International Journal of Engineering and Computer Science
International Journal Of Engineering And Computer Science (IJECS) is a leading international journals for publication of new ideas, the state of the art research results and fundamental advances in all aspects of computer science and engineering. IJECS is a scholarly open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications related to Computer Science and Engineering IJECS would take much care in making your article published without much delay with your kind cooperation.
The Crunchy Part Entails the Innovations Offered In Big Data Analytics and Software Engineering of Smarsh Decision-Making Systems
The emergence of information based technologies had never been witnessed before and it has restructured the decision making process in industries, particularly in the attempt to combine big data analytics and software engineering solutions. This paper discusses the possibility of intelligent, accurate and dynamic decision making with the help of the big data analytics, which can be incorporated into the modern software engineering practices. Big data analytics based on the five dimensions of volume, velocity, variety, veracity, and value are the foundation of generating anything of use out of complex data. This coupled with software engineering innovations including agile engineering Devops pipelines, microservices and cloud-native architecture will ensure development of scalable, reliable, and performance decision-support systems. It is a literature review conducted systematically, the drawbacks of conventional decision-making frames, and the presentation of a conceptual framework that integrates analytics pipelines and software lifecycles of engineering. The research indicates that comparative measures and case studies of the field of healthcare, finance, and governance give increases in decision accuracy, efficiency, and adaptability. The outcome of the study shows that intelligent systems of decision-making may not only minimize risks of operation, but also optimize the forecasting outcome to develop a new level of organizational intelligence, and may encourage the privacy-saving approach and independence of the machine-based learning forecasts. Finally, it highlights its future, which is the application of edge and quantum computing, advanced predictive modeling and autonomous software systems all these are bound to revolutionize the dynamic decision making environment. It is applicable to the academic and industrial process since the study offers a blueprint of how the big data analytics may be integrated into innovations in software engineering so as to come up with robust and intelligent decision-support systems
Copyright (c) 2025 International Journal of Engineering and Computer Science http://creativecommons.org/licenses/by-nc-sa/4.0
AI-Driven Algorithmic Trading: Integrating Machine Learning, Hybrid Technical Indicators, and Risk Management for Momentum Strategies
Algorithmic trading has transformed financial markets by enabling automated, data-driven decision-making at scale. However, many existing systems suffer from fundamental architectural limitations: predominant use of rule-based strategies with limited indicator combinations (typically 1-3 technical indicators), static signal generation logic that cannot adapt to changing market regimes, inadequate real-time data validation leading to spurious trades from stale or erroneous data, and siloed risk management systems that operate independently from signal generation. These design constraints result in suboptimal performance, increased drawdowns, and reduced profitability—particularly during high-volatility periods when market dynamics shift rapidly. This paper addresses these fundamental challenges by presenting an AI-driven, real-time algorithmic trading system for U.S. equities, designed to answer: Can the integration of supervised machine learning, hybrid technical indicators, and dynamic risk management significantly outperform traditional rule-based approaches in both returns and risk control? The proposed system integrates supervised machine learning, hybrid technical indicators (VWAP, MACD, RSI, Bollinger Bands), and dynamic risk management to overcome traditional limitations. It leverages multiple real-time market data providers and alternative data sources (such as news sentiment) to generate high-confidence trading signals while eliminating data reliability issues. Key innovations include topgainer filters and advanced selection criteria for momentum trading during high-activity periods, addressing the timing and selection weaknesses of conventional systems. The modular, configurable platform supports rapid adaptation to changing market conditions and features robust data validation—including multi-provider checks and gap-filling—to ensure signal reliability and eliminate data-driven false signals. Backtesting and live trading simulations across diverse market conditions demonstrate up to 14% higher returns and 7% lower drawdown compared to traditional rule-based strategies, with a Sharpe ratio improvement from 0.8 to 1.7. These results highlight the substantial performance gains achievable by addressing the core architectural limitations of existing algorithmic trading systems through integrated AI, hybrid indicators, and adaptive risk management, demonstrating clear superiority over conventional rule-based approaches.
AI-Augmented Analytical–Numerical Model for Cyclic Behavior of Basalt- FRP Strengthened RC Beams: MATLAB-Based Theoretical Method.
This study proposes a hybrid analytical–AI modeling framework for predicting the cyclic flexural behavior of reinforced-concrete (RC) beams strengthened with externally bonded Basalt Fiber-Reinforced Polymer (BFRP) sheets. The approach combines a MATLAB-based analytical–numerical solver with an artificial-intelligence surrogate predictor to enable accurate and rapid assessment of strength, ductility, and energy dissipation under repeated loading. A fiber-section analytical model was formulated to simulate the nonlinear hysteresis response, incorporating concrete degradation, steel kinematic hardening, and bond-controlled BFRP strain limits. Displacement-controlled cyclic simulations were executed to generate a comprehensive dataset spanning BFRP thicknesses (0.5–2.0 mm) and bonded-length ratios (0.3–0.9). A deep-learning surrogate network, trained on 300 analytical simulations, achieved a high predictive accuracy (R² = 0.964, RMSE = 98.4 kN), confirming strong correlation with both analytical and published experimental data. Parametric results revealed that increasing effective FRP strain up to 0.016–0.018 enhances peak load by ≈ 20 %, while extending the bonded length to Lₐ/L ≈ 0.8 improves ductility and equivalent damping (ξ_eq ≈ 5 %). Beyond these limits, performance gains plateau, defining an optimal retrofit range. The proposed framework establishes a non-experimental digital-twin pathway for performance-based FRP retrofit design, offering a computationally efficient alternative to finite-element or laboratory testing. The model’s scalability supports future integration into AI-driven code calibration and design-chart development for cyclic and seismic applications.
Basalt-FRP (BFRP); cyclic behavior; reinforced-concrete beams; analytical–numerical modeling;
Chest Disease Classification Based on Deep Convolution Neural Network (DCNN)
Abstract— In this paper, a robust method for diagnosing chest illness from chest X-ray images is designed and implemented utilizing deep learning networks. The goals of the suggested system are to ensure that patients receive the appropriate treatment at the appropriate time, enable prompt and accurate diagnosis, and stop a patient's health from declining. We developed a neural network approach that fine-tunes convolutional layers and hyperparameters to categorize chest diseases into 14 groups. Using a U-net model for lung separation and a Res-Net50 for chest diagnosis, our approach can distinguish between 14 categories of chest illnesses and normal patients. The proposed method showed exceptional diagnostic efficiency with a 92.71% classification model accuracy and a 97% segmentation model accuracy across all classes.
Convolutional Neural Networks, Chest Diagnosis, U-Net, ResNet-50, Gamma Correction.
State-of-the-Art Algorithms for Cell Image Processing, Segmentation, andTracking in Time-Lapse Microscopy
In cell bioscience, microscopy has always been an essential tool to study cell conduct in reply to various cultural terms and handling. Propagation and cell demise are often examine using microscopy, however, more knotty statistics like movement pattern and morphology could be analyzed. The cell tracking method poses sensible challenge to the computer vision and pattern recognition. This study conducted a comprehensive review on cell tracking and segmentation studies. This study analyses the current segmentation. It also discusses the cell tracking algorithms. Moreover, it presents the cell tracking and segmentation CNN architectures, and provides description of design cell tracking approach. Finally, it identifies the gaps in cell tracking and segmentation for future research. This review forms a basis for future cell tracking research.
Design and Analysis of a Solar-Assisted Regenerative Charging System for Hybrid Electric Vehicles
Hybrid electric vehicles (HEVs) traditionally rely on a combination of a conventional internal combustion (IC) engine and an electric motor powered by chemical batteries. This paper details a significant enhancement to this standard architecture. Our design incorporates two additional methods for energy capture: solar charging assistance to continually replenish the battery module, and a regenerative braking system that recovers energy typically lost during deceleration. The IC engine is strategically reserved as a last-resort backup system. Specifically, the engine is activated only when both conditions are met: the absence of solar energy input (e.g., during the night or heavy rain) coupled with a critically low or fully drained battery charge. By adopting this optimized power management strategy, we achieve several key benefits, including the maximization of the vehicle's pure electric operational time, optimization of the overall system lifespan, and a substantial reduction in tailpipe emissions, positioning this model as a significantly cleaner and more efficient option compared to vehicles running solely on fossil fuels.
Hybrid Electric Vehicle (HEV), Solar-Assisted Charging, Regenerative Braking, Maximum Power Point Tracking (MPPT), Battery Management System (BMS), Energy Efficiency, Zero-Emission Mobility, Sustainable Transportation.
Toward Smart Bone Healing: A Review of In Vivo Evidence and Translational Perspectives on Bioelectronic Scaffolds and Piezoelectric Biomaterials
The limited self-healing capacity of bone and the disadvantages of traditional grafting methods make large bone defects a significant challenge in regenerative medicine. Recent developments in bone tissue engineering (BTE) have highlighted the therapeutic potential of bioelectronic scaffolds as well as piezoelectric biomaterials. As a result of these smart systems, bone regeneration can be actively controlled by responding to external or internal biophysical cues, including electrical and mechanical stimulation. A critical examination of recent in vivo studies using piezoelectric ceramics and polymers is presented in this review that explores the role of endogenous bioelectric signaling in bone healing. Furthermore, this study assesses the interaction between conductive scaffolds, electrical stimulation modalities, and piezo-responsive materials to improve osteogenesis, osseointegration, and vascularization. A particular emphasis is placed on translational implications, scaffold fabrication techniques such as 3D printing, and the integration of remote-controlled stimulation systems for battery-free and self-sufficient stimulation. The insights provided here provide a roadmap for developing next-generation bioelectronic platforms that can overcome current clinical limitations in orthopedic repair
Bone tissue engineering, electrical stimulation, bioelectronic scaffolds, piezoelectric biomaterials, in vivo regeneration, smart biomaterials, osteogenesis.
A Numerical Solution of Second-Order Linear Boundary Value Problems Using Natural Cubic Splines
In this paper, a natural cubic spline method (NCS) has been developed to solve two-point boundary vale problems (BVPS) of second order1 differential equation. The solution of the BVPs is initially approximated by cubic splines and derived a recurrence relation with natural spline constraints. Replacing this recurrence relation and respective cubic spline approximation in BVPs, obtained a tri-diagonal system of equation. An efficient Thomas algorithm is used to find the solution and represents the results graphically. The famous differential equations from Bessel’s equation, Lane-Emden equation, porous catalyst pellet has been considered to check the developed NCS method. Table values for various step sizes are computed in order to verify the developed method’s accuracy. The outcomes are contrasted with those obtained using the shooting technique and spectral methods as well as with those found in the literature.
Lightweight Convolutional Neural Network for Multi-Class Plant Disease Detection and Classification
Globally, there is rise of farming and cultivation. Now a days due to global warming and unpredicted conditions of nature due to deforestation has become a predominant challenge for the farming and agri-communities to enrich the system. To address the need, it is necessary to save the crop from diseases at early stage for better yield. This paper presents a lightweight convolutional neural network (CNN) model for accurate and efficient plant disease classification using leaf images. Leveraging the publicly available PlantVillage dataset, the proposed model undergoes extensive preprocessing and data augmentation to enhance its robustness and generalization. The model architecture includes convolutional layers for spatial feature extraction, ReLU activations, dropout regularization, and softmax-based classification. Evaluated through metrics such as accuracy, precision, recall, and F1-score, the model achieves high classification performance with strong real-world deployment potential. Visual analysis via confusion matrices and accuracy/loss trends further affirms the model’s reliability. With practical deployment on mobile and edge devices in mind, this work contributes to the development of scalable, AI-driven solutions for early plant disease detection in smart agriculture
Agriculture, CNN, Deep Learning, Classification
Predicting Student Dropout Risk Through Machine Learning Analysis of Behavioral Patterns
Student dropout remains a critical challenge in higher education institutions worldwide, affecting not only individual academic trajectories but also institutional effectiveness and societal development. This research investigates the application of Machine Learning (ML) techniques to predict student dropout risk by analyzing behavioral patterns extracted from Learning Management System (LMS) data. The study synthesizes contemporary research findings and explores how digital traces of student engagement, combined with academic and demographic data, can facilitate early identification of at-risk students. By examining various ML algorithms including k-Nearest Neighbors (k-NN), Neural Networks (NN), Decision Trees (DT), and Naive Bayes (NB), this research demonstrates that behavioral pattern analysis significantly enhances prediction accuracy compared to traditional statistical methods. Empirical validation reveals that k-NN with k=3 achieves optimal performance with 87% sensitivity, while feature correlation analysis identifies strong relationships between test performance, project completion, and final result points. The findings reveal that LMS activity metrics, particularly access frequency, test engagement, and assignment submission behaviors, serve as strong indicators of dropout risk when combined with academic performance data. Furthermore, ROC curve analysis demonstrates that ensemble approaches and optimized k-NN classifiers substantially outperform baseline methods in distinguishing dropout-prone students from persisters. The research contributes to the growing body of knowledge in educational data mining by providing a comprehensive framework for integrating behavioral analytics into institutional retention strategies, ultimately supporting data-driven decision-making for improved student success outcomes.
student dropout prediction, machine learning, behavioral patterns, learning management systems, educational data mining, k-nearest neighbors
Feature Engineering Approaches for Predictive Modeling of Phishing Campaign Effectiveness
Phishing attacks continue to evolve in sophistication, targeting diverse industry sectors with varying degrees of effectiveness. This study investigates comprehensive feature engineering methodologies for developing predictive models that assess phishing campaign effectiveness across multiple dimensions. Through systematic analysis of industry-specific attack patterns, URL-based lexical features, and multi-layered detection approaches, we propose an integrated framework that combines traditional heuristic methods with advanced machine learning techniques. Our methodology leverages feature selection algorithms applied to the ISCX-URL2016 dataset comprising 9,964 phishing URLs and 10,000 legitimate URLs, identifying nine critical features that demonstrate strong discriminative power in predicting campaign success rates. Analysis reveals that financial services, Software as a Service platforms, and webmail systems constitute the primary targets, accounting for sixty percent of phishing campaigns. The multi-layered detection framework integrating list-based, visual similarity, and heuristic machine learning approaches achieves superior performance through optimal feature engineering. This research contributes actionable insights for prioritizing defensive strategies based on industry vulnerability profiles and predictive feature importance rankings.
Phishing campaigns, feature engineering, predictive modeling, machine learning, URL analysis, campaign effectiveness, industry targeting, multi-layered detection, cybersecurity analytics
The AI-Powered Grid: A Systematic Review of Machine Learning for Optimization and Resilience in Smart Energy Systems
Traditional energy grids are evolving into smart grids (SGs), which incorporate advanced communications, real-time analytics, and two-way power flows due to the global shift in the direction of sustainable energy. Complex problems as a result of this paradigm shift include handling large datasets, integrating intermittent renewable energy resources, and ensuring grid security and reliability. This literature review systematically examines the transformative role of machine learning (ML) and artificial intelligence (AI) in addressing these challenges. In addition to summarizing their use in key domains including demand forecasting, predictive maintenance, resource allocation, and anomaly detection, it also presents a selection of ML algorithms ranging from simple models such as SVM to state-of-the-art deep learning (DRL). The review also examines new hybrid architectures, including AI integration with blockchain to ensure secure transactions and federated learning to ensure data security. Despite significant improvements in efficiency and sustainability, AI adoption faces barriers related to data quality, model scalability, computational complexity, and cybersecurity. The study concludes by identifying critical research gaps and proposing future directions such as the development of explainable AI (XAI), lightweight models, standardized frameworks, and robust policy reforms to realize the full potential of self-adaptive smart energy grids.
Smart Grids; Distribution Systems; Renewable Energy Integration; Machine Learning.
Evolution of IMS Voice Services in 6G Networks
As the telecommunications industry transitions into the 6G era, IMS-based voice services are evolving beyond traditional SIP-based session control to accommodate new paradigms in emergency response, enterprise automation, and intelligent service orchestration. This paper outlines the architectural, operational, and AI-driven advancements shaping the next generation of voice services—such as AI-assisted call handling, 911 prioritization, Wireless Priority Services (WPS), and policy-based business call connect. By integrating AI inference engines into the IMS core and leveraging predictive analytics for call session election, this research introduces novel methodologies for dynamic session control. The work further presents three patented mechanisms developed by the authors: Circuit Switched (CS) Selection Optimization, TADS Skip Logic for Call Path Reduction, and Voice Termination Election Logic—each contributing to faster, more reliable, and intelligent call setup in the evolving IMS and 6G environment
IP Multimedia System (IMS), Voice over IP (VoIP), Video over IP, WPS wireless priority services, TADS Terminating domain selection, Network Slicing, Rich Communication Services (RCS), Cloud Platforms, Dynamic Scaling, Predictive Analytics, Machine Learning (ML), Random Cut Forest (RCF), Boost, K-Nearest Neighbors (KNN), R² (Coefficient of Determination), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), SIP Error Codes, Call Success Rate, Call Drop Rate, Registration Success Rate, Scam Call Detection, Voicemail Frequency, Network Congestion, Latency Forecasting, CI/CD Deployment Pipelines, Adaptive Network Configuration, Automated Resource Management component, formatting, style, styling, insert (key words).
Derived conjunction and algorithm to close value block in the database model of block form
In the database model of block form, from the results of the research on derived formulas, this article shows how to construct the derived conjunction to receive Tr as the truth block, the main results of this research has demonstrated the necessary and sufficient conditions for a logical formula to be expressed in terms of the derived equations. From these results, this article continues to propose the concepts of Tr value block, closed value block with multiplication & T*, and propose algorithm to make closed value block in database model of block form, the results of this algorithm have found that the smallest truth block T* contains the smallest truth block satisfying: T* contains the element e unit and the z zero element and closes with the & operator. In special cases when the index set consists of 1 element, the volume degenerates into a relationship, this result coincides with the results researched by the authors in the relational database model.
Derived conjunction and algorithm to close value block in the database model of block form, block.
Machine Learning Approaches for Auto-Repairing Test Steps in UI Automation: A Review
In modern software development, UI automation testing is one of the most important aspects of application quality assurance. Excessive UI changes make automated test scripts fragile, hence resulting in flaky tests and exorbitant maintenance costs that expose QA teams to unbearable stress. To overcome such issues, recent progress in Machine Learning (ML) and Artificial Intelligence (AI) has been leveraged to develop auto-repair mechanisms for automatically detecting and repairing broken test steps, making tests more reliable and reducing human effort. This survey presents an overview of current ML-based solutions to self-healing UI test automation, including element locator repair, flaky test stabilization, and wait automation. We address methods from heuristic to large language model-based repair and dynamic web and mobile test script modification. Leading frameworks describe how the integration of ML can make test maintenance easier and the tests themselves more resilient. Our presentation recognizes that while locator repair approaches have matured and offer immediate practical advantages, newer LLM-based approaches offer improved semantic understanding but at high costs of explainability and computational complexity. We talk about current constraints on the generalizability and explainability of test repair and provide guidelines for future research on advancing intelligent, adaptive QA automation solutions that reduce downtime and enhance software delivery quality
End-to-End Validation of Clinical Trial Management Systems Using Flexible Methodologies
The article is devoted to the investigation and development of a comprehensive model for end-to-end (E2E) validation of clinical trial management systems (CTMS) using Agile methodologies. The relevance of the work is driven by the need to accelerate the market entry of new medicinal products while regulatory requirements for data integrity are simultaneously tightening. The novelty lies in proposing an integrated framework that combines Agile principles, a risk-based approach, and continuous compliance verification into a single process for GxP environments. The study describes the limitations of traditional waterfall validation models and examines modern approaches to software validation in the pharmaceutical industry. Special attention is paid to developing a dynamic validation master plan and adapting CI/CD pipelines for regulated environments. The work sets the objective of developing a conceptual model that enables improved operational efficiency and data quality while maintaining compliance with regulatory standards. To achieve this, the methods used include systematic analysis of the scientific literature, comparative analysis, and conceptual modeling. The conclusion describes the practical significance of the proposed model for pharmaceutical companies and suggests directions for further research. The materials presented in the article will be of interest to validation specialists, quality managers, IT leaders, and data management specialists in the life sciences domain.
end-to-end validation, clinical trial management systems, CTMS, Agile methodologies, Agile, risk-based approach, data integrity, GxP, software validation, pharmaceutical industry.
Unauthorized Access Prevention Model for Secure Data Transmission in Internet of Things-Based Network
The Internet of Things (IoT) has revolutionized connectivity by enabling devices to interact seamlessly in various domains such as healthcare, smart cities, and industrial automation. However, this connectivity also introduces vulnerabilities, with unauthorized access posing significant threats to data security. This paper presents an unauthorised access prevention model for securing data transmitted in the internet of things-based network, with a focus on providing an Intrusion Detection System (IDS) that detects botnet attacks. The approach employs a Multi-Layer Perceptron (MLP) for fault detection, specifically designed to address the challenges inherent in IoT environments. The MLP architecture consists of three dense layers with Rectified Linear Unit (ReLU) activation functions, enabling the capture of intricate data relationships. Through sequential modelling, the MLP accurately identifies complex patterns indicative of botnet attacks. The training process of the MLP yields promising results, with significant accuracy and minimal loss values. The model achieves an accuracy of 99.99% for both training and validation (testing) data. Furthermore, the classification report highlights the model's exceptional precision and recall for classifying normal and IDS classes, demonstrating its reliability for detecting botnet attacks. To bolster security measures, the system incorporates encryption and access control mechanisms. Data encryption, utilizing the Fernet symmetric encryption algorithm, ensures the confidentiality of transmitted data. Access control mechanisms differentiate between authorized and unauthorized devices, enhancing overall security.
Internet of things, data transmission, Random forest, encryption, Access control
Performance Analysis and Simulation of a 1.5 MW Wind Turbine under Specific Wind Conditions
In this research, the performance of a 1.5 MW horizontal-axis wind turbine (HAWT) was studied. The primary objective was to simulate the mechanical to electrical energy conversion process in a small wind turbine system and analyze the turbine output under different wind speeds and blade radii. A mathematical model was developed based on basic aerodynamic principles, including kinetic energy extraction, power coefficient (Cp), and tip speed ratio (λ). The simulation was performed using MATLAB/Simulink, incorporating real wind speed data from selected cities in Nigeria. Key parameters such as output power, mechanical torque, and electrical torque were monitored and analyzed. The results demonstrate the turbine's characteristic power curve and its dynamic response to wind speed changes, achieving a power output close to its rated capacity. The results provide insights into the optimal setup for wind power generation under similar climatic conditions and contribute to feasibility studies for wind farm projects.
Applying Zero Trust Architecture Principles in Digital Infrastructures
This article explores the application of Zero Trust Architecture (ZTA) methods to enhance the cybersecurity of modern digital infrastructures. It outlines the theoretical foundations of the Zero Trust model, including core principles such as continuous verification, least-privilege enforcement, breach presumption, microsegmentation, and dynamic access policy management. Through a comprehensive literature review, the paper identifies key challenges in integrating this model into hybrid digital ecosystems and outlines future directions for research, including the convergence of ZTA with artificial intelligence technologies and the development of analytical mathematical models. The findings suggest that adopting Zero Trust practices significantly reduces the risk of lateral threat movement and increases the adaptability of access control systems—an increasingly critical factor amid ongoing digital transformation. The insights presented will be of interest to academics and cybersecurity professionals, including security strategy developers, information systems architects, and researchers focusing on the integration of Zero Trust methodologies to enhance the resilience of digital environments. Additionally, the discussion offers practical value for project leaders and policymakers engaged in digital transformation initiatives and the optimization of security processes in the face of rapidly evolving cyber threats.
Blood Donation, Emergency Healthcare, Donor–Recipient Matching, Geo-location and Blood Bank.
Democracy in Software Development: The Rise of Vibe Coding
Software development as an engineering discipline is characterized by tension between abstraction and precision. It has undergone a tremendous transformation over the decades, from highly rigid machine language programming to the modern day vibe coding that tends to democratize software development through automation, abstraction, and artificial intelligence (AI). Vibe coding, a term that refers to AI-assisted and intuition-driven software development methodology. This paper first provides the historical trajectory of software development, arguing that each stage has incrementally democratized software development. The current shift powered by Large Language Models (LLMs) represents the most significant stride in the democratization of software development yet. This paper also enumerates the implications of this shift and the evolution of software development expertise. It concludes that while vibe coding has its challenges, it aligns with the historical evolution of software development, which is the relentless pursuit of higher-level abstraction to harness human creativity and collective intelligence.
A Novel Mobile-Based Blood Donation Model For Emergency Situations
Blood donation is essential to modern healthcare, yet shortages and delays in accessing compatible blood remain a global challenge, particularly in low and middle income countries. In emergency contexts such as trauma, obstetric complications, accidents, surgical crises etc, the absence of timely blood transfusion often results in preventable mortality. Traditional donor mobilization strategies, including static registries and manual blood bank processes, are limited by inefficiencies, lack of real-time updates and weak integration with hospital workflows. This study titled a novel mobile-based blood donation model for emergency situations designed to address these limitations. The application incorporates a real-time geo-location services, automated eligibility screening, and secure communication between hospitals, donors and blood banks. By streamlining donor–recipient matching and ensuring data privacy, the system reduces delays and enhances reliability in emergency transfusion support. Also the application seeks to promote sustained donor participation and foster a community-driven culture of regular blood donation by incorporating some educative features. This model runs on Windows 10 or later, with HTML5, CSS3, and Bootstrap for the front-end, ASP.NET with C# for the back-end, and SQL Server for database management. It is hosted on IIS, developed in Visual Studio, and enhanced with JavaScript, JQuery, and ASP.NET Identity for interactivity and security. The findings highlight the potential of mobile health technology to strengthen healthcare resilience, reduce preventable deaths, and improve emergency medical response.
Analysis of Network Performance in Service Level Agreements (SLAs): Proactive Management and Advanced Troubleshooting Strategies in Modern Converged Environments
In today's digital landscape, characterized by cloud infrastructures and software-defined networking (SDN), network performance management has evolved from a technical function to a strategic necessity to ensure business continuity and customer satisfaction. This paper provides a comprehensive analytical review of the critical importance of network performance management and troubleshooting techniques in modern complex environments. A paradigm shift from reactive to proactive management, based on establishing a robust “network performance baseline,” is essential for early detection of performance deviations and potential anomalies. This research paper systematically outlines the steps for establishing an effective baseline, from identifying key performance indicators (KPIs) and critical network devices to determining the optimal duration for data collection. Furthermore, multi-layered troubleshooting frameworks, such as the OSI and TCP/IP models, are examined as structured models for accurate problem diagnosis, with systematic approaches such as top-down, bottom-up, and divide-and-conquer discussed. The research culminates in a classification and evaluation of modern software and hardware tools, including Network Performance Monitoring and Diagnostics (NPMD) platforms and Artificial Intelligence for IT Operations (AIOps), that enable network engineers to enhance operational efficiency and meet the stringent requirements of Service Level Agreements (SLAs) in the era of converged digital solutions.
Network performance management; Service Level Agreements (SLAs); Network troubleshooting; Network Performance Baseline; AIOps (Artificial Intelligence for IT Operations); Software-Defined Networking (SDN).
Methodological foundations of AI observability for enterprise LLM applications
The article describes the methodological foundations of AI observability for large language models integrated into enterprise environments. The relevance is determined by the adoption of LLMs in business alongside the emergence of specific risks: hallucinations, data leaks, and uncontrolled cost escalation that are not covered by traditional monitoring tools. The scientific novelty lies in proposing a multi-level framework that systematizes observability metrics across four dimensions: quality and semantics, cost and performance, security and privacy, responsibility and ethics. The study identifies the limitations of classical MLOps approaches as applied to LLMs and analyzes contemporary methods for detecting anomalous model behavior. Special emphasis is placed on coupling automated metrics with human feedback mechanisms. The purpose of the study is to construct a holistic methodology for designing LLM observability systems. To achieve this goal, methods of systems and comparative analysis, as well as conceptual architectural modeling, are employed. In conclusion, the practical significance of the framework is demonstrated for minimizing risks and increasing the return on investment from LLM applications. The findings presented in this work will be of interest to project managers in Data Science, MLOps engineers, and AI systems architects.
AI Observability, large language models, LLM, LLMOps, AI monitoring, enterprise AI, hallucination detection, responsible AI, Conversational AI, MLOps.
Analysis of Natural Frequencies and Vibrations in Pipes Under Fluid Flow Using Theoretical Modeling
Pipes applications are important and widely used applications in all industrial fields because pipes are used in many fields such as water, oil, gas, etc. Therefore, it was necessary to highlight the study of the pipes and the problems faced by them. These important and complex problems Which cause failures in many pipe networks are the problem of vibrations. Where researchers have dealt with the past years the subject in many respects, but the problem has not ended so far. Therefore, in this research, the problem of controlling the vibration resulting from the flow of fluids inside the pipes was shed light on, in addition to the study of vibration reduction for these pipes, where the research included studying the natural frequency of pipes of various types. Fixation to the pipe at a constant speed and different pressure for two types of fluids (water and oil) at a certain length and diameter, and observing the effect of this addition on the amount of vibration in addition to the natural frequency of the pipe. This study was done by deriving differential equations for pipes for different types of fixations (Pinned-Pinned, Clamped-Pinned, Clamped-Clamped) and the application of boundary conditions for each equation. Where the equations for the natural frequency of the tubes were reached by the theoretical method (analytically) by deriving the differential equations of motion and dealing with them for the purpose of arriving at the general equation so that it is easy to convert it with changing the inputs and outputs and using the Matlab 2018 program, where the final equations were graphed and the results for the tubes were measured.
Data-Driven Migration Strategies: Leveraging GenAI for Relational to NoSQL Cloud Database Migrations
Migrating from on-premises relational databases to cloud based NoSQL database [1] represents a fundamental shift in data architecture [2]. This transition involves moving from ACID-compliant [4], structured data models to eventually consistent, flexible schema designs. Organizations typically pursue this migration to achieve better scalability, reduce operational overhead, and leverage cloud-native capabilities. The process requires careful planning as it involves not just data movement but also application refactoring [5][20]. Modern database migrations can benefit significantly from GenAI [3] capabilities that can analyze source database patterns, predict optimal NoSQL targets, and automate complex decision-making processes. GenAI transforms traditional manual migration planning into intelligent, data-driven recommendations that reduce risks and improve outcomes. AI-powered analysis can examine years of query logs, schema evolution patterns, and performance metrics to recommend the most suitable NoSQL database type and architecture design.
Relational database migration, Cloud-based NoSQL, ACID compliance, Flexible schema, Data architecture transformation ,Scalability ,Operational overhead reduction, Cloud-native capabilities, Automated database migration, GenAI (Generative AI) ,Schema analysis ,Query pattern analysis ,Data-driven decision-making ,NoSQL database selection ,Migration planning automation ,Performance optimization ,Risk mitigation, Intelligent migration tools
Next-Generation Hybrid Databases: Bridging SQL Consistency, NoSQL Scalability, and AI-Driven Optimizations
Next-Generation Hybrid >> Databases: Bridging SQL Consistency, NoSQL Scalability, and >> AI-Driven Optimizations
SQL, NoSQL, hybrid databases, polystores, consistency, benchmarks, AI in databases
A Survey On Using Artificial Intelligence (AI) During Students’ Exams
In modern education, both teachers and students are increasingly trying to consult artificial intelligence. This article examines the extent to which students rely on their own knowledge and on the help of artificial intelligence during an exam. Results of a survey among students of the Informatics program at NBU are presented.
Survey, AI, Informatics, students’opinions
A Novel Multi-modal Graph Neural Network MAGNet for Detecting AI-Generated Scientific Contents
The accelerated development of sophisticated large language models (LLMs) poses a serious and increasingly critical risk to academic integrity as it effortlessly generates high-quality scientific text that it is often barely differentiable to work by humans. Current detection systems, often based on statistical analysis or single-feature analysis, have severe shortcomings in generalizability between AI models and are not available to the broader academic community. In response to these issues, this study introduced MAGNet, a new Multimodal Graph Neural Network architecture that can be used to identify AI-made scientific text with high robustness and flexibility. Our model goes beyond conventional and unites and combines several feature modalities such as syntactic patterns, semantic relationships, and graph-based structural features into a single deep learning model. With such design, MAGNet is capable of finding weak, subtle artifacts and trends that are typical of AI generation that simpler statistical methods fail to detect. Trained on 5,000 scientific texts, a highly rigorously curated dataset, to guarantee a direct and fair comparison with modern studies, our model has an accuracy of 91.0% state-of-the-art, with a precision of 89.5% and a recall of 93.0%. Most importantly, MAGNet has shown a high level of performance in difficult situations, achieving the accuracies of 87.1% in cross-model generalization test and 84.5% in deliberately paraphrased documents, thus mentioning its high resilience to evasion behaviors. We have created and implemented a publicly accessible web application, to connect the gap between research and practice to provide the academic community with a powerful and user friendly interface to use this new advanced detection capability. Not only does our work set a new standard in performance in AI-text detection, but it also provides a highly valuable practical resource of educators, researchers, and publishers interested in protecting the integrity of scientific communication. The study concluded that introduced a new multimodal, graph-based neural network called MAGNet that determines AI-written scientific text. They showed that a combination of semantic, statistical, and structural analysis in the same deep learning system shows the highest performance, and the accuracy of the system is 91.0% on a large-scale, curated dataset.
AI, Deep Learning, Natural language processing, academic integrity and multi-modal fusion
Development of AI-Based Adaptive Algorithms for Predictive Control of Hybrid Energy Systems to Maximize Their Thermodynamic and Economic Efficiency
In the context of global energy transformation and the continuous growth of renewable energy sources (RES) in generation portfolios, managing their stochastic behavior and ensuring their integration into existing power systems has become critically important. This study presents a theoretical foundation and proposes AI-based adaptive algorithms for the predictive control of hybrid energy systems (HES). The objective is to formulate a smart control concept aimed at the comprehensive optimization of both thermodynamic and economic performance of HES. Within this framework, a conceptual SAEO (Smart Adaptive Energy Optimization) model is introduced, integrating RES, energy storage technologies (including hydrogen systems), and a gas-steam combined cycle. The results demonstrate that implementation of the developed adaptive algorithms increases overall system efficiency and reduces the levelized cost of energy compared with traditional control schemes based on fixed logic rules. Based on these findings, it is concluded that the intelligent enhancement of control algorithms is a key prerequisite for achieving a synergistic effect in complex hybrid energy systems. The presented results may be of value to power engineers, AI researchers, and strategic planning specialists in the energy sector.
hybrid energy system, artificial intelligence, predictive control, thermodynamic efficiency, economic efficiency, SAEO, hydrogen energy, renewable energy sources, optimization, SCADA.
Design A VLAN-Based Branch Network: College of Political Sciences as a Case Study
Performance of a network is influenced by the user satisfaction which is related to network services provided. Fault tolerance, scalability, Quality of Service (QoS), and security are four basic characteristics that need to be addressed in term of network architectures to meet user satisfaction. Divers of engineering and architectural aspects should be leveraged in modern converged enterprise campus networks from the design standpoint. In this project, existing network architecture of the political sciences college at the University of Kufa is identified and analyzed from any issues that could affect user satisfaction. The final phase is to propose a new network design that meet user satisfaction without high costs. To achieve the proposed design and network criteria, packet tracer software is used to design and test the new network architecture.