International Journal of Emerging Trends in Engineering and Technology
International Journal of Emerging Trends in Engineering and Technology (IJETET), a double-blind peer-review journal, welcomes original research articles, reviews, case studies, short communications, and technical papers in the emerging fields of Engineering and Technology. Engineering and technology trends reflect the rapid advancements and innovations that shape various industries, societies, and everyday life. These trends often emerge from the continuous development of new techniques, materials, and tools and the integration of interdisciplinary knowledge and research. IJETET is a global platform for interdisciplinary research, promoting advancements in various engineering fields, including electrical, mechanical, civil, computer science, environmental engineering, and emerging technologies.
AI-Driven Cloud-Native Microservices Framework for Secure Healthcare Prescription Automation, Software Reliability, and Scalable Deployment Optimization
Healthcare prescription automation is increasingly dependent on interoperable clinical data exchange, cloud-native service orchestration, secure application programming interfaces, and dependable software delivery pipelines. However, current prescription automation platforms often treat clinical decision support, e-prescribing workflow execution, software reliability engineering, cybersecurity governance, and deployment optimization as separate concerns. This separation creates architectural fragmentation in environments where prescription requests must be clinically valid, auditable, resilient to distributed failure, compliant with privacy obligations, and scalable under fluctuating enterprise workloads. This paper proposes a conceptual AI-driven cloud-native microservices framework for secure healthcare prescription automation, software reliability, and scalable deployment optimization. The framework integrates modular prescription services, AI-assisted clinical and operational intelligence, policy-driven security controls, observability-centered reliability engineering, and container-based deployment automation into a unified architecture. The major contribution of this paper is not an empirical claim of clinical superiority, but a structured reference model for designing prescription automation platforms that can support high-assurance workflows in regulated healthcare environments. The study identifies practical gaps in existing approaches, including a weak linkage between prescription standards and runtime reliability, insufficient integration of defect prediction with development pipelines, limited explainability in AI-assisted prescription decisions, and fragmented governance across APIs, containers, and machine learning components. The paper further presents a comparative methodology, architectural layers, implementation considerations, expected analytical outcomes, risk limitations, and future research directions. The proposed framework can guide healthcare enterprises, cloud architects, software reliability engineers, and AI governance teams in designing secure, scalable, and auditable prescription automation systems without binding the architecture to a single vendor or proprietary platform.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Quality Enhancement via Human-Robot Collaboration in Logistics Workflows
The rapid growth of e-commerce and global supply chains has intensified the demand for high-quality, efficient logistics operations. Traditional automation has improved speed but often falls short in flexibility and quality assurance. This paper explores human-robot collaboration (HRC) as a strategy to enhance quality in logistics workflows by combining the strengths of human judgment and robotic precision. We examine various HRC models, task allocation strategies, and workflow optimization techniques that minimize errors, improve accuracy, and increase operational efficiency. Case studies and emerging technologies, such as AI-enabled robotics and real-time monitoring systems, demonstrate the potential for improved quality metrics in logistics processes. Challenges including human factors, technical limitations, and organizational integration are discussed, and future directions for adaptive, intelligent HRC systems are outlined. The findings suggest that effective human-robot collaboration can significantly elevate the quality and reliability of logistics operations while fostering a synergistic workplace.
Self-Supervised Anomaly Detection in Logistics Quality Data Streams
The increasing digitalization of logistics operations has resulted in continuous streams of quality-related data generated from sensors, automation systems, and information platforms. Traditional anomaly detection approaches often rely on labeled datasets or static rules, which are impractical in dynamic logistics environments where quality deviations are rare, evolving, and context-dependent. This study proposes a self-supervised anomaly detection framework for monitoring logistics quality data streams in real time. The proposed approach learns normal operational patterns directly from unlabeled data by leveraging self-supervised learning techniques, enabling the detection of handling, transportation, storage, and sensor-related anomalies without prior fault annotations. The framework incorporates adaptive anomaly scoring and streaming data processing to address concept drift and operational variability. Experimental results demonstrate that the proposed model outperforms conventional unsupervised and supervised baselines in terms of detection accuracy, robustness, and early warning capability. The findings highlight the potential of self-supervised learning as an effective tool for proactive quality assurance in modern logistics and supply chain systems.
Reinforcement Learning for Dynamic Quality Optimization in Inventory Flow
Efficient inventory flow management requires balancing cost, service level, and product quality in environments characterized by uncertainty and dynamic demand. Traditional inventory optimization models often rely on static assumptions and predefined decision rules, limiting their ability to adapt to real-time changes in quality degradation, demand variability, and operational constraints. This paper proposes a reinforcement learning–based framework for dynamic quality optimization in inventory flow systems. By modeling inventory decisions as a sequential decision-making problem, the proposed approach enables an intelligent agent to learn optimal ordering, holding, and allocation policies that minimize quality loss while maintaining operational efficiency. The framework incorporates quality-aware reward functions and state representations that capture inventory levels, product age, demand uncertainty, and quality decay dynamics. Experimental results demonstrate that reinforcement learning policies outperform conventional inventory control models in reducing quality-related losses, improving service levels, and adapting to fluctuating operating conditions. The findings highlight the potential of reinforcement learning as a powerful tool for achieving dynamic, data-driven inventory quality optimization in modern supply chain systems.
Constrained Fuzzy Arithmetic Approach to Solve the Fully Fuzzy Transshipment Problem
Transportation is the distribution of goods and resources from one place to another. Most of the solving techniques for solving fully fuzzy mathematical programs are based on the standard fuzzy arithmetic operations. The constrained fuzzy arithmetic concept generates efficient solutions for many real-life applications. The transhipment problem is an extension of the transportation problem, shipping from a source to another source, shipping from a destination to another destination and shipping from a destination to any source may be allowed. This paper proposes a fuzzy constrained arithmetic approach to minimize the cost of fuzzy transportation. This paper presents an efficient algorithm for solving the transhipment problem using a fuzzy constrained arithmetic approach. At first, convert the transportation problem into an equivalent transhipment problem and then solve it by using the constrained fuzzy arithmetic algorithm. This novel method gives the minimum cost for the transhipment problem. In this paper, the solution procedure for the fully fuzzy transhipment problem using the CFA approach is explained with the help of a numerical example as another application of this algorithm.
Performance Engineering in Cloud Systems: Designing Efficient Technical Architectures for Scalable Solutions
In the era of cloud computing, ensuring the scalability and efficiency of applications is paramount. Performance engineering offers a structured approach to designing cloud architectures that can adapt to varying workloads while maintaining optimal performance. This paper explores the principles of performance engineering within the context of cloud systems, focusing on strategies for designing scalable and efficient technical architectures. We examine both horizontal and vertical scaling techniques, the role of microservices, and the importance of performance monitoring and optimization. Through a detailed analysis, we provide insights into best practices and emerging trends that guide the development of robust cloud solutions capable of meeting dynamic business demands.
Building Secure, Explainable, and Auditable AI Systems for Modern Software Engineering Applications
Artificial intelligence is now embedded in defect prediction, test prioritization, incident triage, release governance, and service observability. As these capabilities move into production workflows, software engineering organizations must evaluate more than predictive performance. They must show that model behavior is secure, explainable, auditable, and governable under drift, scale, and compliance pressure. This paper proposes a research grounded framework for building secure, explainable, and auditable AI systems for modern software engineering applications. The framework synthesizes trustworthy AI risk management, machine learning software engineering, explainability research, documentation practices, and cloud native observability. It argues that trustworthy AI in software engineering should be treated as a lifecycle systems property spanning data provenance, model design, evidence generation, runtime monitoring, human review, and post deployment accountability. The paper introduces architectural layers and control mechanisms for integrating security, interpretable evidence, and audit artifacts into AI enabled engineering platforms, and maps them to practical use cases including automated testing, defect forecasting, observability, document processing, and workflow governance.
Development of Intelligent Systems for Modern Engineering Challenges
The rapid evolution of engineering systems, driven by increasing complexity, interconnectedness, and dynamic operational environments, has necessitated the development of intelligent systems capable of autonomous decision-making and adaptive behavior. This study explores the design and implementation of intelligent systems to address modern engineering challenges across domains such as manufacturing, infrastructure, energy, and transportation. The research integrates artificial intelligence techniques, including machine learning, deep learning, and optimization algorithms, with cyber-physical systems and real-time data processing frameworks to create adaptive and resilient engineering solutions. A conceptual architecture is proposed that combines sensing, data integration, intelligent analytics, and decision-making layers to enable continuous monitoring and optimization of engineering systems. The study further examines practical applications through case-based or simulation-based validation, demonstrating improvements in efficiency, reliability, and system performance. Key challenges such as data quality, system interoperability, explainability, and cybersecurity are also analyzed. The findings highlight the transformative potential of intelligent systems in enhancing engineering processes and outcomes. This research contributes to both theoretical and practical advancements by providing a structured framework for developing intelligent systems that can effectively address complex and evolving engineering problems in the era of digital transformation.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Prospects, Challenges, Implementation Strategies, and General Outlook for Geothermal Energy Application in Nigeria
Nigeria's national development depends on energy security and sustainability because of the nation's expanding industrial base, growing population, and ongoing electricity shortages. The Earth's interior heat can be used to generate geothermal energy, a renewable resource that is yet mostly unexplored but has the potential to produce consistent, dependable, and eco-friendly power. Nigeria's Benue Trough, Sokoto Basin, Niger Delta, and portions of the basement complex all have geothermal potential, as shown by high geothermal gradients, increased heat flow, and surface manifestations such as the hot springs at Ikogosi, Wikki, and Nasarawa. Despite these signs, Nigeria's geothermal development is still restricted because of a lack of specific legal frameworks, high initial investment costs, poor institutional frameworks, a lack of geo-scientific exploration data, and a lack of public awareness. It will require a comprehensive and thorough evaluation of geothermal resources, capacity building, risk mitigation techniques, supportive regulatory frameworks, and stakeholder engagement to overcome these obstacles. Nigeria might increase energy security, diversify its energy sources, improve electricity reliability, lower greenhouse gas emissions, and promote industrial and rural development if geothermal energy is fully adapted into the country's energy mix. As a sustainable and workable solution to Nigeria's energy problems, this discussion paper emphasizes the strategic significance of geothermal energy and calls for systematic research, policy prioritization, and investment to realize its full potential. Thus, this paper examines the prospects, challenges, implementation strategies, and general outlook for geothermal energy application in Nigeria.
Machine Learning Techniques in Healthcare Business Intelligence: A Comprehensive Review
The rapid growth of healthcare data has intensified the need for advanced analytical approaches to support effective business intelligence and decision-making. This study aims to comprehensively review machine learning techniques applied in healthcare business intelligence and examine their contributions to clinical, operational, and strategic performance. Using a systematic literature review methodology, peer-reviewed journal articles, conference proceedings, and industry reports published in recent years were analyzed and synthesized. The review categorizes machine learning techniques into supervised learning, unsupervised learning, deep learning, and reinforcement learning, highlighting their applications in predictive analytics, clinical decision support, operational optimization, fraud detection, and population health management. Key findings indicate that machine learning significantly enhances predictive accuracy, resource efficiency, and data-driven decision-making in healthcare organizations, although challenges related to data quality, privacy, interpretability, and system integration persist. The study concludes that while machine learning has transformative potential for healthcare business intelligence, successful implementation requires robust data governance frameworks, explainable models, and close collaboration between technical experts and healthcare stakeholders.
Emerging Trends in Corrosion Engineering and Technology for Steel Reinforced Concrete
Corrosion of steel reinforced concrete (RC) is among the major durability challenges of civil engineering infrastructure [1-3]. With the increasing service demands for aging buildings, bridges, and highways, the durability of RC structures is not a secondary design criterion or just an academic concern. Corrosion-related durability of steel reinforced concrete directly influences the safety of the public, the economy, sustainability, and environmental protection. The recent research and development in the field of corrosion engineering have seen enormous technological advancements. Corrosion engineering for steel-reinforced concrete structures is now emerging as a state-of-the-art technology, which is renewing the optimism that engineers can now predict, prevent, and control reinforced concrete corrosion in a better way for civil engineering infrastructure. The need of the hour is to transform the RC corrosion engineering technology from reactive repair to the proactive protection trend. Conventionally, the trend was to repair [4-5] the concrete structures after the corrosion damage was visible. Usual methods used for this include patching of the spalling concrete, replacing the corroded rebars, and applying the surface sealants. However, this concept of reaction repair is expensive and effective only temporarily [5]. The emerging trends are to move away from this mindset and go for proactive corrosion protection by preventing the damage before it reduces the load-carrying capacity by causing cracks in concrete and a reduction in rebar area.
Design and Development of Wheelchair cum Stretcher with IoT
The wheelchair-cum-stretcher is an innovative, multifunctional medical device designed to enhance patient mobility and healthcare efficiency. It integrates the functionalities of both a wheelchair and a stretcher, allowing seamless transformation between the two modes. This dual-purpose design minimizes the need for multiple transfers, reducing patient discomfort and the risk of injury. The device is particularly beneficial in emergency settings, hospitals, and elderly care facilities, where quick and safe patient movement is crucial. Features such as an adjustable frame, foldable components, and ergonomic support ensure ease of use for both patients and caregivers. This study explores the design, materials, and engineering considerations behind the wheelchair-cum-stretcher, focusing on durability, maneuverability, and patient comfort. Various prototypes were analyzed to optimize weight distribution, stability, and ease of operation. The research also highlights the potential impact of this device on healthcare efficiency, particularly in reducing response time and labor costs. The findings suggest that a well- designed wheelchair-stretcher hybrid can significantly improve patient care, making it a valuable addition to modern medical equipment
AI-Augmented Microgrid Design for Renewable Energy Distribution Networks
Renewable energy distribution systems are being improved thanks to AI in microgrids, which solve the issues caused by fluctuations in supply and demand. Intelligent systems that use machine learning and predictive analytics are used to help with the generation, storage and delivery of energy. Using recent information from IoT sensors and earlier energy usage records, LSTM and SVR AI algorithms are able to predict load accurately, balance any changes in load smoothly and allow proactive energy management. As a result, there is effective energy use from solar, wind and hybrid renewables with barely any need for backup generators. Innovations such as using AI for predictive maintenance reduce downtime of equipment by about 30–40% and Genetic Algorithms (GA) can lead to up to a 67% drop in electricity costs in comparison to more traditional approaches. Using adaptive control allows these microgrids to manage the gap between supply and demand, make the grid more stable and prevent problems caused by changing weather by shifting resources early. Rural case studies prove that it is possible to enhance both energy supply reliability and decrease carbon emissions by around 50%. Using AI makes energy networks safer and lets them use demand response to motivate people to use energy when demand is low. With these advancements, AI-enhanced microgrids become key support for reliable, cheaper and greener energy in both areas with and without a main energy network.
Development of Real-Time Reservoir Surveillance Platforms Using Edge Computing and Wireless Sensor Networks
Continuous surveillance in a reservoir is necessary for managing resources properly; yet traditional cloud services encounter delays, limited bandwidth and high energy costs. To overcome these limitations, the study presents a new system that merges edge computing with Wireless Sensor Networks (WSNs). The system achieves faster response times and decreased power use by processing information close to where sensors are placed. ESNs and other reservoir computing structures are key components of the platform for processing unbalanced sensor information smoothly. Real-time detection and prediction of unusual patterns is possible with ESNs because their reservoirs can automatically handle input signals. This process is made better by using a fault-tolerant edge provisioning framework that helps choose the best placement for nodes that balance being reliable, using less energy and delivering services fast. Specifically, the devices can preprocess their data and filter out noisy parts and then encode these important features before they send the state vectors to servers for deeper study. Experiments in water quality monitoring reveal how the system processes all the intended measurements (such as pH and dissolved oxygen) despite not having much bandwidth. The use of inkjet-printed sensors along with analog pulse-based telemetry helps to both lower costs and use less energy. There is a 40% decrease in delay time for transferring data and a 30% rise in successful fault detection, as shown by the results, compared to models that depend on the cloud.
An Autonomous and Decentralized IoT Control Framework for Real-Time Management of Enhanced Oil Recovery Systems
Managing Enhanced Oil Recovery (EOR) operations must be well planned, with detailed data inputs and active monitoring to improve production and avoid risks. In these environments, traditional monitoring and control systems do not handle the unique, spread-out structures of today’s oilfields well. This framework was created to make autonomous and decentralized decisions in the control of EOR systems. The architecture uses IoT sensors, edge devices and intelligent agents to support local processing, reliable communication between devices and smart changes in the EOR system. Distributing control software to devices at the edge with up-to-date information allows the framework to tightly control activities, strengthen its ability to resist disruption and handle a rising number of distributed fields. The framework has been shown to maintain precise control, save energy and adapt rapidly to temporary changes in reservoir and equipment conditions via experimental validation. It provides a new way to handle EOR operations, using artificial intelligence spread out across systems.
Adaptive Signal Processing Algorithms in Biomedical Devices for Remote Monitoring
Adaptive Signal Processing (ASP) algorithms have revolutionized remote health monitoring systems through integration into the biomedical device. With the pressure on global healthcare systems to deliver scalable, patient-centric monitoring frameworks, ASP algorithms can help transform how healthcare services are provided now and in the future. The algorithms adapt to different physiological conditions, suppress noise while extracting critical features in real-time and improve the accuracy and reliability of biomedical diagnostics. In this paper, current advancements and applications of ASP within remote biomedical monitoring are presented, and several adaptive filtering techniques (Least Mean Square (LMS), Recursive Least Squares (RLS), Kalman filters) are described. Wearable biosensors, IoT and ASP work in tandem to augment the capabilities of e-health systems to process physiological data continuously and in real time. A number of case studies (including ECG monitoring, EEG-based brain computer interfaces) are described, which demonstrate the practical utility of ASP in diagnosing cardiovascular anomalies, detecting epileptic seizures and monitoring of respiratory irregularities. This paper also assesses algorithmic performance in terms of convergence rate, efficiency via computational cost and signal-to-noise ratio (SNR). These discussions are supported by a literature review including recent results in medical engineering and signal processing. An experimental setup is proposed by the methodology section that uses simulated biomedical signals, the prototyping of hardware using Arduino and MATLAB-based signal analysis. Our results demonstrate significant improvement in noise suppression and anomaly detection over traditional signal processing techniques. The results support the protean promise of ASP in telemedicine and personalized medicine.
Federated Learning for Privacy-Preserving Smart Healthcare: An Architectural Overview
The combination of Artificial Intelligence (AI) and Internet of Things (IoT) in the healthcare realm has led to intelligent healthcare systems that provide real-time and data-driven medical services that are personalised to an individual. Yet, due to the favorable nature of healthcare data, privacy, security, and regulatory compliance (such as HIPAA and GDPR) become challenging. Existing, traditional centralized machine learning paradigms are often a poor fit for healthcare because they require the consolidation of all the data, which can heavily erode patient privacy. As a new approach, Federated Learning (FL) allows model training over various decentralized devices without patients’ data being sent to a central server. This provides an architectural overview of FL in privacy-preserving smart healthcare. The study then investigates the fundamental components, enabling technologies, communication protocols, security enhancements and performance measurement metrics for FL architectures. We also examine real-world use cases, including remote patient monitoring (RPM), disease prediction and medical image analysis. Moreover, an extensive literature study, comparative analysis and a proposed framework which integrates differential privacy and secure multiparty computation are presented to increase the security of the data as well as model robustness. Latency, model accuracy and communication efficiency are discussed using simulated datasets and potential key performance indicators of a hybrid system where a group of real-world participants led by an expert proxy provide input to an ML engine. This culminates in a detailed discussion on the challenges, future directions and the transformational potential of federated learning in establishing a truly secure and intelligent smart healthcare ecosystem.