Artificial Intelligence (AI) and automation have become pivotal in transforming Human Resource Management (HRM), automating routine tasks, enabling predictive analytics, and personalizing employee experiences across recruitment, performance management, engagement, and learning and development (L&D). The integration of AI in HRM has led to significant efficiency gains, such as reducing hiring time by up to 75%, improving worker retention by 25-65%, and augmenting skill development through personalized, adaptive learning pathways. This paper reviews current academic research highlighting both the transformative potential and ethical challenges of AI adoption, such as algorithmic bias and data privacy concerns. The study advocates for hybrid human-AI models that combine the efficiency of automation with human empathy, emphasizing the need for HR professional upskilling and transparent governance frameworks for sustainable integration.

Self-driving vehicles are envisioned as automated and safety-focused vehicles facilitating smooth movement on roads. This research proposes a novel, robust, and intelligent navigation framework for such vehicles through an integrated fusion of advanced technologies like predictive analytics with remote sensing and detection for accurate obstacle/object detection. TaskTrek, ViewVerse, and RuleRise form the core of the essential model governing vehicle-environment interaction. TaskTrek handles kinematic trajectory synthesis and space-time traffic modeling, ViewVerse provides LiDAR-based volumetric perception and radar-assisted navigational intelligence, and RuleRise manages topological localization, vehicle actuation, and autonomous decision-making through multimodal sensory fusion. The model applies an iterative Multi-FacBiNet method, which uses the cognitive Fully Convolutional Neural Network (FCNN) method to detect and classify obstacles during vehicle movement on the road. Upon stimulation during vehicle movement, the model provided an encouraging outcome. The fusion of predictive intelligence, Radar, and sensing technologies gave 95.3% proficiency. Minimum obstacle detection, processing, and response delays of 0.116 seconds, 0.105 seconds, and 0.36 seconds, respectively, are recorded. The computed mean obstacle detection accuracy for right, left, front and back camera angles are 88.3%, 83.8%, 91.4%, and 89.9%, respectively. Further, a comprehensive analysis of the model’s performance in different on-road scenarios considering metrics like traffic load, road type, and region density was done. The model generated a very impressive accuracy of obstacle detection on all parameters. The results of this study not only aid in accelerating the development of precise navigation-enabled self-driving vehicles but also in the context of environmentally friendly mobility/motion tracking solutions.

The application of Artificial Intelligence (AI) in project management is transforming decision-making processes, enhancing task execution, and improving risk management. This study aimed to elucidate the challenges raised by AI in project management (PM) using a scientometric and qualitative analysis. The research employs both quantitative and qualitative analysis using VOSviewer. The scientometric analyses reveal a substantial increase in AI in PM publications, with “project management,” “artificial intelligence,” “machine learning,” “cost reduction,” “decision making” and “supply chain management” as the most influential co-occurrence. The systematic review the implementation of challenges and strategies. The analysis identifies the publication trends, most significant keywords, leading institutions and researchers, prominent collaboration connections, primary publication venues, and the most-cited publications. This research enhances understanding of AI in PM, promotes the utilization of artificial intelligence technologies for gaining insights during certain phases of project development, and improves project management efficiency. The utilization of AI technologies, including machine learning, natural language processing, and predictive analytics, markedly improves project efficiency by enhancing decision-making, effectiveness, and risk mitigation.

Aim: This study examines how intelligent drilling optimization systems (IDOS), driven by machine learning (ML) and automation technologies, can reduce nonproductive time (NPT) and enhance well delivery outcomes in the oil and gas industry. It aims to synthesize recent developments in artificial intelligence (AI)-based drilling systems, highlighting their operational benefits, performance improvements, and potential challenges. Study Design: A comprehensive review of recent advancements in intelligent drilling optimization between 2020 and 2025, focusing on the integration of ML algorithms, automation frameworks, and real-time data analytics in upstream petroleum operations. The review emphasizes the practical impact of these technologies on NPT reduction, drilling efficiency, and sustainable well delivery. Methodology: A systematic review was conducted, sourcing publications from Google Scholar, Scopus, ScienceDirect and IEEE Xplore. Studies were selected based on relevance to predictive analytics, automation in well control, and data-driven optimization. Results: Findings reveal that ML and automation technologies significantly improve drilling performance by enabling predictive maintenance, real-time anomaly detection, and autonomous control of drilling parameters. Algorithms such as artificial neural networks (ANNs), support vector machines (SVM), and reinforcement learning (RL), applied in predictive and real-time optimization, achieved 20–35% reductions in NPT. Integration of digital twins, IoT, edge computing, and cloud analytics improved simulation accuracy, minimized operational risks, and facilitated adaptive decision-making, supporting continuous optimization and enhanced well delivery. Conclusions: Intelligent drilling systems remain limited by challenges such as data heterogeneity, lack of model standardization, and skill gaps in AI implementation. Future research should focus on hybrid modeling approaches that combine physics-based and ML-driven analytics, as well as developing unified frameworks for cross-field data integration to enhance scalability and interpretability.

Quality assurance has fundamentally evolved from a selling mechanism to a governance mechanism with an enterprise importance in sustainability and operational excellence. Quality assurance governance today includes predictive analytics for defect trends and release risk forecasting, enabling teams to proactively address quality issues before deployment and integrated DevOps processes, as it relates to a structured project framework that delivers on high defect reduction and minimizes infrastructure cost. This has been accelerated in regulated industries, such as the banking and insurance industries, foundational business units in professional services, retail, telecommunications, and automotive verticals, where compliance and operational continuity add regulatory expectation to compliance requirements and the practical need of putting quality frameworks in place. As organizations have evolved into proactive quality assurance governance, things like automated dashboards, virtualization processes, and Multi-tenant release orchestration with client-specific validation checkpoints ensures that each banking or retail client receives tailored deployment assurance without compromising shared infrastructure that have measurable value in terms of increasing release cadence, customer satisfaction, and major cost reductions. Today's quality assurance leaders have moved into more supportive roles, pushing beyond traditional testing to implement governance frameworks to move organisations to vector towards zero-downtime migrations, compliance assurance, and citizen safety validation. It is through the strategic positioning of quality assurance into organisational architecture that demonstrates its importance to enable sustainable digital transformation and operational resiliency in all types of technology and regulatory environments.

The rapid rise of artificial intelligence (AI) is revolutionizing the way that companies view data, technology and process automation across all industries from marketing to financial decisions. Through this quantitative study the essay will examine how AI predictive analytics may help to enhance marketing performance and financial strategy outcomes for SMEs. To develop data, 350 management and analytics professionals working for organizations that had implemented AI-driven predictive tools were surveyed in a cross-sectional survey design. This paper aimed to measure the influence of artificial intelligence (AI) and machine learning (ML) applications in marketing KPIs and financial performance KPIs. As a whole, our findings suggest that the adoption of AI is quite positively related to advancements in marketing and financial performance, leaving those not adopting AI behind. Regression results suggest that the depth of AI implementation is a key factor in the enhancement of performance. We then included the important moderators as data quality, zero-momentum and non-generation. These are the takeaways: Businesses can get huge strategic advantages from moving to AI, only if they have good data governance, digital infrastructure and people trained in use of the technology. The variance analysis also reflects that some industries are more receptive to the integration of artificial intelligence, such as finance and service industries, due to a possibility for stronger data capacity and better data-processing approaches. The results from this investigation reveal that today’s AI-powered predictive analytics can function as a strategic asset to businesses by boosting operational effectiveness, strengthening financial wellbeing and directing better marketing choices. But companies must also spend on the basics, like quality of data, skills of staff and governance around ethical use of AI, to make the most from predictive capabilities. This research contributes to the growing literature on AI strategy and arm business leaders with actionable information that can help them be more competitive in a data-driven global economy.

A Contemporary Paradigm for Value-Based Medicine in Vascular Care: Challenges and Opportunities.

A Survey of Practical Tools in Clinical Practice-A Survey of What Is in Use or Soon to be at Use in Clinical Practice.

Background: Artificial intelligence (AI) and robotic technologies are reshaping modern surgery, evolving from mechanical aids into cognitive collaborators. Their impact on outcomes, workflow and surgical autonomy remains debated. This review synthesises the highest-level evidence from the past decade examining AI-assisted surgery in humans. Methods: A comprehensive PubMed and Embase search (January 2015–October 2025) identified peer-reviewed human studies using combinations of “artificial intelligence”, “machine learning”, “deep learning”, “computer vision” and “robotic surgery”. Eligible publications included systematic reviews, meta-analyses and large observational cohorts. Thirty-eight studies were narratively appraised and grouped into seven domains: foundational frameworks, comparative outcomes, predictive analytics, intra-operative computer vision, specialty exemplars, training and ergonomics and ethics and safety. Results: Across high-quality meta-analyses and multicentre cohorts, AI-assisted or robotic surgery consistently reduced blood loss (20-35%), shortened hospital stay (by 1-2 days), and lowered conversion rates (up to 40%) without compromising oncological margins or increasing complications. Machine-learning models outperformed conventional risk scores for morbidity, mortality and postoperative delirium prediction. Computer-vision and augmented-reality systems enhanced anatomical recognition, navigation and instrument precision. Specialty-specific studies demonstrated reproducible advantages in hepatobiliary, colorectal, urological, thoracic and paediatric surgery. Training platforms incorporating AI shortened learning curves and reduced ergonomic strain, while ethical analyses emphasised the necessity of transparency, data security and sustained human oversight. Conclusions: Evidence from 38 studies shows that AI augments rather than replaces surgical expertise. When ethically implemented, it enhances precision, safety and efficiency across disciplines. The future of operative care rests on a partnership between human judgement and algorithmic insight.

A comprehensive diagnostic framework for hepatitis C using structured data and predictive analytics

Abstract Background Artificial intelligence (AI) marks an inflection point in medical education systems built on scarcity of resources. These designs privilege standardisation and recall‐heavy examinations over reasoning and adaptive expertise, defined as the capacity to apply knowledge flexibly in uncertain clinical contexts, producing learners who memorise content but struggle with ambiguity, integration across domains and decision‐making under pressure. Objectives To outline a conceptual roadmap for integrating AI into medical education that strengthens adaptive expertise, productive struggle and assessment integrity rather than eroding them. Methods Conceptual analysis using educational, assessment and cognitive science frameworks to contrast scarcity‐era logics with emerging AI capabilities and synthesise illustrative use cases. Results We describe how AI can scaffold knowledge acquisition and inquiry; support authentic practice via virtual patients and educator‐created, AI‐enabled teaching tools and reshape assessment through blueprint‐aligned items and predictive learning analytics. We highlight AI's double‐edged nature: risks of undermining integrity, promoting cognitive deskilling and bypassing productive struggle, defined as purposeful, scaffolded difficulty that feels effortful yet achievable and that strengthens long‐term learning. We propose enabling conditions: trust, transparency, structured difficulty, and deliberate cognitive redistribution, defined as intentional reallocation of cognitive work between humans and AI tools, which offloads routine lower‐yield tasks to machines to preserve and advance human judgement, values, relationships and professional identity formation. Conclusions AI will either accelerate superficial shortcuts or amplify humane, expert practice, depending on how pedagogy, assessment and culture are redesigned. Intentional alignment can reclaim time and cognitive space for the uniquely human work at the heart of education.

Predictive modeling and cohort data analytics for student success and retention.

<p>Progress in mobile technology, the internet, cloud computing, digital platforms, and social media has substantially facilitated interpersonal connections following the COVID-19 pandemic. As individuals increasingly prioritise health, there is an escalating desire for novel methods to assess health and well-being. This study presents an internet of things (IoT)-based system for remote monitoring utilizing a long range (LoRa), a low-cost and LoRa wireless network for the early identification of health issues in home healthcare environments. The project has three primary components: transmitter, receiver, and alarm systems. The transmission segment captures data via sensors and transmits it to the reception segment, which then uploads it to the cloud. Additionally, machine learning (ML) methods, including convolutional neural networks (CNN), artificial neural networks (ANN), Naïve Bayes (NB), and long short-term memory (LSTM), were utilized on the acquired data to forecast heart rate, blood oxygen levels, body temperature patterns. The forecasting models are trained and evaluated using data from various health parameters from five diverse persons to ascertain the architecture that exhibits optimal performance in modeling and predicting dynamics of different medical parameters. The models' accuracy was assessed using mean absolute error (MAE) and root mean square error (RMSE) measures. Although the models performed similarly, the ANN model outperformed them in all conditions.</p>

Implementing machine learning for predictive analytics: An empirical study of employee turnover

Bioleaching of waste-derived rare earth elements: An integrated approach with meta-analysis and predictive analytics for scale-up

Impact of artificial intelligence on hospital admission prediction and flow optimization in health services: a systematic review.

Advancing predictive analytics in child malnutrition: Machine, ensemble and deep learning models with balanced class distribution for early detection of stunting and wasting

This quantitative study investigated the integration of Lean Six Sigma (LSS), artificial intelligence (AI), and digital twin (DT) technologies as a unified framework for achieving measurable performance improvement in smart manufacturing systems. The research aimed to evaluate the extent to which AI-enabled digital twins could enhance Lean Six Sigma’s analytical and process control capabilities and to determine the quantitative impact of this integration on operational efficiency, defect reduction, and production reliability. Data were collected from 150 participants across 20 manufacturing organizations that had implemented digital transformation initiatives involving LSS, AI, and DT frameworks. Using descriptive, correlational, and multiple regression analyses, the study examined how these independent variables jointly influenced key performance indicators, including mean time between failures (MTBF), overall equipment effectiveness (OEE), and defect rate. The results indicated that the integration model was statistically significant, with an adjusted R² of 0.719, confirming that approximately 72% of the variance in performance outcomes could be explained by the combined influence of LSS, AI, and DT. Correlation analysis revealed strong positive associations between AI integration and OEE (r = 0.816) and between DT utilization and MTBF (r = 0.802), while defect rate demonstrated significant negative correlations with all three predictors. Reliability testing produced Cronbach’s alpha values exceeding 0.85 for all constructs, confirming instrument consistency, while validity testing established clear construct alignment through factor analysis. Regression coefficients demonstrated that AI integration had the highest predictive strength (β = 0.447, p < 0.001), followed by digital twin synchronization (β = 0.389, p < 0.001) and Lean Six Sigma implementation (β = 0.312, p < 0.001). These findings provided empirical evidence that combining process improvement methodologies with intelligent simulation and predictive analytics produced significant, quantifiable enhancements in manufacturing performance.

Harnessing Artificial Intelligence, Business Intelligence, and Digital Technologies for achieving supply chain excellence in Oman: Investigating the mediating role of predictive analytics

Artificial Intelligence in Diagnosis and Clinical Decision-Making.