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71 articles for “deployment strategies”
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Health-Seeking Behavior among Women Living in Internally Displaced Persons Camp in Kagara, Niger State
Abstract: This study investigates health-seeking behavior among women in the Internally Displaced Persons (IDP) camp in Kagara, Niger State. Using a cross-sectional design, the study surveyed 400 women to understand factors influencing their health-seeking practices, including barriers to accessing healthcare. The findings of this study reveal that a combination of financial constraints, deeply ingrained cultural beliefs, logistical hurdles, and a general lack of awareness among internally displaced persons (IDP) women significantly …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 1, 2025 · pp. 19–28 Read article
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Review of Current Trends in Vaccine Development against Zoonotic Viruses: Challenges and Innovations
Abstract: The emergence of zoonotic viruses poses a significant threat to global public health, necessitating rapid advancements in vaccine development. This review highlights the current trends and innovative strategies employed in the creation of vaccines against zoonotic pathogens, including but not limited to viruses responsible for diseases such as Ebola, Zika, Nipah, and more recently, SARS-CoV-2. We discuss various vaccine platforms, including inactivated, live attenuated, mRNA, and viral vector vaccines, focusing …
Published in International Journal of Virus Studies · Vol. 1, Issue 2, 2024 · pp. 1–11 Read article
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Cloud-based Application Development and Optimization
Abstract: As cloud computing powers today’s applications, optimizing cloud-based development is crucial to achieve performance, cost effectiveness, and scalability. This research focuses on enhancing the design, deployment, and maintenance of cloud applications, tackling challenges in resource management, scalability, and resilience. We specifically explore dynamic resource allocation algorithms that use predictive analytics for auto-scaling based on workload variations, aiming to cut costs while preserving high performance. The study also investigates cross-cloud optimization …
Published in Journal of Open Source Developments · Vol. 12, Issue 1, 2025 · pp. 37–42 Read article
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Reinforcement Learning for Adaptive Sensing with Shape Memory Polymer-Based IoT Nodes
Abstract: The rapid expansion of intelligent sensing in the Internet of Things (IoT) has revealed the pressing need for materials and algorithms capable of self-adaptation in volatile environments. Conventional polymer-based sensors and static control strategies often fail to capture nonlinear thermo-mechanical dynamics, leaving them unsuitable for unpredictable operating conditions. Although prior studies have improved polymer composites or introduced algorithmic optimization independently, few attempts have coupled the adaptability of smart materials with …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 370–391 Read article
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Cloud-driven Fraud Detection: Evaluating Decision Tree and Random Forest Classifiers for Credit Card Transaction Security
Abstract: With the alarming rise in global financial fraud, necessitating substantial annual losses, modern techniques for fraud detection are continuously evolving across various business domains. Fraud detection involves constant monitoring of user activities to estimate, perceive, or prevent undesirable behaviour. Cloud Computing emerges as a promising solution, accelerating application deployment, fostering creativity and innovation, reducing costs, and enhancing overall business acumen. This study introduces a cloud-driven approach to fraud detection, specifically …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 1, 2024 · pp. 13–27 Read article
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Intelligent Earth: AI As A Catalyst For Climate Action
Abstract: Artificial Intelligence (AI) is assuming an increasingly influential role in climate science, providing advanced tools capable of interpreting vast, complex, and multi-dimensional environmental datasets. Traditional climate modeling approaches, while grounded in physical principles, frequently struggle to deliver high-resolution, real-time, and region-specific forecasts because of heavy computational demands, incomplete observations, and uncertainties in representing small -- scale processes. Artificial intelligence (AI) techniques, especially machine learning and deep learning, provide strong substitutes …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 4, Issue 1, 2026 · pp. 48–52 Read article
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Adaptive E-Learning Algorithms and Heutagogy: A Systematic Analysis
Abstract: The proliferation of artificial intelligence (AI) and machine learning (ML) technologies has transformed the digital education landscape by enabling adaptive e-learning systems capable of personalizing content and optimizing learning paths. This study provides a systematic analysis of adaptive e-learning algorithms within the framework of heutagogy, an educational paradigm that emphasizes learner autonomy, self-direction, and capability development. The convergence of adaptive technologies with heutagogical principles offers new avenues for creating more …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 33–38 Read article
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Open Source Software Empowering Artificial Intelligence, Machine Learning, and Cyber Security: A Comprehensive Research Study
Abstract: Open Source Software (OSS) has become a foundational pillar for rapid innovation across Artificial Intelligence (AI), Machine Learning (ML), and Cybersecurity. This paper delivers a comprehensive, journal-length analysis of OSS-driven ecosystems, emphasizing collaborative development, transparency, and accelerated deployment. By providing freely available libraries, tools, and frameworks, OSS makes it easier for developers and researchers to experiment, build models, and deploy solutions quickly. This study examines how OSS can be combined …
Published in Journal of Open Source Developments · Vol. 13, Issue 1, 2026 Read article
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AI-Driven Robotics for Sustainable Solutions in Disaster Management
Abstract: Disasters, whether natural or man-made, present significant challenges to societies worldwide. Efficient response, recovery, and mitigation strategies are crucial to minimizing human suffering, loss of life, and economic damage. Traditional disaster management strategies, while effective to some degree, often face limitations related to human resources, response time, accessibility, and safety. The integration of artificial intelligence (AI) and robotics into disaster management offers transformative potential for overcoming these challenges. This paper …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 3, Issue 1, 2025 · pp. 24–30 Read article
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RF Energy Harvesting Techniques for Wireless Sensor Networks
Abstract: In contemporary applications like environmental monitoring, healthcare systems, industrial automation, agriculture, military surveillance, and smart cities, Wireless Sensor Networks (WSNs) are crucial. However, the limited battery life of sensor nodes remains a major challenge because many sensor devices are deployed in remote or difficult-to-access locations. Frequent battery replacement or recharging increases maintenance cost, reduces network reliability, and limits long-term operation. In contemporary applications like environmental monitoring, healthcare systems, industrial automation, …
Published in Recent Trends in Sensor Research & Technology · Vol. 13, Issue 2, 2026 Read article
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Advancements in Humanoid Robot Locomotion: A Review of Control Strategies and Kinematic Models
Abstract: Humanoid robot locomotion has significantly improved over the past few decades, driven by improvements in control strategies and kinematic models. Researchers aim to develop robots that can walk, run, and navigate complex terrains with efficiency and stability. This review explores recent developments in humanoid locomotion, highlighting control strategies such as model predictive control, reinforcement learning, and central pattern generators. Additionally, it examines kinematic models, including inverted pendulum models and zero …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 3, Issue 1, 2025 · pp. 24–30 Read article
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The Role of Simulation and Digital Twins in Enhancing Mechanical Production Efficiency: A Systematic Review
Abstract: In the evolving landscape of smart manufacturing, simulation technologies and digital twin (DT) systems have emerged as pivotal tools for enhancing the efficiency, agility, and sustainability of mechanical production processes. This systematic review investigates how the integration of simulations and DTs contributes to performance improvements across various stages of mechanical manufacturing—ranging from design and process optimization to predictive maintenance and real-time monitoring. While simulations provide the ability to model, test, …
Published in Journal of Production Research & Management · Vol. 15, Issue 2, 2025 · pp. 31–36 Read article
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Techniques for Congestion Mitigation in Hybrid Electricity Markets
Abstract: In hybrid electricity markets, managing congestion is a crucial issue that impacts market efficiency, grid stability, and the integration ofrenewable energy sources. In orderto efficiently detect and manage crowded zones, this study suggests an enhanced congestion mitigation strategy by introducing the notion of Average Transmission Congestion Distribution Factor (ATCDF). In order to improve grid dependability, the research focuses on integrating Wind Power Generation (WPG) with Battery Energy Storage Systems (BESS) …
Published in Trends in Electrical Engineering · Vol. 15, Issue 3, 2025 · pp. 1–6 Read article
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Anti-Soiling Coating Technologies for PV Panels Under Dust Storm Conditions
Abstract: Background Deployment of photovoltaics (PV) in arid and semi-arid areas where there is ample sunlight has been increasing quickly because of their great solar irradiation. The rising number of dust storms, as well as depositing particulate matter to the air, have caused a significant decrease in energy production due to reduced transmittance of light through the surface of the PV panel from the dust on the panel’s surface. This has …
Published in Journal of Thin Films, Coating Science Technology & Application · Vol. 13, Issue 2, 2026 · pp. 26–36 Read article
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An Examination of Energy-Saving Techniques for UAV Communication
Abstract: Unmanned aerial vehicles (UAVs), or drones, have become essential in various industries due to their low deployment costs and exceptional versatility. As a result, they are widely used in industries like mining, agriculture, logistics, and search and rescue. The effectiveness of UAV applications is largely dependent on robust communication technology, which is crucial for control, data transmission, and coordination. However, the limited capacity of the low-power batteries used in UAVs …
Published in International Journal on Drones · Vol. 1, Issue 1, 2025 · pp. 1–7 Read article
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Blockchain Adoption in Indian Public Services: A Holistic Empirical Investigation
Abstract: The Indian public sector is pivotal in the country’s governance and public welfare. It is worth noting that there is a significant number of intermediaries involved in the execution of tasks that are compromising data transparency. Currently, the public sector banks lack standardization and validation as major obstacles in the deployment of blockchain technology. The research paper explores the scope and effectiveness of blockchain technology in India’s public sector through …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 3, 2024 · pp. 48–57 Read article
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Leveraging AI and Machine Learning for Early Prediction and Prevention of Non- Communicable Diseases in Resource-Limited Settings
Abstract: Populations in these regions face persistent structural barriers, such as underdeveloped healthcare infrastructure, shortages of trained health professionals, and fragmented or incomplete health information systems. These limitations delay timely diagnosis, restrict access to preventive care, and compromise effective disease management. In recent years, rapid progress in artificial intelligence (AI) and machine learning (ML) has opened promising avenues to mitigate these challenges. Practical applications already emerging include mobile health platforms for …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 15, Issue 1, 2026 · pp. 9–15 Read article
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Proof-of-Minimum Privacy Leak Consensus Strategy in Blockchain
Abstract: In this study, we propose a novel consensus algorithm to preserve the security and privacy of a transaction. We propose a Proof-of-Minimum Privacy Leak consensus strategy. This means that the competing nodes which participate in the competition to mine the next block should give a proof of minimum privacy leak during its transaction. Only this proof will give highest votes to that node, and it will be elected as the …
Published in E-Commerce for Future & Trends Read article
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Virtual Machine Cost & Computing comparison between cloud service
Abstract: Cloud computing has revolutionized enterprise IT infrastructure with virtual machines forming the cornerstone of Infrastructure as a Service deployment. This study provides a detailed comparative evaluation of virtual machine pricing and computational performance among three leading cloud computing platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Through quantitative analysis of current pricing data from September 2025, independent performance benchmarks from Cockroach Labs 2021 Report, and market …
Published in International Journal of Mobile Computing Technology · Vol. 4, Issue 2, 2026 Read article
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Enhanced Multimodal Disease Prediction Using Hybrid Ensemble Learning and AutoML Techniques
Abstract: The integration of hybrid ensemble learning and automated machine learning (AutoML) is revolutionizing disease prediction by addressing the complexity, imbalance, and high dimensionality inherent in medical datasets. This paper proposes an advanced pipeline that combines diverse ensemble learning models with AutoML-based optimization to predict chronic diseases such as kidney diseas-e, Parkinson’s disease, and lung cancer. Publicly available datasets from UCI and PhysioNet repositories were preprocessed using outlier removal, normalization, and …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 Read article