The increasing convergence of quantum computing and artificial intelligence (AI) in health technology innovation is examined in this case study. The study explores how quantum computing is being positioned as a supplementary capability to AI in addressing intricate, data-intensive healthcare concerns, drawing on secondary sources. Quantum computing has potential benefits in optimization, molecular simulation, and high-dimensional data processing, yet AI still dominates clinical decision support, imaging, and predictive analytics. The case examines how AI–quantum integration may improve the speed, accuracy, and scalability of health technology solutions through institutional and industry-led efforts. To assess technology preparedness, ecosystem collaboration, and governance considerations, the study takes an innovation and systems viewpoint. The focus of the case is discussion on a suitable balance between prospective clinical benefits and related hazards in the face of uncertainty around quantum maturity, high prices, and skill gaps. The scope also includes identifying healthcare sub-domains where AI–quantum integration is currently possible, such as drug discovery, precision medicine, health operations, system optimization, medical imaging etc. Analyzing the ethical, legal, and data governance issues that could influence responsible adoption in line with both public interest and innovation goals shall remain a critical concern to address.

Every data science pipeline begins with a measurement. When that measurement is made by a quantum mechanical system—exploiting superposition, entanglement, and coherence—the sensor can in principle surpass the classical shot-noise precision limit. Atomic clocks already underpin GPS; superconducting magnetometers guide epilepsy surgery; single-photon LiDAR systems detect methane leaks at parts-per-million from 200 meters. This position paper argues that quantum sensing is naturally viewed as an optimization–inference stack whose central design problems are semidefinite programs (SDPs) and whose downstream layers connect directly to statistical inference and operational decision-making familiar to the INFORMS data science community—adaptive bandits, reinforcement learning, inverse problems, stochastic programming, and ML surrogates. The two SDP formulations are developed from first principles and shown to bound every other quantity in the pipeline; they are best understood as oracle tools that certify precision ceilings rather than as the paper’s primary novelty. An end-to-end methane emissions case study traces the complete stack from photon physics through SDP design to operational crew dispatch, providing a concrete quantum-versus-classical benchmark.

In an era of rapid digital transformation, customer experience management (CXM) has emerged as a critical factor in building lasting relationships between businesses and consumers. Artificial Intelligence (AI) is reshaping this landscape, offering new opportunities for automating interactions, personalizing customer service, and enhancing overall satisfaction. This study examines the impact of AI-driven automation in CXM platforms, specifically within the context of Saudi Arabian businesses. By employing tools such as chatbots, recommendation engines, and sentiment analysis, we analyze how AI can streamline response times, align service offerings with individual preferences, and elevate customer satisfaction. The experimental results reveal that AI-driven solutions significantly improve key CXM metrics. The treatment group, utilizing AI-based tools, experienced a 48% reduction in response times and a 28% increase in customer satisfaction scores compared to traditional service methods. Additionally, the AI-powered recommendation engine achieved higher personalization scores, reflecting its ability to tailor interactions to customer needs. These findings underscore the value AI-Driven Automation for Enhanced ... 97 of integrating AI into CXM strategies, especially in a market as dynamic as Saudi Arabia. This study contributes to the broader understanding of AI’s role in enhancing customer experience and offers insights for organizations aiming to adopt AI-driven CXM solutions effectively. Keywords: Artificial Intelligence, Customer Experience Managemen

Natural Language Processing (NLP) based Artificial Intelligence (AI) conversational systems have become essential tools for enterprises aiming to improve customer experience and service efficiency. These intelligent systems, commonly referred to as chatbots or virtual assistants, enable organizations to provide automated, personalized, and real-time responses to customer queries. The integration of NLP technologies allows machines to understand, interpret, and respond to human language effectively. Enterprises increasingly adopt conversational systems to reduce operational costs, increase response speed, and enhance customer satisfaction. This study reviews the role of NLP-based conversational agents in enterprise environments and analyzes their impact on customer service, communication efficiency, and service automation. The paper discusses technological frameworks, implementation strategies, advantages, challenges, and future research directions. The findings suggest that NLP-driven conversational systems significantly improve customer engagement and operational efficiency when integrated with enterprise knowledge bases and machine learning techniques

This chapter explores the application of hybrid artificial intelligence (AI) frameworks in the domain of smart agriculture and precision farming. We present a comprehensive overview of how the integration of various AI techniques, including machine learning, deep learning, and ensemble methods, can revolutionize agricultural practices. A novel hybrid AI framework is proposed, designed to leverage data from diverse sources such as IoT sensors, drones, and satellites to provide actionable insights for farmers. The chapter details a research methodology for developing and evaluating a crop yield prediction system based on this framework. A synthetic dataset is created to simulate real-world agricultural conditions, and a comparative analysis of different machine learning models, including Random Forest, XGBoost, Gradient Boosting, and a hybrid ensemble, is conducted. The results demonstrate the superior performance of the hybrid ensemble model in accurately predicting crop yields. The chapter concludes with a discussion on the implications of these findings for the future of agriculture and outlines potential directions for future research.

The increasing demand for food production, coupled with climate variability, resource scarcity, and environmental degradation, has accelerated the adoption of intelligent technologies in modern agriculture. Machine learning has emerged as a transformative approach for precision agriculture by enabling data-driven decision-making that improves crop productivity while optimizing the use of agricultural resources. This paper presents a comprehensive review of machine learning-based precision agriculture with a focus on intelligent crop yield prediction and resource optimization. It examines how supervised, unsupervised, and deep learning models leverage heterogeneous data sources, including satellite imagery, unmanned aerial vehicle (UAV) observations, Internet of Things (IoT) sensor networks, weather records, and soil characteristics, to generate accurate yield forecasts and support precision farm management. The study further evaluates machine learning applications in irrigation scheduling, fertilizer recommendation, pest and disease detection, nutrient management, and water-use efficiency. In addition, it discusses the challenges associated with data quality, model interpretability, scalability, computational requirements, and the adoption of artificial intelligence technologies in diverse agricultural environments. An integrated conceptual framework is proposed that combines multi-source agricultural sensing, machine learning analytics, predictive modeling, and decision support to enhance productivity, sustainability, and resource efficiency. The paper concludes by identifying future research directions, including explainable artificial intelligence, federated learning, digital twins, edge intelligence, and climate-resilient predictive systems for next-generation precision agriculture. The findings provide valuable insights for researchers, policymakers, agricultural practitioners, and technology developers seeking to improve food security through intelligent, sustainable, and data-driven farming systems.

At the moment, the advancement in technology is being extensively used for development aroundtheworldnowadays. One of the advancements in technology is the use of artificial intelligence (AI) for smart farming for the productionof crops and animals in agriculture. Special powers can be programmed into artificial intelligence (AI) systems as needed.Working with agricultural systems, artificial intelligence (AI) helps to raise the standard of agriculture in the worldnowadays.The use of this new technology in fundamental industries like agriculture is nothing new as we speak. Utilizing the most recentpaper trends will help enhance agricultural yields in a variety of places. This is essential since there is a rising needfor foodsources and less land is accessible for agriculture use in Nigeria. So, utilizing the features from the most recent year, thissystematic review tries to gather the most recent trends in AI studies for Smart Farming publications, and using suchasystemwill help enhance the production of the crops. The impacts of artificial intelligence for smart farming to enhance cropyieldarealso discussed in detail along with its various applications. We have seen how these sensors can be combined to improvethecrop yield production.

The integration of artificial intelligence (AI) is revolutionizing intelligent ocean exploration, offering transformative solutions for next-generation autonomous underwater vehicles (AUVs). This paper systematically reviews current research on AUVs, identifying three critical challenges: limited underwater endurance due to energy constraints (limited battery capacity and inefficient recharge methods), insufficient adaptability of autonomous control systems in complex marine environments (dynamic obstacle avoidance, unpredictable current disturbances and poor algorithm generalization), and degraded visual perception caused by underwater turbidity and lighting variability (underwater turbidity, light reflection, illumination, mirrored targets, suspended particles, etc.). We analyze these technical bottlenecks through an AI-driven lens and propose targeted improvement strategies. Furthermore, emerging development trends are projected, emphasizing the synergistic advancement of four key areas: Wireless power transfer for AUVs, autonomous underwater vehicle–manipulator systems with artificial intelligence; advanced sensing, navigation and autonomy for AUVs; and collaborative operations (underwater robot swarm operations). The analysis highlights how these innovations will enable adaptive, energy-efficient and environmentally resilient AUV systems, ultimately accelerating sustainable ocean exploration and resource utilization.

Unmanned aerial vehicles (UAVs) are increasingly adopted for border surveillance because they can deliver persistent coverage, rapid deployment, and real-time situational awareness across wide and remote areas at comparatively low operational cost. This study presents the design, development, and implementation of an intelligent quadcopter-based surveillance platform that combines autonomous navigation with live monitoring and on-board vision processing. The proposed system integrates a Pixhawk 2.4.8 flight controller, GPS waypoint navigation, and FPV video transmission, supported by Python-based computer vision modules for real-time object detection and tracking using OpenCV. To enable end-to-end operation, a custom ground-control web application was developed using React and Java-based REST APIs, with IoT communication protocols to support mission planning, live video viewing, continuous aircraft telemetry/status monitoring, and manual override when required. System validation was performed through a structured set of laboratory and field experiments, including propulsion characterization, ESC and sensor calibration, PID tuning for flight stability, and multi-scenario flight trials. The experimental results indicate stable autonomous flight at altitudes up to 15 m, dependable real-time video streaming, effective detection and tracking of moving targets under practical conditions, and robust fail-safe behavior through return-to-launch during abnormal or emergency events. Overall, the implemented platform provides a scalable and cost-efficient approach to strengthen border-security operations and offers a flexible foundation for future enhancements such as thermal imaging, advanced AI recognition models, and autonomous charging or docking stations.

High-speed near-ground flight presents critical challenges for large-inertia UAVs carrying payloads, including complex obstacles and communication-denied environments. Unlike agile small drones, these platforms require both rapid path planning and strict adherence to trajectory tracking constraints for safe obstacle avoidance. This paper proposes a two-stage autonomous navigation framework tailored for large-inertia UAVs. The framework integrates: (1) an enhanced LiDAR model with physical optical noise for improved simulation fidelity; (2) an ESDF + OctoMap dual-map construction method supporting global search and local optimization; and (3) a global BIT* planner combined with a B-spline local optimizer embedding dynamic, smoothness, and tracking accuracy constraints to ensure path feasibility and trackability. Simulation results demonstrate an average planning time of 0.86 ms, outperforming NAVIGATION, Informed RRT*, MPC Planner, and ESDF Optimization by 29.6–52.0%, with a 100% obstacle avoidance success rate and trajectory tracking RMSE of 0.28 m over a 350 m flight distance, along with strong parameter and noise robustness. Actual flight tests on a 9.4 kg quadrotor UAV confirm the algorithm’s effectiveness in map construction, path planning, and obstacle avoidance in environments with 15 obstacles, while maintaining computational overhead suitable for onboard deployment. These results establish the proposed framework as an effective solution for high-speed autonomous navigation of large-inertia UAVs in complex near-ground environments

Monogenic diabetes mellitus comprised a heterogeneous group of disorders caused by single-gene mutations affecting pancreatic β-cell function or insulin action, accounting for approximately 1--2% of all diabetes cases yet frequently misdiagnosed as type 1 or type 2 diabetes. The advent of CRISPR-Cas9 gene editing technology had introduced unprecedented opportunities for definitive genetic correction of these conditions. This narrative review critically examined the therapeutic potential and translational challenges of CRISPR-Cas9 applications in monogenic diabetes. A comprehensive literature search was conducted using PubMed, Embase, and Web of Science databases (2015--2025) with terms including "CRISPR," "gene editing," "monogenic diabetes," "MODY," and "neonatal diabetes." Principal findings reveal that CRISPR-Cas9 has demonstrated remarkable efficacy in correcting pathogenic mutations in patient-derived induced pluripotent stem cells and animal models of monogenic diabetes, with successful restoration of glucose-stimulated insulin secretion and normoglycemia. However, substantial barriers persisted including off-target mutagenesis, delivery vehicle limitations, immunogenicity concerns, and regulatory complexities that collectively impeded clinical translation. Emerging base editing and prime editing technologies offered enhanced precision with reduced double-strand breaks, potentially mitigating safety concerns. The evidence supported cautious optimism that CRISPR-based therapeutics may eventually provide curative interventions for monogenic diabetes, contingent upon resolution of safety, delivery, and ethical challenges through rigorous preclinical validation and carefully designed clinical trials.

Genome editing technologies, particularly CRISPR and its derivatives, have revolutionized molecular biology and medicine by enabling precise and efficient alterations to the genome. This systematic review provides a comprehensive overview of CRISPRCas systems and next-generation genome editing tools, focusing on their therapeutic potentials, challenges, and ethical considerations. Emerging technologies, such as base editing, prime editing, and RNA-targeted CRISPR systems, offer enhanced precision and expanded applications for genetic diseases, cancer therapies, and beyond. However, issues related to off-target effects, delivery mechanisms, and regulatory hurdles remain significant challenges. Ethical debates surrounding germline editing and equity in access to these therapies are also critical considerations that must be addressed. This review synthesizes current advances and explores future directions for genome editing technologies in precision medicine.

The ability to precisely modify genomic information has transformed modern biology and medicine, enabling direct interrogation and correction of genetic determinants underlying disease, development, and physiological function. Early gene manipulation strategies, although foundational, were constrained by limited specificity, low efficiency, and unintended genomic alterations. The emergence of next generation gene editing technologies has revolutionized genomic engineering by introducing programmable, highly precise, and adaptable tools capable of targeted DNA and RNA modification. Among these, CRISPR based systems have become the cornerstone of modern genome editing, offering unprecedented ease of design, scalability, and versatility. Building upon this foundation, advanced platforms such as base editing and prime editing have further refined genome manipulation by enabling single nucleotide changes and precise sequence insertions or deletions without inducing double strand DNA breaks. Parallel advances in epigenome editing and transcriptional control have expanded the scope of genomic engineering beyond sequence modification, allowing dynamic regulation of gene expression and chromatin architecture. These innovations have catalyzed rapid progress in therapeutic applications, including the treatment of inherited genetic disorders, cancer, infectious diseases, and regenerative medicine. However, challenges related to delivery efficiency, off target effects, immunogenicity, and ethical governance remain critical considerations for clinical translation. This review comprehensively examines the evolution of gene editing technologies, the mechanisms and innovations underlying next generation tools, and their expanding therapeutic and non medical applications, while addressing safety, ethical, and regulatory dimensions shaping the future of genomic engineering

CRISPR-Cas9 has rapidly emerged as a gold standard for accurate genome editing, which boasts remarkable potential for human therapies. This brief review article summarizes the most up-to-date developments in CRISPR-Cas9genetherapy with a focus on single-gene disorders such as sickle cell disease, β-thalassemia, and Leber congenital amaurosis. We explain how Cas9 generates a double-stranded break in DNA and how subsequent repair leads to gene correction. We describe base and prime editing methods that raise precision and drastically reduce off-target effects. Still, oneofthe most significant challenges in the therapeutic context is the delivery of the editing machinery to the desiredcells. There are many approaches under investigation, such as the use of adeno-associated viral vectors, lipid nanoparticlesorelectroporation, which are being evaluated for their efficacy, safety, and their ability to home in on the right tissue. Immunogenicity, undesired mutations, and long-term genetic stability remain major concerns. Besides the technical concerns, we also go over the ethical and legal issues, such as the germline gene editing, equitable access, and informedconsent, which emphasize the whole world perspective on responsible use. Eventually, the therapeutic scopeofCRISPR-Cas9 may be expanded by joining forces with RNA-targeting technologies, epigenetic modulators, andAI-based design software. Despite the hurdles, CRISPR-Cas9 is expected to revolutionize the field of precision medicine, thereby providing extraordinary possibilities for safe and efficacious genome-based medical interventions

The 21st century is witnessing immense achievements in human history, starting from home science to space science. Artificial Intelligence (AI) is a salient one among these feats, the critical factor of the 4th industrial revolution. Health is the primary and essential asset for the continuity of human civilization on this planet. Not only must we address the deadly existing diseases like Cancer, AIDS, Alzheimer's, heart diseases, gastrointestinal diseases, etc., but on top of that, we must effectively predict, prevent and respond to potential pathogens capable of causing havoc like the recent outbreak caused by SARS-CoV-2. AIenabled technology with the computational capacity of a computer and reasoning ability of humans saves surplus labor and time that is majorly consumed in target validation, lead optimization, molecular representation, and designing reaction pathways, which traditionally is a decade-long way of searching, visualizing, studying, imagining, experimenting and maintaining a ton of data. This article would focus on how AI will help find the drug-like properties in the compound screening phase predicting the Structure-Activity Relationship (SAR) and ADMET properties in lead identification and optimization phases, sustainable development of chemicals in the synthesis phases up to AI's assistance in the successful conduct of clinical trials and repurposing.

This article examines the significance of entropy production of two-phase Casson nanomaterial considering microbes cell, compelled by the Homann stagnant point past a vertical deformable sheet. The flow is modeled via porous medium laden by Darcy-Forchheimer drag effects, influenced by heated radiation and buoyancy effect. The suction/injection and convective flow have been applied on the boundary conditions. The double diffusive heat/mass theory model has been considered to signify the influences of thermal/solutal relaxation time in contrast to traditional Fourier and Fick’s equations. Employing similarity alteration, the constitutive equations are dimensionalized and rehabilitated into a system of ODE’s. These are then semi-analytically considered through a robust Homotopy analysis method (HAM) along convergence analysis on MATHEMATICA 12.0. The various profiles have been examined against the various physical variables. The findings clarify the combined effects on entropy production along transfer mechanism of the fluid factor, inertia coefficient, buoyancy factor, heated radiation factor, and thermal/ mass relaxation time. Moreover, Casson fluid parameter is declined the velocity filed. Moreover, thermophoresis factor is greatly enhanced for the temperature and concentration fields. Additionally, the entropy production is declined via the Brickman number and porosity factor but boosted through the Bejan number. This study sheds new light on the thermal efficiency of biological-nanofluidic structures, which may find usage in solar and thermal collectors, medicinal tools, including innovative energy technologies.

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