Browsing by Author "Ali, Guma"
Now showing 1 - 20 of 69
Results Per Page
Sort Options
Item A secure and efficient blockchain and distributed ledger technology-based optimal resource management in digital twin beyond 5G networks using hybrid energy valley and levy flight distributer optimization algorithm.(IEEE, 2024-08-19) Kumar, K. Suresh; Alzubi, Jafar A.; Sarhan, Nadia M.; Awwad, E. M.; Kandasamy, V.; Ali, GumaThis paper aims to establish a virtual object management system, as well as optimal task scheduling using the foundation of Digital Twins (DT), to improve the user’s experience with management and to accomplish the task efficiently. On the other hand, offloading tasks using IoT gadgets to edge computing, fails to speed up control by users. The capabilities of the DT are provided by executing processes such as visualization, virtualization, synchronization, and simulation. The optimal selection of the virtual objects for the DT is done by utilizing the implemented Hybrid Energy Valley with Lévy Flight Distribution Optimization (HEV-LFDO) in order to optimally offload the task by the edge devices. The optimal selection of the virtual objects is done with the aid of the HEV-LFDO in the DT by considering the total cost of executing all tasks using the selected virtual objects and the decision variables to determine whether a virtual object is taken for executing a task or not as the constraint. The data for performing resource management is secured using the blockchain or distributed ledger technology. This accounts for the minimization of the local loss function. Finally, the secured data is considered for optimal resource management tasks. The optimal resource management is done using the same HEV-LFDO. This optimal resource management is carried out by considering the constraints like the cost of assigning a virtual object for the task to the edge device, and the cost of assigning the task to the edge device. These two costs are analyzed by taking the network’s bandwidth, energy consumption, and computational resources into consideration. Experimental verifications are conducted on the executed optimal resource management scheme to prove the ability of the implemented model to be integrated with the edge computing network. The overall processing time as well as the latency are also minimized by executing the optimal resource management scheme.Item A Survey on artificial intelligence and blockchain applications in cybersecurity for smart cities(2025-01-10) Asiku, Denis; Adebo, Thomas; Wamusi, Robert; Aziku, Samuel; Kabiito, Simon Peter; Zaward, Morish; Sallam, Malik; Ali, Guma; Mijwil, Maad M.Smart cities rapidly evolve into transformative ecosystems where advanced technologies work together to improve urban living. These interconnected environments use emerging technologies to offer efficient services and sustainable solutions for urban challenges. As these systems become more complex, their vulnerability to cybersecurity threats also increases. Integrating artificial intelligence (AI) and Blockchain technologies to address these challenges presents promising solutions that ensure secure and resilient infrastructures. This study provides a comprehensive survey of integrating AI, Blockchain, cybersecurity, and smart city technologies based on an analysis of peer-reviewed journals, conference proceedings, book chapters, and websites. Seven independent researchers reviewed relevant literature published between January 2021 and December 2024 using ACM Digital Library, Wiley Online Library, Taylor & Francis, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, IGI Global, and Google Scholar. The study explores how AI can enhance threat detection, anomaly detection, and predictive analytics, enabling real-time responses to cyber threats. It examines various AI methodologies, including machine learning and deep learning, to identify vulnerabilities and prevent attacks. It discusses the role of Blockchain in securing data integrity, improving transparency, and providing decentralized control over sensitive information. Blockchain’s tamper-proof ledger and smart contract capabilities offer innovative solutions for identity management, secure transactions, and data sharing among smart city stakeholders. The study also highlights how combining AI and Blockchain can create robust cybersecurity frameworks, enhancing resilience against emerging threats. The survey concludes by outlining future research directions and offering recommendations for policymakers, urban planners, and cybersecurity professionals. This study identifies emerging trends and applications for enhancing the security and resilience of smart cities through innovative technological solutions. The survey provides valuable insights for researchers and practitioners who aim to utilize AI and Blockchain to improve smart city cybersecurity.Item A survey on artificial intelligence in cybersecurity for smart agriculture: state-of-the-art, cyber threats, artificial intelligence applications, and ethical concerns.(Mesopotamian Academic Press, Imam Ja'afar Al-Sadiq University, 2024-07-20) Ali, Guma; Mijwil, Maad M.; Buruga, Bosco Apparatus; Abotaleb, Mostafa; Adamopoulos, IoannisWireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review described advancements from conventional agriculture to modern SA, including architecture and emerging technology. It digs into SA’s numerous uses, emphasizing its potential to transform farming efficiency, production, and sustainability. The growing reliance on SA introduces new cyber threats that endanger its integrity and dependability and provide a complete analysis of their possible consequences. Still, the research examined the essential role of AI in combating these threats, focusing on its applications in threat identification, risk management, and real-time response mechanisms. The survey also discusses ethical concerns such as data privacy, the requirement for high-quality information, and the complexities of AI implementation in SA. This study, therefore, intends to provide researchers and practitioners with insights into AI’s capabilities and future directions in the security of smart agricultural infrastructures. This study hopes to assist researchers, policymakers, and practitioners in harnessing AI for robust cybersecurity in SA, assuring a safe and sustainable agricultural future by comprehensively evaluating the existing environment and future trends.Item A Survey on securing smart finance using artificial intelligence and blockchai(Peninsula Publishing Press, 2026-01-10) Ali, Guma; Otim, Emmanuel; Mijwil, Maad M.; Buruga, Bosco Apparatus; Eslahi, Aida Vafae; Adamopoulos, IoannisThe rapid digitalization of financial services has given rise to smart finance ecosystems that integrate FinTech platforms, Internet of Things (IoT) devices, cloud infrastructures, and decentralized applications. While these systems enhance automation, operational efficiency, and financial inclusion, their highly distributed, data-intensive architectures introduce critical security, privacy, and trust challenges. In this context, artificial intelligence (AI) and blockchain have emerged as complementary technologies capable of addressing these challenges through intelligent decision-making, advanced threat detection, data integrity, and transparent operations. This survey provides a comprehensive review of recent research on securing smart finance systems using AI- and blockchain-based approaches. The survey comprehensively analyzed research published between 2023 and 2026 using the Scopus database, focusing on the keywords “AI,” “blockchain,” and “smart finance.” The analysis reveals extensive use of AI-driven security mechanisms, including credit scoring and risk assessment, transaction monitoring and fraud detection, anti-money laundering (AML) and know-your-customer compliance, identity verification, cyber threat detection, smart contract security analysis, behavioral biometrics, insurance fraud detection, and market risk prediction. In parallel, the survey examines blockchain-enabled security solutions, including secure payment and settlement systems, cross-border remittances, AML and counter-terrorism financing frameworks, digital identity management, smart contracts, asset tokenization, decentralized finance, auditability, and secure interbank communication. The integration of AI and blockchain offers significant advantages, including improved fraud detection accuracy, enhanced transparency and traceability, stronger data integrity, automated compliance, real-time threat response, and increased system resilience. Despite these benefits, key challenges persist, particularly in scalability, privacy preservation, interoperability, regulatory and ethical compliance, energy efficiency, explainability, and post-quantum security. The survey concludes by outlining future research directions and design guidelines for developing secure, scalable, and trustworthy smart finance systems that effectively leverage the integration between AI and blockchain.Item Agricultural waste to biofuel: Transforming crop residue into next-generation aviation fuel.(IEEE, 2025-12-29) Maury, Shyam; Mangaiyarkarasi, V.; Madaminjonugli, Bakhriddinov Makhamadali; MuhamedAle, Hasssan; Arunkumar, E.; Ali, Guma; Rakhimov, Navruzbek; Shetty, ChinmaiThe rapid expansion of international aviation has significantly contributed to greenhouse gas emissions. The existing type of jet fuel consumes a considerable amount of crude oil, which has compounded the demand. As a result, the industry accounts for a carbon footprint that reduces the pressure on alternative energy sources with lower carbon emissions. Agricultural waste comprises straw, husks, and stalks, which denotes one such available lignocellulosic feedstock that is not exhaustively utilised to offer a solution to environmental and economic dilemmas in the aviation energy production. The given research paper proposes an integrated solution to transform agricultural waste materials into high-energy-density biofuels, which can be utilised in the aviation industry through a two-stage biochemical and thermochemical treatment, followed by subsequent fuel upgrading to produce a high-quality product. Pre-treatment options, which were attempted on a lab scale, included steam explosion, dilute acid hydrolysis, enzyme saccharification, fermentation, pyrolysis, and Fischer-Tropsch catalytic upgrading. The parameters of the process conditions were optimised to achieve a high yield and minimise energy consumption. Results were statistically analysed to ensure reproducibility, and fuel properties were compared to ASTM D7566 standards to verify that they conformed to conventional jet fuel specifications. The results show that biofuels produced from agricultural waste have an energy density similar to that of Jet A fuel, with notable reductions in carbon and particulate emissions, making them a viable option for mitigating greenhouse gas emissions caused by aviation. The techno-economic analysis also demonstrates the viability of large-scale implementation, based on the availability of feedstock, process effectiveness, and compliance with regulations. Twith regulations. The practice is also compatible with the principles of the circular economy , which emphasises the value of agricultural residues, agrarian eco nomies, and sustainable waste management. Moreover, it is possible to optimise it with AI, emulate the use of blockchains to track feedstock, and adopt the concept of hybrid biofuel electric to make the future of biofuel work more efficiently and easily tr acked. Comprehensively, the paper demonstrates that agricultural waste can be a feasible and sustainable aviation biofeedstock of the next generation, as it can help minimise carbon footprints, make biofuels economically viable, and promote the current trend of carbon neutral aircraft in the global community.Item AHNet: Design and execution of adaptive hybrid network for credit risk prediction using spatio-temporal attention-based convolutional autoencoder features in the banking sector(Springer Nature, 2026-01-07) Ahmad, Ahmad Y. A. Bani; Shukla, Madhu; Jayaprakash, B; Bharathi, B; Ali, Guma; Yogapriya, JOver the past ten years, pattern recognition experts have become very interested in market financial predictions. To support investment decision-making processes, it is crucial to develop an intelligent financial forecasting model in the financial markets. However, multivariate financial time series prediction is still difficult. Time series data are typically used for market analysis, and the high degree of fluctuation in this type of data necessitates the use of highly effective classification tools that are prevalent in the state of the art, such as Convolutional Neural Networks (CNNs) systems like AlexNet, residual network, Inception, and so forth. Researchers must start from scratch when training new tools as a result. These procedures could take a long time. Therefore, it is crucial to address the many issues related to using conventional methods for financial forecasting. The benchmark resources are used to gather the necessary financial data for verification in the first step. Next, the collected data are offered to the Spatio-Temporal Attention-based Convolutional Autoencoder (STA-CAE)-based feature extraction phase. The financial prediction phase receives the essential features used for validation extracted in this phase. Here, a novel Adaptive Hybrid Network (AHNet) is employed to perform the prediction procedures. The developed AHNet is an integrated version of Ridge Regression with Stacked Residual Recurrent Neural Network (RR-SResRNN). Moreover, the parameters of AHNet are optimized utilizing the Improved Random Array-based Secretary Bird Optimization Algorithm (IRA-SBOA) that helps to improve the prediction efficiency of the developed prediction technique. The efficiency of the created technique is compared to classical frameworks by executing various tests after collecting financial prediction outcomes from the AHNet model.Item AI Driven railway crack detection system using Convolutional Neural Network and IoT(IEEE, 2025-06-02) Sudha, M.; Saranya, R.; Ali, Guma; Ganesh, C; Umamaheswari, S.The Railway Track fractures identification Using AI with IoT project aims to increase railway safety by automating the identification of fractures in railway tracks using artificial intelligence (AI) and Internet of Things (IoT) technology. Even minor cracks in railway tracks, which are crucial components of the transportation system, can cause major accidents if they not immediately identified also repaired. Conventional manual assessing technique are expensive, time-consuming, and error-prone. Convolutional Neural Networks, a kind deep learning model, will used in this study to automatically identify cracks in photos of railroad tracks. The model trained on a dataset of track photographs in order to discern parts are defective (cracked) and non-defective (intact). Once trained, The CNN model is employed to analyze images captured by cameras mounted on trains or inspection vehicles as part of a real-time monitoring system driven by the Internet of Things. Every time a fracture is discovered, the system sends the information to the Blynk IoT platform, notifying and alerting maintenance personnel. Additionally, an LCD display and a buzzer alarm are activated in the field to alert technicians to the detected defect. By combining AI and IoT, the initiative aims to reduce overall maintenance costs, improve safety through early fault detection, and speed up the track inspection process. By providing a more automated, accurate, and efficient method of tracking the present state of railroad lines, the system ultimately increases the harmlessness and reliability of railway transit.Item AI-driven pathogen detection systems for rapid and accurate diagnosis(IEEE, 2025-09-24) Deepika, M.; Murugan, Sundara Bala; Subasini, V.; Rufus, N. Herald Anantha; Atkinswestley, A.; Ali, GumaPathogen detection at speed along with precision serves as a basis for disease diagnosis and overall control effectiveness. Several current detection methods based on culture-based techniques and PCR face drawbacks of extended processing times, high expenses, and the need for expert operators. It adopts deep learning Convolutional Neural Networks with an attention mechanism in a system designed to overcome existing limitations of pathogen detection. The model was trained using a multi-modal dataset that combined spectral and biological signals through feature extraction optimization and privacy-protected training methods based on federated learning. The proposed system reaches a 96.8% detection accuracy together with 15% reduced false alarm rates and 35% faster operation speeds than conventional models. Experimental data measurements showed that this system achieved 92.3% F1-score together with 97.1% AUC for real-time identification of pathogens. AI has proven its effectiveness through these findings in changing the way pathogen diagnostics operate. Further work will concentrate on extending datasets while creating real-time deployment protocols for clinical use.Item AI-Driven real-time social media analytics for consumer trust enhancement.(IEEE, 2026-02-20) Sharon; Gnanaroy, E. Rushit; Gupta, Anish; kizi, Sotivoldiyeva Sarvinoz Kahramon; Kannimuthu, S.; Ali, GumaIn a hyperconnected digital ecosystem, companies have never faced as much pressure to maintain consumer trust as they do at present, as content surrounding social media spreads at an alarming pace, along with opinions, feedback, and false information disseminating in real-time. Conventional monitoring approaches, based on batch processing and periodic review, are insufficient for monitoring the dynamism and rapid pace of online interactions. This factor often leads to sluggish reactions to reputational risks and consumer dissatisfaction. In response to this issue, we propose an AI-based, real-time social media analytics system that will continuously track multimodal data streams, including text, photos, and video, to detect signs of consumer mood and trust occurrences. The system combines contextual sentiment analysis using transformer-based natural language processing models, mapping social influence and information propagation through graph neural networks, and anomaly detection algorithms to detect sudden changes in perception or possible misinformation. Streaming pipelines based on distributed computing infrastructure can guarantee lowlatency processing, and real-time alerting and predictive modelling can ensure proactive engagement approaches to reduce risks as they occur. Throughout the simulations, the framework outperforms previous analytics systems, achieving accuracy, precision, recall, and F1-score scores in the 85-90% range across various social media platforms. The system enables organisations to react proactively to negative sentiment, avoid reputational loss, and strengthen consumer trust in real-time by delivering actionable insights and instant alerts. The findings affirm that the combination of developed AI and real-time data processing will provide a scalable technology-based methodology that can not only increase operational efficiency but also provide the company with a sound mechanism for maintaining consumer trust in the dynamic online environment.Item AI-Powered collaborative robotics for next-gen industrial automation(IEEE, 2026-02-20) Ramya, M; Subburam, S.; Patel, Pushplata; Seetaram, J.; Singh, K. Ranjith; Ali, Guma; Salami, Zayd AjzanThe runaway development of Industry 4.0 has brought very interconnected, intelligent, and automated production systems in place, but conventional robotics based on rules cannot react to dynamic production conditions, unpredictable working processes, and real-time disruptions, which tend to result in lower efficiency, time delays, and bottlenecks in the work process. Among common challenges faced by factories are inefficiencies in task allocation, the absence of collaborative coordination among robots, poor predictive maintenance, and vulnerabilities in data exchange during interactions. To address those issues, this research proposes AI-Powered Multi-Agent Collaborative Robotic System (AI-MACROS), a highly sophisticated industrial automation system integrating swarm intelligence, reinforcement learning, digital twin simulation, and blockchain-based security. AI-MACROS allows teams of robotic agents to work together, learn dynamically based on environmental interactions, optimize task distribution and deal with unexpected events in the real world. Through digital twins, it provides virtual replicas of robots and work processes, with which to monitor the system in real time, preemptive maintenance and simulate scenarios without disrupting the existing processes. Also, blockchain protocols guarantee integrity and safety of communication and data across the industrial network. Compared to a typical rulebased robotics, one may observe that AI-MACROS is executed by the high performance score (i.e. higher efficiency, accuracy, recall, and F1-scores), and the coordination and energy efficiency is additionally simplified with the help of particle swarm optimization, genetic algorithms and deep reinforcement learning. The proposed solution addresses the disadvantages of the traditional automation, making the operations more resilient in addition to improving the security and scale, not mentioning that it is deemed as not only a potential game changer but also as a next-generation smart factory solution. In a clever synergy, predictive analytics and safe decentralized information processing, AI-MACROS creates high-performance prototype of adaptable, fruitful and credible automation of production under realistic production scenarios.Item AIoT-driven smart agri-grid (ASAG) for sustainable precision agriculture(IEEE, 2025-12-29) Sundaram, N. Kalyana; Rajendran, Megala; Ehssan, Muhamed; Soy, Aakansha; Anandhi, K.; Begum, T Ummal Sariba; Ali, Guma; Dhananjaya, BBy advising and teaching farmers on how to apply modern farm practices that embrace Artificial Intelligence (AI) and the Internet of Things (IoT), precision agriculture is revolutionising sustainable farming by optimising for usages that are as much as possible and waste as little as can be afforded. In this research, we propose an AIoT-driven Smart Agri Grid (ASAG) framework that integrates real-time nanosensor networks, an AI-operational control microclimate, an autonomous decision-support system, and secure data sharing via a blockchain using encrypted statistical data. To achieve real-time analytics, edge computing is used in the framework for real-time data analytics, predictive algorithms for dynamic irrigation & nutrient management, and federated learning for distributed AI training, which maintains privacy and scalability. In addition, the system uses AI-based waste-minimisation techniques, such as predictive harvest timing and the conversion of bio-waste into organic fertilisers, thereby reducing post-harvest losses. Experimental results show that ASAG can improve crop yield by 20 to 30%, reduce water waste by up to 50%, and reduce chemical overuse by up to 30%, with its economic and environmental benefits. The feasibility of such deployment on a large scale in precision agriculture is further confirmed by a cost-benefit analysis. The results reinforce the power of AI and IoT in transforming contemporary farming into a self-optimising, climate-resilient system. For long-term sustainability in global agriculture, quantum AI will be used to predict soil health, monitor AI-assisted carbon sequestration, and enable genomic AI for climate-resistant crops.Item An Editorial vision for civil engineering: navigating intelligence and innovation(Mesopotamian Academic Press, 2025-11-01) Mijwil, Maad M.; Adamopoulos, Ioannis; Ali, GumaThe integration of Artificial Intelligence in civil engineering has seen a major advancement over the applications of primitive data-driven models, reaching advanced hybrid physics-informed models. This evolution represents a significant change of paradigm from the basic predictive analytics to what is now called "structural cognition." But in this cutting-edge paradigm, the evolutionary learning of AI beyond the structural prediction is further developed, with the ability to learn underlying causal relationships in complicated engineering systems. This not only allows them to diagnose potential problems, but also to proactively propose well-targeted remedial measures, giving engineers a greater understanding of the situation as well as better, better-informed decision-making with respect to infrastructure sustainability and safety [1], [2]. A critical element to this evolution is the emergence of "perceptive infrastructure." This idea is further greatly supported by similar inventions like wavelet-diffusion architectures, and it is very useful in the design of the most robust vision-based monitoring systems. The systems are especially well-suited to demanding environments, such as low-light and other bad weather conditions where conventional surveillance techniques tend to break down. By equipping infrastructure with the ability to sense and make real-time sense of their environment with high fidelity, these technologies are ushering in a new era of self-sensing, self-reporting and even self-predicting civil assets, heading toward constant intelligent self-diagnosis [1]. Figure 1 illustrates artificial intelligence tools in civil engineering.Item Analysing the connection between ai and industry 4.0 from a cybersecurity perspective: defending the smart revolution(Mesopotamian Journal of Big Data, 2023-05-05) Bala, Indu; Mijwil, Maad M.; Ali, Guma; Sadıkoğlu, EmreIn recent years, the significance and efficiency of business performance have become dependent heavily on digitization, as jobs in companies are seeking to be transformed into digital jobs based on smart systems and applications of the fourth industrial revolution. Cybersecurity systems must interact and continuously cooperate with authorized users through the Internet of Things and benefit from corporate services that allow users to interact in a secure environment free from electronic attacks. Artificial intelligence methods contribute to the design of the Fourth Industrial Revolution principles, including interoperability, information transparency, technical assistance, and decentralized decisions. Through this design, security gaps may be generated that attackers can exploit in order to be able to enter systems, control them, or manipulate them. In this paper, the role of automated systems for digital operations in the fourth industrial revolution era will be examined from the perspective of artificial intelligence and cybersecurity, as well as the most significant practices of artificial intelligence methods. This paper concluded that artificial intelligence methods play a significant role in defending and protecting cybersecurity and the Internet of Things, preventing electronic attacks, and protecting users' privacy.Item Artificial intelligence in corneal topography: A short article in enhancing eye care(Mesopotamian Journal of Artificial Intelligence in Healthcare, 2023-06-17) Ali, Guma; Eid, Marwa M.; Ahmed, Omar G.; Abotaleb, Mostafa; Alaabdin, Anas M. Zein; Buruga, Bosco ApparatusThe eye is a critical part of the human being, as it provides complete vision and the ability to receive and process visual details, and any deficiency in it may affect vision and loss of sight. Corneal topography is one of the essential diagnostic tools in the field of ophthalmology, as it can provide important information about the cornea and the problems that appear in it. Artificial intelligence strategies contribute to the development of the healthcare domain through a group of approaches that have a significant and vital impact on improving the field of ophthalmology. The primary purpose of this paper is to highlight the efficiency of artificial intelligence in extracting features from corneal topography and how these techniques contribute to helping ophthalmologists diagnose corneal topography. Furthermore, the focus is on the performance of AI algorithms, their diagnostic capabilities, and their importance in helping physicians and patients. The effects of this paper confirm the effectiveness and efficiency of artificial intelligence algorithms in the clinical diagnosis of various eye concerns.Item Artificial intelligence solutions for health 4.0: overcoming challenges and surveying applications(Mesopotamian Journal of Artificial Intelligence in Healthcare, 2023-03-10) Al-Mistarehi, Abdel-Hameed; Mijwil, Maad M.; Filali, Youssef; Bounabi, Mariem; Ali, Guma; Abotaleb, MostafaIn recent years, the term Health 4.0 has appeared in health services and is related to the concept of Industry 4.0. The term Health 4.0 focuses on replacing traditional care in hospitals and medical clinics with home health services that are based on artificial intelligence techniques through the use of telemedicine applications that allow the monitoring of patients in a virtual environment. This term is utilized to represent digital change in the healthcare sector. Governments aim to develop the level of medical care in hospitals and clinics to ensure the provision of healthcare benefits at low costs and increase patient satisfaction. It has become vital for hospitals to grow their environment into digital environments in their services through the use of a set of computer programs based on artificial intelligence. Artificial intelligence techniques in Health 4.0 provide a set of procedures that benefit patients and healthcare workers, including early diagnosis, make inquiries into treatment, data analysis, reports on the patient's condition, and others. The primary purpose of this article is to determine the significance of Health 4.0 and AI techniques in healthcare by mentioning the most important benefits and weaknesses of using AI techniques in healthcare.Item Augmented heritage: AR and QR code integration for interactive cultural storytelling in the UAE(IEEE, 2025-12-05) Lal, Mily; Dash, Soumyakant; Keerthika, J; Kumar, Bura Vijay; Ali, Guma; Shukla, Vinod KumarThis study has outlined a mobile-augmented reality (AR) storytelling framework utilizing Quick Response (QR) code technology to provide cultural engagement at heritage sites in the United Arab Emirates. The project set out to merge traditional heritage with augmented immersive technology and focused on a four-phase methodology: (1) Content curation and narrative design through historians and cultural experts; (2) Planning QR codes in spaces cooperatively designed through spatial design and semiotics of culture; (3) Content development of AR experiences in Unity and Vuforia using finite state machine for stability and usability; and (4) User testing in three major heritage locations. The quantitative and qualitative evaluations measured user engagement, narrative retention, usability of the system, and cultural sensemaking. Relative to baseline digital experiences, the AR-QR platform saw significant gains: 25-30% improvement in engagement; 20-25% improvement in narrative retention; and over 15% improvement in usability and cultural relevance. The study evidence demonstrates the promise of AR-QR storytelling and approach to deliver contextually rich and interactive heritage experiences. Although there are limitations in device affordances/conceivability and longer-term evaluations, this study suggests---immersive storytelling can make heritage more engaging, accessible, and meaningful to a diverse population.Item AZTEM: A Self-evolving zero-trust model for adaptive cloud security using AI-driven threat mitigation and quantum-resilient encryption(IEEE, 2026-02-20) Khemraj, Ikhar Avinash; Buckshumiyan, A.; Balassem, Zayd; Shekar, A.; Arunkumar, B.; Ali, Guma; Jabborov, NurboyThe new digital ecosystems are being based on cloud environments but they are highly vulnerable to the shifts in cyber threat, which can take advantage of the stationary trust models, loose access rules and encryption functions that hangs in the air with the advent of quantum computing. The traditional models of perimeter based security fail to operate in highly dynamic cloud environments with insider based mauling, cross-lateral mauling and the zero day mauling bypassing the traditional security models. In this work, A Self-evolving Zero-Trust Model of Adaptive Cloud security with AI-based Threat Mitigation and Quantumresilient Encryption is introduced and is capable of addressing these emerging problems. AZTEM eliminates the implicit trust, validating and permitting all user, device, and microservice relations through the utilization of a contextsensitive trust engine that is reinforced by reinforcement learning algorithms. To counter the rapid adaptations of attack vectors, the system relies on the deep-learning-based anomaly detection methods such as LSTM-based sequence classifiers on cloud workload telemetry, network flow logs, and publicly accessible datasets (UNSW-NB15 and CICIDS2018). An adaptive policy controller is the basis of dynamic orchestration of mitigation strategies and uses AI-based response mechanisms to quarantine, reroute, or restrict malicious sessions in real time. Besides, the model includes quantum-resilient encryption that extends the protection of both data-at-rest and data-in-transit by using lattice cryptography to secure quantum decryption threats where confidentiality is ensured at the long-term scale. An initial deployment of AWS on Kubernetes clusters demonstrated that threat detection accuracy and false positives decreased by 27 percent and 34 percent respectively over a baseline zero-trust deployment, and performance overhead is less than 8 percent. Its application in adversarial context was assessed through measures such as precision, recall, F1-score and confidence intervals and proved it to be qualified. The architecture is not only making the cloud more resilient to the present, and the infrastructures of the quantum age impervious to the threats of the quantum age, but it is also providing businesses, governments and hard-to-secure areas in need of cloud security without a trade-off an ethical and scaleable and deployment-ready path.Item BERT and CNN for automated detection of detrimental discourse(IEE, 2026-02-19) Devarapalli, Thejasree; Yarlagadda, Nagalakshmi; Haldorai, Anandakumar; Sivaramakrishnan, A; Ali, Guma; Sharma, VinodThe modern online age is competitive and full of information that requires sensing dangerous materials in order to maintain online honesty and facilitate positive forms of communication. This paper presents a new model that takes a hybrid approach to using BERT Base embeddings and convolutional neural networks (CNNs) to deal with the problem of detecting harmful material in a variety of settings. BERT Base embeddings are used to dynamically gain the fined tuned features of semantics of text that is then processed by a CNN to classify. Compared to the previous studies mainly concentrated on individual contexts with LSTM, RNN, or more advanced transformer models, we use the effectiveness of CNNs to recognize patterns in contexts and also work with different types of harmful content at the same time. To evaluate it, we applied benchmark datasets a reputable Mendeley dataset of hate speech detection, as well as Kaggle datasets of cyberbullying and emotional distress. The experimental results point to the power of our method, which reached almost 92 percent of accuracy in categories. This study merges semantic expressiveness and CNN effectiveness and contributes to content moderation policies and meaningful insights to create safer and more inclusive websites.Item Blockchain and deep Q-learning for trusted cloud-enabled drone network in smart forestry: A Survey(Imam Ja’afar Al-Sadiq University, 2025-12-14) Ali, Guma; Wamusi, Robert; Mijwil, Maad M.; Al-Hamzawi, Hassan A. Hameed; Al Sailawi, Ali S. Abed; Salau, Ayodeji OlalekanThe convergence of drone technology, cloud computing, and intelligent decision-making is revolutionizing precision forestry. However, deploying large-scale drone networks in smart forestry faces challenges such as trust, security, data integrity, and autonomous coordination. This survey examines how combining Blockchain technology with deep Q-learning (DQL) can address these issues within cloud-enabled drone networks. Drawing on 102 peer-reviewed sources published between 2022 and 2025 from reputable platforms such as ACM Digital Library, Frontiers, Wiley Online Library, PLoS, Nature, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, Taylor & Francis, Sage, and Google Scholar, this work highlights recent advancements in secure and intelligent drone ecosystems. Blockchain provides a decentralized, tamper-resistant framework for validating transactions and securing data exchange among autonomous drones, ensuring the integrity, confidentiality, and authenticity of environmental data. This is critical in forestry, where data manipulation and unauthorized access pose significant risks. Complementing this, DQL enables drones to make autonomous decisions by interpreting real-time environmental data and learning from past experiences, allowing drones to adjust their flight paths, optimize resource utilization, and enhance data collection in dynamic forest environments, such as wildfires or illegal logging operations. Together, Blockchain and DQL create a resilient, scalable architecture that supports secure, real-time, and intelligent forest monitoring. This framework lays the groundwork for developing autonomous and trustworthy drone networks that promote sustainable and climate-smart forestry management.Item Blockchain and federated learning in edge-fog-cloud computing environments for smart logistics(Mesopotamian Academic Press, 2025-07-22) Ali, Guma; Adebo, Thomas; Mijwil, Maad M.; Al-Mahzoum, Kholoud; Sallam, Malik; Salau, Ayodeji Olalekan; Adamopoulos, Ioannis; Bala, Indu; Al-jubori, Aseed Yaseen RashidThe rapid growth of smart logistics, driven by IoT devices and data-intensive applications, necessitates secure, scalable, and efficient computing frameworks. As the edge-fog-cloud (EFC) paradigm supports real-time data processing, it faces significant security threats and attacks, including privacy risks, data breaches, and unauthorized access. To address these security threats and attacks, blockchain and federated learning (FL) have gained popularity as potential solutions in EFC computing environments for smart logistics. This survey reviews the current landscape in EFC computing environments for smart logistics, highlighting the existing benefits and challenges identified in 134 research studies published between January 2023 and June 2025. The applications of blockchain and FL demonstrate their ability to enhance data security and privacy, improve real-time tracking and monitoring, and ensure inventory and supply chain optimization. Although these technologies offer promising solutions, challenges such as scalability issues, data quality, interoperability and standardization hinder their effective implementation. The survey suggests future research directions focused on developing advanced blockchain and FL, integrating emerging technologies, developing policies and regulations, fostering collaborative research, and ensuring cross-industry adoption and interoperability. Integrating blockchain and FL within the EFC model offers a transformative path toward building secure, intelligent, and resilient logistics systems.