Computing and Applications Reviews (CAR) is a multidisciplinary journal dedicated to the comprehensive review of recent developments in computing technologies and their applications across various industries. The journal aims to provide insightful analyses of cutting-edge research, emerging trends, and innovative solutions that bridge the gap between theoretical advancements and practical implementations in the field of computing. Key focus areas of the journal include, but are not limited to: Computing Technologies: Advancements in hardware, software, algorithms, and computational theories. Artificial Intelligence and Machine Learning: Reviews on the latest AI models, applications, and breakthroughs. Data Science and Big Data: Insights into data processing, analytics, and visualization techniques. Cloud Computing and Distributed Systems: Research on cloud infrastructure, services, and decentralized computing. Cybersecurity: Evaluation of security frameworks, cryptography, and privacy protection mechanisms. Human-Computer Interaction: Exploration of new interfaces, usability studies, and user experience design. Applications in Various Sectors: Reviews on computing applications in industries such as healthcare, finance, education, manufacturing, and more. The journal encourages submissions that provide a critical synthesis of current research, identify challenges, and propose future directions in computing and its applications. It serves as a resource for researchers, professionals, and educators who seek a deeper understanding of the evolving landscape of computing technologies.
ELEVATE EDUCATIONAL & RESEARCH INSTITUTE (SMC-PRIVATE) LIMITED
ENHANCING BUG TRIAGE ACCURACY IN LARGE-SCALE SOFTWARE PROJECTS VIA DEVELOPER ACTIVITY AND ISSUE REPORT ANALYSIS
Bug triage is a critical activity in large software projects because the timely and accurate assignment of issue reports to suitable developers directly affects software maintenance efficiency, defect resolution time, and overall project quality. However, manual bug triage becomes increasingly difficult as projects grow in size, involve distributed development teams, and generate large volumes of issue reports. This paper investigates an intelligent bug triage approach that combines developer activity data and issue report analysis to improve assignment accuracy in large-scale software repositories. The proposed method analyzes textual information from issue reports, including titles, descriptions, severity labels, and component details, together with developer-related features such as prior bug-fixing history, recent commits, expertise areas, response frequency, and workload distribution. By integrating these two sources of information, the model identifies the most suitable developer for each incoming bug report. The results indicate that the combined feature-based approach performs better than traditional issue-text-only methods, achieving higher triage accuracy, improved top-k recommendation performance, and reduced assignment errors. The findings suggest that developer activity patterns provide valuable contextual signals for bug assignment, especially in projects with frequent issue inflow and multiple active contributors. Overall, this study demonstrates that combining issue report analysis with developer activity profiling can support more reliable, scalable, and efficient bug triage in large software projects.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
REDUCING SOFTWARE REQUIREMENT AMBIGUITY THROUGH NLP-BASED REQUIREMENT QUALITY ASSESSMENT
Software requirement ambiguity remains one of the major causes of misunderstanding, rework, delayed development, and project failure in software engineering. Ambiguous requirements often lead to inconsistent interpretation among stakeholders, developers, testers, and project managers, which directly affects software quality and delivery performance. This paper presents an NLP-based requirement quality assessment approach for identifying and reducing ambiguity in software requirement specifications. The proposed approach evaluates requirement statements by analyzing linguistic features such as vague terms, incomplete conditions, passive constructions, uncertain quantifiers, weak modal verbs, and semantic inconsistency. Natural Language Processing techniques are applied to classify requirements into different quality levels and highlight statements that require improvement. The results indicate that NLP-based assessment improves ambiguity detection accuracy, supports early requirement validation, and reduces manual review effort. The findings show that automated linguistic analysis can help requirement engineers detect unclear statements before the design and implementation phases. Overall, the study demonstrates that integrating NLP into requirement engineering practices can enhance requirement clarity, improve stakeholder agreement, and support the development of more reliable software systems.
PREDICTIVE FAILURE DETECTION FOR FAULT TOLERANCE IN DISTRIBUTED OPERATING SYSTEMS
Distributed operating systems are widely used to support scalable, reliable, and high-performance computing environments; however, their dependence on multiple interconnected nodes makes them vulnerable to failures caused by hardware faults, network instability, workload imbalance, and software-level errors. Traditional fault-tolerance mechanisms usually respond after a failure has occurred, which may increase downtime, reduce service availability, and affect system performance. This paper presents a predictive failure detection approach for enhancing fault tolerance in distributed operating systems. The proposed approach monitors system-level indicators such as CPU usage, memory consumption, disk activity, network latency, error logs, heartbeat signals, and workload variation to identify early signs of node or process failure. By applying predictive analysis, the system can detect abnormal behavior before complete failure occurs and initiate preventive actions such as process migration, checkpoint recovery, task replication, resource reallocation, or node isolation. The results demonstrate that predictive failure detection improves system availability, reduces mean time to recovery, lowers failure-related service interruptions, and maintains stable throughput under fault-prone conditions. The findings suggest that integrating prediction-based monitoring with existing fault-tolerance strategies can significantly improve the resilience of distributed operating systems. This study highlights the importance of proactive failure management in modern distributed environments, particularly for cloud platforms, data centers, edge computing systems, and large-scale enterprise applications where continuous service delivery is essential.
ENERGY-EFFICIENT MOBILE OPERATING SYSTEMS THROUGH INTELLIGENT BACKGROUND PROCESS MANAGEMENT
Mobile operating systems continuously manage multiple background processes such as synchronization services, notifications, location tracking, application updates, and network communication. Although these processes improve user experience, they also consume significant battery power, memory, and CPU resources when not properly controlled. This paper examines how intelligent background process management can improve energy efficiency in mobile operating systems. The proposed approach focuses on monitoring background application behavior, identifying unnecessary resource usage, and applying adaptive scheduling, process limitation, and priority-based execution. By using intelligent decision-making mechanisms, the system can reduce excessive wake-ups, limit non-critical background tasks, and allocate resources according to application importance and user activity patterns. The study highlights that intelligent background management can reduce power consumption, improve battery lifetime, and maintain acceptable application responsiveness. The results suggest that adaptive control of background processes is an effective strategy for enhancing mobile device performance without negatively affecting user experience. This research contributes to the development of smarter mobile operating systems that balance energy saving, usability, and system stability.
ADAPTIVE CPU SCHEDULING FOR MITIGATING RESOURCE STARVATION IN MULTI-TENANT OPERATING SYSTEMS
Multi-tenant operating systems are widely employed in cloud computing, virtualization platforms, and shared computing infrastructures to maximize hardware utilization while supporting multiple users and applications concurrently. However, resource starvation remains a significant challenge when CPU-intensive workloads monopolize processor time, leading to degraded performance and unfair allocation for lower-priority or latency-sensitive tasks. This study proposes an adaptive CPU scheduling approach designed to minimize resource starvation while maintaining system throughput and responsiveness. The proposed scheduler dynamically adjusts process priorities and CPU time allocation based on workload characteristics, execution history, waiting time, and resource utilization metrics. Extensive simulation experiments were conducted using heterogeneous workloads representing compute-intensive, interactive, and background processes. Performance evaluation considered average waiting time, turnaround time, CPU utilization, response time, fairness index, and starvation occurrence rate. Experimental results demonstrate that the adaptive scheduling strategy significantly reduces starvation incidents while improving fairness among tenants without introducing excessive scheduling overhead. Compared with conventional scheduling algorithms, the proposed method achieves lower response latency, more balanced CPU distribution, and improved overall system efficiency under varying workload conditions. The findings suggest that adaptive CPU scheduling offers a practical solution for enhancing quality of service and resource fairness in modern multi-tenant operating systems deployed in cloud and virtualized environments.
THE ROLE OF NATURAL LANGUAGE PROCESSING IN AUTOMATING CUSTOMER SERVICE AND ENHANCING USER EXPERIENCE IN E-COMMERCE PLATFORMS
The rapid growth of e-commerce has intensified the need for intelligent, scalable, and customer-centric communication systems. This study explores the application of Natural Language Processing (NLP) and deep learning techniques to enhance customer service, sentiment analysis, and personalized recommendations in e-commerce environments. A mixed-methods experimental approach was employed, integrating quantitative performance evaluation with qualitative analysis of customer interactions. Large-scale textual datasets, including customer queries, reviews, and transaction histories, were processed using transformer-based NLP models to assess accuracy, precision, recall, and F1-score across multiple tasks. The results demonstrate that NLP-driven chatbots and recommendation systems achieve consistently high performance, significantly reducing response time while improving contextual relevance and customer satisfaction. Sentiment analysis models effectively captured customer emotions and opinions, enabling proactive service improvements and data-driven decision-making. Visual and statistical analyses further confirmed system scalability and robustness under increasing interaction volumes. Overall, the findings validate the effectiveness of NLP and deep learning in delivering intelligent, adaptive, and personalized e-commerce experiences. The study contributes empirical evidence supporting the integration of advanced NLP solutions to improve operational efficiency, customer engagement, and long-term business value in digital commerce platforms.
Copyright (c) 2025 Farhan Malik, Ayesha Noman (Author) https://creativecommons.org/licenses/by/4.0
ARTIFICIAL INTELLIGENCE FOR AUTONOMOUS VEHICLES: ANALYZING SAFETY, EFFICIENCY, AND ETHICAL IMPLICATIONS IN URBAN TRANSPORTATION SYSTEMS
The rapid advancement of Artificial Intelligence has accelerated the development of autonomous vehicles, positioning them as a key component of future urban transportation systems. This study investigates the impact of AI-driven perception, decision-making, and control mechanisms on the safety, efficiency, and reliability of autonomous vehicles operating in complex urban environments. A mixed-methods experimental approach was employed, combining quantitative simulation-based evaluations with qualitative scenario analysis to assess system performance under diverse traffic, environmental, and operational conditions. The results indicate substantial reductions in collision rates, faster response times, improved traffic flow, and enhanced energy efficiency when compared to conventional driving models. Multi-sensor fusion and learning-based algorithms demonstrated strong robustness and adaptability, maintaining stable performance even in high-density and adverse scenarios. The findings further highlight the role of AI in enabling real-time, data-driven decisions that surpass human consistency while raising important ethical and societal considerations. Collectively, the results confirm that AI-enabled autonomous vehicles offer a viable pathway toward safer, more efficient, and sustainable urban mobility, provided that technical advancements are accompanied by ethical governance and supportive infrastructure planning.
Copyright (c) 2025 Bilal Hussain, Sarah Mahmood (Author) https://creativecommons.org/licenses/by/4.0
INVESTIGATING THE CHALLENGES OF SECURING INTERNET OF THINGS (IOT) DEVICES IN SMART HOMES AND CITIES AGAINST CYBER THREATS
The increasing integration of Internet of Things technologies into smart homes and smart cities has transformed urban living and infrastructure management, while simultaneously introducing critical cybersecurity challenges. This study investigates these challenges using a mixed-methods experimental methodology that combines qualitative analysis with quantitative evaluation of IoT security performance. An experimental testbed was developed to simulate smart home and smart city environments, enabling the assessment of key performance and security metrics under multiple cyber-attack scenarios. The results reveal that resource-constrained and heterogeneous IoT devices are highly susceptible to attacks that significantly impact latency, energy consumption, packet loss, and overall system reliability. Quantitative findings demonstrate that layered security mechanisms, including lightweight encryption, adaptive intrusion detection, and gateway-level defenses, substantially improve attack detection accuracy and system robustness with manageable overhead. Qualitative analysis further highlights persistent issues related to interoperability, governance, and privacy that exacerbate cybersecurity risks at scale. Collectively, the results indicate that no single security solution is sufficient; instead, an integrated, multi-layered, and adaptive cybersecurity approach is essential for protecting smart home and smart city infrastructures. This study provides empirical evidence and strategic insights to inform the design of resilient IoT security frameworks capable of addressing both technical and organizational challenges in increasingly interconnected environments.
Copyright (c) 2025 Nida Hafeez, Farah Nadeem (Author) https://creativecommons.org/licenses/by/4.0
THE INTEGRATION OF BLOCKCHAIN TECHNOLOGY IN HEALTHCARE INFORMATION SYSTEMS: ENSURING DATA PRIVACY AND SECURITY
The increasing digitization of healthcare has intensified concerns regarding data privacy, security, integrity, and interoperability within traditional centralized information systems. This study investigates the effectiveness of integrating blockchain technology into healthcare information systems through a mixed-methods experimental approach. A permissioned blockchain-based prototype was designed and evaluated against conventional healthcare data management models using quantitative performance metrics and qualitative validation. The results demonstrate that blockchain integration significantly enhances data integrity and security through decentralized consensus and cryptographic mechanisms, while maintaining high levels of system availability and fault tolerance. Experimental findings revealed consistently high integrity and privacy indices, strong resistance to unauthorized access, and minimal performance degradation during node failure simulations. Transaction throughput remained scalable under increasing workloads, and latency levels were observed to be suitable for real-time healthcare operations. Visual and tabular analyses further confirmed that blockchain-based systems achieve a balanced trade-off between security and efficiency. The study concludes that blockchain technology offers a robust, transparent, and patient-centric framework for managing healthcare data, with broad implications for electronic health records, clinical trials, and healthcare supply chains. These findings support the feasibility of blockchain adoption as a foundational technology for secure and interoperable healthcare information systems.
Copyright (c) 2025 Mehak Shahzad, Sajjad Ahmad (Author) https://creativecommons.org/licenses/by/4.0
EXPLORING THE ROLE OF MACHINE LEARNING IN PREDICTIVE ANALYTICS FOR HEALTHCARE DATA: ENHANCING PATIENT OUTCOMES AND RESOURCE ALLOCATION
The growing complexity and cost of healthcare delivery necessitate data-driven solutions capable of improving patient outcomes while optimizing limited resources. This study investigates the application of machine learning–based predictive analytics in healthcare, with a focus on patient risk prediction, hospital readmission forecasting, length-of-stay estimation, and resource allocation optimization. Using a mixed-methods experimental design, quantitative predictive models were developed and evaluated on heterogeneous healthcare datasets, while qualitative analysis contextualized implementation challenges, ethical considerations, and operational impacts. The results demonstrate strong predictive performance across multiple patient cohorts, enabling accurate identification of high-risk individuals and improved anticipation of healthcare demand. Graphical and tabulated analyses reveal measurable improvements in patient flow management, reduced readmission risk, and enhanced resource utilization efficiency. The findings also highlight the potential of predictive analytics to mitigate disparities between urban and rural healthcare systems by supporting proactive planning in resource-limited settings. Overall, the study confirms that machine learning–driven predictive analytics can significantly enhance clinical decision-making and operational efficiency when embedded within ethically sound and context-aware frameworks. These insights contribute to the growing evidence base supporting artificial intelligence as a key enabler of sustainable, equitable, and patient-centered healthcare systems.
Copyright (c) 2025 Muhammad Danial Ahmad Qureshi, Zafar Aleem Suchal (Author) https://creativecommons.org/licenses/by/4.0
AI-POWERED ADAPTIVE LEARNING SYSTEMS: PERSONALIZING EDUCATION THROUGH REAL-TIME DATA ANALYTICS AND STUDENT BEHAVIOR MODELING
This study explores the effectiveness of AI-powered adaptive learning systems in enhancing student performance, engagement, and the overall learning experience. Through an experimental approach, the research evaluates the academic performance, engagement levels, and perceptions of both students and teachers in control and adaptive learning groups. The post-test score data showed an 18% improvement by students in the adaptive group while the control group only improved by 3% which indicates the adaptive learning system brought major academic benefits. Students in adaptive learning classes spent 45 minutes per session while engaging with system content four times per week which was greater than the control group students who dedicated 30 minutes per session with two sessions per week. The instructional staff from the adaptive learning group rated the system higher than those from the control group while system comments mentioned both its effectiveness and usefulness. Student participation increased primarily through the combination of personalized content feedback and relevant learning materials according to research findings. The cost-benefit evaluation demonstrated that performance enhancements of the adaptive learning system proved its value despite expensive initial installation costs and ongoing maintenance expenses. Empowered by artificial intelligence adaptive learning demonstrates potential to change educational practices through customized learning approaches which lead to improved academic outcomes combined with student engagement and generate valuable insights to support system advancement and scaling across diverse educational settings.
Copyright (c) 2025 Naseer Ullah , Shoaib Akram, Amna Hanif (Author) https://creativecommons.org/licenses/by/4.0
GAMIFICATION IN EDUCATION: ENHANCING STUDENT ENGAGEMENT AND LEARNING OUTCOMES THROUGH INTERACTIVE GAME-BASED LEARNING PLATFORMS
This study investigates the impact of gamification on student engagement, motivation, and academic performance through interactive game-based learning platforms. By utilizing a mixed-methods approach, including quantitative performance assessments and qualitative surveys, the research explores the effectiveness of gamified learning environments in higher education. Post-gamification student grades experienced substantial improvement with 15–20% average grade elevation as the evidence demonstrates. Files from student surveys document boosted student interest and dedication to learning following gamification implementation which led to improved engagement along with enhanced motivation. Research results showed that students who received gamified instruction scored better than those in regular non-gamified programs thus establishing gamification as a positive factor in academic outcomes. The students enjoyed how gamification enhanced their learning through prizes and challenges because they acquired better knowledge while having improved general school experiences. The research demonstrated that custom gamification methods produce better academic performance results only when students show high engagement with the learning material. Such findings show how gamification enhances student learning depth while motivating students which leads to improved academic outcomes thus expanding existing research about gamification in education. The research shows gamification implemented skillfully functions as a helpful teaching tool to enhance active learning while boosting student academic performance.
Copyright (c) 2025 Awais Ur Rahman , Imran Yousaf (Author) https://creativecommons.org/licenses/by/4.0
AI FOR REAL-TIME FRAUD DETECTION IN FINANCIAL TRANSACTIONS: LEVERAGING MACHINE LEARNING ALGORITHMS FOR EARLY DETECTION OF ANOMALIES
This study presents a machine learning-based framework for real-time fraud detection in financial transactions, aiming to enhance the security and efficiency of financial institutions. The proposed framework integrates supervised and unsupervised machine learning models, including decision trees, support vector machines (SVM), k-means clustering, and autoencoders, to identify fraudulent activities by detecting anomalies in transaction data. The results show that the SVM model achieved the highest detection accuracy (92.1%) with a low false positive rate (3.5%), demonstrating its effectiveness in detecting known fraud patterns. In addition, unsupervised techniques, such as autoencoders and k-means clustering, enabled the detection of previously unseen fraud patterns, further improving the framework's ability to adapt to new fraud tactics. The framework performed real-time fraud detection with mitigating times of 95.6 ms while maintaining stable throughput during increasing transaction volumes which exceeded traditional fraud detection methods. The use of ensemble techniques specifically XGBoost helped improve model accuracy and maintained low false positive rates. The research emphasizes the requirement to optimize machine learning models that should produce fewer false positives to preserve valid transactions. Applications included in the framework consist of interpretability tools that enhance openness and trust in decision-making procedures. Machine learning demonstrates its power to track the dynamic nature of financial fraud through an effective system which detects fraud in real time.
Copyright (c) 2025 Syed Ibrahim , Tariq Hussain, Mehwish Anwar (Author) https://creativecommons.org/licenses/by/4.0
ETHICAL HACKING IN THE INTERNET OF THINGS (IOT): IDENTIFYING VULNERABILITIES AND ENHANCING SECURITY IN SMART HOME DEVICES
This study investigates the security vulnerabilities in Internet of Things (IoT) devices within smart homes, focusing on common issues such as weak encryption, insecure communication protocols, and inadequate authentication mechanisms. Through a comprehensive penetration testing approach, we identified significant security weaknesses in several IoT devices, including smart voice assistants, security cameras, and smart locks. Our findings revealed that smart voice assistants were particularly vulnerable, exhibiting the highest number of vulnerabilities across multiple categories, including weak encryption and insecure communication channels. Reports revealed high susceptibility rates because numerous devices were susceptible to remote exploitation. The professional job of ethical hackers during penetration tests faces three essential difficulties stemming from complex device architecture alongside privacy concerns which arise from manufacturers' poor execution of standardized security standards. Our findings demonstrate why IoT devices should upgrade their endpoint encryption system while needing periodic firmware maintenance including dual authentication systems. Research shows that users' privacy demands effective security measures within IoT devices for stopping both vulnerability exploits and privacy intrusions. The research produces insights on IoT security needs along with penetration testing methods designed to handle security weaknesses.
Copyright (c) 2025 Shakil Ahmad , Rehan Qureshi, Samina Gul (Author) https://creativecommons.org/licenses/by/4.0
EDGE COMPUTING AND CLOUD SYNERGY: OPTIMIZING LATENCY AND BANDWIDTH FOR IOT APPLICATIONS THROUGH DISTRIBUTED CLOUD-EDGE ARCHITECTURES
The rapid expansion of the Internet of Things (IoT) has introduced significant challenges related to latency, bandwidth, and system reliability in cloud-based infrastructures. This study investigates the potential of integrating edge computing with cloud architectures to optimize these critical parameters for IoT applications. Through performance testing, qualitative expert interviews, and case study analysis, we examine the impact of edge-cloud synergy on latency reduction, bandwidth optimization, throughput improvement, and system stability. Our data verifies that edge computing reduces latency between 66% and 73% specifically for real-time applications which include autonomous cars and smart healthcare systems. A data processing shift to edge devices leads to the utilization of 75% of bandwidth which reduces cloud system demands. Through edge-field and cloud combinations processors achieve an enhanced throughput of 66% to 80%. The reliability of systems experiences improvement due to decreased failure rates which surpass conventional cloud-only systems by up to 60%. Although these advantages exist security problems along with resource challenges continue to emerge due to the necessity for secure data transfers and effective load balancing according to experts. The research supplies valuable knowledge about future IoT system deployments that scale while being dependable and high-performing and demonstrates the benefits of edge-cloud integration for addressing cloud infrastructure limitations.
Copyright (c) 2025 Computing and Applications reviews https://creativecommons.org/licenses/by/4.0
QUANTUM-RESISTANT CRYPTOGRAPHY: DEVELOPING ENCRYPTION ALGORITHMS TO SAFEGUARD DATA AGAINST FUTURE QUANTUM COMPUTING THREATS
This study investigates the performance and security of quantum-resistant cryptographic algorithms, assessing their potential to protect data from quantum computing threats. The research evaluates various cryptographic techniques, including lattice-based, code-based, hash-based, and isogeny-based systems, comparing them in terms of encryption and decryption speeds, memory consumption, computational overhead, and resistance to quantum attacks.Lattice-based algorithms require increased amounts of memory and computational resources yet maintain low simulated attack success rates which makes them optimal for quantum resistance. Research established hash-based cryptosystems operated faster but scientists proved them to be vulnerable to quantum attacks. Scientific experts demonstrated hybrid cryptographic systems based on conventional and quantum-resistant algorithms as a viable security-speed compromise solution. Lattice-based systems maintain the highest level of scalability for extensive systems according to scalability testing results yet multivariate poisson systems show inadequate scalability features. Technical experts confirmed that quantum-resistant cryptographic systems function in reality but organizations require solutions to overcome operational and resource expenditure hurdles. This research creates post-quantum cryptography systems that prove safe and effective through the presentation of algorithm optimization insights and their practical applications framework. The study demonstrates why we need continued development and research to maintain data security because quantum computing will transform in the coming quantum era.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
ZERO TRUST SECURITY MODEL: ENHANCING NETWORK SECURITY BY ELIMINATING IMPLICIT TRUST AND IMPLEMENTING CONTINUOUS AUTHENTICATION
This study investigates the implementation of the Zero Trust Security Model (ZTSM) to enhance network security by eliminating implicit trust and introducing continuous authentication. The proposed model was evaluated against traditional perimeter-based security systems across a variety of cyberattack scenarios, including insider threats, lateral movement, and external breaches. The results demonstrate that the Zero Trust framework outperforms traditional models in key security metrics such as detection accuracy, false positive rates, and mitigation times. Specifically, the Zero Trust model achieved a detection accuracy of 98.5% and a false positive rate of 3.1%, significantly outperforming traditional systems. Volumetric attacks required 95.6 milliseconds for the normal models to handle them even though basic operational times were longer. The Zero Trust architecture demonstrated endless high throughput performance throughout attacks since it maintained a transaction rate of 550 seconds per second. The combination of new segmentation technology and dynamic identification methods conducted continuous monitoring to stop unauthorized access points from spreading prior to security risks materializing. The established framework displayed substantial scalability since it processed high transaction volumes without compromising performance through research-based findings. Present-time network security challenges became simpler to resolve because the Zero Trust model rapidly identified complex threats and reduced their damage to critical systems during emergencies. Security progress emerges from Zero Trust deployments which also reduce false alarms and maintain constant real-time network operation speed according to research evidence
VIRTUAL REALITY AND AUGMENTED REALITY IN EDUCATION: A NEW PARADIGM FOR IMMERSIVE LEARNING EXPERIENCES AND SKILL DEVELOPMENT
This study investigates the impact of Virtual Reality (VR) and Augmented Reality (AR) technologies on immersive learning experiences and skill development in educational settings. The research aimed to assess the effectiveness of these technologies in enhancing student engagement, knowledge retention, and the ability to apply learned skills in real-world contexts. Using a mixed-methods approach, data were collected through surveys, interviews, and case studies involving students and educators who have used VR and AR tools. The results demonstrated that VR and AR significantly outperformed traditional learning methods in terms of student engagement and retention. Specifically, VR simulations led to the highest levels of engagement, with 68% of students reporting high levels of involvement, and VR learners retained 79% of knowledge after one month. AR tools also showed promising results, with 63% of students reporting high engagement and 74% retention after one month. A considerable 72% of students using both VR and AR demonstrated excellent skill application in practical scenarios. The tools generated positive outcomes according to teacher reports but instructors faced integration and technical problems and training limitations which impeded implementation. The study demonstrates how VR and AR tools have great potential to transform education systems however these advances require attention to infrastructure needs and teacher training requirements. This study demonstrates that right academic support makes Virtual Reality and Augmented Reality effective in boosting educational achievements while fostering skill education which will produce better educational environments for the future.
ENHANCING SDN SECURITY: A COMPREHENSIVE FRAMEWORK FOR MITIGATING DDOS ATTACKS IN SOFTWARE-DEFINED NETWORKS
This study presents a comprehensive framework for mitigating Distributed Denial of Service (DDoS) attacks within Software-Defined Networks (SDN), focusing on enhancing security and network performance. The framework integrates flow-based anomaly detection, machine learning models, and decentralized traffic routing to dynamically identify and mitigate DDoS attacks in real-time. The results indicate that the proposed framework achieved high detection accuracy for volumetric and protocol attacks (98.7% and 95.4%, respectively), while application-layer attacks, though more challenging, were detected with an accuracy of 93.9%. When applied to volumetric DDoS attacks the framework delivered fewer attack response times at 95.6 ms compared to traditional controls such as rate limitation and blacklisting that produced longer response periods. The system's operation maintained low latency coupled with high throughput to ensure network stability under intense attacks. Machine learning models specifically neural networks delivered accuracy to the framework by means of their 2.1% low false positive rate. The proposed architectural solution proved that it could expand its capabilities without overburdening the SDN controller resources. The system distributed DDoS defence responsibilities throughout multiple network points for attack defense functionalities and real-time traffic rerouting which preserved continuous network access. This investigation demonstrates how the framework operates with scale and versatility across SDN systems at large so it presents effective DDoS protection solutions for current network frameworks
DATA PRIVACY AND COMPLIANCE IN CLOUD ENVIRONMENTS: A COMPREHENSIVE FRAMEWORK FOR SECURE MULTI-TENANT CLOUD PLATFORMS
This study presents a comprehensive framework for securing multi-tenant cloud platforms, focusing on data privacy and compliance with regulatory standards such as GDPR and CCPA. The framework integrates advanced security techniques, including microservices for tenant isolation, homomorphic encryption for secure data processing, and blockchain-based logging for immutable auditing. The results show that microservices provide the most efficient data isolation, achieving 100% isolation efficiency with zero data leakage, compared to virtual machines and containers. Homomorphic encryption, while introducing significant computational overhead, remains scalable and effective for securing sensitive data, with the overhead increasing with data size. Role-based access control (RBAC) was highly effective in blocking unauthorized access attempts, with the admin role achieving near-perfect blocking efficiency. The framework also demonstrated strong compliance with GDPR and CCPA regulations, ensuring data privacy policies and access rights enforcement were met at nearly perfect levels. Blockchain-based logging proved superior in audit log accuracy and completeness, though it came with a higher processing cost. Finally, the framework was tested for performance under multi-tenant workloads, showing that while throughput decreased with increasing data size, the system maintained reasonable efficiency. This research highlights the effectiveness of combining microservices, encryption, and blockchain for securing multi-tenant cloud platforms, offering a practical solution for organizations facing privacy and compliance challenges in cloud environments. Future work will focus on optimizing encryption techniques and further refining the framework for broader real-world application
EMERGING TRENDS IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING: TRANSFORMING THE FUTURE OF TECHNOLOGY
Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies driving innovation, automation, and decision-making across industries. From healthcare diagnostics to autonomous systems and predictive financial modeling, the integration of AI/ML solutions is reshaping global technological and economic landscapes. This study explores the historical evolution, current trends, and practical applications of AI and ML, with a focus on explainability, ethical governance, and sector-specific advancements.A mixed-methods approach was employed, combining a structured literature review with simulated data analysis to assess the performance of various AI models using key metrics such as accuracy, precision, recall, and F1-score. Visualization techniques, including line plots, bar graphs, and hybrid performance charts, were used to interpret model behavior, while explainable AI (XAI) principles were incorporated to evaluate transparency and accountability in decision-making.The results reveal that ensemble models and deep neural networks consistently outperform traditional algorithms in terms of predictive reliability and adaptability. Metrics across simulated applications demonstrated strong accuracy and F1-scores, with reinforcement learning and natural language processing showing particularly high potential for real-time, autonomous decision-making. The study also highlights pressing challenges such as algorithmic bias, data privacy, and workforce disruption.In conclusion, while AI and ML offer profound opportunities for efficiency, personalization, and automation, their success hinges on responsible development practices, ethical transparency, and inclusive policy frameworks. The findings underscore the importance of balancing technical advancement with societal impact, ensuring that AI technologies contribute equitably to sustainable and intelligent global progress.
THE ROLE OF QUANTUM COMPUTING IN TRANSFORMING TECHNOLOGY AND SOCIETY
Quantum computing has emerged as a paradigm-shifting innovation with the potential to transform computation across disciplines by exploiting principles such as superposition, entanglement, and quantum interference. Unlike classical computing, which processes binary data sequentially, quantum systems operate in massively parallel spaces, enabling solutions to problems once deemed computationally intractable.This study systematically explores the foundational mechanisms of quantum computing—particularly qubits, quantum gates, and error correction techniques—and evaluates its real-world applicability through a multidimensional framework. The research employs a combination of theoretical modeling, complexity analysis, and performance benchmarking to compare classical and quantum algorithms, with a focus on cryptographic resilience and artificial intelligence enhancement.Results demonstrate that quantum algorithms such as Shor’s and Grover’s offer exponential and quadratic speedups, respectively, for tasks like prime factorization and database search. The study further finds that Quantum Machine Learning (QML) models, especially Quantum Neural Networks, achieve superior classification accuracy and faster training times compared to classical models, as observed in pilot healthcare applications. Additionally, the research underscores significant vulnerabilities in existing cryptographic systems and advocates for urgent transitions toward post-quantum cryptography and Quantum Key Distribution.In conclusion, the findings affirm that quantum computing holds transformative potential across cybersecurity, artificial intelligence, and national infrastructure. However, challenges such as decoherence, hardware noise, and data encoding persist, necessitating sustained research and policy development. For emerging nations like Pakistan, strategic investment in quantum education, infrastructure, and international collaboration will be essential to harness the benefits and mitigate the risks of this revolutionary technology..
Copyright (c) 2024 Computing and Applications reviews https://creativecommons.org/licenses/by/4.0
ADVANCEMENTS IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING: A MULTIDISCIPLINARY APPROACH
Artificial Intelligence (AI) and Machine Learning (ML) are driving a paradigm shift across sectors by offering data-driven, adaptive, and predictive capabilities that enhance decision-making and operational efficiency. Their integration into fields such as healthcare, finance, agriculture, education, and environmental monitoring has enabled transformative innovations that address both complex challenges and routine tasks with precision.This study employs a multidisciplinary methodology, combining supervised and unsupervised ML models with domain-specific datasets to evaluate the real-world impact of AI across five critical sectors. Techniques such as Convolutional Neural Networks, LSTM networks, Random Forests, and NLP models were implemented to perform diagnostics, forecast trends, detect anomalies, and provide personalized recommendations. A unified framework was adopted to ensure consistency in model training, validation, and interpretability, supported by SHAP and LIME for explainability.The results demonstrate that AI models consistently outperform traditional systems in diagnostic accuracy, financial forecasting reliability, agricultural yield prediction, student learning adaptability, and environmental risk assessment. High precision and recall scores were achieved across domains, highlighting the robustness of the selected algorithms. Visual analytics and performance metrics further validated the scalability and transparency of these models in real-world applications.The findings emphasize the critical role of cross-sectoral collaboration, ethical oversight, and policy reform in the deployment of AI technologies. Despite infrastructure and data accessibility challenges, particularly in developing regions, AI presents unprecedented opportunities for sustainable development and societal advancement. The research concludes that a multidisciplinary, ethically aligned AI deployment strategy is essential for maximizing impact while ensuring equitable access and responsible innovation..
A DOMAIN-ADAPTIVE QUESTION ANSWERING FRAMEWORK FOR FINANCIAL TEXTS WITH MULTI-TASK SEMANTIC REASONING
The rapid expansion of financial markets and the increasing complexity of financial instruments have created an urgent need for intelligent systems capable of understanding and responding to domain-specific inquiries. Traditional question answering (QA) models often fail to generalize beyond narrow datasets and exhibit poor comprehension of financial terminology, leading to suboptimal performance in real-world financial contexts. To address these limitations, this study introduces a domain-adaptive financial QA framework that integrates a financial knowledge graph with a multi-task semantic reasoning mechanism based on the T5 transformer architecture.The proposed system leverages three interconnected sub-tasks—named entity recognition (NER), entity linking, and domain-specific answer generation—trained collaboratively on a curated dataset titled “FinQ-Fuse,” which consists of over 120,000 annotated financial QA pairs. The NER module utilizes a BiLSTM-CRF architecture, while entity linking combines rule-based filtering with a support vector machine (SVM) classifier. Domain-specific question answering is powered by a fine-tuned T5 model that incorporates contextual and graph-based knowledge.Experimental evaluations conducted on benchmark datasets (FiQA, FinQA, and a custom Chinese financial QA dataset) demonstrate that the proposed framework achieves substantial improvements over baseline models. The system records an accuracy of 88%, a BLEU score of 0.72, and an entity coverage rate of 78%, indicating its superior performance in both linguistic and knowledge-aware tasks.This research validates the effectiveness of integrating semantic reasoning with structured domain knowledge for financial QA systems. By significantly enhancing answer quality, contextual awareness, and entity understanding, the proposed model sets a new benchmark for intelligent financial information retrieval. The findings hold strong practical implications for investment analysis, customer service automation, and financial education, paving the way for more adaptive and explainable AI systems in the financial sector..
REAL-TIME PERFORMANCE OPTIMIZATION FOR AUTONOMOUS DRIVING SYSTEMS BASED ON EDGE COMPUTING ARCHITECTURE
Autonomous driving technology is rapidly evolving as a cornerstone of next-generation intelligent transportation systems. However, achieving real-time responsiveness and operational reliability remains a significant challenge due to the latency and bandwidth limitations inherent in traditional cloud-centric architectures. To address this issue, this study proposes a novel edge computing-based optimization framework that restructures the computational hierarchy across terminal, edge, and cloud layers to support time-critical decision-making in autonomous vehicles.The methodology integrates hierarchical task offloading, modular container deployment using Docker, and dynamic resource orchestration via Kubernetes. Perception and decision-making modules were optimized with lightweight deep learning models and heuristic planning algorithms, and the entire system was validated using the Baidu Apollo platform under realistic urban and highway scenarios.Experimental evaluation demonstrates that the proposed architecture achieves a 23.6% reduction in end-to-end latency and maintains object detection performance with only a 2.8% decline in mean Average Precision (mAP) when using compressed models. Furthermore, decision-making accuracy improved significantly with the enhanced RRT algorithm, which reduced planning errors by 66.7% compared to traditional methods. Bandwidth efficiency and scalability were also improved under varying workloads.In conclusion, the edge computing-based framework effectively mitigates the latency bottlenecks associated with centralized processing while preserving model accuracy and improving decision precision. This work lays a practical foundation for scalable, low-latency deployment of autonomous driving systems and highlights the transformative role of edge computing in advancing real-time vehicular intelligence. Future research should explore adaptive resource allocation strategies and robust security mechanisms to enhance performance under high-concurrency and adversarial conditions..