American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140)
The American Online Journal of Science and Engineering (AOJSE) ISSN: 3067-1140 is an international, peer-reviewed, open-access journal dedicated to publishing high-quality research in science, engineering, and applied technologies. The journal provides a scholarly platform for researchers, academicians, scientists, and industry professionals to present new discoveries, advanced methodologies, and innovative solutions that contribute to scientific and technological progress. AOJSE is committed to fostering global research visibility through free and unrestricted access to all published content, enabling the rapid dissemination of scientific knowledge for the benefit of academic communities, practitioners, and society. Open Access & Publication Model AOJSE operates under a complete open-access publishing system. All articles are freely available for reading, downloading, and sharing without subscription fees or access barriers. To ensure high-quality publication processes—including peer review, editorial support, production, and digital archiving—publication fees are applied only after a manuscript is accepted. This model ensures: Global reach and accessibility Enhanced citation potential Rapid communication of scientific findings Support for open scientific collaboration Journal Metrics Below 18% Acceptance Rate 15 days Time to first decision 50 days Review time 60-90 days Acceptance to publication Editorial & Peer-Review Ethics AOJSE follows rigorous ethical standards and international publishing guidelines. Submitted manuscripts must be: Original and unpublished Not under review in any other journal Prepared in accordance with ethical research and reporting principles Peer-Review Process Initial Editorial Screening Manuscripts are examined for relevance, originality, clarity, and alignment with the journal’s scope. Double-Blind Peer Review Each submission is evaluated by expert reviewers with concealed identities, ensuring fairness, objectivity, and quality. Final Editorial Decision Decisions are based on reviewer recommendations, scientific contribution, technical accuracy, and overall merit. AOJSE enforces strict policies against: Plagiarism Data falsification or manipulation Unethical experimentation Misuse of authorship Conflicts of interest not disclosed Preservation & Archiving Policy AOJSE ensures the long-term preservation and accessibility of its published work through: Secure digital archiving Redundant backup systems Maintenance of stable and reliable storage formats These measures ensure that all published articles remain permanently accessible, safeguarding the scientific record for future researchers. Retraction & Correction Policy To maintain academic integrity, AOJSE may issue: Corrections For minor errors that do not affect research validity. Expressions of Concern When serious issues arise that require further investigation. Retractions For substantiated cases involving: Ethical misconduct Plagiarism Faulty or fraudulent data Significant methodological errors Duplicate or unauthorized publication The journal addresses all concerns transparently and professionally. Privacy & Data Handling Policy AOJSE prioritizes the privacy and security of personal data. Any information collected during submission, review, or publication workflows is: Stored securely Used solely for editorial and administrative purposes Accessible only to authorized editorial personnel Personal information will not be shared externally without consent. Authors may request corrections or removal of their personal data at any time. Contact Information For submissions, editorial inquiries, or journal-related support, authors may contact: Email: editor@aojse.com The editorial team is available to guide authors throughout the submission, review, and publication process. Scope of AOJSE The American Online Journal of Science and Engineering (AOJSE) publishes original research, review articles, technical reports, and short communications across a wide range of scientific and engineering disciplines. Primary Areas of Interest Engineering Disciplines Electrical and Electronics Engineering Mechanical Engineering Civil and Structural Engineering Chemical Engineering Industrial and Manufacturing Engineering Materials Engineering and Nanotechnology Computer Science & Computational Technologies Artificial Intelligence and Machine Learning Data Science and Big Data Analytics Computer Networks and Cybersecurity Software Engineering Cloud Computing and Distributed Systems Physical & Applied Sciences Physics and Applied Physics Chemistry and Chemical Analysis Environmental Science Renewable Energy and Sustainability Earth and Atmospheric Sciences Mathematics & Computational Modeling Applied Mathematics Numerical Methods Statistical Analysis Scientific Modeling and Simulation Emerging and Interdisciplinary Technologies Robotics and Automation Internet of Things (IoT) Smart Systems and Embedded Technologies Biotechnology and Bioengineering Advanced Sensors and Instrumentation AOJSE encourages interdisciplinary studies that integrate scientific principles with innovative engineering solutions to address real-world challenge
Leveraging Data Analytics for Tax Compliance Monitoring in Public Revenue Administration
This study presents evidence-based analysis of data-driven tax compliance monitoring within public revenue systems, emphasizing rigorous methodology, transparent reporting, and measurable outcomes. Emerging trends in the digitalization of the economy and public service delivery increasingly motivate the use of big data analytics by tax authorities for efficient revenue collection, including compliance monitoring. Political pressure or judicial rulings often push decision-making and applied analytics into unsafe territories. In contrast, peer-reviewed modelling and analysis support legitimate and transparent data-driven compliance monitoring. Digital economy taxation gap analytics is one area gaining widespread research and implementation attention. Another core aspect of tax compliance risk assessment is the detection of tacit and explicit noncompliance. Risk-assessment support for audit selection or the identification of high-risk sectors and segments is increasingly provided by data-analytical methods. While demonstrated in a financial-supply-chain context, the approach is transferable beyond the scope and context of any one implementation. Empirical examinations validate the proposed concepts, metrics, and models, establishing the methodological foundation for a broader application across public revenue systems. Supply-side data companies maintain data about every firm across digital supply chains. Cross-jurisdictional public-sector data-sharing silos constitute uncontested evidence of tax gap size and sector non-compliance risk or willingness summaries. Non-participation in regulatory audits, data-feedback mechanisms, and information-exchange strategies results in an ever-increasing compliance gap. Tax administrations and regulators have decision responsibility and operational authority for compliance governance, yet any failure to embrace any one of the risk-and-remediation strategies should also be held to account.
Data; Tax compliance monitoring; Public revenue systems; Decision support; Policy evaluation; Policy design; Administration arrangements; Policy instruments; Tax gaps; Revenue risk
Copyright (c) 2025 American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140)
Advancing Credit Card Fraud Detection with an Optimized Light Gradient Boosting Machine
The immense expansion of online sales has made credit cards the major payment method, and at the same time, the number of fraudulent activities has increased in a parallel manner. Fraud detection systems that work well are, therefore, a must-have in the financial world, as they will keep the financial transactions safe and also cut down the economic losses that would otherwise occur. In this paper, an intelligent credit card fraud detection method is suggested that it makes use of an Optimized Light Gradient Boosting Machine (OLightGBM). The OLightGBM model uses Bayesian hyperparameter optimization for LightGBM to learn faster and provide more accurate predictions. The model was tested on two public credit card datasets containing both legitimate and fraudulent transactions from the real world. The performance of the OLightGBM model is compared with that of traditional machine learning algorithms and found to exceed the latter in terms of accuracy, precision, AUC, and F1-score. The findings prove its capability and appropriateness for use in real-time fraud detection systems.
Credit Card Fraud Detection, LightGBM, Bayesian Optimization, Machine Learning, Electronic Commerce.
A Multi-Context AI Framework for Real-Time Detection and Prevention of Transaction Fraud
The research introduces a Multi-Context AI Framework that is capable of identifying and preventing transaction fraud in the financial sector in real-time, even where the fraud is evolving quickly. The traditional methods of detecting fraud through rules and using machine learning are hard as the context gets changed rapidly to detect new frauds. The framework is able to combine behavioral context, temporal context, geospatial cues, device intelligence, and transactional risk indicators to create a unified fraud score. It uses adaptive learning, drift-aware updating, and a hybrid ensemble which consists of deep learning and tree-based models to constantly improve the predictions. The framework is equipped with a streaming architecture that allows for immediate anomaly flagging with extremely low latency, which makes it ideal for large-scale digital payment ecosystems. The results of the experiments show that the new method is more accurate, resilient against changing fraud patterns, and has a much lower rate of false positives than the traditional methods of fraud detection. The framework thus provides a modern, real-time, scalable, and smart solution for financial security systems.
Transaction fraud detection, multi-context intelligence, real-time analytics,anomaly detection.
Dynamic Urban Air-Quality Forecasting through Multi-Modal Machine Learning on Transport, Weather, and Sensor Data
Urban air pollution is a global challenge that urgently requires effective and timely solutions. City governments must allocate resources to protect their citizens from the adverse effects of air pollution while being optimally effective and cost-efficient. A growing number of low-cost air-quality sensors may help with timely intervention. However, they typically deliver low accuracy and sparse coverage. A novel forecasting approach is presented that leverages existing transport and weather data to develop an easy-to-implement model capable of producing dense air-quality estimates at multiple horizons. In addition, contradictory space–time behavior is investigated and a multi-modal forecasting model is proposed. By capturing short-term predictions and longer-term trends, the model effectively incorporates spatio-temporal correlations across different time scales. The approach is validated on sensor data from London, indicating that a multi-modal model combining transport, weather, and sensor data outperforms pure multi-task or single-task setups. Well-calibrated, short-term, and contradicting forecasts enable practical air-quality forecasting when an abundance of noisy sensor measurements is available. Cities can leverage existing data and create easy-to-implement, low-cost, dynamic air-quality forecasting solutions, facilitating timely interventions and resource allocation. Forecasting air-quality measures at arbitrary spatio-temporal scales is a challenging task. Although Low-Cost Sensors (LCSs) have recently offered the opportunity to improve prediction quality by supplying inexpensive and often expressed spatio-temporal forecasts, they come with significant downsides: relatively low accuracy and limited coverage. A general multi-modal forecasting framework capable of providing LCS-based predictions at multiple time horizons is thus highly attractive, as it allows the combination of high-accuracy and low-cost measurements while addressing their individual downsides.
Agentic AI, Autonomous Data Engineering, Adaptive Enterprise Systems, Business Integration, Beyond Automation
Cross-Disciplinary Deep Learning Framework for Urban Sustainability: Linking Transport Efficiency, Material Durability, and Environmental Health
Transport efficiency, material durability, and environmental health are essential for urban sustainability. However, deeper temporal, spatial, and causal connections among these domains remain underexplored. Deep learning can augment information from traditional empirical, deductive, and a priori techniques. A cross-disciplinary educational framework rooted in urban planning, systems engineering, machine learning, and education theory can nurture the required competencies. The proposed approach is structured along three pillars: data architectures, scalable deep learning methods, and practical applications for a specific context or combination of contexts. Evidence from the transport domain, where the model is most mature and where synergies with the material durability domain are clearest, supports three conclusions. Spatiotemporal modeling of urban mobility based on deep learning can enrich transport outcomes, detect mobility-related disruptions, and inform preventive measures. The development is guided by neural network architectures tailored for infrastructure networks, which capture and exploit hidden or explicit relationships. Finally, appropriate reinforcement learning formulations facilitate the definition of dynamic transport optimization problems that emerge naturally from the referenced spatiotemporal modeling. Underlying deep-learning methods for material durability and lifecycle assessment are predictive models of material longevity and surrogate models for lifecycle assessment. The predictive models incorporate inputs from internal or external non-destructive inspection signals that detect and quantify degradation processes and uncertainties associated with their inference. Validation and calibration rely on field data. The surrogate models combine physics-based and data-driven components for efficient yet accurate computation. Evidence from the environmental health domain provides additional utilities for transport and material developments. Urban exposure models for short-term concentrations draw on low-cost sensors and exploited spatiotemporal redundancy to manage data sparsity. Information fusion approaches support joint estimation for chemical-species mixtures. Spatiotemporal exposure-response relationships allow risk mapping and uncertainty characterization.
Urban Sustainability, Transport Efficiency, Material Durability, Environmental Health, Spatiotemporal Modeling, Deep Learning, Reinforcement Learning, Systems Engineering, Data Architectures, Surrogate Models, Lifecycle Assessment, Non-Destructive Inspection, Degradation Prediction, Urban Exposure Models, Information Fusion, Risk Mapping, Infrastructure Networks, Mobility Disruptions, Predictive Modeling, Cross-Disciplinary Frameworks.
A Theoretical Model for Carbon-Neutral Concrete Mix DesignConsidering Diverse Curing Conditions
The greening of concrete is a critical research focus in civil engineering, driven by the increasing global demand for aggregates, the worldwide enforcement of decarbonization policies, and the urgent challenge of climate change. As one of the largest energy consumers and CO2 producers, the cement and concrete industry must align structural efficiency with ambitious sustainability targets. This paper develops a conceptual theoretical model within a numerical framework, based on first principles, to analyze the impact of various curing conditions on the environmental-structural performance of carbon-neutral concrete mix designs. The model is built upon three intertwined pillars: (1) an Environmental Pillar focused on reducing Portland cement content via green substitutes like fly ash and slag, using embodied carbon as a key metric; (2) a Mechanical Pillar ensuring pg. 2 engineering performance (strength, durability) through optimized mix proportions; and (3) a Curing Conditions Pillar that accounts for the effects of different curing methods (e.g., wet, steam, CO2) on carbon performance and long-term structural response. This integrated approach allows for the study of complex interactions between environmental, technical, and structural factors. The model provides a foundation for future computational simulations and the development of standardized performance metrics for sustainable construction codes, offering a practical framework for engineers, scholars, and policymakers to advance toward carbon-neutral concrete.
Carbon-Neutral Concrete, Sustainable Mix Design, Curing Optimization, Supplementary Cementitious Materials, Embodied Carbon Reduction, Life Cycle Assessment, CO₂ Mineralization, Geopolymer Concrete, Theoretical Modeling, Infrastructure Sustainability.
Samer S. Abdulhussein Samer S. Abdulhussein
Reinforcement-Learning Based Optimal Scheduling of Multi-Reservoir Irrigation Systems in Semi-Arid Regions
Multi-reservoir irrigation systems are vital for ensuring agricultural production and food security in temperate and semi-arid regions. Reservoir-reservoir interactions must be considered, since water is released from different reservoirs to irrigation networks with different priorities. Reinforcement-learning methods provide a framework to establish such interactions, but their performance has not been compared with heuristic and model-based methods in a multi-reservoir irrigation context. Here, a multi-reservoir irrigation system is modeled and controlled with a reinforcement-learning approach that is evaluated against two routing policies and two techniques from the RL literature. The reward signal favours high water-use efficiency, penalizes missed irrigation demand, and rewards reliable supply. The explorative agent learns the complete operational policy, including transfers among reservoirs, directly from simulation. The results reveal the RL agent's superior ability to maximize water-use efficiency with acceptable deficits. The proposed method also proves robust against drought conditions, delivering reliable schedules under severe alterations of inflow, unrestricted by time. The need to allocate limited water resources efficiently has pushed the development of different water distribution methods, each with pros and cons. These might still be shaped by reinforcement-learning techniques that move toward real-time decision-making and climate-adaptive management, paving the way for an intelligent water-resources management system able to interact with the hydrological model of an irrigation system.
Reinforcement learning (RL), multi-reservoir systems, irrigation scheduling, basin operation, water use efficiency, water supply reliability, multi-objective optimization.
Significance of Negative Matric Suction Pressures on Infiltration in the Swash Zone
The swash zone&#39;s complex hydrodynamics can be influenced by infiltration and exfiltration processes, which are not yet fully understood. This study explores the role of negative matric suction pressures in swash-zone infiltration, focusing on seven naturally occurring beach media samples. Using a combination of tensiometer and in-contact filter paper methods, we examine the prevalence of matric suction pressures for beach media ranging from fine pebbles to fine sand. The results highlight the existence of matric suction pressures within the range 0-2 kPa for the examined media samples, with maximum values observed within the grain-size range 0.85-0.71 mm. Our results reveal a previously underexamined aspect of swash-zone infiltration, highlighting the potential significance of matric suction in influencing coastal processes. The findings suggest that omitting matric suction effects is shown to be an invalid assumption under certain conditions, as these pressures can exceed gravitational potential.
In-contact Filter Paper method, Infiltration, Matric Suction Pressure, Swash Zone, Tensiometer, Unsaturated media.
The Wetlands as Natural Sewage Treatment Plants for cost-effective water management in peri-urban areas
The city of Hyderabad has a small river, a tributary of Krishna river viz., Musi river and due to huge and unplanned constructions and destruction of the catchment area, the flow in the river has been gradually reducing and at the same time huge sewage water flows in the river due to increasing population living in the peri-urban areas without any infrastructure. Thus, the river in the down-stream carries mostly the sewage water particularly in the non-rainy season. Conveniently, there exist a number of natural wetlands on the bank of the river and they help in purifying the sewage under natural conditions. Therefore, to check the efficacy of this purification an experiment was carried out by allowing the swage water of the river to pass through a designated wetland and it was found that the nitrate was reduced to more than 90% after passing the sewage through the wetland for a time period of 48 hrs. In addition to Nitrate reduction, the phosphate and Sulphate too have been reduced considerably. Based on this experiment, a few schemes of irrigation through the constructed wetlands have been suggested using wastewater.
Wetlands, Natural Sewage Treatment, Cost-effective Water Management, Peri-urban Areas, Wastewater Treatment, Ecological Engineering, Sustainable Water Management, Pollution Control, Ecosystem Services, Constructed Wetlands
Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment
Data engineering and machine learning workflows suffer from this confusion and often employ separated pipelines for each of them. Summaries on how companies build machine learning models reveal that they employ numerous components (e.g., Spark, Airflow, TensorFlow). They also use special-purpose versions of such components to address particular concerns (e.g., Airflow for Machine Learning, Google TensorFlow Data Validation). Different versions of components in separated pipelines prevent companies from achieving the automation of model development achieved by Continuous Integration/Continuous Delivery (CI/CD) of traditional software. Therefore, companies should strive to achieve a unified pipeline that supports both data engineering and machine learning to fulfill the objective of Automated Model Deployment. Data engineering prepares the data required by an organization. Machine learning extracts knowledge from data; it needs to consume the data made available by data engineering. Both data engineering and machine learning pipelines can never be separated; therefore, they should never be implemented using separated tools and schedules. A roadmap is necessary that guides enterprises toward building a unified pipeline and, in doing so, helps enterprises achieve Automated Model Deployment.
Data Engineering, Machine Learning, Workflow Integration, Separated Pipelines, Spark, Airflow, Tensor Flow, Specialized Components, Pipeline Automation, Continuous Integration, Continuous Delivery, Automated Model Deployment, Unified Pipeline, Data Preparation, Knowledge Extraction, Pipeline Scheduling, Model Development, Enterprise Roadmap, ML Data Validation, Automation Framework.
Copyright (c) 2023 American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140)
Beyond Automation: The 2025 Role of Agentic AI in Autonomous Data Engineering and Adaptive Enterprise Systems
Agentic AI concerns the use of decision-making capabilities to perform tasks in an autonomous way, with a focus on AI applied to autonomous systems. This paper explores agentic AI’s role beyond traditional automation, particularly in autonomous data engineering and adaptive enterprise systems. By 2025, agentic AI will be increasingly embedded in enterprise data pipelines—allowing systems to self-configure and automate business processes related to data engineering tasks. Such implementations leverage AI to provide holistic system autonomy, moving beyond what AI-centric solutions dedicated to individual subtasks can achieve. Data engineering and adaptive enterprise systems offer an instructive arena for observing the transition from automation to agentic AI: it is most apparent when intelligent agents experienced in data engineering are added to oneself-solving, self-configuring, and adaptive enterprise systems. Although these developments come with inherent ethical considerations, the transition from automation to agentic AI is best described as evolution rather than revolution.
IMPROVED SEGMENTATION AND DEFORMITY CORRECTION IN MEDICAL IMAGING SYSTEMS
Medical image segmentation plays an important role in computer-aided diagnosis by providing exact marking of or tracing of anatomical structures. In this domain, although deep convolutional neural networks have demonstrated impressive performances, the segmented outputs usually do not show the value of stability and accuracy that are required for clinical deployment. We present in this article the novel SESV (Segmentation Error-based Segmentation and Verification) framework, aimed at further improving the segmentation performance of pre-existing DCNN models while leaving their basic architecture intact. Instead of attempting to improve segmentation accuracy alone, SESV identifies and corrects segmentation errors by first producing error maps which serve as priors. The error map, the original image, and the initial segmentation mask then enter the re-segmentation network, which looks to derive a refined segmentation. In order to make sure, the verification network is set up to do verification and decide whether it is proper to accept the corrected segmentation. Experimental results show that the SESV framework considerably boosted the quality of the segmentation and offered a sound alternative toward medical
Deep Convolutional Neural Networks (DCNNs)
CyberKids: A Video Game to Promote Cybersecurity Awareness Among Children
The Industrial Internet of Things (IIoT) has transformed industries with large-scale data accumulation and analysis capabilities. While increasing cloud storage-based dependency on IIoT data comes with its major security vulnerabilities and privacy issues, traditional methods of encryption grant privacy while being inefficient for processing and limiting access to control over the shares. For remediation, this work proposes SmartCrypt as an efficient, intelligent IIoT data sharing and storing scheme. SmartCrypt incorporates symmetric homomorphic encryption that facilitates computations over encrypted data without decryption and incurs low computation overhead. Furthermore, it makes use of fine-grained access control to secure the retrieval and processing of information by only qualified parties. Compared to traditional practices, SmartCrypt is privacy-preserving analytics-suitable, especially for time-series IIoT applications like predictive maintenance and anomaly detection. We measure the efficiency of the system with real-world datasets and prove that SmartCrypt enhances query response time by 17% and throughput by 9% against current techniques. These findings authenticate its capability in offering a secure, efficient, and scalable means of IIoT data management. With assurance of strong security without compromising on data utility, SmartCrypt provides a promising solution to addressing the twin challenges of privacy and accessibility in industrial data ecosystems.
Towards Autonomic Wireless Systems: Integrating Agentic AI with Advanced Semiconductor Technologies in Telecommunications
Contemporary society is reliant on complex technical systems that provide important functions and services, including safety, health, and economic prosperity. Creation of advanced wireless and telecommunications systems that are embedded in the physical, social, and economic fabric of modern life depends on resolution of difficult management challenges regarding the performance and evolution of such systems. We argue that dynamic autonomous management of large scale advanced wireless systems using Agentic AI will result in new organizational forms of advanced wireless systems. These collective entities will function as natural sociotechnical partners for people and organizations in resolving pressing problems in society and directly dealing with high dimensional uncertainty that people and traditional organizations are ill suited to address. To achieve this vision, new approaches are needed to address the dual challenge of integration of communication, computation, and control in Trusted Infrastructure and Trusted Intelligence foundations of Agentic AI. Achieving trust and minimizing risks of catastrophic failure are quintessential issues for the new systems. Foundational work in socio-technical design is needed to establish methods to address organizational and ethical issues for trusted collaboration between human organizations, Agentic AI, and new design choices associated with advanced semiconductor technology for systems design and operation. Agentic AI and controlled adversarial intelligence provide new capabilities for dynamic trusted networked control of infrastructure systems for telecommunications and associated high dimensional uncertainty. Sociology, economics, and philosophy provide important foundations for discussions of agentic AI capabilities and risks. Addressing these issues will shape the new ecosystem of sociotechnical entity partnerships and organizational forms of future telecommunications and wireless systems, and are requisite to avoiding a possible dystopian world of dis-integrated Agentic AI technology.
autonomic wireless systems, agentic AI, advanced semiconductor technologies, telecommunications, AI integration, self-optimizing networks, adaptive systems, machine learning, autonomous decision-making, real-time resource management, intelligent communication networks, semiconductor advancements, AI-driven optimization, dynamic spectrum allocation, energy efficiency, network automation, autonomous agents, edge computing, AI-enhanced signal processing, intelligent edge devices, wireless communication protocols, smart antennas, system self-healing, scalable networks, cognitive systems, next-generation wireless technologies, AI-driven network slicing.
Copyright (c) 2024 American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140)
Employing digitization and spatial analysis tools in GIS to manage educational institutions: The College of Engineering at Mustansiriya University as a case study
Geographic Information Systems (GIS) are one of the most important tools for organizing the development of integrated spatial databases for educational institutions. These databases support infrastructure analysis, resource management, expansion planning, and improve planning and decision-making efficiency. Therefore, this research aims to use corrected satellite imagery to produce an integrated digital map of the study area (College of Engineering/Al-Mustansiriya University) within a GIS environment. This map will then be linked to an integrated database and spatial analysis will be conducted on this data. Field surveys will be used to collect the database for each department within the College of Engineering. This data will be linked to its geographic location on the digital map. Spatial analyses will be conducted to arrive at the final results, which will serve as an integrated database for department management and forecasting future plans for expansion of faculty, students, and staff. This analysis will also produce multiple strategies for decision-making regarding the required number of undergraduate, graduate, and evening students, and linking these numbers to the facilities, laboratories, and infrastructure that will accommodate the acceptable number of students.
GIS, Spatial Analysis, Mustansiriyah, Bab Al-Moazam, Symbology.
Next-Generation Healthcare: Merging AI, ML, and Big Data for Accelerated Disease Diagnosis and Personalized Treatment
The rapid growth of information technologies generated unprecedented data, offering scientific discoveries not conventionally accessible. The integration of Big Data into daily aspects of life has enabled holistic functioning in various sectors. However, the potential for faster and fractionated individual care and individualized treatments needs to be materialized in a broadly adaptable manner. Disease prevention and amelioration is the pinnacle of care, and new therapeutic interventions and individualized approaches tailored to patients and their lifestyle can help health systems cope with the challenges they face. Currently, management largely involves one or more medicines following established treatment guidelines and protocols. New therapeutic interventions, including gene and cell therapies as well as pharmacogenetic treatments informed by AI and ML predictions, offer the promise of turning medicine into a far more proactive and individualized practice. However, their application is limited mainly to population-positive methodologies and sites of expedient integration. There are numerous approaches to designing medicines and conceiving of treatments. Development, approval, and delivery processes are challenging and tedious, which limits deeply integrated designs and turns therapies into largely population-positive methodologies. Moreover, tailorable solutions are limitedly available at the facility level. Both clinician and administrative knowledge sources are ill-equipped to integrate badly needed treatments into the system. Moreover, individual care and rapid interventions are thought to be mostly infeasible, and ADAPT is needed. There is a conceptually unlimited space within the input domains of care where predictions of new drugs can be made on the timescale of hours. However, effective care addressing individual complexities is a conceptually intractable task demanding the development of unexplored advanced predictive methodologies. Similar efforts are underway in personalized healthcare, on the opposite end of the quality spectrum. Knowledge-intensive complex networks are being comprehensively modeled by advanced AI and ML technologies to offer faithful modelling of patients and hence precise predictions of their responses to treatments. Information technology-enhanced ways of extracting data from deliverables can vastly improve the quality of modeling done by machines of large systems involving complex inputs. Although actively pursued, taken individually, there are little couplings between the two approaches shaping drastically different and non-compatible solutions to addressing the existing and resisted challenges of health systems.
AI, ML, healthcare, big data, analytical toolsArtificial Intelligence in Healthcare,Machine Learning Diagnosis,Big Data Analytics,Personalized Medicine,Predictive Healthcare Models,AI-Powered Diagnostics,Precision Medicine,Clinical Decision Support Systems,Healthcare Data Integration,Real-Time Patient Monitoring.
Architecting Scalable Multi-Cloud Environments for Telecom OSS with Advanced Infrastructure Automation Tools
Telecom companies need to rapidly deploy new digital services. The evolving multi-cloud landscape containing public and private clouds and edge computing resources can assist telecom companies in scaling up IT operations. However, public clouds have limited service assurance. To make the best use of the public cloud while ensuring service quality, telecommunication companies need to build their own multi-cloud environments, using multiple public clouds together with edge and private clouds. It is not easy to build a multi-cloud environment as a good mix of several clouds is quite complex but those environments are essential. This approach comprises four components. The first component does the service modeling and design to create a service model containing cloud resource requirements, deployment and scaling policies. The second component finds suitable clouds for the service model. The third one selects the cloud instances of the clouds, and all deployment and scaling policies, making sure that constraints against service execution are not violated. The last component periodically monitors the execution status of the service and triggers the deployment and scaling policies when needed. While the first three components have been studied and implemented in earlier works, this last step was not addressed before. Large-scale experiments on the footprint of a telecom OSS workload at publicly available clouds demonstrate that the solution operates quickly and correctly, and the assurance of the telecom OSS service on public clouds is well satisfied.
Cloud, orchestrator, multi-cloud, telecom operator, service orchestration,Multi-Cloud Architecture,Telecom OSS,Infrastructure Automation,Scalability,Cloud Orchestration,DevOps for Telecom,CI/CD Pipelines,Network Function Virtualization (NFV),Infrastructure as Code (IaC),Cloud-Native Technologies,Hybrid Cloud Management,Kubernetes for Telecom,Service Assurance,API-Driven Automation,Cloud Infrastructure Monitoring.
Advances in Therapeutic Strategies for Alzheimer’s Disease: Bridging Basic Research and Clinical Applications
This book summarizes new developments in therapeutic strategies for Alzheimer’s disease (AD) and how to translate basic research to the clinic. AD is a heterogeneous and difficult disease to diagnose. No current treatment can modify the development and progress of the disease or delay the appearance of neurodegeneration. The failure of multiple drug candidates is attributed to a lack of understanding of AD pathophysiology, including the factors that promote neurodegeneration, the variances in patient populations that may reflect distinct but overlapping disease mechanisms, and the limited ability to predict the efficacy and safety of drug treatments. The articles compiled in this book attempt to describe what we consider the most important problems and challenges to advancing therapeutic strategies. The objectives of this book are to bridge the gap between basic and clinical research, questioning current concepts or finding new ideas to identify susceptible patients, slow down AD, and develop new drugs or new approaches for AD therapy. All the chapters in this book have scientific originality or present different views on exciting aspects of AD treatment. There are several classes of drugs with a confirmed or potential beneficial effect on AD symptoms, but there is not a single therapeutic agent that can slow or arrest neurodegeneration. Though the scenario is bleak, the emergence of new avenues such as antibodies against tau pathology, tau-tubulin interaction inhibitors, or hippocampal stimulation by optogenetics show potential for the development of new AD treatments. Potential AD therapies targeting gut microbiota, astrocytes, or unique pathways such as those regulating circadian rhythms, currently considered to be peripheral components of AD, offer exciting therapeutic strategies, opening the door to the slow or arrest neurodegeneration processes that define AD
Alzheimer’s Disease Therapy, Translational Research for AD, Neurodegeneration Mechanisms, Heterogeneous AD Pathophysiology, AD Drug Development Challenges, Predictive Efficacy in AD Treatments, Tau Pathology Antibodies, Tau-Tubulin Interaction Inhibitors, Hippocampal Optogenetic Stimulation, Gut Microbiota in AD Therapy, Astrocyte-Targeted Treatments, Circadian Rhythm Pathway Modulation, Neurodegeneration Arrest Strategies, Patient Stratification in AD, Clinical and Basic Research Bridge, Innovative AD Therapeutic Approaches, Failures in AD Clinical Trials, AD Susceptibility Identification, Peripheral Pathways in AD, Future Directions for AD Treatment.
Integration of artificial intelligence and machine learning in national food service distribution networks
The unprecedented COVID-19 pandemic has revealed a chaotic vulnerability in the distribution of food service. People around the world were prevented from getting food products, which weakened international relations and placed governments on high alert. Meanwhile, in developed countries, an oversupply of food commodities caused serious food-waste problems, while in developing countries, food scarcity became critical. In addition, about 1 million people worldwide are food-insecure and in need of food assistance. How to effectively allocate food for food service is key to reduce excess surplus and perform food distributions. This chapter considers the complex problem of food distribution networks by integrating artificial intelligence and machine learning techniques in the process of demand-supply forecasting, food distribution allocation, and vehicle routing. The demand data from food banks and the supply-inventory data from food suppliers can only be better predicted through effective analytics and algorithms, and the transportation routes can only be better optimized through effective routing intelligence. In the post-Covid-19 era, the new technologies brought by these artificial-intelligent and machine-learning techniques provide the food industry with greater opportunities for enhancements. Food assistance is essential to society, where a supply surplus cannot solve hunger, but distributions do; however, the transportation of food products from suppliers to food service recipients is complicated, as these services are often provided by multiple food banks and distributors. With the aid of the latest artificial-intelligence and machine-learning algorithms, the food shortage can be addressed and the food waste shortage can be reduced through better demand forecasts and larger allocation efficiencies. To minimize the transportation times and maximize the safety of food recipients, shorter transportation routes are also desirable.
COVID-19, Food Distribution, Food Insecurity, Food Surplus, Food Scarcity, Artificial Intelligence, Machine Learning, Demand Forecasting, Supply Allocation, Vehicle Routing, Food Banks, Supply-Inventory Data, Routing Intelligence, Post-Pandemic, Transportation Optimization, Distribution Efficiency, Food Waste Reduction, Demand-Supply Balance, Allocation Algorithms, Food Assistance.
ANFIS BASED APPROACH TO PREDICT SEDIMENT REMOVAL EFFICIENCY OF VORTEX
The intricate flow dynamics within a vortex settling basin (VSB) make it challenging to establish a generalized regression model for accurately estimating sediment removal efficiency. Therefore, this study proposes an alternative approach using the Adaptive Neuro Fuzzy Inference System (ANFIS) to predict the sediment removal efficiency of VSB. The model is developed based on a comprehensive and reliable database sourced from literature, encompassing a wide range of hydraulic and geometrical variables from laboratory and field studies. The sediment removal efficiency of the VSB is modelled as a function of five key variables: water abstraction ratio, depth ratio, width ratio, diameter ratio, and particle Reynolds number. Training and testing data are extracted from laboratory and field datasets in various reputable references. Numerical tests reveal that ANFIS yields more accurate VSB removal efficiency predictions than previous empirical approaches. Sensitivity analysis further indicates that the particle Reynolds number exerts a more significant influence on sediment removal efficiency than the other independent parameters. This ANFIS-based approach offers an enhanced understanding and prediction capability for the complex processes occurring within a VSB.
Total Quality Management, Construction Industry, Project Efficiency, Cost Reduction, Saudi Vision 2030, Continuous Improvement, Quality Standards.
Multimedia Encryption and Decryption using AES, ECC and Chaos Based Techniques
In today’s digital world, protecting multimedia data like text, images, audio, video, and documents from unauthorized access is more important than ever. This project introduces a powerful yet easy-to-use solution that combines the strengths of three advanced techniques: Advanced Encryption Standard (AES) for fast and efficient data encryption, Elliptic Curve Cryptography (ECC) for secure key management, and chaos- based encryption to add an extra layer of unpredictability. This combination makes it much harder for attackers to find patterns and exploit them. The result is a Django-based web application that smoothly integrates both the frontend and backend, allowing it to handle large multimedia files with ease while maintaining top- notch security and performance. By tackling the shortcomings of existing systems, this project offers a practical and reliable way to secure data in modern communication networks. Our tests show that this approach significantly boosts data confidentiality and integrity, especially during encryption. This project not only tackles current security issues but also sets the stage for future encryption systems to be even more flexible and robust.
Data security, AES, ECC, Chaos encryption, Multimedia protection
Harnessing Big Data and Deep Learning for Real-Time Demand Forecasting in Retail: A Scalable AI-Driven Approach
Retailers worldwide are expanding their investments in advanced AI-driven solutions to improve demand forecasting in retail operations. Empowered by recent progress in big data technologies and deep learning algorithms, there is potential to develop a scalable and real-time intelligent system to forecast demands at SKU level. This system can help improve prediction accuracy and operational efficiency in the retail sector. Deep learning and big data models are proposed to capture complex nonlinear temporal patterns in a large number of time series with multi-scale and multi-mobility characteristics. These include (1) SoxNeT that integrates big mobility data into extreme learning machines with a deep neural network based on LSTM cells to effectively extract temporal features and spatial correlations, (2) DemandNet that applies convolutional neural networks and RNNs to model groundsill demand and investigate urban land-use context effects, and (3) EKO, a deep learning ensemble framework that combines many big data boosting and popular models with categorical embedding layers, to capture complex and dynamic spatial-temporal patterns in high-dimensional traffic data. Several challenges need to be addressed: (1) most existing methods do not explicitly capture the seasonal effect of nonlinear combinations of many factors; (2) deep learning models are data-intensive and their training often requires cross-validation to tune hyperparameters, making them difficult to scale to many SKUs; and (3) most proposed approaches are not optimized for computational efficiency or deployment-ready. Therefore, it is important to develop a scalable deep learning algorithm that accommodates big retail data and runs efficiently, hence improving the performance of demand forecasting models. An innovative AI-driven approach is proposed that effectively forecasts demand at SKU level, benefited by the development of three scalable deep learning algorithms, including a decomposable local-global network design and an ensemble of traditional time series and CNN-based predictors. The prediction process allows for endogeneity effects and the marketing optimization problem is formulated as a sliding-horizon control strategy. Various AI algorithms are tested on a revenue management problem with purchase pre order decisions. An alternative AI framework is provided which automates the development of decision-time, dynamic pricing policies based on a class of MDP models.
Big Data,Deep Learning,Real-Time Forecasting,Demand Prediction,Retail Analytics,AI-Driven Approach,Scalable Solutions,Machine Learning,Predictive Modeling,Supply Chain Optimization,Retail Demand Planning,Data-Driven Insights,Neural Networks,Consumer Behavior Analysis,Inventory Management.