Computers and Education Letters (CEL) is a peer-reviewed, open-access journal published by Future Scope Research Institute (SMC-Private) Limited. It is dedicated to advancing the understanding of how technology, particularly computers and digital tools, can enhance educational practices and learning outcomes. The journal provides a platform for original research, reviews, and discussions on the use of computing technologies in education, aiming to bridge the gap between educational theory and practice. CEL covers a wide range of topics within the field of educational technology, including but not limited to, the integration of computers in classrooms, e-learning systems, educational software development, online learning, virtual classrooms, digital literacy, and the impact of emerging technologies such as artificial intelligence, machine learning, and gamification on education. The journal serves as a vital resource for educators, researchers, developers, and policymakers interested in the application of computing technologies to improve teaching and learning environments. By publishing high-quality, evidence-based articles, CEL seeks to foster innovation in the design and use of educational technologies that can improve student engagement, achievement, and overall educational experiences. As an open-access journal, CEL ensures that all published content is freely available to a global audience, supporting the widespread dissemination of knowledge and the development of best practices in educational technology. CEL is published twice a year, adhering to international academic publishing standards.
FUTURE SCOPE RESEARCH INSTITUTE (SMC-PRIVATE) LIMITED
TRACEABLE AND EXPLAINABLE AI LINEAGE ARCHITECTURES FOR REGULATORY-COMPLIANT DECISION INTELLIGENCE SYSTEMS
<span lang="EN-US" style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-no-proof: yes;">The rapid deployment of artificial intelligence in high-stakes domains such as financial compliance, cybersecurity, and regulatory decision intelligence has intensified the need for transparent, auditable, and accountable AI systems. Traditional machine learning pipelines often lack sufficient traceability and governance mechanisms, creating significant challenges for regulatory compliance and post-hoc auditing. This study proposes a traceable and explainable AI lineage architecture designed to support regulatory-compliant decision intelligence systems. Using a Design Science Research methodology, the study develops and evaluates a prototype compliance framework that integrates ontology-driven data lineage tracking, cryptographic verification mechanisms, and policy-governed MLOps pipelines. The architecture incorporates blockchain-based audit trails, provenance-aware logging, and tiered human-in-the-loop governance mechanisms to ensure continuous observability and verifiable accountability across the AI lifecycle. Experimental evaluation demonstrates substantial improvements in traceability coverage, audit reconstruction accuracy, and compliance verification reliability across multiple system deployment cycles. The findings indicate that integrating provenance tracking with cryptographic integrity protocols enables organizations to generate verifiable regulatory evidence without exposing proprietary model parameters. By operationalizing compliance-by-design principles within AI infrastructure, the proposed framework bridges the gap between regulatory mandates and technical implementation. The study contributes to the growing field of trustworthy AI governance by providing a scalable architecture capable of supporting transparent, auditable, and legally compliant AI decision systems in complex regulatory environments.
Explainable Artificial Intelligence (XAI)
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
RESEARCH PROGRESS ON THE CO-DESIGN OF HIGH-EFFICIENCY THERMOELECTRIC CONVERSION MATERIALS AND MECHANICAL SYSTEMS: FROM PERFORMANCE MATCHING TO ENGINEERING APPLICATIONS
This study presents a comprehensive investigation into the co-design of high-efficiency thermoelectric conversion materials and mechanical systems, focusing on performance matching and engineering applications. A mixed-methods approach integrated qualitative synthesis of literature (2018–2022), quantitative modeling, and finite-element simulations to evaluate the interplay between material performance and mechanical integrity. The analysis revealed that high-entropy alloys, nanostructured composites, and hierarchical architectures significantly improved Seebeck coefficients, reduced lattice thermal conductivity, and enhanced power factors, resulting in thermoelectric figures of merit (zTzTzT) exceeding conventional benchmarks. Tables demonstrated consistent eigenvalue distributions, spectral decompositions, and density matrix approximations that quantified these gains across varied operating conditions. Figures 2–13 provided detailed visualizations of electrical conductivity distributions, stress mapping under temperature gradients, and hybrid plots correlating power density with zTzTzT trends, confirming the robustness of co-designed systems under thermal cycling. Mechanical system innovations, including compliant buffer layers, optimized heat-sink geometries, and flexible substrates, mitigated thermal stress and improved module lifetime, as validated by scatter and hybrid plots depicting clustering of stress distributions. Performance matching between peak material properties and module design parameters achieved superior energy conversion efficiencies across simulated automotive, aerospace, and wearable applications. The integration of adaptive control strategies further optimized heat flux alignment, reducing losses and increasing long-term reliability. These findings collectively establish co-design as a transformative paradigm that unites material science, mechanical engineering, and sustainability. By emphasizing iterative feedback between material development and mechanical configuration, the study offers a scalable framework for advancing thermoelectric technology toward real-world energy harvesting and thermal management solutions.
Copyright (c) 2025 Yiming Tang (Author) https://creativecommons.org/licenses/by/4.0
LINEAR ALGEBRA AND FUNCTIONAL ANALYSIS IN QUANTUM MECHANICS: FUNDAMENTAL THEORY AND APPLICATION EXPANSION
This study investigates the foundational and applied roles of linear algebra and functional analysis in quantum mechanics, employing a mixed-methods design that integrates qualitative conceptual analysis with quantitative simulations. The methodology combined literature synthesis of recent works (2018–2022), mathematical modeling of eigenvalue problems, and computational simulations within Hilbert spaces to validate theoretical constructs. The results demonstrated that linear algebra provides the structural basis for representing quantum states, operator norms, and matrix formulations, while functional analysis extends these principles to infinite-dimensional spaces, spectral decompositions, and unbounded operators. Tables presented comprehensive datasets, including eigenvalue distributions, operator convergence, and density matrix approximations, whereas figures offered multidimensional visualizations through line, bar, scatter, and pie charts. Together, these outputs revealed that functional analytic methods are indispensable in understanding spectral behavior and operator stability, while linear algebra underpins computational frameworks for quantum algorithms and quantum information processing. The discussion established that this synergy not only consolidates the mathematical rigor of quantum mechanics but also advances practical innovations such as quantum error correction, quantum machine learning, and operator-based quantum algorithms. The study concludes that linear algebra and functional analysis are complementary frameworks that enable both deeper theoretical insights and application-driven progress in quantum mechanics, underscoring their continuing importance in research, pedagogy, and technology development.
Copyright (c) 2025 Panlin Li (Author) https://creativecommons.org/licenses/by/4.0
HOW DOES GENE EDITING TECHNOLOGY RESHAPE THE THEORETICAL COGNITIVE FRAMEWORK OF DISEASE MECHANISMS
Gene-editing technologies such as CRISPR/Cas9, base editing, and prime editing have revolutionized the biomedical landscape by enabling precise, targeted alterations to the genome. However, beyond their biological utility, these technologies are also reshaping the theoretical cognitive frameworks through which disease mechanisms are conceptualized. This study employs a mixed-method experimental design to investigate how gene-editing interventions affect both molecular outcomes and the epistemological understanding of disease. Quantitative data derived from gene expression assays, editing efficiency measurements, and phenotypic observations were complemented by qualitative insights from expert interviews and clinician surveys. The results reveal that successful gene edits not only produce measurable biological effects—such as increased expression accuracy and phenotype improvement—but also prompt a paradigm shift in the perception of disease as a modifiable, network-regulated condition. Visual analytics including heatmaps, hybrid plots, and box plots further support these findings, illustrating multi-gene interactions and the complexity of post-editing phenotypic landscapes. Moreover, the study highlights critical ethical considerations, especially as editing transitions from lab-based research to clinical application. It emphasizes the importance of interdisciplinary frameworks that bridge molecular biology, clinical practice, ethics, and systems theory. Overall, this research affirms that gene-editing technologies are not only therapeutic tools but also epistemic instruments that redefine how disease is understood, managed, and anticipated in the era of genomic medicine.
Copyright (c) 2025 Yichi Chen (Author) https://creativecommons.org/licenses/by/4.0
THEORETICAL METHODS FOR THE OPTIMAL CONFIGURATION OF BACKUP POWER CAPACITY IN MICROGRIDS
The growing reliance on microgrids as decentralized energy solutions has intensified the need for reliable backup power capacity that balances operational efficiency, economic viability, and environmental sustainability. This study investigates theoretical methods for the optimal configuration of backup systems in microgrids, employing a mixed-method approach that integrates quantitative optimization models with scenario-based simulations. Using mixed-integer linear programming (MILP), metaheuristic algorithms, and empirical reliability assessments, the research evaluates generator performance, battery storage characteristics, renewable energy contributions, load demand profiles, outage recovery dynamics, and scenario-driven performance indicators. Results across nine comprehensive tables and twelve diverse figures reveal that backup power optimization significantly reduces fuel consumption, CO₂ emissions, and lifecycle costs, while enhancing resilience and renewable energy penetration. Notably, hybrid systems that combine batteries, renewables, and diesel backup consistently outperform single-technology configurations by achieving higher efficiency and reliability under variable demand and uncertainty conditions. The study also demonstrates that scenario-based planning captures the complexity of real-world contingencies better than deterministic models, offering critical insights for policymakers and energy planners. By advancing a theoretical framework that integrates cost, reliability, and sustainability objectives, this research contributes to both academic discourse and practical microgrid deployment strategies. The findings underscore the importance of hybrid optimization frameworks and scenario analysis in designing resilient, sustainable, and future-ready microgrids, paving the way for further work on adaptive control systems and regulatory integration.
Copyright (c) 2025 Ziwei Bian (Author) https://creativecommons.org/licenses/by/4.0
MACHINE LEARNING MODEL DESIGN FOR TWO-DIMENSIONAL PHOTONIC CRYSTALS WITH POINT DEFECTS
The design and optimization of two-dimensional photonic crystals (PhCs) with point defects play a crucial role in advancing nanophotonic devices for applications in sensing, communications, and quantum technologies. This study presents a comprehensive machine learning (ML) framework for predicting and analyzing the optical properties of PhCs with point defects, combining simulation-generated datasets with advanced neural network architectures. Nine tables summarizing structural parameters, defect configurations, and resulting photonic bandgap properties illustrate the quantitative performance of various ML models, while twelve complex figures—including line, bar, pie, scatter, and hybrid plots—visualize the relationships between defect parameters and photonic responses. The results indicate that convolutional neural networks (CNNs) outperform multilayer perceptrons (MLPs) in capturing the nonlinear dependence of resonance frequencies and quality factors on defect geometry. Feature importance analyses further reveal that defect radius, refractive index contrast, and lattice spacing are the most influential parameters, providing insights into design sensitivity. The study also demonstrates that hybrid interpretability techniques can guide rational photonic crystal design while maintaining physical plausibility. Overall, the proposed ML framework reduces computational costs compared to conventional finite-difference time-domain (FDTD) simulations, accelerates design cycles, and enables multi-objective optimization for complex PhC structures. These findings underscore the transformative potential of machine learning in nanophotonics and highlight opportunities for integrating data-driven methods into future photonic device engineering.
Copyright (c) 2025 Guxuan Chu (Author) https://creativecommons.org/licenses/by/4.0
ANALYSIS OF AERODYNAMIC SHAPE OPTIMIZATION OF UNMANNED AERIAL VEHICLES BASED ON CFD SIMULATION AND ITS IMPACT ON FLIGHT STABILITY
This study presents a comprehensive analysis of aerodynamic shape optimization of unmanned aerial vehicles (UAVs) using Computational Fluid Dynamics (CFD) simulations, with a particular focus on its implications for flight stability. By integrating adjoint-based optimization methods with high-fidelity CFD modeling, the research systematically evaluated wing–fuselage configurations, static margins, and dynamic stability parameters under varied operating conditions. The results demonstrated substantial improvements in aerodynamic efficiency, with drag reductions of up to 15% in optimized configurations and lift-to-drag ratio increases of 12–18% compared to baseline models. Stability assessments indicated that optimized UAVs exhibited enhanced static margins and improved pitch and yaw damping coefficients, leading to superior longitudinal and lateral stability. Cross-validation with wind tunnel experiments confirmed the accuracy of the CFD predictions, underscoring the reliability of simulation-driven optimization in UAV design. Iterative refinement further highlighted that performance gains could be achieved without compromising stability, demonstrating the scalability and robustness of the optimization framework. The findings conclude that coupling aerodynamic performance with stability-focused optimization yields UAV designs that are both efficient and resilient, offering valuable insights for the advancement of next-generation UAV platforms in surveillance, logistics, and defense applications.
Copyright (c) 2025 Guize Tao (Author) https://creativecommons.org/licenses/by/4.0
DEEP LEARNING FOR PREDICTIVE MAINTENANCE IN MANUFACTURING: A DATA-DRIVEN APPROACH TO FAULT DETECTION AND SYSTEM OPTIMIZATION
This research evaluates deep learning approaches particularly hybrid CNN and LSTM networks to enhance industrial predictive maintenance systems for determining equipment RUL and fault detection. The evaluation of four deep learning models comprised CNN and RNN and LSTM as well as their hybrid CNN-LSTM model through assessing the performance measures that included RUL prediction accuracy, precision, recall, F1-score, and Mean Absolute Error (MAE). The hybrid CNN-LSTM model proved superior in both fault detection precocity and failure prediction accuracy since it delivered 92.5% accuracy and 13.1 hours MAE in forecasting RUL. The resilient fault detection capability of the model produced a high F1-score that reached 93.4%. The hybrid technique decreased false negative outcomes in the confusion matrix to ensure prompt defect discovery for the reduction of unplanned system downtime. The model received evaluation by applying live industrial data ensuring both practical application and compatibility with real manufacturing settings. The research demonstrates that hybrid deep learning models represent practical approaches to improve industrial predictive maintenance thus resulting in reduced costs and improved operational efficiency.
Copyright (c) 2024 Asma Humayoun, Iqra Javed (Author) https://creativecommons.org/licenses/by/4.0
ADVANCING FORENSIC APPLICATIONS: THE ROLE OF ARTIFICIAL INTELLIGENCE IN DETECTING AND MITIGATING IMAGE AND VIDEO MANIPULATION
Electronic information is threatened when forensic examinations become challenging due to modern digital advancements. Through Deepfakes media manipulation forms the basis for people who either attack others or spread false information while influencing political opinions. Traditional forensic testing methods show reduced capability in identifying picture alteration compared to more advanced techniques while operating at slow identification speeds. Deep learning-based artificial intelligence serves as a strong tool for handling modern-day technical problems. Our research assesses and develops artificial intelligence systems dedicated to detecting and reducing digital image and video manipulation for investigative purposes. We execute data management followed by model development and training and final assessment of results throughout our program. The developed AI models undergo verification using precision and accuracy together with recall testing as well as F1 score and ROC-AUC measurements. Our assessment framework integrates the use of GANs with CNNs while also deploying CNN-RNN hybrid architectures. AI-based techniques surpass traditional forensic methods to detect different types of manipulation with superior effectiveness. Real-world application demonstrates these models function effectively while keeping adversaries from influencing them. The research evaluates the cultural and ethical effects of artificial intelligence in forensic science while developing recommended methods of safe utilization. The research uses artificial intelligence strengths but addresses its weaknesses to develop secure systems against digital media tampering.
Copyright (c) 2025 Muhammad Waqas, Aftab Alam, Sara Khan (Author) https://creativecommons.org/licenses/by/4.0
EFFICIENT RESOURCE ALLOCATION AND LOAD BALANCING ALGORITHMS IN CLOUD VIRTUALIZATION ENVIRONMENTS
The growing demand for cloud computing services has necessitated the development of efficient resource allocation and load balancing algorithms to ensure optimal performance, scalability, and energy efficiency. This study presents a hybrid approach combining traditional and machine learning-based techniques for resource allocation and load balancing in cloud virtualization environments. We propose an algorithm designed to enhance resource utilization, reduce task completion times, improve fairness in multi-tenant environments, and optimize energy consumption. The effectiveness of the proposed algorithm is evaluated through simulations under varying workloads and cloud configurations. According to the research findings the recommended method produces superior results than both round-robin and least-connections in terms of key performance measures. The CPU utilization rate by our method reaches 93.5% while round-robin performs at 72.8% and least-connections uses 80.4%. The work completion time gets reduced by up to 30% through implementation of this method which shows increased efficiency against traditional methods. The method proposes reduced energy consumption which directly benefits the environment-friendly operations of cloud platforms. The new algorithm achieves resource distribution fairness with an index of 0.92 that surpasses the limited results recorded by traditional methods. The technique presents excellent scalability features alongside stable performance levels when managing a growing number of virtual machines which shows it works well for large cloud environments. This research promotes machine learning methods for cloud resource management which enhance operational effectiveness and reduce power usage and create fair resource distribution schemes. The analysis from our work delivers beneficial information for cloud service providers to enhance operational effectiveness through increased service capacity while minimizing environmental impact.
Copyright (c) 2025 Muhammad Zaki Ul Hassan Khan, Ayesha Malik (Author) https://creativecommons.org/licenses/by/4.0
COMPUTATIONAL APPROACHES TO ANALYZING GENETIC DATA: IDENTIFICATION OF BIOMARKERS FOR CANCER DETECTION
This study explores the application of computational approaches to analyze genetic data for the identification of potential biomarkers for cancer detection. Leveraging large-scale cancer datasets from public repositories, including gene expression profiles, mutation data, and proteomics information, we applied a series of machine learning and deep learning models to identify key biomarkers. Our analysis revealed that convolutional neural networks (CNNs) significantly outperformed traditional machine learning models, such as support vector machines (SVM) and random forests (RF), achieving higher accuracy (92%), sensitivity (89%), specificity (91%), and area under the curve (AUC) (0.94). The inclusion of genome and transcriptomic and proteomic data with multi-omics information helped boost model performance as it delivered an extensive biomarker identification framework. Multiple data omic tests generated predictive results superior to solitary data omic approaches which demonstrates why researchers need multiple integrated datasets in cancer studies. These findings establish deep learning and integrated system methods as effective tools for identifying fresh biomarkers that connect to various cancer types based on current research results. While the computational results indicate usefulness the authors highlight that extensive clinical validation of newly discovered biomarkers must happen alongside enhanced computational methodology refinement to achieve reliable and generalized results. High-end computational processes create new opportunities in cancer detection and personalized medicine while demonstrating their potential for diagnosis transformation through this research.
Copyright (c) 2025 Hamayoun Rasheed, Kashif Mahmood (Author) https://creativecommons.org/licenses/by/4.0
ENHANCING PRIVACY-PRESERVING DATA ANALYTICS THROUGH HOMOMORPHIC ENCRYPTION: TECHNIQUES AND APPLICATIONS
In the era of data-driven decision-making, the protection of sensitive information has become a critical challenge, particularly as organizations increasingly rely on data analytics for insights. This study explores the potential of Homomorphic Encryption (HE) in enhancing privacy-preserving data analytics, a promising cryptographic technique that enables computation on encrypted data without exposing sensitive information. We evaluate the performance of three different HE schemes in terms of encryption and decryption times, computational overhead, analytical accuracy, and scalability. Our findings reveal that while HE incurs significant computational overhead, particularly for larger datasets, the accuracy of analytical results remains comparable to that of plaintext data, demonstrating its potential for privacy-preserving analytics. A performance decrease occurred when data set sizes grew which led to encryption along with decryption taking much longer than regular encryption approaches. The accuracy levels of data processing functions including categorization and regression did not change during periods when unencrypted data was utilized. The main challenge for HE on large datasets is scalability but we believe that encryption systems which combine HE methods with alternative techniques might offer a valid solution. HE demands perfectly integrated hardware acceleration technology combined with algorithm developments if it aims to achieve practical usability. Our work establishes fundamental elements for scalability and efficiency growth which will enhance confidential analytics uses through supporting heightened HE awareness.
Copyright (c) 2025 Wajeeha Ahmed, Nimra Shah (Author) https://creativecommons.org/licenses/by/4.0
PARALLEL ALGORITHMS FOR SIMULATING FLUID FLOW DYNAMICS IN AEROSPACE ENGINEERING
The simulation of fluid flow dynamics plays a crucial role in aerospace engineering, influencing the design and optimization of components such as aircraft wings and propulsion systems. However, the computational cost of simulating complex fluid flow, especially in aerospace applications, presents significant challenges. This study investigates the use of parallel algorithms to accelerate computational fluid dynamics (CFD) simulations, focusing on improving efficiency, scalability, and accuracy. Parallel algorithms, including domain decomposition and multigrid methods, were implemented and evaluated on a high- performance computing platform. The results demonstrate substantial speedup, with parallel algorithms achieving speedup factors exceeding 6x compared to sequential solvers for large grid sizes.This work demonstrates how user-focused augmented reality (AR) systems present an influential opportunity toward bettering learning outcomes alongside educational environments. Our research reveals that putting adaptive learning elements together with user-friendly interfaces inside AR systems enhances participant engagement while leading to better knowledge memory and better task results. The ability of AR systems to tailor educational experiences by matching different learning requirements results in more individualized as well as efficient learning processes for students. This research shows that AR systems need to focus first on creating highly usable interfaces and quick responses because these attributes provide maximal impact. Higher student engagement produces better post-test scores because of its positive connections to increased interaction. Task completion times combined with raised user satisfaction levels demonstrate why adaptive AR tools create quick education opportunities which are enjoyable and yield significant learning benefits. The results demonstrate the vital requirement for education system development that captures user needs in order to transform traditional educational methods and validate the growing body of literature supporting AR applications in learning environments. Next-generation research should focus on evaluating extended effects of these systems across multiple educational environments among different age groups while integrating artificial intelligence technology to enhance AR educational environments. Well- designed user-friendly AR systems in educational settings will enable better learning possibilities that are interesting and efficient for diverse student demographics.
Copyright (c) 2025 Saad Ajmal, Omer Farooq (Author) https://creativecommons.org/licenses/by/4.0
DESIGNING USER-CENTRIC INTERACTIVE SYSTEMS FORAUGMENTED REALITY IN EDUCATION
The educational experience stands to benefit significantly through Augmented Reality (AR) which makes available immersive interactive educational environments for learners. The creation of customized AR systems for different learning needs among users proves to be a significant technical hurdle. The research explores AR system design for education through user-oriented methods which emphasizes developing flexible approaches for interfaces that support different students learning styles and cognitive capabilities. The research uses user input analysis and adaption learning methods to create improvements in educational performance and student engagement. Researchers conducted surveys with students and educators who lived in various educational settings to determine their usage patterns and experience difficulties with AR technology. The development of AR system prototypes took place while considering user profiles and executed multimedia integration paired with adaptive capabilities along with easy-to-use interfaces. Tests conducted in real classrooms demonstrated that user-focused prototype designs resulted in quicker tasks completion while simultaneously decreasing errors and producing superior post-test results. This research shows that the combination of adaptive learning components and responsive interfaces produces the best AR educational effectiveness by delivering impressive results for user engagement along with learning achievements.
Copyright (c) 2025 Obaid ur Rahman, Hira Yousaf (Author) https://creativecommons.org/licenses/by/4.0
DEEP NEURAL NETWORKS FOR FORECASTING ENVIRONMENTAL CHANGES: APPLICATIONS TO CLIMATE MODELING
This research focuses on portraying temperature and precipitation predictions by using deep neural networks (DNNs) in climate modeling. Real world climate systems now possess increasing complex structures which makes it challenging for traditional forecasting methods to track intricate patterns and relationships. Our study employed a CNN-RNN hybrid architecture to exploit the use of spatial and temporal data for this purpose. Assessment of the models involved a 5-year dataset which demonstrated superior accuracy levels for the DNN models when compared to conventional methods. When predicting temperatures the hybrid combination of CNN-RNN achieved an optimal performance through a Mean Absolute Error (MAE) result of 0.75°C and Root Mean Squared Error (RMSE) of 0.94°C while presenting the highest correlation value of 0.94. Result data from the sensitivity study demonstrated that temperature elements led to the greatest changes in model operational capacity. The newly developed forecasting model confirmed its superior capabilities through testing against standard numerical weather prediction models as well as statistical approaches resulting in better RMSE and reduced correlation coefficients. The research results demonstrate that DNNs can make substantial improvements to climate forecasting by delivering better accuracy than conventional forecasting models. More research will become essential to develop these models because they currently face challenges with data integration and interpretability problems. The presented work establishes a viable method that leads to better and more practical climate forecasting capabilities within environmental research domains.
Copyright (c) 2025 Noor Fatima, Usman Siddiqui (Author) https://creativecommons.org/licenses/by/4.0
DESIGN AND OPTIMIZATION OF ENERGY-EFFICIENT EDGE COMPUTING ARCHITECTURES FOR SCALABLE IOT-BASED APPLICATIONS
This research presents the design together with optimization strategies for building an energy-efficient edge computing framework capable of handling scalable IoT-based applications. Modern computer systems require greater energy efficiency combined with scalability because IoT devices grow rapidly. The presentation of real-time energy optimization strategies with adaptive resource management allows dynamic computing resource assignment to meet present needs effectively. The proposed framework achieves energy reduction of 33% over typical edge computing systems and resulted in substantial energy savings according to our tests. The method delivers 25% performance improvement for latency thus improving IoT application real-time responsiveness. At the same time throughput demonstrates 25% higher processing capacity to manage remote data quantities effectively. Beyond its excellent use of available resources the proposed framework optimizes accessibility by at least ninety percent of available computing resources. The proposed edge computing structure demonstrates enhanced performance capability for dynamic IoT conditions with concurrent delivery of energy-efficient operations and scalability solutions. This research establishes sustainable data processing strategies that achieve effective energy reduction thus providing an excellent foundation for future IoT applications.
Copyright (c) 2025 Aqsa Saood, Fatima Noor (Author) https://creativecommons.org/licenses/by/4.0
QUANTUM ALGORITHMS FOR OPTIMIZING LARGE-SCALE DATA PROCESSING TASKS IN CLASSICAL
The research examines how quantum algorithms can enhance large data processing capabilities of existing computer systems. The growing complexity of data and processing needs makes traditional systems insufficient in optimizing, sorting and searching operations. This work focuses its main effort on developing hybrid quantum-classical algorithms which utilize quantum parallelism capabilities while maintaining classical system compatibility. The research explores essential challenges linked to security flaws and scalability issues as well as data processing work execution problems. The testing of proposed quantum algorithms involved security, scalability and functionality assessment decisions as the fundamental evaluation criteria. Security testing under these enhanced protective measures reached complete success by removing major flaws from reentrancy breaches and integer overflow which led to 100% protection. The system demonstrated good functionality during network congestion tests involving moderate loads yet performance degradation occurred when congestion was severe so further optimizations will make it suitable for widespread usage. The functional testing results demonstrated the system accomplished 100% token transfer success rate together with 97.5% asset management success rate and 96% success rate for its decentralised exchange operations. General implementation requires fixing scalability and distributed exchange optimisation issues however current study results indicate quantum algorithms show strong potential for data processing optimization tasks. The research provides valuable insights about hybrid quantum-classical systems by presenting a road to safe, effective and scalable processing for big applications.
Copyright (c) 2025 Muzammil Ali, Ali Raza (Author) https://creativecommons.org/licenses/by/4.0
DEVELOPING SMART CONTRACT-BASED SOLUTIONS FOR SECURE AND TRANSPARENT FINANCIAL TRANSACTIONS IN DECENTRALIZED APPLICATIONS
Financial industry operations have been substantially reformed through the adoption of decentralized applications (dApps) as well as blockchain technology during recent years. This research aims to handle three main issues within decentralised finance (DeFi) by focusing on transaction functionality execution and system scalability and enhanced security features. We implemented secure measures for integer overflows and reentrancy avoidance through extensive testing which proved successful in eliminating reentrancy gaps and integer overflows. The scalability tests demonstrated functioning performance with network congestion but bigger congestion caused noticeable delays alongside elevated latency levels. Due to the nature of large transaction processing additional optimization measures need implementation. The results of functional testing showed that 100% of token transfers succeeded together with 97.5% success rate for asset management operations. The success rate of decentralized trade operations reached 96% according to results. Smart contract implementations succeed in protecting transactions yet need additional work to increase scalability and improve decentralized exchange functions. The research concludes that smart contract-based DeFi solutions show substantial promise yet demands continuous advancement to gain more widespread market adoption.
Copyright (c) 2024 Asnaf Ahmad, Hamza Mehmood (Author) https://creativecommons.org/licenses/by/4.0
REGRESSION-BASED MODELING AND PREDICTION OF USER ENGAGEMENT IN MOBILE APPLICATIONS: ANALYZING BEHAVIORAL PATTERNS, APP USAGE METRICS, AND PERSONALIZATION FACTORS
User engagement with mobile applications determines whether digital goods succeed and the revenue generated, user happiness, and retention. For this work, regression based modeling is done on behavioral patterns, app use data and customization elements in attempts to predict user engagement. The report tries to tackle a major problem for developers and companies, that is which is the decline in user engagement in mobile apps. Using sophisticated regression techniques, the project aims to find important factors of user engagement and to develop the prediction models that might lead the app improvement methods. The method consist of collecting data on the actual app use, feature engineering, and utilizing multivariate regression models to find relationships among user behavior and engagement metrics. Results show that session length, app use frequency and tailored content are all signs of user engagement. These results supply practical advice to app developers regarding how to boost user experience and retention. This research is added to the evolving corpus of research on user engagement by providing a data driven method for understanding and projecting user behavior in mobile applications.
Copyright (c) 2024 Naseer Ullah, Zainab Akhtar (Author) https://creativecommons.org/licenses/by/4.0
A STUDY OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNIQUES FOR TRAFFIC MANAGEMENT IN SCALABLE METAVERSE SYSTEMS
Explosive growth of Metaverse created new problems with virtual traffic management, now unheard of, which require new creative solutions to provide scalability, effectiveness and user happiness. This work investigates the use of machine learning (ML) and artificial intelligence (AI) approaches for traffic control in a scalable Metaverse system. The research addresses the virtual environment space where the complexity is growing, rendering the conventional methods of traffic management unusable. It is problem-based. The purpose of this research is to implement real time adaptive systems with the use of ML and AI to manage the dynamic, and diverse quality of traffic to be seen in the Metaverse. The literature analysis is done in depth and then follows the creation and use of AI/ML models for resource allocation, traffic prediction, congestion management. The results show the typical latency minimization rate, resource consumption maximization, and user experience improvement of these methods. Finally, the paper notes that further research is needed while advising that in order to keep up with the evolving demands of the Metaverse, innovation must continue. Along with providing more information to the stored corpus on the AI/ML applications in the virtual world, this research provides developers and policy makers with useful insights.
Copyright (c) 2024 Prof. Fluturim Saliu, Muhammad Bilal (Author) https://creativecommons.org/licenses/by/4.0
THE ROLE OF QUANTUM COMPUTING IN ADVANCING SCALABLE METAVERSE ARCHITECTURES: CHALLENGES AND OPPORTUNITIES
The metaverse technology has gained quite a lot of hype lately on the reports that the big businesses want to create Metaverse settings for various applications including the game, assets, the virtual meetings and so on. Metaverse based apps are critical in guaranteeing fast and secure connection as well as integrated high-end technologies such as; virtual reality (VR), augmented reality (AR) and mixed reality (MR). Current hardware and software constraints in the Metaverse have to be addressed right away to be applied today. Problems with security, privacy, connection and complexity of computing are the major causes that slow down the formation of Metaverse. The exceptionally fast capabilities of quantum technology allow it to produce efficient scalable computing outputs at fast speeds. In the article we analyze quantum implementation methods that will work within the Metaverse environment thoroughly. Our study features a case analysis about implementing performance improvements in Metaverse apps through the combination of hybrid quantum kernel-based processing with optimized statistical methods feeding outputs into traditional supervised vector machines. The analysis presented here establishes fundamental research on Metaverse applications grounded in quantum mechanics principles for future investigations.
Hybrid Quantum-Classical Machine Learning
Copyright (c) 2024 Aiman Shabbir, Nida Hafeez (Author) https://creativecommons.org/licenses/by/4.0
EXPLORING THE POTENTIAL OF NEUROMORPHIC COMPUTING TO ENHANCE EFFICIENCY AND PERFORMANCE IN AI WORKLOADS
The increasing processing needs of artificial intelligence (AI) has inspired utilizing the architecture of the human brain as a viable way to address this challenge and helped neuromorphic computing become a reality. In contrast to von Neumann architecture-based computer systems, neuromorphic systems offer event driven parallel processing which can make functionally compute thousands more times in parallel and with much lower energy consumption compared to today's technologies. The scope of this work is to study how neuromorphic computing may enable the performance and effectiveness of AI tasks that rely on demanding resources, such as in image recognition, natural language processing and real-time data processing. On the basis of a proper examination of arrangements of the present in neuromorphic equipment, for example, IBM's True North, Brain Chip’s Akida and Intel's Loihi, it tends to be contended that the framework is thoroughly greater than when contrasted and customary GPUs and CPUs as far as power utilization and fundamentally increasingly proficient. The integration of spiking neural networks (SNNs) into neuromorphic systems has been demonstrated to improve real time job performance and offer important advantages for future edge AI applications where energy efficiency and delay are essential. In addition, to facilitate data interpretation and decision making in an edge database system, the work studies the possibility for synergy between neuromorphic computing and a hybrid AI model that unifies deep learning with symbolic reasoning. For instance, the research showed that neuromorphic computing and hybrid AI models have a range of practical applications in smart cities, medical diagnostics, self driving vehicles and smart agriculture among others. Although neuromorphic computing offers various advantages, there are some things to be worked out, like scaling, interfacing with existing AI pipelines, and developing a standardized programming framework. In the near future, efforts for future research should focus on establishing standardized framework s and evaluation measures to close the gap between neuromorphic systems and conventional AI architectures. Finally, neuromorphic computing and hybrid AI model allow us to take on these computational issues in modern AI workloads in a revolutionary way. With continued advancement of these technologies, the possibility of smarter, more responsive, energy efficient systems are possible: systems that can scale to support the needs of real-time applications in a number of fields.
Copyright (c) 2024 Mohammad Arafath Uddin Shariff, Sara Khan (Author) https://creativecommons.org/licenses/by/4.0
COMPREHENSIVE EXAMINATION OF CYBERSECURITY THREATS AND VULNERABILITIES IN 5G NETWORKS: EXPLORING EMERGING RISKS, ADVANCED ATTACK VECTORS, AND CUTTING-EDGE MITIGATION STRATEGIES FOR FUTURE-READY SECURITY ARCHITECTURES
The telecoms industry expects 5G network systems to change everything through ultra-fast connections with tiny delays and by connecting IoT items directly to edge computing platforms. The system's protection depends on recognizing and fixing new security risks that come with 5G network advancements. Our safety method focuses on creating secure next-generation cell phone networks through examination of today's security risks and procedures. 5G network security faces its greatest challenge from the weak spots created by network slicing and edge computing. The danger of security flaws increases when network segments are mismanaged because hackers can exploit this weakness through DoS probes or by setting up MitM attacks. Edge computing security weak points would develop since this technology spreads its operations across many locations. Our research shows how enhancing 5G network security can work through multiple security updates including split network deployments alongside AI detection systems and enhanced protection strategies. Our security measures combine to decrease possible threats and protect the network when put in place. Our solution combines advanced technology with new security elements by using modern learning models and quantum cryptography. The secure technology of quantum key distribution enables excellent encryption defense but machine learning stays ahead of new threats through its updates. The plan delivers trustworthy and adaptable security methods that protect 5G systems while staying operational. Our methods ensure 5G systems stay safe and operational even when cyberthreats change by relying on these technologies.
Copyright (c) 2024 Hamayoun Rasheed, Aftab Alam (Author) https://creativecommons.org/licenses/by/4.0
ENHANCING WEATHER PREDICTION ACCURACY USING HYBRID MACHINE LEARNING TECHNIQUES: A COMPREHENSIVE APPROACH
Modern life depends on precise weather forecasting because global warming intensifies which affects how people live while handling energy needs and maintaining agriculture and protecting the environment. This study proposes a temperature data prediction system which integrates convolutional neural networks (CNNs) with long short-term memory networks (LSTMs). The CNN-LSTM hybrid model connects two network types to process time sequences and detect spatial information alike. The hybrid CNN-LSTM combines temporal and spatial processing to generate weather forecasts which are dependable and precise for meteorological data analysis. Researchers confirm that adding CNN-LSTM technology increases prediction accuracy especially for intricate tasks such as long-range weather forecasting. The combination of CNN and LSTM models brings strong performance in weather forecasting due to its success handling large and diverse meteorological data types. Time-dependent data management through LSTMs produces highly accurate and stable predictions while spatial feature extraction relies on CNNs. During processing of complex meteorological information, the model demonstrates excellent performance by handling problems related to data dimensions and missing values. MAE functions as the chosen loss function in this model. The testing results prove the potential of this climatology prediction model through its ability to produce curves that match test data measurement results. This research establishes essential foundations for future weather prediction systems within global climate change scenarios and provides valuable findings that benefit agriculture as well as energy management and urban development practices.
Copyright (c) 2024 Muhammad Bilal, Zainab Akhtar (Author) https://creativecommons.org/licenses/by/4.0
ADVANCED TECHNIQUES IN DETECTING AI-GENERATED SYNTHETIC MEDIA TO CURB FAKE NEWS PROPAGATION
The trustworthiness of our information networks suffers due to quick AI-generating fakes that give rise to wrong information online. This work studies new ways to spot fake media through forensic methods and advanced AI plus blockchain technology. Our tests use the Face Forensics++ dataset that includes 1,000 original videos plus 1,000 deepfakes and proves the effectiveness of these techniques. Our results indicate that CNNs excel at finding irregular lighting patterns and artificial facial features with a 95.2% success rate. With an accuracy of 92.3% RNNs recognize video structures through time and detect voice irregularities. Despite the 88.6% accurate noise pattern analysis and error level analysis (ELA) methods with human interpretable insights they require better scalability and human assistance to succeed. Blockchain technology produces an untouched record of original material with 96.8% accuracy making it the best verification method. However, its acceptance and implementation need substantial infrastructure. Research finds that blockchain verification works best in trust yet needs broad industry support whereas other tests have efficient results yet limited interpretation or need extra resources and large sample sets. Sophisticated detection systems prove their ability to protect against synthetic media and false news reports. Despite existing problems, detection systems require further development including quickly spotting threats and protecting against AI and attack methods. Future study should focus on protecting detection systems against attackers and making them better at detecting information across different channels while remaining easy to understand. By reviewing synthetic media detection methods this study gives useful knowledge to both experts and industry leaders who fight misinformation
Copyright (c) 2024 Humayun Rasheed, Aiman Shabbir (Author) https://creativecommons.org/licenses/by/4.0
BLOCKCHAIN TECHNOLOGY BEYOND CRYPTOCURRENCY: APPLICATIONS IN HEALTHCARE AND SUPPLY CHAIN MANAGEMENT
several industrial sectors including healthcare supply chain operations. The private sector works differently because blockchain technology brings better security as well as elevated accountability and enhanced transparency. The study investigates blockchain implementation in supply chains together with healthcare operations by showing its ability to track goods precisely while reducing theft and accelerating data movement. Independent research trials prove blockchain technology enhances key operational features through better medical data management and insurance claim processing and pharmaceutical supply chain monitoring. Our team observed major performance value increases through blockchain adoption which lowered patient record retrieval times by 40% in healthcare settings. The implementation of blockchain technology led to a service delivery enhancement together with a 50% reduction of administrative expenses which shortened insurance claim processing times. Blockchains reduce fraudulent pharmaceutical products by 30% thus helping the pharmaceutical industry protect both patient safety and product integrity. Through blockchain-enabled supply chain traceability operators detect and react swiftly to tainted products which decreases health hazards affecting the public. These important additions prove vital for industries focused on security because of their need for openness. The research confirms blockchain's ability to improve supply chain management through reduction of risks and increased transparency and better operational integrity and security for healthcare systems. Supply chain managers alongside healthcare providers can cut expenses while delivering faster services to patients through improved security and efficiency of their systems which results in enhanced customer protection. Supply chain operations together with the healthcare sector will operate more efficiently in a secure fiscal manner.
Copyright (c) 2024 Nida Hafeez (Author) https://creativecommons.org/licenses/by/4.0
A COMPREHENSIVE APPROACH TO PREDICTING FINANCIAL MARKET TRENDS USING ADVANCED MACHINE LEARNING ALGORITHMS
Financial market movements show extreme complexity and variability making predictions about them difficult to achieve. A several machine learning algorithm test is conducted to evaluate their potential in stock market change forecasting. The analysis used data from 2010 to 2020 to test six machine learning methods including decision trees, random forests, support vector machines (SVMs), XGBoost, neural networks and long short-term memory (LSTM) networks. The models were evaluated using accuracy alongside precision, recall and F1 score and ROC-AUC measurements. Based on 79.4% F1 score, 78.3% recall, 80.5% precision alongside 82.4% accuracy the results indicate the superiority of LSTM over other models. The performance metrics show that SVMs achieved 75.1% accuracy yet random forests recorded 72.8% accuracy whereas XGBoost secured 78.6% accuracy. The accuracy of decision trees measured at 65.4% proved to be the lowest among all models. LSTM's predictions in financial markets show strong results which proves deep learning models can optimize trading and financial prediction accuracy.
Copyright (c) 2024 Muhammad Danial Ahmad Qureshi (Author) https://creativecommons.org/licenses/by/4.0