Academic International Journal of Engineering Science
type: channel
description: Academic International Journal of Engineering Science AIJES is a peer-reviewed, open-access journal that publishes original research, review articles, case studies, field studies, and commentaries on engineering aspects as shown in the below categories: – Aerospace engineering Architecture Engineering Biomolecular Engineering Biomedical engineering Chemical Engineering Civil Engineering Communication Engineering Computer Engineering Computer Science Data Science Electrical Engineering Engineering Physics Environmental engineering Industrial Engineering Materials Science and Engineering Mechanical Engineering Nuclear Engineering Systems Engineering
publisher: Academic International Publishers
language: en
publication name: Academic International Journal of Engineering Science
issn: 2984-7745
items: Adaptive Control Strategies for Energy-Efficient Smart Grids
items: Green Synthesis of Nanomaterials for Advanced Water Desalination Systems
items: Machine Learning–Based Predictive Maintenance in Industrial Robotics
items: Secure and Scalable Blockchain Frameworks for Internet of Things (IoT) Networks
items: A Review of Self-Healing Concrete Technologies for Sustainable Infrastructure
items: Biometric Payment Systems in India – A Case Study of ThumbPay by Proxgy
items: AI-Driven Security Framework for Internet of Mobility Things: A Mixed-Methods Analysis of Machine Learning Applications in Connected Vehicle Networks
items: Slope Stability Considering the Top Building Load
items: A SPICE-Compatible Physics-Inspired Model for Molecular-Type Atomic Switches in Memory and Neuromorphic Circuits
items: Smart Logistics Integration: 5G Networks and IoT Technologies in Modern Supply Chain Management
items: Machine Learning Techniques and Insights for Cardiovascular or Heart Disease Prediction
items: A Systematic Review of Single-Tuned Passive Filters for Harmonic Mitigation in Distribution Networks
items: A Comprehensive Review of Top-Loading Effects on Slope Stability: Load Magnitude, Distance, and Geological Heterogeneity
items: Advances and Limitations of Level Set Methods for Modeling the Freshwater–Saltwater Interface in Coastal Aquifers: Insights from the Henry Problem
items: Radial Basis Function Neural Networks for Rainfall Prediction and Urban Tree Management in Tropical Malaysia
items: Artificial Intelligence driven Customer Behavior Analytics for Healthcare: A Systematic Review of Patient Experience, Engagement, and Decision-Making Patterns
items: Exploring Rice Husk Ash in Uncontrolled Condition as Supplementary Cementitious Materials for Enhanced Performance in Construction Materials
items: State Of Art: Carbon Fiber Reinforced Concrete A Review
items: Retrofitting of RCC Beams in the Shear Region Using Carbon Fiber-Reinforced Polymer
items: Review of High-Strength Concrete Structures with Hybrid Fibers
items: State of Art: Seismic Failure Impact on Concrete Bridges
items: Intrusion Detection Systems: A Revisit of Performance Evaluation Parameters
items: Effect of Training on the Construction Project Performance Using System Dynamic

Adaptive Control Strategies for Energy-Efficient Smart Grids
type: item
description: The global move toward sustainable energy systems has led to the creation of smart power distribution networks that are able to balance the various energy sources, changing demand curves and real time operating limits. Adaptive control strategies are another form of new approach to smart grid control, which allows dynamically optimizing the flow of energy, load balancing, and integration of renewable energy. This review looks into the core principles, technological structures, and implementation issues of adaptive control systems in the present-day smart grids. The paper presents major control methodologies such as model predictive control, artificial intelligence-based control and distributed control architecture. It is a critical analysis of how these strategies can improve grid stability, reduce energy losses, and absorb intermittency of the renewable energy generation. Also, the paper reviews regulatory issues, cybersecurity standards, and interoperability standards that regulate smart grid activities across the world. The results reveal that adaptive control systems with the appropriate inclusion of robust communication infrastructure and modern sensing devices can greatly enhance grid efficiency by minimizing energy losses up to 20 percent, reducing operational expenses, and speeding up the process of integrating decentralized energy sources like renewable energy sources but maintaining the reliability and resilience of the system.
subject: Articles
  topic: section
subject: Adaptive control, Smart grids, Model predictive control, Renewable energy integration, Artificial intelligence,
  topic: keywords
creator: William H. Paker
rights: Copyright (c) 2023 Academic International Journal of Engineering Science https://creativecommons.org/licenses/by/4.0
date: 2023-12-04
publication date: 2023-12-04
starting page: 40
ending page: 48
doi: 10.59675/E125

Green Synthesis of Nanomaterials for Advanced Water Desalination Systems
type: item
description: Water scarcity around the world has led to increased demands of effective and sustainable desalination processes that can transform sea water and brackish water to drinking water. Nanotechnology solutions are transformational in that the membrane performs better, the membrane is able to endure fouling better, and the membrane can remove contaminants more optimally. Nevertheless, the traditional techniques of producing nanomaterials such as the use of toxic substances, energy intensive processes and production of toxic wastes are against the concept of sustainability required to curb the water problems. Environmentally benign alternatives have been found through green synthesis methods which use environmental friendly biological agents such as plant extracts, microorganisms and biopolymers with high-performance nanomaterials made with minimal ecological footprint. In this paper the analyses will be of green synthesis techniques applicable in the fabrication of nanoparticles, nanocomposites, and nanostructured membranes that are specifically developed to serve the advanced water desalination systems. It has a critical analysis of synthesis mechanisms, techniques of characterization, as well as performance measurements such as salt rejection rates, permeate flux, antifouling properties, and stability during operation. The paper compares the use of applications of reverse osmosis, forward osmosis, membrane distillation, and nanofiltration technologies, the scale-up issue, and economic viability. This study also shows through case studies of Middle Eastern, Asian, and African desalination plants that green-synthesized nanomaterials exhibit salt rejection rates that are more than 99% with energy reduction of up to 30. The paper summarizes that green nanotechnology is a viable way forward in meeting water security by having environmentally responsible desalination plants in line with the concept of the circular economy and the United Nations Sustainable Development Goals.
subject: Articles
  topic: section
subject: Green synthesis, Nanomaterials, Water desalination, Reverse osmosis, Membrane technology, Plant-mediated synthesis,
  topic: keywords
creator: Stanley N. Gathekia
rights: Copyright (c) 2023 Academic International Journal of Engineering Science https://creativecommons.org/licenses/by/4.0
date: 2023-09-12
publication date: 2023-09-12
starting page: 30
ending page: 39
doi: 10.59675/E124

Machine Learning–Based Predictive Maintenance in Industrial Robotics
type: item
description: The fast development of the industry 4.0 technologies is radically changing the operations and maintenance approaches of manufacturing activities in the global arena. Machine learning predictive maintenance is a paradigm shift of the old reactive and preventive methods which allow real time fault detection and predictions of the remaining useful life in industrial robots. In this paper, the technical framework and operation models of predictive maintenance in robotic systems by integrating machine learning algorithms with sensor networks and Industrial Internet of Things infrastructure are analyzed along with their implications. It is a critical analysis of different methodologies such as supervised learning, unsupervised learning, and deep learning approaches, and it discusses implementation issues involving the data quality, model interpretability, and system integration. The paper also assesses the regulatory factors and cybersecurity systems needed to deploy in industries. Using automotive, electronic, and aerospace manufacturing case studies, this study shows that predictive maintenance with the help of ML can reduce unexpected downtimes by up to 40 percent and increase the life of components. It is concluded in the paper that machine learning-driven predictive maintenance systems are revolutionary systems to provide efficient, reliable, and cost-effective industrial operations in the smart manufacturing era.
subject: Articles
  topic: section
subject: Predictive maintenance, Machine learning, Industrial robotics, Industry 4.0, Deep learning, Remaining useful life, Condition monitoring
  topic: keywords
creator: Naseer Hamzah Mohammed
rights: Copyright (c) 2023 Academic International Journal of Engineering Science https://creativecommons.org/licenses/by/4.0
date: 2023-08-23
publication date: 2023-08-23
starting page: 21
ending page: 29
doi: 10.59675/E123

Secure and Scalable Blockchain Frameworks for Internet of Things (IoT) Networks
type: item
description: The increasing exponentially growing Internet of Things (IoT) networks have posed hitherto unseen challenges, in security, data integrity, and scaling in distributed systems. It provides an ideal technology that solves numerous intrinsic weaknesses of traditional IoT designs such as single point of failure, unauthorized access, and data distortion, which can be resolved by the use of blockchain technology. In this paper, the author will discuss how blockchain frameworks can be implemented in conjunction with IoT networks, the technical structure, consensus algorithms, and security protocols intended to improve the level of reliability and trustworthiness of a system. It is a critical analysis of several blockchain applications such as the public as well as the private model and consortium model and also discusses scalability issues of each model of computational overhead, energy usage, and transaction throughput. The paper discusses novel solutions that include lightweight consensus algorithms, off-chain processing, sharding, and directed acyclic graph designs which are specifically designed to support the resource-constrained IoT devices. This study is shown using case studies in smart cities, healthcare, supply chain management and industrial automation industries to show how blockchain-IoT integration can help minimize security breaches by up to 60 percent without compromising system performance. The paper concludes that scalable and secure blockchain platforms are disruptive platforms to construct trusted, transparent and robust IoT networks in the era of pervasive computing.
subject: Articles
  topic: section
subject: Blockchain, Internet of Things, IoT security, Distributed ledger, Consensus mechanisms, Smart contracts, Scalability, Edge computing
  topic: keywords
creator: Tamara Saad Mohamed
rights: Copyright (c) 2023 Academic International Journal of Engineering Science https://creativecommons.org/licenses/by/4.0
date: 2023-08-10
publication date: 2023-08-10
starting page: 11
ending page: 20
doi: 10.59675/E122

A Review of Self-Healing Concrete Technologies for Sustainable Infrastructure
type: item
description: Self-healing concrete is a revolutionary innovation in the science of construction materials, which has an autonomous capacity to repair, which is one of the primary drawbacks of concrete cracking and progressive weakening. This review looks at the key self-healing strategies that have been developed over the last 20 years such as autogenous healing in which the surrounding hydration and carbonation promote the healing process, bacterial-based biological systems in which microbial induced calcite precipitation acts to promote healing, and encapsulated chemical healing agents which are released once the crack is formed. The evaluation of each technology is in terms of efficiency of healing, constraints in terms of practical crack width, environmental necessities, cost implications and practicability. The article examines the latest progress in the encapsulation devices, bacterial spore protectant mechanism, and the hybrid systems of combining various mechanisms of healing in order to have the overall effect of crack healing. It has been shown in performance data that bacterial systems are capable of healing cracks with a width of up to 0.5-0.8 millimeters and a high recovery of the strength and that autogenous techniques can seal micro-cracks with a width of less than 0.15 millimeters. Issues of implementation such as scalability of production, standardization of quality control, long-term validation of performance and integration of the construction practices are critically looked at. The life-cycle assessment has shown that the potential environmental impact can be reduced by 20-30 percent by using longer structural life and lowering maintenance rates. The results indicate that self-healing concrete technology, specifically hybrid solutions have a lot of potential to revolutionize sustainable infrastructure construction with a significant portion of construction budgets estimated to be spent on construction repair and maintenance being diminished by a significant m…
subject: Articles
  topic: section
subject: Self-healing concrete, Autogenous healing, Bacterial concrete, Microbial induced calcite precipitation, Encapsulated healing agents
  topic: keywords
creator: Fe del Mundo
rights: Copyright (c) 2023 Academic International Journal of Engineering Science https://creativecommons.org/licenses/by/4.0
date: 2023-07-12
publication date: 2023-07-12
starting page: 01
ending page: 10
doi: 10.59675/E121

Biometric Payment Systems in India – A Case Study of ThumbPay by Proxgy
type: item
description: The rapid evolution of the digital payment ecosystem in India is changing the way citizens access and use financial services. Developed by Proxgy, ThumbPay represents an innovative leap in integrating Aadhaar-based biometric authentication with the Unified Payments Interface, UPI, thereby allowing users to authorize transactions with just their fingerprints. Contrasting with the Amazon One or Tap-and-Pay by Tencent, which have an appeal toward convenience and luxury, ThumbPay has been created with a vision for accessibility and inclusion—solving the problems of unbanked populations and those who cannot use smartphones. This paper examines the technical framework, the model of operation, and the implications of ThumbPay regarding the promotion of financial inclusion in India. It also critically analyzes various issues related to data privacy and security and its readiness concerning regulatory guidelines under the frameworks provided by the RBI, NPCI, and DPDPA (2023). The study concludes that biometric payment systems, like ThumbPay, might act as a transformative agent in achieving an inclusive, secure, and transparent digital financial ecosystem in India.
subject: Articles
  topic: section
subject: Biometric payment systems, ThumbPay, Proxgy, Aadhaar, UPI, Financial inclusion, Data privacy, Digital India, FinTech, DPDPA 2023
  topic: keywords
creator: Abhinab Mondal
creator: Surjadeep Dutta
rights: Copyright (c) 2025 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
date: 2025-11-20
publication date: 2025-11-20
starting page: 51
ending page: 55
doi: 10.59675/E324

AI-Driven Security Framework for Internet of Mobility Things: A Mixed-Methods Analysis of Machine Learning Applications in Connected Vehicle Networks
type: item
description: Internet of Mobility Things (IoMT) is the future of transport in a nutshell. It is a car mash-up that combines chat, self-driving technology, and intelligent road technology, utilizing high-tech communications. This article combines two methods to understand how AI can be used to make IoMT safe: a systematic literature review and hands-on experiments. We filtered 45 papers in 202025 and also tested machine-learning models on typical car information. The review featured such AI giants as deep neural networks, federated learning, and adversarial training. At the end of the data, campus area networks CAN bus attack data was proven to yield our convolution neural networks CNNs a 99.2% detection rate and a 0.58% false positive result. Fed-learning models made 97.8% correct with data confidentiality. Altogether, artificial intelligence AI introduces numerous benefits in terms of security; yet we continue to encounter limitations due to the limitations of computer power and the necessity to operate in real-time. We consider that the hybrid AI-plus-traditional model is the most suitable solution to IoMT security. This paper will help establish standard AI security systems for next generation connected mobility.
subject: Articles
  topic: section
subject: Internet of Mobility Things, AI security, federated learning, intrusion detection, vehicular networks, cybersecurity.
  topic: keywords
creator: Tamara Saad Mohamed
rights: Copyright (c) 2025 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 36
ending page: 50
doi: 10.59675/E323

Slope Stability Considering the Top Building Load
type: item
description: In this paper, the effect of building loads on the stability of slopes was analyzed through three dimensions of the numerical model. Various load pressures that include 120KPa, 200 KPa, and 300KPa were applied to a specified slope model about geometric and mechanical characteristics. The analysis was performed using FLAC3D software for displacement, strain, as well as the safety of the tunnel. The following conclusions can be drawn from the conclusions as they show a strong correlation between different loads and the distances from the shoulder of the slope to determine the stability of the slope and also is valuable information as far as engineering is concerned.
subject: Articles
  topic: section
subject: Green Concrete
  topic: keywords
subject: Construction projects
  topic: keywords
subject: construction
  topic: keywords
creator: Stanley Njoroge
rights: Copyright (c) 2024 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0/
date: 2024-06-20
publication date: 2024-06-20
starting page: 22
ending page: 27
doi: 10.59675/E213

A SPICE-Compatible Physics-Inspired Model for Molecular-Type Atomic Switches in Memory and Neuromorphic Circuits
type: item
description: Molecular-type atomic switches are an emerging technology in solid-electrolyte electrical devices, serving as nonvolatile memory devices and neuromorphic computers. However, there are no compact physics-inspired behavioral models to enable their integration into circuit-level simulations. This paper introduces a simple SPICE-compatible behavioral model that combines ion-density dynamics, filament growth, and exponential resistance growth to recreate the major experimental behaviors of these devices, such as bistable switching, rate-dependent plasticity, and quantized conductance steps. The model is represented as a set of coupled ordinary differential equations using a two-element state vector, and it is then converted into SPICE primitives as behavioral sources and state holding capacitors in practice. Calibration plans for measurable experimental quantities, such as switching time as a function of bias, threshold voltages, and quantized step magnitudes, are suggested. The resulting framework allows for quick exploration of circuits at the circuit level and evaluates the entire system at the system level, while considering material-specific parameterization demands and possible quantum effects refinements. It is a synthesis of proven experimental and theoretical literature, making it directly applicable to modern memory and neuromorphic applications.
subject: Articles
  topic: section
subject: Atomic switch; SPICE modeling; Filament growth; Plasticity; Calibration; Neuromorphic computing
  topic: keywords
creator: Ahmed Ramil Suma
rights: Copyright (c) 2023 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
date: 2023-02-15
publication date: 2023-02-15
starting page: 01
ending page: 15
doi: 10.59675/E111

Smart Logistics Integration: 5G Networks and IoT Technologies in Modern Supply Chain Management
type: item
description: The combination of 5G and internet of things IoT technology is fundamentally a game-changer in smart logistics. This paper will consider the impact of 5G-enabled IoT networks on the entire supply chain, examining innovative network designs such as network slicing, multi-access edge computing (MEC), and low-power wide-area networks (LPWAN). In essence, the paper demonstrating that such technologies can increase warehouse efficiency by 20% and that the use of solutions rooted in narrowband IoT (NB-IoT) may help increase yard dispatch efficiency by 87%. Our systematic digital twin simulations and real-world tests have shown that bespoke network slices and edge computing are highly effective in meeting numerous logistics requirements. They improve throughput, strip off latency and enhance reliability. The paper provides specific, concrete information that IT professionals, telecom engineers, and supply-chain managers can apply in practice when implementing next-generation logistics solutions.
subject: Articles
  topic: section
subject: Smart logistics, 5G, Internet of Things, supply chain management, network slicing, edge computing.
  topic: keywords
creator: Tamara Saad Mohamed
rights: Copyright (c) 2025 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
date: 2025-06-24
publication date: 2025-06-24
starting page: 36
ending page: 45
doi: 10.59675/E314

Machine Learning Techniques and Insights for Cardiovascular or Heart Disease Prediction
type: item
description: Heart disease has been one of the major health concerns over the decades regardless of age, weight, or gender. Here in the article, we try to highlight the importance of early detection and the prevention of life-threatening heart-related diseases, including heart attacks and strokes. For this review paper, we include raw data from clinics and also include the famous Framingham Heart Study dataset, which is a popular cardiovascular dataset, including patient records that emphasize important human risk factors including age, blood pressure, cholesterol, smoking status, and family history. Precision, recall, accuracy, F1-score, and ROC-AUC metrics are such techniques and algorithms that are used to evaluate the implementation and performance of advanced machine learning approaches, including Gradient Boosting Machines (GBM), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Convolutional Neural Network (CNN) and many more. Here, we
subject: Articles
  topic: section
subject: Heart Disease Prediction, Early Detection, Cardiovascular Health, Gradient Boosting Machines, K- Nearest Neighbors, Artificial Neural Networks.
  topic: keywords
creator: Mujiba Shaima
creator: Mazharul Islam Tusher
creator: Estak Ahmed
creator: Sharmin Sultana Akhi
creator: Rayhan Hassan Mahin
rights: Copyright (c) 2025 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
date: 2025-06-27
publication date: 2025-06-27
starting page: 22
ending page: 35
doi: 10.59675/E313

A Systematic Review of Single-Tuned Passive Filters for Harmonic Mitigation in Distribution Networks
type: item
description: This paper is a systematic literature review on the relevance of passive filtering on eliminating harmonic distortion in distribution networks by optimal use and sizing of single-tuned passive filters (STPFs). The review explains how competing goals of power quality augmentation, loss reduction and cost minimization are balanced in the multi-objective optimization models through a critical analysis of the models. The key findings indicate that properly selected and designed STPFs are important to minimize the Total Harmonic Distortion (THD) and Current Harmonic Distortion (CHD) and at the same time enhance the voltage profiles and minimize power losses. These conclusions can be supported by simulations on IEEE standard test feeders, specifically the IEEE 33-bus system, and by empirical research of real distribution networks. The results have verified that the optimization-oriented filter allocation planning is much better than the traditional methods with significant power quality improvement under operating conditions.
subject: Articles
  topic: section
subject: harmonic mitigation, tuned passive filters, distribution system, multi-objective optimization, power quality.
  topic: keywords
creator: Ameer J. Mawla
rights: Copyright (c) 2023 Academic International Journal of Engineering Science https://creativecommons.org/licenses/by/4.0
date: 2023-02-15
publication date: 2023-02-15
starting page: 56
ending page: 68
doi: 10.59675/E115

A Comprehensive Review of Top-Loading Effects on Slope Stability: Load Magnitude, Distance, and Geological Heterogeneity
type: item
description: Top-loading slope stability has now been a key issue to infrastructure development on or near slopes. This review will be a synthesis of the empirical, numerical, and analytical research studies that investigate the effect of top-building loads on slope response, especially the magnitude of the load, the distance between the slope shoulder and the load, and the heterogeneity of the load material. Top-surface loads are always noted as influential causes of slope instability in contemporary literature (2020-2023), which can change the possible slip surfaces, shear-strain patterns, and the factors of safety. Combining three-dimensional numerical modelling, parametric tests, and seismic factors prove that the magnitude of loads and the distance to the slope shoulder is a significant parameter of the potential to slip, the movement of the environment, and the safety level. Heterogeneity of rock-contents and geological variability have a great impact on the ultimate loading capacity and failure modes. Dual failure modes, namely, local foundation failure and global slope failure, require integrated assessment frameworks, under the combined effects of both the static and seismic loading. The synthesis highlights that geology-based, holistic and site-specific evaluations are critical in laying plans of buildings on or close to the slope.
subject: Articles
  topic: section
subject: slope stability, top building loading, numerical modelling, soil-rock heterogeneity, seismic loading, factor of safety.
  topic: keywords
creator: Simon A. Laru
rights: Copyright (c) 2023 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
date: 2023-02-15
publication date: 2023-02-15
starting page: 44
ending page: 55
doi: 10.59675/E114

Advances and Limitations of Level Set Methods for Modeling the Freshwater–Saltwater Interface in Coastal Aquifers: Insights from the Henry Problem
type: item
description: Coastal intrusion of salt water into freshwater aquifers is a major threat to the freshwater security in the world. This paper is a critical review of the level set technique as applied in monitoring and modeling the freshwater-saltwater interface during the coastal aquifer system, based on the case of Henry problem. Combined with high-order numerical discretization, and with a suitable reinitialisation approach, level set formulations have demonstrated the ability to accurately solve interface dynamics and achieve better geometric flexibility than traditional modeling methods. Nevertheless, there remain formidable mathematical problems such as numerical diffusion that affects the sharpness of the interface, inefficient maintenance of mass balance over long simulations and computational inefficiencies, especially in the three-dimensional and long-term modeling cases. The study suggests the hybrid methodological frameworks to help eliminate these limitations by using a combination of level set tracking with the volume-of-fluid corrections to improve mass conservation and make it more applicable to real-life field studies.
subject: Articles
  topic: section
subject: saltwater intrusion, level set method, Henry problem, coastal aquifers, interface tracking, density-dependent groundwater flow.
  topic: keywords
creator: Stanley G. Njoroge
rights: Copyright (c) 2023 Academic International Journal of Engineering Science https://creativecommons.org/licenses/by/4.0
date: 2023-02-15
publication date: 2023-02-15
starting page: 33
ending page: 43
doi: 10.59675/E113

Radial Basis Function Neural Networks for Rainfall Prediction and Urban Tree Management in Tropical Malaysia
type: item
description: The current research paper summarizes a wide body of information on Radial Basis Function Neural Networks (RBFNNs) to predict rainfall and its application in urban trees management in tropical Malaysia. Several peer-reviewed articles were analyzed to determine the predictive accuracy, architecture, feature engineering, and integration issues of RBFNNs. It was found that the architectural properties of RBFNNs were appropriate for nonlinearity and nonstationarity in the precipitation processes. Relative performance assessments invariably ranked RBFNNs as one of the most efficient tools compared to other machine learning models, showing better Root Mean Square Error (RMSE) and generalization capabilities across various hydro-climatic regimes. It was identified that incorporating rainfall predictions using RBFNNs with Geographic Information Systems (GIS)-based applications like GeoTrees and the Kuala Lumpur Multi-Hazard Platform (KL-MHP) is a valid solution to facilitate predictive maintenance, reduce costs, and alleviate hazards in tropical urban settings. The results support the use of RBFNNs as a practical and, in many cases, better forecasting element in urban tree management systems, offering quantifiable advantages in maintenance planning, risk reduction, and resource distribution in municipal arboriculture programs.
subject: Articles
  topic: section
subject: RBFNN; rainfall prediction; tropical Malaysia; urban tree management; GIS integration; predictive maintenance; urban forestry.
  topic: keywords
creator: Zenaa Qudrat Nazri
rights: Copyright (c) 2023 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
date: 2023-02-15
publication date: 2023-02-15
starting page: 16
ending page: 32
doi: 10.59675/E112

Artificial Intelligence driven Customer Behavior Analytics for Healthcare: A Systematic Review of Patient Experience, Engagement, and Decision-Making Patterns
type: item
description: The study discussed systematic literature review regarding the usage of Artificial Intelligence (AI) technologies as tools for analyzing, understanding and predicting consumer behavior in healthcare ecosystems, including patterns of patient engagement, service utilization and decision-making. Design/methodology/approach - A systematic literature review was performed across the major academic databases including PubMed, Scopus, Web of Science and IEEE Xplore, capturing articles published from 1980 to 2024. The literature review provided a synthesis of 87 peer-reviewed articles, which focus on the application of AI for customer behavior analytics in the healthcare context, categorized into five thematic groups: predictive analytics, personalization engines, sentiment analysis, mapping patient journeys, and decision support systems. Findings - AI technologies, in particular machine learning and natural language processing, are making strides in understanding complex patient behaviors, with predictive accuracy rates exceeding 85% in appointment adherence and compliance with treatment plans. Deep learning models are emerging as particularly useful in identifying patients who are at-risk and personalizing healthcare interventions, which have also resulted in increased patient satisfaction scores and lower healthcare costs. Research limitations/implications – Although technology has advanced significantly, obstacles remain related to data privacy, bias in algorithms, the transparency of AI models, and integration with current healthcare information systems. This review identifies a research gap in longitudinal studies and promotes further research into ethical frameworks for AI implementation in healthcare contexts. Practical implications – Healthcare organizations can use patient data and AI-informed behavioral insights to improve patient experiences, better allocate resources, decrease no-show rates, and develop health promotion initiatives targeting specific populations…
subject: Articles
  topic: section
subject: Artificial Intelligence, Healthcare Analytics, Patient Experience, Digital Health, Patient Engagement
  topic: keywords
creator: Surjadeep Dutta
creator: Sarthak Sengupta
creator: Arivazhagan.R
rights: Copyright (c) 2025 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
date: 2025-10-18
publication date: 2025-10-18
starting page: 12
ending page: 35
doi: 10.59675/E322

Exploring Rice Husk Ash in Uncontrolled Condition as Supplementary Cementitious Materials for Enhanced Performance in Construction Materials
type: item
description: This study investigates the potential of Rice Husk Ash (RHA), an abundant agricultural byproduct in Bangladesh, as a sustainable supplementary cementitious material (SCM) in concrete. Ordinary Portland Cement (OPC) was partially replaced with locally produced, unprocessed RHA at proportions of 5%, 10%, 15%, and 20% by weight, with a control mix (0% RHA) for comparison. Mechanical performance was evaluated through compressive and tensile strength tests at 7, 14, and 28 days, complemented by durability assessments in saltwater conditions. Microstructural and chemical analyses were conducted using X-ray Diffraction (XRD), Energy Dispersive X-ray Spectroscopy (EDX), and Scanning Electron Microscopy (SEM). Results revealed that 10% RHA replacement achieved the optimal balance between strength and sustainability, with compressive strength reaching 22.06 MPa and tensile strength showing improved pozzolanic activity over time. Higher replacement levels (15–20%) significantly reduced strength due to dilution effects. Microstructural studies confirmed the high amorphous silica content and favorable morphology of RHA, supporting its pozzolanic reactivity. Incorporating RHA reduces cement demand, lowers emissions, and offers an environmentally friendly solution for managing agricultural waste. The findings establish that a 10% substitution of OPC with unprocessed RHA provides an effective, eco-friendly, and cost-efficient pathway toward green construction in Bangladesh and other rice-producing regions.
subject: Articles
  topic: section
subject: Sustainable Development, Rice Husk Ash, Concrete Strength, Waste Management.
  topic: keywords
creator: Md. Zakir Hossain Khan
creator: Md. Rokonuzzaman
creator: Md Shajahan Ali
creator: Md Mohaiminul Alam
creator: Md Mahamudul Hasan Tayef
creator: Saif Muhammad Nasrun Nabi
rights: Copyright (c) 2025 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0
date: 2025-09-06
publication date: 2025-09-06
starting page: 01
ending page: 11
doi: 10.59675/E321

State Of Art: Carbon Fiber Reinforced Concrete A Review
type: item
description: Because plain concrete can break easily, checking how well it can bend, and handle being pulled apart is important. Even though plain concrete is tough and strong, it's not so good at resisting being stretched. To fix this, the construction industry uses different kinds of fibers to make concrete more flexible, lasting, and able to resist being pulled. Steel fiber is often used because it's strong but can get rusty. Conversely, Carbon Fiber (CF) is a viable choice for Fiber-Reinforced Concrete (FRC). because it doesn't rust, is lightweight, and has strong tensile strength. However, some researchers have noticed that CF hasn't been used as much as it could be. This research gives a summary of CF, its use in fixing and restoring projects, and the properties of CF Reinforced Concrete (CFRC). It looks at the strength, toughness, and bending qualities of CF, and studies if different types of CF can be used to fix and rehab buildings.
subject: Articles
  topic: section
subject: Carbon, concrete, Carbon Fiber, Fiber-Reinforced Concrete, Vapor-Grown Carbon Fibers.
  topic: keywords
creator: Dhafar Salam Mahdi
creator: Hussam Ali Mohammed
rights: Copyright (c) 2024 Journals Academic international journal of engineering science https://creativecommons.org/licenses/by/4.0/
date: 2024-11-13
publication date: 2024-11-13
starting page: 09
ending page: 17
doi: 10.59675/E222

Retrofitting of RCC Beams in the Shear Region Using Carbon Fiber-Reinforced Polymer
type: item
description: In recent years, Fiber Reinforced Polymers (FRPs) have gained significant popularity over traditional materials for enhancing the shear strength of Reinforced Concrete (RC) beams, particularly when they are deficient in shear or prone to shear failure. FRPs are advanced composite materials widely used to retrofit existing RC beams and improve the shear resistance of new ones. Among these, Carbon Fiber Reinforced Polymers (CFRP) stand out due to their lightweight nature, exceptional tensile strength, superior corrosion resistance, and ease of application in construction. Shear failure is commonly observed under seismic and impact loading conditions, making CFRP reinforcement a complex but essential solution. Although extensive research has been conducted in this field, existing studies remain insufficient. This study focuses on strengthening RC beams in shear using CFRP. A total of six simply supported RC beams (150 mm × 200 mm × 1800 mm) were subjected to four-point bending tests. The primary objective is to enhance the shear load-bearing capacity of the beams. The research involved testing RC beams reinforced with CFRP, including two control specimens and four beams wrapped with a single layer of CFRP. The wrapping configurations included two beams with 356 mm CFRP wrapping and two with 508 mm wrapping. The average maximum load capacity of the unwrapped beams was recorded at 84.74 kN, while those wrapped with 356 mm CFRP reached 120.46 kN. Meanwhile, the beams wrapped with 508 mm CFRP demonstrated an increased load capacity of 155.815 kN. Cracking was observed earlier in the unwrapped beams compared to those strengthened with CFRP, indicating improved structural performance. The failure patterns of the beams varied, particularly between those with and without CFRP wrapping. This study highlights CFRP's potential and provides a foundation for future research on optimizing its configurations for infrastructure rehabilitation.
subject: Articles
  topic: section
subject: RCC
  topic: keywords
subject: Retrofitting
  topic: keywords
subject: Shear Region
  topic: keywords
subject: Carbon Fiber
  topic: keywords
subject: Beam
  topic: keywords
creator: Md. Zakir Hossain Khan Khan
creator: Tanvir Hasan
creator: Md. Atik Shariar
creator: Intisar An Nafi
creator: Fahim Shariyr Akash
rights: Copyright (c) 2025 Academic International Journal of Engineering Science https://creativecommons.org/licenses/by/4.0/
date: 2025-03-29
publication date: 2025-03-29
starting page: 09
ending page: 21
doi: 10.59675/E312

Review of High-Strength Concrete Structures with Hybrid Fibers
type: item
description: This work provides a thorough evaluation of high strength concrete (HSC) constructions augmented with hybrid fibers, emphasizing their mechanical properties and structural performance. High-strength concrete, characterized by a compressive strength of 55 MPa or greater, is extensively employed in contemporary construction, especially in skyscrapers, owing to its capacity to reduce column dimensions. Nonetheless, conventional HSC demonstrates deficiencies, such as inadequate tensile strength and ductility, which may jeopardize structural integrity. The integration of fibers into concrete mixtures has been increasingly prevalent during the past decade to tackle these issues. Recent research has investigated the utilization of diverse fiber types in hybrid combinations to enhance various performance attributes of HSC. This review consolidates research findings from 2021 onwards, emphasizing the role of hybrid fibers in improving the mechanical properties of high-strength concrete. The paper examines the ramifications of these advancements for future research and practical applications, highlighting the necessity for additional exploration of fiber interactions, performance assessments under varied conditions, and the establishment of standardized protocols for the utilization of hybrid fibers in construction. This review seeks to further the development of concrete technology and its utilization in high-performance constructions.
subject: Articles
  topic: section
subject: High strength concrete, fibers, hybrid fibers, ultra high performance
  topic: keywords
creator: zaid jaffer
creator: Hussam Ali Mohammed
rights: Copyright (c) 2025 Academic International Journal of Engineering Sciences https://creativecommons.org/licenses/by/4.0/
date: 2025-01-31
publication date: 2025-01-31
starting page: 1
ending page: 8
doi: 10.59675/E311

State of Art: Seismic Failure Impact on Concrete Bridges
type: item
description: Seismic failures in concrete bridges can lead to severe structural damage, resulting in partial or complete collapse, significant economic losses, and potential disruptions to transportation networks. Such failures may arise from inadequate design, aging materials, or insufficient seismic detailing, making bridges vulnerable during earthquakes. The impact includes immediate structural damage and long-term consequences for public safety and infrastructure resilience, highlighting the need for effective seismic-resistant designs and retrofitting strategies to minimize risks.
subject: Articles
  topic: section
subject: Seismic , Concrete , Bridges , Earthquakes , Flexural.
  topic: keywords
creator: Liqaa Salam Mahdi
creator: Hussam Ali Mohammed
rights: Copyright (c) 2024 Academic international journal of engineering science https://creativecommons.org/licenses/by/4.0
date: 2024-11-12
publication date: 2024-11-12
starting page: 01
ending page: 08
doi: 10.59675/E221

Intrusion Detection Systems: A Revisit of Performance Evaluation Parameters
type: item
description: The effectiveness efficiency of various intrusion detection systems (IDSs) have always been evaluated on various datasets using different techniques. This evaluation normally covers a range of metrics, ranging from those that evaluated their accuracy to those that focus on their usability and performance. The evaluation of several attributes of IDS depends on the use of several metrics which have been proposed and used in various studies. Regrettably, there are no existing benchmark metric for the network intrusion detection systems as studies are still ongoing in this regard. Quantitative evaluation of the performance of IDS is mainly based on the use of several performance but in this article, most of the existing performance metrics were reviewed and used for the evaluation of the performance of various IDSs using different benchmark datasets. Furthermore, this study will aid in better understanding of the various performance metrics used for the evaluation of the performance of novel IDSs.; it will also further the insights to those that have found interest in the uses and the development of IDSs and related areas.
subject: Articles
  topic: section
subject: Computer systems security, Intrusion detection system, Network Security, Performance evaluation metrics, Cyber-Physical Systems.
  topic: keywords
creator: Tamara Saad Mohamed
creator: Saad Mohammed Khalifah
rights: Copyright (c) 2024 Academic international journal of engineering science https://creativecommons.org/licenses/by/4.0
date: 2024-06-20
publication date: 2024-06-20
starting page: 15
ending page: 21
doi: 10.59675/E212

Effect of Training on the Construction Project Performance Using System Dynamic
type: item
description: The sector of construction is important for the country's economic, community, and ecological agendas and therefore takes an important role in Iraq's future; managers of the project are in charge of activities planning and coordinating, and at the same time, obtaining the best charge, period, resources of the human and material so that objectives of the predetermined project are finally gotten. Hence, it is productivity can be increased if the skills of the managers of the projects are improved the paper aims to determine the effect of training on the project's performance in terms of both time and quality to obtain the causal effect on the projects. The paper's methodology consists of a questionnaire distributed to 140 experts in different sectors to obtain the problem that affects the time and quality of the projects and then analyze these problems using the system dynamic technique. Depending on the effort of the research, the subsequent assumptions can be utilized: The training system in Iraqi construction is very weak and very negligible and doesn't contain any supervision Most of the engineers are not multidiscipline, and even if they work on the other discipline, the luck the principle of it. The nature of the construction industry in Iraq tends to use traditional methods in construction and refuses to use modern techniques. There are a series of shortages in communication between different disciplines and that creates a gap that leads to becoming an obstacle in the front of project completion.
subject: Articles
  topic: section
subject: Training, System Dynamic, Construction Projects.
  topic: keywords
creator: Suha Falih Mahdi
creator: Roaa Faleh Mahdi
rights: Copyright (c) 2024 Academic International Journal of Engineering Sciences
date: 2024-04-08
publication date: 2024-04-08
starting page: 01
ending page: 14
doi: 10.59675/E211