International Journal of Emerging Research in Engineering and Technology
Energy-Optimized Steering and Braking Coordination in Software-Defined Electric Vehicles
This article is a proposal of an energy-efficient steering and braking coordination scheme of the next generation Software-Defined Electric Vehicles (SD-EVs). The contemporary EVs are being turned to centralized, software defined architectures that enable dynamism in controlling vehicle subsystems using software abstraction layers. Nonetheless, the rise in the complexity of computational and control would demand efficient approaches of energy reduction without compromising safety and comfort of the ride. This paper suggests an integrated control architecture that can bring steer-by-wire (SbW) and brake-by-wire (BbW) together in a software-defined control stack with the ability to implement energy-efficient strategies in real time. The abstract addresses the reasons why it is better to use SDV architectures, restrictions of conventional hardware-based control systems, and the benefits of centralized electronic control units (ECUs). The solutions suggested are based on the model predictive control (MPC) and optimal energy allocation principles, and theory of motion coordination principles. We present a dynamic car model in which steering dynamic and braking dynamic are included in a combined manner and thus, allow coordination of tire forces and yaw forces. Our architecture will also enhance energy efficiency by dynamically reallocating braking effort between regenerative braking and friction braking paths, as well as steering actuator torques to reduce power consumption. Expert verification in a high-fidelity vehicle simulator and Hardware-in-the-Loop (HIL) test bench are done to test the performance in the different driving conditions, including slalom maneuvers, emergency braking, and urban stop-go cycles. Findings show that there was great saving of energy as opposed to traditional independent SbW and BbW systems. The offered strategy also leads to the improvement of the dynamic situation by minimizing the unwanted yaw moments and optimizing the use of tires. Additional information dwells on safety assurance, the modularity of software-defined control stacks, cyber-physical convergence and fault-tolerant behavior. This study demonstrates the opportunities available in software-defined vehicle systems in facilitating comprehensive energy optimization policies that would otherwise be challenging to implement in the older systems.
End-to-End Validation of Electric Power Steering using Hardware-in-Loop and Vehicle Bench Systems
This Paper of its better steering comfort, higher fuel efficiency and ability to be used with Advanced Driver Assistance Systems (ADAS), Electric Power Steering (EPS) has been a secondary subsystem in passenger cars becoming a standard element of any present-day vehicle. A strong multi-layered validation approach is needed to make sure that its reliability is ensured under real-world and extreme operating conditions. The present paper provides an end-to-end experience of EPS validation based on Hardware-in-Loop (HIL) and Vehicle Bench Systems, which combines the simulation-based validation testing as well as the physical testing of subsystems. The methodology facilitates ECU level verification, real-time testing of actuators and complete system verification behavior, cutting down the development time and failures in the field by a significant margin. The suggested validation system combines model simulation, sensor simulation, torque overlay test, steering map testing, thermal profiles, fault injection, and closed loop dynamic testing. There is a detailed EPS functional verification workflow, communication integrity testing workflow, failure mode diagnostics workflow and durability assessment workflow. Components of cost, engineering flexibility, risk mitigation, and test coverage are among the factors that are systematically compared in the study, between the conventional vehicle validation and modern HIL-based validation. The results of the experiments prove that the number of prototype iterations reduces greatly, the quality of controlling algorithms is increased, and the correlation between testing and simulation and testing of the vehicles is demonstrated. Experiments involving a real-time dSPACE-based HIL system and high fidelity EPS mechanical rig indicate that there are only ±3 percent differences on assist torque prediction between the system and the vehicle level. Moreover, fault simulation, such as torque-sensor drift, CAN bus delay, and motor overcurrent was tested without exposing the hardware to unjustifiable risks. The results demonstrate that incorporating HIL and vehicle bench systems will result in the quicker debugging process, enhanced security, and extremely trustworthy EPS controllers in this or that situation. The paper ends with the conclusion and recommendation of hybrid validation process to use in cases of OEM and Tier-1 suppliers that would reduce the time frame of development, warranties costs, and adherence to ISO 26262 functional safety specification.
Disease Diagnostic Systems based on AI-Applications in Healthcare: Models, Challenges, and Future Directions
Artificial Intelligence (AI) is improving clinical decision-making, accuracy, and efficiency, which is revolutionizing disease diagnosis systems. AI-powered diagnostic technologies can now evaluate medical images, including magnetic resonance imaging, computed tomography, and radiographs, allowing doctors to identify patients more rapidly and precisely. AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), are being used to handle both organized and unstructured healthcare data at a rapid pace. It has been shown that Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB), Logistic Regression (LR), and AdaBoost algorithms may successfully analyze clinical parameters of illnesses, including diabetes, renal disease, and heart disease. Time-series analysis, signal processing, and medical imaging all make extensive use of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), two key models in the DL paradigm. Medical imaging, cardiovascular prediction, chronic disease management, neurological assessment, infectious disease detection, and precision medicine are among the applications supported by AI-based systems. Although the creation of AI in healthcare is associated with issues regarding data quality, standardization, interpretability, bias, and compliance with regulations, the future of AI is marked by progress in robotics, oncology, digital pathology, genomics, and integration of AI with the IoT. AI-based diagnostic tools are anticipated to enhance the current state of healthcare delivery by increasing accuracy, efficiency, and accessibility.
Review of Secure API Development and Authentication Mechanisms in ASP.NET Core Applications
ASP.NET Core applications API development and authentication. It discusses the security design of ASP.NET Core, which is structured as a modular design, an HTTP request pipeline built on middleware, and is integrated with CoreCLR, CoreFX, and Kestrel web server. Some of the key security features discussed in the study include authentication, authorization, data protection, enforcement of the HTTPS protocol, and security headers. The different authentication systems, such as claims-based authentication, token authentication using JWT, OAuth 2.0, OpenID Connect, etc., are examined in terms of the working principles, tokens, and delegated authorization. Additionally, the paper covers the common API security concerns, including injection attacks, broken authentication, insecure data transmission, and denial-of-service attacks. It also recommends best practices, such as rate restriction, safe code, API gateway, and continual monitoring, to mitigate these threats. The traditional and modern security methods are compared and contrasted to demonstrate the superiority of the token-based security model and the zero-trust model for scalable, cloud-native ASP.NET Core frameworks.
Software-in-the-Loop Validation of Electric Vehicle Battery Systems Using AI-Augmented Digital Twins
The increasing reliance on software-controlled battery management and protection functions has made software-in-the-loop (SiL) validation a critical challenge for electric vehicle battery systems. Conventional battery validation approaches, which depend heavily on physical testing and late-stage verification, are costly, limited in coverage, and poorly suited to exploring rare or compounded operating conditions. While digital twins and data-driven models have been widely studied for battery modeling and health estimation, their use as validation-centric instruments for safety-critical battery software remains limited. This paper presents a software-in-the-loop validation framework based on AI-augmented digital twins, integrating physics-based electrothermal battery models with machine learning based residual and risk modeling. The framework enables scalable validation of battery control logic under both measured and synthetically generated operating scenarios, with validation objectives focused on limit enforcement, thermal derating, and fault-handling robustness. The approach is demonstrated using publicly available national-laboratory battery datasets, showing that synthetic scenario execution significantly expands validation coverage compared to experimental testing alone and reveals software-mediated risk conditions that are difficult to detect through conventional methods. The results demonstrate that AI-augmented digital twins can transform battery validation into a continuous, lifecycle-oriented process, supporting shift-left verification and improved safety assurance in software-defined electric vehicles.
Copyright (c) 2026 International Journal of Emerging Research in Engineering and Technology
AI-Augmented Approval Workflows: A Dual-Authority Framework for Clinical Decision Making
High-stakes clinical decisions are increasingly influenced by artificial intelligence systems, yet no formal framework exists specifying when AI may approve or recommend medical interventions. Existing advisory-only mechanisms are prone to alert fatigue, with override rates exceeding 90% for warnings of drug-drug interactions in commercial systems. This paper addresses such challenges by proposing a dual-authority framework for establishing explicit approval responsibilities of both human clinicians and AI systems. The framework includes risk-based authority on delegation models, formal disagreement resolution protocols, and liability allocation mechanisms. A lightweight dual-signature protocol enables cryptographic verification of the parties' signoffs over crucial decisions. An integration with established EHR systems and sub-500ms response times allow the system architecture to work conveniently. The proposed evaluation targets sensitivity above 90% for safety-related decisions, reducing override rates below 20%. By codifying shared accountability, the framework meets regulatory requirements such as California’s 2024 Physicians Make Decisions Act while also facilitating meaningful AI inclusion in clinical decision-making.
Smart Vision Systems with Edge AI: Semiconductor-Based Architectures for Real-Time Facial Recognition and Object Detection
Smart vision systems have become central to modern digital infrastructure, supporting applications such as intelligent surveillance, autonomous mobility, industrial inspection, and smart retail analytics. Many existing computer vision systems rely on cloud-based processing, where image data is transmitted to remote servers for model inference. While this approach enables large-scale computation, it introduces latency, bandwidth dependency, and concerns related to privacy and data governance. Recent developments in Edge AI provide an alternative paradigm in which inference is performed directly on local devices using optimized neural networks and specialized hardware accelerators. This study examines the role of semiconductor-based architectures in enabling real-time facial recognition and object detection within edge-deployed vision systems. The paper presents a conceptual framework that integrates image sensors, embedded processors, and dedicated AI accelerators designed to support efficient deep learning inference at the device level. The proposed architecture emphasizes parallel processing, optimized memory pipelines, and lightweight model deployment to support low-latency visual analytics. Performance considerations such as inference latency, throughput, energy consumption, and detection accuracy are examined through a comparative evaluation of different deployment platforms, including cloud GPU systems, edge CPUs, and semiconductor-assisted edge accelerators. The analysis indicates that semiconductor-assisted edge architectures substantially reduce inference latency while improving computational efficiency compared with conventional cloud-based pipelines. In addition to performance gains, localized inference reduces the need to transmit sensitive visual data to external infrastructure, which strengthens privacy protection and improves system reliability in environments with limited connectivity. The findings highlight the growing importance of semiconductor-enabled Edge AI as a foundation for scalable and responsive smart vision systems across multiple sectors
Serverless computing has emerged as a dominant cloud paradigm due to its automatic scaling, event-driven execution model, and pay-per-use cost structure; however, these advantages are often constrained by performance unpredictability, cold-start delays, and suboptimal resource allocation strategies [25],[24]. Recent studies show that as applications grow more latency-sensitive and distributed, traditional serverless platforms struggle to balance cost efficiency and high performance, especially under bursty or heterogeneous workloads [23],[26]. This paper investigates optimization approaches spanning cold-start reduction, adaptive memory tuning, workflow orchestration improvements, and predictive scaling techniques. Drawing on empirical findings that link runtime selection and concurrency management to significant latency reductions [4],[28], this study develops an integrated optimization framework designed to enhance both execution performance and cost efficiency in Function-as-a-Service (FaaS) environments. Experimental evaluation demonstrates that combining lightweight runtime containers, machine-learning-guided resource allocation, and improved state management can substantially reduce end-to-end latency while lowering operational cost. The results contribute to ongoing efforts to make serverless platforms more predictable, scalable, and suitable for enterprise-grade workloads.
Performance Analysis of Convolutional Neural Network Architectures for Automated Melanoma Detection
Deep-learning-based automated melanoma detection has shown good diagnostic capability, but its potential in clinical environment is not only based on the predictive power but also on the trust and acceptance of clinicians. This paper presents the convolutional neural network (CNN) based melanoma recognition with a survey-based measure of the adoption intentions of clinicians. Three pre-trained CNN models, efficientnetb0, ResNet50 and MobileNetV2 were tested in terms of the SIIM-ISIC 2020 dermoscopic image dataset on standardized preprocessing, data augmentation and training conditions. The accuracy of EfficientNetB0 was 0.9817, the ResNet50 was at 0.9804, and the MobileNetV2 was 0.9829 which is indicative of good classification. A survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and trust-ethics concepts and developed on the survey of 100 clinicians was carried out to supplement the technical assessment. The rank-order analysis of correlation of Spearman (n = 100) showed that acceptance of AI-based melanoma detection positively correlated with performance expectancy (r = 0.540), effort expectancy (r = 0.591), social influence (r = 0.571), facilitating conditions (r = 0.636), trust (r = 0.537), and ethical/privacy concerns (r = 0.544) and with p ≤ 0.01. These statistically significant associations indicate that technological and trust-related factors are determinants of clinicians' intentions to adopt them. The results indicate that high technical performance should be combined with trust, ethical transparency, and clinician acceptance to facilitate the real-world implementation of AI-based diagnostic tools.
AI-Driven Cyber Threat Detection and Response
The rapid digitization of modern society has dramatically expanded the scale, complexity, and sophistication of cyber threats. Traditional cybersecurity mechanisms, which rely heavily on static rules, signature-based detection, and manual analysis, struggle to keep pace with evolving attack vectors such as advanced persistent threats, zero-day exploits, ransomware campaigns, and coordinated distributed denial-of-service attacks. Artificial intelligence has emerged as a transformative force in cybersecurity, enabling automated, adaptive, and real-time threat detection and response. AI-driven cyber defense systems leverage machine learning, deep learning, anomaly detection, behavioral analytics, and automated decision-making frameworks to identify malicious activities, predict potential vulnerabilities, and orchestrate rapid mitigation strategies. By analyzing vast volumes of network traffic, user behavior patterns, system logs, and endpoint data, AI systems can detect subtle indicators of compromise that would otherwise evade conventional defenses. This article provides a comprehensive and in-depth exploration of AI-driven cyber threat detection and response, examining foundational technologies, system architectures, detection methodologies, response automation, adversarial challenges, ethical considerations, and future research directions. It highlights how intelligent cybersecurity systems enhance resilience, reduce response times, and enable proactive defense strategies in an increasingly interconnected and high-risk digital environment.
Graph Neural Networks for Complex Network Analysis
Complex networks are fundamental structures underlying numerous real-world systems, including social interactions, biological processes, transportation infrastructures, communication systems, and financial markets. Traditional machine learning techniques often struggle to model such systems effectively due to their irregular, relational, and non-Euclidean nature. Graph Neural Networks (GNNs) have emerged as a powerful deep learning paradigm designed specifically to operate on graph-structured data, enabling scalable and expressive representation learning for nodes, edges, and entire graphs. By integrating principles from graph theory and neural network architectures, GNNs provide a unified framework for capturing structural dependencies, dynamic interactions, and hierarchical patterns within complex networks. This article presents a comprehensive exploration of Graph Neural Networks for complex network analysis, examining their theoretical foundations, architectural developments, training methodologies, scalability considerations, and real-world applications across diverse domains. It further discusses challenges such as over-smoothing, interpretability, computational efficiency, and robustness, while outlining emerging research directions that aim to enhance the capability and reliability of GNN-based systems. Through detailed analysis, this work demonstrates how Graph Neural Networks are reshaping the study and application of complex networks in modern artificial intelligence.
AI-Based Predictive Maintenance Models in Smart Manufacturing Environments
Equipment downtime incurs direct financial losses, reduced end-customer service levels, and negative environmental impact. Downtimes often result from equipment failures that could be prevented through proper maintenance planning. Predictive maintenance promises to minimize unplanned downtimes by exploiting the data generated by Industrial IoT devices. While these types of data are usually plentiful, their quality is sometimes unsatisfactory, making machine learning models difficult to develop. Available datasets for predictive maintenance build upon condition monitoring, and usually cover conditions that are rarely met in real workloads. Therefore, finding approaches for handling rare events is essential for training reliable failure prediction models. Observing the factors that impact these paradigms, several predictive-maintenance models can be proposed. Supervised learning methods can be applied for predicting future failures based on labelled data, while anomaly-detection models can be explored to identify abnormal use conditions. Unsupervised clustering techniques can help identify outliers in usage patterns, and self-supervised setups exploit the rich information available in sensors, but do not require predictions of rare failures. The proposed methods cover the full predictive-maintenance lifecycle, from identifying, collecting, and preprocessing data, to context-based training-validation-testing pipelines, to interpretation and hints on integration with maintenance management systems.
Intelligent Maintenance Management Systems
AI-Driven Knowledge Management Systems for Enterprise IT Operations
Artificial Intelligence (AI) is increasingly being integrated into Knowledge Management (KM) systems to provide enhanced decision-making support to Information Technology (IT) operations teams. AI-driven systems are empowering these teams to reduce service resolution times, streamline troubleshooting processes, and share knowledge more effectively by improving the speed, relevance, and contextual accuracy of knowledge retrieval and understanding. Alongside these advantages, such AI-powered capabilities are redesigning the nature of knowledge work, augmenting the collaborative aspect of KM efforts, and shifting the composition and skill requirements of IT operations teams. Fundamental considerations for developing AI-driven KM systems for IT operations are explored, including a discussion of deployed architectural patterns, data quality and governance concerns, and deployment and processing options. Further, the impact of these systems and capabilities on the KM aspect of IT operations teams is examined. Together, these insights provide a foundation for the adoption of AI capabilities within KM systems used by IT operations teams.
Decision Intelligence for AI-Driven Agile Lifecycle Governance: Linking Architecture-Centered Management to Defect Risk Forecasting
Agile delivery improves responsiveness but can amplify architectural drift, defect leakage, and governance ambiguity when quality controls are not explicitly linked to measurable risk signals. In practice, many teams implement defect prediction or operational analytics as standalone dashboards rather than as decision drivers for sprint planning, release gating, and architecture investment. This paper presents DI-AIGLG, a decision intelligence-based governance framework that links architecture-centered management with defect risk forecasting and observability evidence to produce repeatable, auditable, and low-overhead governance actions across the agile lifecycle. DI-AIGLG formalizes governance as a closed-loop system consisting of evidence capture, predictive risk modeling, decision policies, and outcome learning. We define an architecture exposure score, propose a calibrated risk score that combines predicted defect likelihood with architectural blast radius, and map risk to actionable controls spanning backlog selection, CI/CD gating, and release readiness. A worked example demonstrates risk budgeted sprint planning under capacity constraints. The framework is designed to be implementable using CI/CD telemetry, automated testing signals, and AI-enhanced observability pipelines.
Human-Centered Ethical AI in Healthcare Contact Centers
The use of AI in healthcare contact centers presents opportunities and challenges for triage and care routing as well as for clinical decision-making, particularly with respect to algorithmic amplification of bias, opacity in decision-making, and inequality in health. This article provides a proposed framework for the human-centered and ethically mindful design, development, and deployment of AI in healthcare contact centers based on five interdependent principles of fairness, transparency, explainability, accountability, and patient autonomy. The framework outlines the technical requirements for bias auditing, explainable AI to improve the transparency of clinical tools, and governance frameworks with clinical and regulatory oversight bodies. Evidence suggests that increasing transparency and explainability can improve operator trust and that AI tools with interpretable results improve response time and accuracy. The article describes bias present within healthcare data, including socio-economic status, race/ethnicity, geographic access distribution, and insurance status. It discusses methods used to address bias, including rebalancing training data, altering algorithmic weight, and adopting fairness constraints. Working iteratively through existing and new governance models at increasingly mature levels helps in providing HIPAA-compliant and nascent AI models to organizations with varying resource levels. Positioning trust as a prerequisite of, and not an outcome from, adoption creates the opportunity for healthcare organizations to be efficient, protect patient rights, deliver health equity outcomes, and become leaders of responsible healthcare technology innovation.
Implicit Channel Inference Techniques for Pilotless OFDM Reception in Next-Generation Wireless Systems
Orthogonal Frequency Division Multiplexing (OFDM) is now the de-facto modulation scheme of contemporary broadband wireless communication systems, such as 5G New Radio (NR), Wi-Fi 6/7, and emerging sixth-generation (6G) systems, because it has superior resistance to multipath fading, is a high spectral efficiency, and can support flexible resource allocation. Traditional OFDM receivers are highly dependent on pilot symbols or reference signals to estimate the channel, synchronize and equalize it. Yet, pilot insertion adds a large overhead in spectral, decreases the efficiency of the energy and constrains the system throughput, particularly in ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC) and high-mobility conditions. Such constraints have fueled the creation of pilotless OFDM reception techniques demand instinctive inferring of channel state information (CSI) at the receiver of received data without explicit pilot signals. The objective of implicit channel inference methods is to recover the response of the wireless channel based on statistical signal characteristics, data driven learning model, blind estimation applications, and sophisticated signal processing structures. These strategies minimize pilot overhead, enhance spectral efficiency, and support dynamic environment adaptive communication. The latest developments in machine learning, specifically deep neural networks, reinforcement learning and self-supervised learning, have further made pilotless reception possible as they have allowed the receivers to acquire channel information by simply observing signals. Further, novel intelligent wireless building of artificial intelligence and physical layer design has portrayed substantial gains of channel estimation precision, obstinacy to interference, and efficiency. This study is an intensive research study on implicit channel inference methods in case of pilo less OFDM reception among next-generation wireless networks. The proposed study is a hybrid framework that uses statistical signal modelling, blind estimation algorithms, and deep learning based inference to modify channel information without pilot symbols. In the methodology, there is extraction of features in received OFDM symbols, use of Temporal correlation, frequency domain inference and adaptive equalization. To demonstrate the interrelation between the signals transmitted, the effect of channel and observed received, under pilotless conditions, mathematical modeling of the OFDM system is given. Also, optimization methods are presented to reduce the estimation error as well as improve the signal detection accuracy to different signal-to-noise rate (SNR) conditions. The performance analysis of the proposed implicit inference framework is based on simulation where it is shown that the proposed framework works optimally or better than the traditional pilot-based performance on bit error rate (BER), spectral efficiency and computationator adaptability. The findings show that pilotless OFDM reception has the potential to reduce transmission overhead by a significant magnitude and does not impair the performance of reliable communication within highly mobile and highly interfered communication conditions. In addition, the deep learning integration can offer better resilience to the channel variability and noise uncertainty, which means that the approach will be applicable to the next-generation wireless networks 6G, smart transportation, Internet of Things (IoT), and satellite communications. The most significant finding of this study is that implicit channel inference can be a disruptive technology in the development of wireless communication systems in the future. Removing the need for pilot signals and the use of intelligent inference mechanisms, pilotless OFDM reception can make systems more efficient, lower latency, and enable network system connectivity to scale up to the latest network designs. The suggested framework serves in the current research results in the creation of smart, versatile and energy efficient communication systems that can satisfy the stringent demands of power-hungry wireless applications in the next generation.
Development of a Generative AI-Assisted Network IDS for Intelligent Cloud Cybersecurity Monitoring
In the cloud security paradigm, threat detection and response exhibit structural and functional symmetry, where each detected threat triggers a corresponding automated or manual response. Cloud security is critical due to the increasing reliance on cloud computing to store, process, and transmit sensitive data across various sectors. In order to resist insider threats and external attacks to cloud systems, the present study examines features of DDoS attacks detection and classification based on CICDDoS2019 data set consisting of 88 features and millions of records. Preprocessing was done to eliminate categorical and duplicate features, deal with infinite values, label encoding and min-max normalization. Principal Component Analysis (PCA) was used to select features and therefore dimensionality was maintained by including vital information. An effective model of complex data was acquired by training a Variational Autoencoder (VAE) model (made up of an encoder and decoder) to maximize the evidence lower bound (ELBO). The model had 99.79% accuracy, 98.57% precision, 98.55% recall and 98.56% F1-score, which was better than classical machine learning methods such as RNN, Kalman Backpropagation Neural Networks, and Logistic Regression proving good, dependable, and efficient cloud-based cybersecurity threat detection.
Intrusion Detection and Protection System
Transforming Organizational Decision-Making Using Power BI Dashboards
In the era of digital transformation, organizations increasingly rely on data-driven intelligence to enhance strategic and operational decision-making. This paper discusses the role of Power BI dashboards in changing the process of decision making in organizations by connecting data with models, visualization, and governance into one Business Intelligence model. The study suggests the implementation of a multi-layer architecture of a dashboard which will centralize the information of heterogeneous sources of the enterprise, use the organized data modeling and transformation methods, and provide the interactive visual analytics with reference to the key performance indicators (KPIs). It employs a case-based approach to methodology and thus assesses the performance improvement after and before the implementation of the dashboard. Findings are significant improvement in accuracy of data, decrease in the time of decision cycle, increasing cross-departmental visibility and operational efficiency. Reporting delays were reduced and manual intervention was minimized due to real time reporting and automated ETL processes which allowed executives to make fast and better decisions. Moreover, the use of dashboard transparency helped to promote a more potent strategic alignment and encouraged the culture of data democratization further down the organizational hierarchy. The results indicate that Power BI dashboards are not only reporting systems but also strategic decision intelligence systems, which yield evidence-based management. Although there are still some limitations associated with data quality and excessive use of visual metrics, the implementation of scalable architecture and governance systems can help to guarantee sustainable adoption. The research makes a conclusion that interactive BI dashboards are an important part of contemporary enterprise performance streamlining and digital transformation efforts.
Designing Resilient Distributed Workflows: Stage-Aware API Failure Handling and Operational Trade-offs
Distributed applications increasingly rely on API-driven workflows that span multiple independently deployed services. While this architecture improves scalability and modularity, it also exposes systems to partial and ambiguous failures that can leave workflows in inconsistent states. The impact of a failure depends on factors such as the transaction stage, the certainty of the API outcome, system throughput, and business priorities, which makes one-size-fits-all recovery strategies ineffective. This paper analyzes API call failures in distributed workflows and evaluates context- and stage-aware mitigation strategies. Using an order management system as a case study, it examines failures during inventory reservation, payment processing, and shipping initiation. For each API, the paper considers stage-specific recovery mechanisms, including retries, compensation or rollback, deferred processing, and manual intervention. The analysis highlights trade-offs between correctness, operational cost, throughput, latency, and customer experience. The paper demonstrates that resilience in distributed workflows must be explicitly designed with stage and outcome awareness, combining automated recovery with manageable manual intervention to achieve practical and sustainable operations.
From Field Observations to Verified Closeout: An Inspection-to-Closure Method for Architectural QA/QC in High-Rise Construction
Late-stage closeout problems in high-rise projects often look like isolated punch items, but many behave like repeat offenders: they disappear in a meeting, reappear under a new photo, and consume time precisely when access, sequencing, and inspection windows are tight. This paper documents an anonymized coastal Florida case study built from six third-party architectural field observation reports with photographs and a consolidated issue log. Rather than treating the dataset as a “defect census,” the study treats it as a reliability signal stream produced under periodic, exposure-limited conditions. The contribution is an inspection-to-closure method that turns each observation into a verifiable record that can survive turnover. The workflow standardizes (1) how findings are converted into checkable records, (2) how the controlling reference is selected and locked (permit detail, approved shop/system detail, manufacturer requirement, or consultant-issued field detail), and (3) how closure is granted only when proof matches the risk type. The method organizes closeout into practical closure lanes, supported by a compact “closure evidence pack” that preserves traceability from finding to governing basis to correction to objective verification. The result is not a new code requirement or a new material. It is a repeatable control process that reduces ambiguity, limits reopen, and converts field observations into defensible closeout outcomes.
Design of an EPICS-Driven Real-Time Control System for Dental CBCT Imaging and Dose Management
Dental cone-beam computed tomography (CBCT) provides high-resolution three-dimensional imaging but introduces concerns regarding patient radiation exposure [1]–[3]. While conventional dose optimization strategies rely on static protocols [4], [5], real-time adaptive control for CBCT remains largely unexplored [6], [9], [10]. This paper proposes a conceptual EPICS-driven real-time control architecture for dental CBCT imaging and dose management [7], [8]. The architecture introduces a supervisory control layer that interfaces with CBCT hardware, radiation monitoring systems, and adaptive optimization logic, enabling low-latency feedback and safety-aware operation without disrupting existing imaging pipelines [6], [9]. Key features include modular hardware abstraction, closed-loop dose management, and extensible supervisory interfaces for visualization and clinical oversight [7], [8]. Feasibility analysis indicates that the proposed framework is compatible with existing CBCT platforms, supports adaptive dose control within diagnostic constraints, and provides a foundation for future research into intelligent, patient-centric imaging systems [1]–[5]. Future work will focus on prototype implementation, digital twin-based validation, and integration with data-driven optimization strategies [6], [9], [7].
Intelligent Air Cooling Control in Thermal HVAC Systems Using Deep Learning
Occupancy-based control of Heating, Ventilation, and Air Conditioning (HVAC) systems with the use of machine learning is described. The common HVAC systems tend to be inefficient because they are either programmed or sensor-driven and thus they waste energy and poor the indoor environmental setup. With real-time occupancy data, HVAC systems can also adjust on a real-time basis by adjusting heating, cooling, and ventilating based on the presence or absence of occupants. This paper suggests a machine learning-based model using the UCI Energy Efficiency Dataset to model and predict performance with regard to cooling. Several models were assessed and compared based on standard regression measures, such as MLP, NN, LSSVR, and Random Forest (RF). The Random Forest model provided the highest level of accuracy and strength with an R² of 96.4 and very low prediction errors (MAE = 0.01, MSE = 0.01, RMSE = 0.076). As the outcomes show, the suggested RF-based model successfully determines the nonlinear dependencies between thermal and environmental factors, which is why it can be deemed as a sound solution to air cooling control and optimization of air conditioning systems with consideration of energy efficiency.
System-Level Design and Orchestration of Large-Scale Cellular Access Networks for Regulatory-Compliant Financial Services
The high pace of computerization of the financial services has radically changed the performance, reliability and compliance standards required of the communication infrastructures. The requirements of financial transactions, algorithm trading systems, digital banking applications, real-time payment systems, distributed ledger frameworks, low latency are of utmost need as well as high availability, high data confidentiality, and regulatory compliance across borders. Cellular access networks on a large scale and, especially, those developed with the 5G or even newer 6G paradigm provide unprecedented flexibility as facilitated by virtualization, disaggregation, and cloud-native orchestration. Nevertheless, the adoption of these networks into regulatory-compliant financial networks brings with it the challenge of complexities at the system level with multi-domain control, service-level agreement (SLA) assurance, privacy-preserving analytics, and deterministic tail-latency management. This paper introduces a mature system-level design and orchestration architecture of the large-scale cellular access network to financial services that are regulatory-compliant. The architecture being proposed combines disaggregated cellular modules, multi-domain control plane, cloud-native Radio Access Network (RAN) deployment and edge-to-cloud integration to make possible secure and SLA-aware service provisioning. The structure uses primal-dual control schemes to optimize under constraints, committee-based reinforcement learning to policy-constrained optimization of the control process, telemetry-based control to respond promptly to market fluctuations in financial markets, and prediction by the time series to anticipate a traffic explosion in response to the fluctuations in the financial market. The architecture is designed with privacy-sensitive analytics, cryptographic isolation, secure enclave-based computation, and digital-twin-assisted validation, whether by enforcing regulatory correctness (such as data residency requirements, auditability requirements, transaction traceability, and privacy regulations). Under the digital twin environment, simulations in the case of scenario validation, resilience testing, as well as stress analysis with extreme financial workloads, can be performed. Mechanisms for tail-latency engineering are provided to put an upper bound on the 99.999th percentile latency, which high-frequency trading and payment clearing systems can use. The orchestration problem is formulated mathematically as a constrained optimization problem that minimizes the latency and operational cost and subject to compliance, availability, and security constraints. The primal-dual approach will guarantee convergence at the dynamic workloads, whereas reinforcement learning will improve the adaptive choice of the policies within the non-stationary traffic conditions. Closed-loop service assurance Telemetry-based feedback allows corrective orchestration actions to ensure that the service source operates correctly and with continuous monitoring. The proposed framework was shown to achieve 38, 42 and 31 percent improvements in tail-latency, SLA violations, and regulatory audit traceability metric, respectively over the usual centralized RAN architectures, by virtue of the simulation-based validation. The edge deployment solutions also minimize the exposure to cross-border data transfer, which is in line with the financial regulatory requirements. The findings affirm that the cellular access networks can be strategically designed to facilitate mission-evidential financial applications in the event when the orchestration mechanisms are compliance cognizant, latency driven as well as security-focused. The paper offers an integrated architecture model, mathematical program optimization and orchestration strategy between telecommunications engineering and financial regulatory infrastructure design. The suggested solution creates a blueprint of next-generation secure financial connectivity which can use cloud native and edge integrated cellular networks
Policy-constrained reinforcement learning
Latency-Aware Scheduling and Resource Control Algorithms for Emergency and Public Safety Wireless Networks
Emergency and public safety wireless networks assist in mission-critical communications that must adhere to ultra-reliable and low-latency transmission in highly dynamic and resource-constrained conditions. The conventional scheduling and resource assignment tools suitable to either the best-effort or enhanced mobile broadband traffic cannot accommodate the high-end to end latency, reliability and service-level agreement (SLA) criteria of a mission-critical application like the emergency medical response, disaster recovery and the national and law enforcement operations. In this paper, a detailed analysis of latency-conscious scheduling and cross-layer resource management algorithms specifically to emergency and public safety wireless networks is described. We introduce a queue and delay conscious framework of scheduling which combines stochastic optimization of networks, restricted Markov decision processes (CMDPs), and the degree of deadline missibility breaking down of a plan. It is proposed to take into account traffic urgency, queues, chain conditions, and key performance indicators (KPI) that are important in the missions together. End-to-end latency, queue stability and convergence property analytical models are obtained. A high degree of analysis shows that there is a huge enhancement in latency reduction, improvement in reliability, and SLA when compared to traditional scheduling methods. The suggested architecture is ideally applicable to the cases of ultra-reliable low-latency communication (URLLC) and future-generation public safety networks.
Automated Sales Performance Optimization Using AI-Driven Cloud Analytics on Salesforce Platforms
Today, businesses are working within very dynamic markets, where customer experiences are fragmented, customers are involved in multi-channel communication, and demand is quickly evolving. It is in this context that sales organizations have a growing pressure to get their conversion efficiency, forecast accuracy and predictability of revenues and at the same time manage the operations cost. The solution to these challenges is offered by cloud-native customer relationship management (CRM) platforms alongside artificial intelligence (AI) and analytics abilities; they are a breakthrough opportunity. My current paper presents the proposal of the detailed AI-related cloud analytics framework of the automated sales performance optimization that is introduced on the Salesforce platforms. The article combines machine learning, predictive analytics, behavioral scoring and closed-loop optimization methods to optimize pipeline velocity, opportunity prioritization and resource allocation. The suggested architecture builds on the ingestion data, feature engineering, and model orchestration layers that are directly coupled to the CRM processes. The framework uses prescriptive intelligence as compared to the traditional reporting based systems, which results in automated recommendations and adaptive decision policies. The main analytics elements will be the lead propensity modeling, churn risk estimation, the dynamic opportunity scoring, and the recommendation engines based on reinforcement. A systematic performance assessment program is proposed to gauge the effectiveness of performance improvement that is measured in sales effectiveness terms such as win rate, sales cycle time, quotas achieved and indexes of revenue growth. The experimental findings show that AI-led analytics can massively improve the results of sales by minimizing information asymmetry and cognitive bias of the manual models of decisions. The results reveal that there are significant increase in the coefficient of predictability and conversion with continued retraining of the model as well as feedback loops. Besides, the study discloses governance, scalability, and ethics necessary to deploy enterprise systems with such provisions as data quality assurance, model interpretability, and privacy adherence. The study provides a practitioner-focused, but theoretically-based framework of implementing AI analytics into sales ecosystems with a CRM center. The framework allows data-driven sales plans that can be tailored to changing business environments through the integration of automation, prediction, and optimization. The suggested solution gives a background to discuss future research in autonomous revenue systems, intelligent CRM additions, as well as adaptive enterprise decision infrastructures.
AI-Driven Intrusion Detection in Internet of Things Networks Using the Edge-IIoTset Dataset
Network Intrusion Detection System (NIDS) is an essential tool in securing cyberspace from a variety of security risks and unknown cyberattacks. A number of solutions have been implemented for Machine Learning (ML), and Deep Learning (DL) based NIDS. In this paper, a deep learning-based multi-class intrusion detection model is introduced on the Edge-IIoTset dataset that includes realistic IIoT network traffic and attack variants. The dataset was preprocessed through feature refinement, categorical encoding, robust scaling, and imbalance handling to enhance model reliability. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) were implemented and evaluated in terms of stratified 5-fold cross-validation. Experiments prove that the suggested LSTM model is much more effective than CNN and some traditional machine learning methods, and it has an accuracy of 99.96 with close-to-perfect precision, recall, and F1-score. The analyses of confusion matrix and ROC curve also reinforce the high discriminatory power of the model in all attack classes, such as DDoS, Injection, Malware, and Information Gathering. The results indicate the success of sequential deep learning to model intricate traffic patterns in IIoT systems. The study provides a very dependable and scalable intrusion detection system that can be applicable in IIoT cybersecurity in practice.
Automated Regulatory (RegTech) Compliance Monitoring in the Financial Landscape Through Machine Learning Models
Regulatory Technology (RegTech) has become an imperative solution in automated compliance monitoring in the financial sector, especially in detecting fraud in imbalanced data of transactions. This paper presents a machine learning-based Regulatory (RegTech) compliance monitoring system based on the Credit Card Fraud Detection (CCFD) dataset. Data integrity and consistency were ensured by conducting comprehensive data preprocessing solutions, such as missing value management, duplicate elimination, and categorical transformation, feature selection, and normalization using StandardScaler. Class imbalance was compensated using Synthetic Minority Over-sampling Technique (SMOTE). The data was split into training and testing subsets. Convolutional Neural Network (CNN) and Random Forest (RF) model have been built and tested on the basis of accuracy, precision, recall, and F 1 -score. The experimental findings prove suggested CNN to be better in comparison to RF, as it only shows 99.8% accuracy and 99.9% precision, recall, and F1-score. The results prove the efficiency of deep learning-based solutions in improving automated financial regulatory compliance monitoring systems.
AI-Driven Load Forecasting for Smart Grids under High Renewable Penetration
The introduction of renewable energy sources into the contemporary power system also brings a lot of variability, uncertainty, and nonlinearity in electricity demand, which makes the conventional load forecasting techniques challenging. Artificial intelligence (AI) and machine learning (ML) have become promising methods to capture complex temporal and spatial relationship of load data, which provide better prediction capabilities of renewable-integrated smart grids. This paper critically evaluates AI-based load predictive designs, which comprise artificial neural networks, support networks, decision tree ensembles, and deep learning devices, e.g., recurrent neural networks, long short-term memory networks, and gated recurrent units and convolutional neural networks. Hybrid and ensemble models, which combine statistical models and signal decomposition methods, are also presented. The main variables affecting the forecasting performance, such as the meteorological, socio-economic, and grid-level variables are mentioned, as well as the data sources, feature engineering approaches, and metrics. Issues of data quality, generalization of models, scalability, and uncertainty are discussed and future research possibilities, such as probabilistic forecasting, real-time adaptive learning, and integrated load-renewable modeling are mentioned. This review will offer a unified source of information on how AI-based load forecasting practices can be developed and implemented within a renewable-abundant smart grid setting to researchers and practitioners.
Cloud-Oriented Data Lake Architectures for AI-Driven Salesforce Business Intelligence Systems
The blistering digital transformation of businesses has seen the creation of structured and unstructured business information in customer relationship management (CRM), enterprise resource planning (ERP), marketing automation, and external digital ecosystems increasing with a rapid rate. Modern companies using Salesforce platforms are accumulating enormous amounts of transactional, behavioral and customer contact data which needs to be processed and analyzed in order to permit real-time business intelligence (BI)-driven and artificial intelligence (AI)-driven decision-making. Conventional data warehouses, which are mainly built to accomplish admirable batch analytics, are becoming inadequate to serve changing AI workloads, predictive modeling, and elastic multi-tenant cloud environments. A new paradigm cloud-oriented data lake architectures has come out to overcome these obstacles. Data lakes are flexible in their schema-on-read, have object storage that scales, are distributed, and are specifically integrated to machine learning frameworks. Data lakes provide the ability to scale compute, use serverless analytics, and orchestrate AI services when deployed on the hyperscale cloud platforms Amazon Web Services, Microsoft Azure, and Google Cloud Platform. The features allow business to combine Salesforce CRM data with IoT feeds, social media feeds, financial systems and third-party APIs to create cohesive business intelligence dashboards, and AI-generated insights. The paper introduces a detailed research on Cloud-Oriented Data Lake Architectures adapted to AI-questions Salesforce Business Intelligence Systems based on the IEEE standard. The analysis covers design principles of architecture, data ingestion pipeline, governance framework, security control, AI model integration, and performance optimization. It is a suggestion of a layer-based reference architecture that includes a data ingestion layer, data storage layer, data processing join with AI analytics layer, semantic modeling, and BI visualization layers. The methodology uses the distributed computing paradigms, metadata management, role based access control (RBAC), encryption mechanism and automated model retraining processes. A quantitative appraisal model is presented to judge scalability, decrease in latency, model precision, increase in data quality and cost reduction. Experimental study shows that experimental analysis has enhanced query performance, predictive analytics accuracy and operational efficiency compared with the traditional enterprise data warehouse methods. The findings suggest that cloud-based data lake systems can potentially improve the performance of AI models because they make it possible to engage in real-time streaming ingestion, distribute feature engineering, and create scalable training systems. Moreover, when Salesforce CRM information is unified with the model of unified lakehouses, customer segmentation, prediction of churn, sales, and optimized campaigns can be implemented. The paper ends with determining the research gaps in the field of governance automation, multi-cloud interoperability, and AI ethics in enterprise CRM systems. It stresses the importance of standard cloud-native architectures to facilitate business intelligence transformation sustainably with AI-driven transformation in large-scale companies.
Integrating ETL with other data management approaches
ETL activity is one of the key elements of the current data ecosystem, and its presence guarantees trustworthy integration and conversion of data across multiple sources. Although ETL has always been connected to data warehousing, it gained additional functions due to popularization of big data and real-time analytics, as well as regulatory needs. This paper discusses ETL integration with five mainstream methods of data management, which include data warehousing, data lakes, master data management (MDM), data governance and data virtualization. Through a series of studies based on synergies, challenges and emerging trends, the study has indicated the changing role of ETL in helping organizations attain data consistency, quality and strategic value.