION
This paper builds upon the foundational work of simulating linear interpolation motion in CNC turning by advancing into the realm of CNC lathe programming. It focuses on the design and development of program code specifically tailored for simulating linear interpolation motion. By integrating G-code programming techniques with advanced simulation tools, this study provides a robust framework for visualising and analysing the precise motion paths in CNC lathe operations. The results contribute to improved accuracy and efficiency in CNC machining processes, offering valuable insights for both academic research and practical applications in the field of CNC programming.
Material removal, Metal cutting, Turning, G-codes, Program blocks
ADVANCED TOOLPATH VERIFICATION IN CNC DRILLING: APPLYING NEWTON’S INTERPOLATION THROUGH MATLAB
This paper presents an advanced methodology for toolpath verification in CNC drilling, utilising Newton's interpolation method along with Matlab. The primary objective is to develop a highly accurate model that precisely captures the dynamic behaviour of CNC drilling machines, allowing for meticulous control over the drill bit's trajectory. Newton's interpolation is crucial for accurately modelling the toolpath, which is essential for ensuring precision in drilling operations. Matlab is employed as the computational platform for implementing the interpolation method, enabling the generation of detailed visualisations of the CNC drilling process. This approach enhances drilling accuracy and efficiency, reduces tool wear, and effectively predicts the dynamic motion of the drill bit, offering significant benefits for both professionals and researchers in the field of precision machining.
Trajectory, Drill bit, CNC interpolator, Interpolating polynomial, Modelling
NEW RESULTS ON FIXED POINT THEOREMS IN 2-BANACH SPACES
In this article, we prove a common fixed point result for interpolative Kannan type contraction mappings in 2-Banach spaces. On the other hand, we introduce interpolative Dass and Gupta rational type contraction mappings on 2-Banach spaces. In particular, we discuss the existence of a fixed point of such a mapping in 2-Banach spaces.
Fixed point theorems, 2-Banach spaces, complete metric spaces.
MONITORING AND TRANSMISSION OF THE PROCESS PARAMETERS ON AGRO INDUSTRIAL FACILITY IN A GSM NETWORK
In modern technological and industrial development, the application of electronics in measuring, processing, storing and transferring process data in industrial facilities is increasing. In this processing of process parameters, it is essential that users have timely and accurate information about the measurement data in their industrial complex. Modern electronic systems provide the ability to process and store measurement data on-site and transfer it to the IoT and GSM network. On the other hand, the application of sophisticated electronic systems is noticeable not only in power plants, but also in healthy and quality food industries, such as agriculture. We witness daily tribunes and panel discussions sponsored by companies and even governments for the introduction of the terminology of digital agriculture, i.e. the introduction of smart electronic solutions in agriculture. In addition to the commitment to so-called green energy, i.e. obtaining energy from renewable sources, the development of digitization in agriculture is an area in which an enormous application of smart electronic systems is expected in the future. Guided by these reasons, in this paper an electronic system is designed that provides a solution to a problem in hydro melioration system, i.e. the development, design and practical implementation of the Smart electronic system which enables the measurement of the process parameters of an agro-industrial facility and their transfer to the GSM SIM network. The solution enables the visualization of process parameters locally on LCD displays and remotely on an mobile device in GSM SIM network. A data log file is also provided for store the value on the process parameters ​​on a local computer.
WEB-BASED EDUCATIONAL GAME FOR EARLY SCREENING AND SUPPORT DYSCALCULIA
Dyscalculia, a specific learning disorder affecting numerical and arithmetic processing, remains underdiagnosed due to the limited accessibility and engagement of traditional screening tools. This study presents the development and validation of a web-based educational game designed for the early screening and support of dyscalculia in children aged 9–12 years. Leveraging gamification and adaptive learning principles, the platform integrates psychometrically validated tasks within an interactive environment to assess core deficits in number sense, arithmetic fluency, and working memory. Experimental evaluation was conducted, with 16 participants (8 experimental, 8 control) undergoing pre- and post-test assessments using the standardized TEDI-MATH diagnostic tool. Results demonstrated statistically significant improvements in the experimental group (t(14) = 16.72, p < 0.001, Cohen's d = 8.36), indicating the game's effectiveness as both a screening instrument and intervention tool. These findings contribute to the growing body of research on digital learning technologies for special educational needs, offering a scalable solution that bridges the gap between early detection and intervention
ENHANCING GEOGRAPHIC INFORMATION SYSTEMS WITH SPATIAL DATA MINING
This paper explores how Geographic Information Systems (GIS) can be combined with Spatial Data Mining (SDM) to improve the understanding and utilization of large volumes of spatial data. The study begins by defining spatial data and highlighting its significance. It then examines the fundamental concepts of GIS and the methods for managing, analyzing, and modeling spatial data. Furthermore, the research identifies and explains the challenges that arise when integrating GIS with SDM, proposing possible solutions to overcome these issues. This paper provides a detailed insight into the synergy between GIS and SDM tools, aiming to enhance the management and analysis of large spatial data collections.
Geographic Information System, Spatial Data Mining, big data.
INTEGRATING AI AND CLOUD COMPUTING FOR EFFICIENT AUDIO ANALYSIS
Abstract. This paper propose a pipeline for sentiment analysis of audio inputs by using AI and techniques for speech recognition. A key aspect of our approach involves determining audio chunks based on silence detection and decibel height, allowing for more effective segmentation and analysis of the input data. These techniques will be instrumental in categorizing audio materials, enabling more organized and accessible content management. In the scope of this paper, we will compare and utilize several Python packages for audio detection, including Librosa, PyDub, and SoundFile, which offer various functionalities for audio analysis and manipulation. Additionally, we discuss the high computational demands associated with training and deploying of models, highlighting the potential cost barriers for many organizations. To address these challenges, we propose the use of cloud-based solutions, specifically AWS Spot Instances, Azure Spot VMs, and Google Cloud Preemptible VMs, which offer substantial cost savings for processing audio data. By leveraging these cloud resources, organizations can significantly reduce expenses while maintaining high performance in AI audio processing tasks.
STATE-OF-THE-ART COMPARISON OF MOBILESECURECOMM WITH MODERN SECURE COMMUNICATION PLATFORMS FOR TACTICAL OPERATIONS
Modern military and critical missions depend on secure, fast, and flexible communication systems that can perform reliably across various operational environments. Legacy platforms such as SINCGARS, EPLRS, and CERT-based emergency systems have offered foundational services, but they fall short when it comes to adapting to new threats, supporting modern data-intensive operations, or integrating with advanced technologies like artificial intelligence and quantum encryption. This paper presents a state-of-the-art comparison between existing secure communication platforms and a newly proposed system called MobileSecureComm. Designed for tactical operations, MobileSecureComm combines modular architecture, quantum-ready encryption, and AI-driven routing to meet the growing demands of modern warfare and emergency response scenarios. The paper draws from extensive literature covering secure mobile platforms, military-grade messaging systems, mobile edge computing, and real-world deployment models. Using simulation-based analysis, the study compares MobileSecureComm to 25 secure communication platforms in terms of latency, throughput, security, interoperability, and resilience. The results show that MobileSecureComm consistently achieves lower latency, higher data throughput, and stronger security safeguards than traditional platforms. It also offers seamless integration with NATO-compliant systems and the flexibility to scale across multi-domain operations. This paper highlights the importance of designing secure communication platforms that are not only technically advanced but also adaptable to changing threats and diverse mission requirements. MobileSecureComm provides a forward-looking solution that can help bridge the gap between existing systems and the future of tactical communication.
ANALYSIS OF THE MOVEMENT OF FLUCTUATIONS AND TRENDS OF THE GROSS DOMESTIC PRODUCT IN THE REPUBLIC OF NORTH MACEDONIA AND FORECASTS
In this paper, an analysis will be made of the movement of fluctuations and trends in the gross domestic product, in the period from 2000 to 2024 by quarter with current prices expressed in millions of denars in North Macedonia, using time series. The analysis will be done using the Python program. In the end, with the help of the previously prepared program and model, a prediction is made for the movement of trends and fluctuations in GDP in the period from 2024 to 2028.
AUTOMATED DOOR STATE DETECTION USING DEEP LEARNING: A COMPUTER VISION APPROACH WITH ROBOFLOW PLATFORM
This paper presents a deep learning-based approach for automated door state detection, capable of classifying doors as open, closed, or semi-open. The implementation leverages the Roboflow platform for comprehensive image processing and model development workflow. Our methodology encompasses data collection, annotation, augmentation, and model training using state-of-the-art deep learning architectures. The system demonstrates robust performance in real-world scenarios, offering potential applications in building automation, security systems, and smart home technologies. This work contributes to the growing field of automated building monitoring by providing a practical solution for door state recognition that can be integrated into existing surveillance and security infrastructures.
SMART CITY: A REVIEW OF CURRENT DEVELOPMENTS AND IMPLEMENTATION OF SMART GRID TECHNOLOGY
To ensure better living standards for the population, it is necessary to develop smart cities that utilize modern technology and operational efficiency to facilitate daily life. The primary goal of implementing (or creating) a smart city is to maintain, balance, and continuously upgrade economic, social, and environmental resources while managing costs. Emphasis is placed on maximizing the use of natural resources through energy conservation (utilizing renewables), efficient water usage, time savings, land optimization, environmental quality, traffic management, and the extensive use of information and communication technology systems and services for effective management and their ongoing enhancement. This work aims to create improved functionality, smart connections, and better organization among people, while maximizing the use of natural resources, minimizing environmental pollution, and improving public health.
technology, natural resources, upgrading, green energy
DEEP LEARNING-BASED DETECTION AND CLASSIFICATION OF DOCUMENT ELEMENTS USING ROBOFLOW
Recent advances in deep learning have enabled significant improvements in document understanding and processing. This paper presents a workflow for detecting and classifying key document elements—paragraphs of text, tables, signatures, and stamps—using deep learning models implemented through the Roboflow platform. We address the unique challenges of document element detection, including complex spatial relationships, overlapping boundaries, and varying formats across different document types. Our methodology leverages Roboflow's comprehensive toolkit for dataset preparation, model training, and deployment, with particular attention to data augmentation techniques specific to document processing. The proposed system achieves robust detection and classification of document elements. Experimental results demonstrate the system's effectiveness in real-world applications. Our findings indicate that the proposed approach offers significant accuracy and efficiency of automated document processing, particularly in scenarios requiring the precise identification and extraction of structured and unstructured document components.