Global Trends in Science and Technology (GTST) is a prestigious international peer-reviewed journal published by the Global Alliance for Scientific Studies and dedicated to showcasing cutting-edge advancements, transformative research, and forward-looking insights across a wide array of scientific and technological domains. GTST serves as a vital platform for researchers, academics, and industry experts to exchange ideas and present innovations that tackle global challenges and inspire progress. Core Focus Areas GTST encompasses a diverse range of interdisciplinary research, including: Development of emerging technologies in science and engineering. Innovations in materials science, nanotechnology, and biotechnology. Renewable energy solutions, sustainability practices, and environmental advancements. Progress in information technology, artificial intelligence, and robotics. Explorations in astrophysics, space research, and groundbreaking physics studies. Pioneering discoveries in healthcare, biomedical sciences, and the pharmaceutical industry. Ethical perspectives and policy considerations in science and technology. Aims and Scope Original Research: Publishes pioneering articles, in-depth reviews, and case studies that shape the future of science and technology. Interdisciplinary Approach: Promotes research that integrates diverse scientific fields for holistic problem-solving. Global Reach: Encourages contributions from experts around the globe, fostering an inclusive and collaborative exchange of knowledge. Open Access Commitment: Ensures unrestricted availability of high-quality research to empower global communities. GTST is devoted to driving scientific and technological progress, addressing critical global issues, and contributing to a sustainable and innovative future through impactful research and groundbreaking ideas.
Machine Learning and Artificial Intelligence for Next-Generation Intelligent Data Analytics: A Cross-Domain Review of Healthcare, Cybersecurity, Blockchain, and Supply Chains
In the rapidly evolving landscape of today's world, Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing intelligent data analytics, empowering decision-making across various sectors with enhanced data-driven insights. This review covers concepts from the foundation up, next-generation analytic techniques, cross-domain applications and some key challenges as well as future directions. Supervised, unsupervised and reinforcement learning are some of the core ML approaches which enable predictive and adaptive systems. Real-time analytics, edge computing, and explainable AI are some of the emerging technologies that improve efficiency and transparency. Healthcare, cybersecurity, blockchain and supply chain management are all examples of areas where we see considerable advances in prediction, security, and optimization. But data challenges like privacy, quality, data scalability, and ethical issues remain. The future of federated learning, quantum computing, and autonomous systems, among other trends, holds the potential of continued innovation, propelling intelligent, secure, and scalable analytics ecosystems across the globe.
Copyright (c) 2026 Alaxendra Harry (Author) https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence and Machine Learning for Intelligent Data Analytics: Current Trends, Challenges, and Future Directions in Healthcare, Cybersecurity, Blockchain, and Supply Chain Systems
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the landscape of modern data analytics, facilitating intelligent, automated, and data-driven decision-making across various industries.AI and ML are transforming the way modern data is analyzed, by allowing for intelligent, automated and data-driven decision-making in many different areas. This review delves into their use in healthcare, cybersecurity, blockchain, predictive analytics, and supply chain management, offering insights into their ability to enhance precision, streamline processes, and provide timely insights. AI powered systems enable a range of applications such as disease diagnosis, threat detection, secure data sharing, forecasting, and logistics optimization. While these advantages are notable, obstacles like data quality problems, privacy concerns, algorithmic bias, and computational challenges are still formidable hurdles. New technologies such as generative AI, federated learning and explainable AI are overcoming these challenges. In conclusion, AI and ML are transforming data analytics into a more intelligent and adaptive field.
Copyright (c) 2026 Mohammad Ali (Author) https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence and Workforce Performance Optimization in Government Supply Chains
This review examines how Artificial Intelligence (AI) can be used to improve the performance of the workforce in government supply chains. It explores essential AI tools like machine learning, natural language processing, and automation, as well as their usage in procurement, logistics, inventory management, and risk identification. The study emphasizes the role of AI in optimizing the workforce, enabling better decision-making, automating repetitive tasks, and implementing sophisticated monitoring systems. It also explains how AI is being used to improve public supply chains over the years and its advantages such as reducing costs, delivering transparency, and providing speedy service. But obstacles like data quality, ethical issues, cybersecurity risks and a lack of employee acceptance are recognized. The review also covers workforce implications and governance issues and directions for future research. In conclusion, AI has the potential to be a powerful enabler for improving government supply chain efficiency and workforce productivity, provided it is used responsibly and effectively.
Artificial Intelligence, Government Supply Chains, Workforce Performance Optimization, Machine Learning, Public Procurement.
Copyright (c) 2026 Mussavir Mustafa Shaikh (Author) https://creativecommons.org/licenses/by-nc/4.0
A Review of Lean Manufacturing Practices in Modern Industries
Lean manufacturing is the name for a methodical way of running production systems to get maximum value and minimum waste. A philosophy that has come a long way from its roots in the Toyota Production System, it is now found in everything from the automotive and service sectors to healthcare. This paper reviews the ins and outs of lean: the principles, the challenges, the benefits and where it is headed. We look at how techniques such as JIT, 5S and Kanban are used to keep costs down and quality up. While there are obstacles to implementation like skill shortages or inertia, new technologies from the Industry 4.0 era are making the approach all the more effective. In short, it is about operational excellence and the kind of continuous improvement that keeps an organization ahead.
Copyright (c) 2026 Muhammad Saad (Author) https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence-Based Healthcare Systems: A Review of Machine Learning, Deep Learning, Data Analytics, Supply Chain Management, and Electrical Engineering Technologies
AI is in the process of remaking modern medicine. Systems powered by machine and deep learning, data analytics and electrical engineering are at the heart of it. This translates to hospital operations that run with greater efficiency, a more patient-focused approach and the sort of predictive analytics needed for decisions made on the spot. Take deep learning, for instance: it has given us a better handle on interpreting medical images and clinical data. And where supply chains are concerned, AI has put things in order through its management of demand and inventory. Then there is the hardware side of things, the sensors and imaging equipment, which is down to electrical engineering. There are still hurdles to clear, not least when it comes to infrastructure or concerns about privacy and how interpretable these tools are. But for all that, AI is proving itself to be an enterprise that puts the patient first and makes healthcare all the more efficient.
Copyright (c) 2026 George Edison (Author) https://creativecommons.org/licenses/by-nc/4.0
Additive Manufacturing and Precision Engineering in Medical Devices
This review shows how the integration of additive manufacturing with precision engineering is changing the face of medical device development. We have covered the main AM processes – FDM, SLA, SLS, SLM and EBM – and their role in everything from prosthetics to dental and surgical equipment. The paper examines the metals, polymers and composites that are essential for performance and biocompatibility. While there are challenges to be had with regulation and scaling, the advantages in efficiency and design are hard to ignore. With new trends in AI and bio printing on the horizon, the outlook for intelligent, patient-specific innovations is very promising.
Copyright (c) 2026 Mannan Mohammed, Salman Kunnipurayil (Author) https://creativecommons.org/licenses/by-nc/4.0
AI-Powered Data Science: A Review of Current Developments and Emerging Opportunities
Data Science with AI combines the capabilities of AI and traditional data science, offering improved data analysis, prediction, and decision-making functions. This review delves into key concepts like machine learning, deep learning, and big data, as well as cutting-edge advancements such as AutoML, generative AI, explainable AI, and real-time analytics. It showcases real-world examples from industries such as healthcare, finance, manufacturing, retail, transportation and education, pointing to its broad industry applications. The study also covers emerging areas such as human-AI collaboration, predictive analytics and democratization of data science. Issues of privacy, bias, scalability and transparency are explored. Lastly, ethical AI, federated learning and autonomous systems development are highlighted as future directions.
Copyright (c) 2026 Surdesh Kumar Oad (Author) https://creativecommons.org/licenses/by-nc/4.0
Data-Driven Intelligence: Emerging Architectures for Scalable and Responsible AI Systems
This review examines information-driven intelligence and future AI designs with emphasis on scalability and responsible system design. It underlines the use of large-scale data, enhanced machine learning models, and distributed computing in the modern AI to achieve high performance in a variety of applications. Among the major ones are deep learning, federated and edge computing, data governance, explain ability, robustness, and ethical aspects. The paper also looks at real-life use in the field of healthcare, finance, smart cities, and autonomous systems with a particular emphasis on their effect on society. It addresses key challenges including bias, privacy, computational cost and security and open research directions towards constructing transparent, efficient and trustworthy AI systems.
Copyright (c) 2026 A Singh (Author) https://creativecommons.org/licenses/by-nc/4.0
Neural Networks in Electrical Engineering Applications: A Review
Neural networks are an essential component of electrical engineering, as they can model nonlinear, complex and data-driven systems. Here, we review their use in power systems, control systems, electrical machines, power electronics and smart grids. They enhance load prediction, fault diagnosis, stability, motor control and energy management, as well as efficiency and reliability. New techniques like deep learning and hybrid models enhance their use. While facing issues such as data requirements and interpretability, research is making progress. In summary, neural networks play a significant role in creating smart, adaptable and efficient electrical engineering systems.
Copyright (c) 2026 Fawad Khan (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning for Big Data Analytics: A Comprehensive Review
This review examines the convergence of machine learning and big data analytics, showcasing how machine learning has contributed to big data analytics and the extraction of valuable insights from large-scale data. It explores the properties of big data, principles of machine learning, scalable algorithms and data pipelines that are backed by distributed and cloud computing platforms. The research explores use cases in healthcare, finance, retail, social media, and smart cities, showing the broad reach of data science. Challenges including scalability, data quality, privacy, and interpretability are discussed, as are emerging areas such as federated learning, explainable AI, and edge computing. It concludes that this integration is critical to future "smart, fast and fair" analytics.
Copyright (c) 2026 Tsendayush Erdenetsogt, Mehtab Jamal (Author) https://creativecommons.org/licenses/by-nc/4.0
Advanced Manufacturing and Process Optimization in Medical Device Production
Process optimization and advanced manufacturing of medical devices have revolutionized technology in healthcare by facilitating production of medical devices that are precise, efficient and of high quality. Key manufacturing methods in this review include additive manufacturing, CNC machining and micro fabrication as well as advanced materials like polymers, metals and ceramics. It also addresses the process optimization techniques such as Lean, Six Sigma and statistical process control that enhance efficiency and decrease errors. More productivity and quality assurance is provided with the help of automation, digital manufacturing, and Industry 4.0 technologies. The future of medical device production is influenced by new trends, including AI, intelligent devices, and customized manufacturing despite traditional barriers like cost, regulation, and scalability.
Copyright (c) 2026 Salman Kunnipurayil, Mannan Mohammed (Author) https://creativecommons.org/licenses/by-nc/4.0
AI in Business Data Analytics: A Review of Current Practices and Future Direction
Artificial Intelligence (AI) is changing the way business data analytics is processed by allowing organizations to process vast amounts of data, discover insights, and make decisions based on the data at a level never seen before. This review will discuss the existing AI applications, such as machine learning, natural language processing, predictive analytics, and AI-powered visualization, in major business processes like marketing, finance, supply chain, and human resources. It further brings out the advantages of AI increased efficiency, decision making, cost reduction, and competitive advantage and touches issues such as data quality, ethical issues, cost of implementation, and skills shortages. New developments, including explainable AI, generative AI, and democratized analytics, are addressed, which offer future adoption guidelines.
Copyright (c) 2026 Vidya Devi Oad (Author) https://creativecommons.org/licenses/by-nc/4.0
AI-Powered Healthcare Transformation: A Review of Machine Learning, Predictive Analytics, Block chain Applications, Data Analytics, Supply Chain Efficiency, and Cybersecurity Challenges
The application of Artificial Intelligence (AI) is revolutionizing healthcare using machine learning, predictive analytics, data analytics, block chain, supply chain optimization and cybersecurity. These technologies improve diagnosis, treatment and efficiency in operations and make the personalized and preventive care possible. Clinical decision-making is enhanced by machine learning and predictive models, whereas data analytics facilitate evidence-based practice. Block chain guarantees safe, transparent management of health data, and AIs optimize the supply of medical resources. Nevertheless, there are the issues of data privacy, algorithm bias, transparency deficit, and cybersecurity risks. Nevertheless, despite these problems, the adoption of emerging technologies can provide a strong model of creating effective, safe, and patient-centered healthcare systems in the future.
Role of AI in Modern Healthcare: From Data Analytics to Intelligent Supply Chains and Secure Block chain Systems
Healthcare is undergoing transformation using artificial intelligence (AI), data analytics, machine learning, and block chain technology. The innovations allow effective analysis of big healthcare data to enhance disease prediction, diagnosis, treatment plans, and patient outcomes. AI-based systems assist in making clinical decisions, improving healthcare supply chains, and improving operational efficiency. Thanks to predictive analytics, it is easier to detect diseases and manage resources at the earliest stage, and block chain guarantees the safety, transparency, and inability to alter the information. The use of AI is pushing to the desired goal of personalized and preventive healthcare despite the obstacles facing it, including data privacy, bias, and interoperability challenges. This review identifies how AI has been used, challenges relating to AI usage, and its future in the current healthcare systems.
Copyright (c) 2026 Bimalendu Pendy (Author) https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence for Big Data Analytics: A Review of Trends and Challenges
Artificial Intelligence (AI) has played a major role in transforming the field of Big Data analytics because it allows processing and interpretation of large and complex datasets efficiently. This review examines the basics of Big Data, concepts of AI, and how they could be combined to improve the decision-making process based on the data. It identifies automated machine learning, real-time analytics, edge computing, and generative AI as important trends as well as healthcare, finance, smart cities, e-commerce, and cybersecurity. Other significant issues that are addressed in the study are data quality, scalability, privacy, and ethical concerns. Moreover, it examines key tools and technologies and establishes the way of future research. By and large, intelligent decision-making using AI-based Big Data analytics have the potential to provide potent solutions.
Integration of AI in Data Science: A Systematic Review
This review paper discusses how Artificial Intelligence (AI) has been incorporated in data science and how it has influenced contemporary data-driven operations. It examines the important AI methods, such as machine learning, deep learning, natural language processing, and computer vision, and their use in different industries. The review points out the advantages of AI, including increased accuracy, automation, and scalability, and covers the issues of data quality, bias, and ethical issues. Moreover, it also talks about the future tendencies and research focus that contributes to the discipline. In general, the article focuses on the revolutionary application of AI in the improvement of data science and its use in making wise decisions.
A Broad Review of AI and Machine Learning in Healthcare Informatics for Predictive Analytics, Data Analytics, and Healthcare Supply Chain Systems
The healthcare informatics field is being transformed by Artificial Intelligence (AI) and Machine Learning (ML) that allow making decisions based on data, predictive analytics, and operating more efficiently. This literature review addresses the situation of the AI and ML in predictive analytics, data analytics, and healthcare supply chain management, with its possible benefits being the ones to improve patient outcomes, resource allocation optimization, and personalized care. It is mainly used in the early detection of diseases, risk stratification, population health management, and smart supply chain forecasting. Although these are beneficial, data quality, privacy, interoperability, ethical issues and biased algorithms are important challenges. New technologies, such as Explainable AI, federated learning, integration with the IoT, and block chain, will improve transparency, security, and efficiency. All in all, AI and ML play a role in the making of healthcare a proactive, patient-centered, and intelligent system.
Copyright (c) 2026 Mohammad Ali Ali (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning in Healthcare: Revolutionizing Clinical Decision-Making with Data Analytics
Machine learning (ML) is reshaping healthcare through the following ways: clinical decision-making through data, diagnostic precision and tailored treatment plans. Using a wide range of data, such as electronic health records, medical imaging, genomics, and wearable devices, ML models detect the trends, forecast the disease progression, and provide optimal patient care. The applications are applicable in oncology, cardiology, neurology, and chronic disease management with large improvements in the outcomes and efficiency. Although such issues as the quality of data, the clarity of algorithms, ethics, and the ability to integrate them into clinical activities have hindered its development, new advances in the field of deep learning, real-time decision support, and collaborative models of human-AI technology make ML one of the foundations of modern medicine, building a predictive, accurate, and patient-centered future.
Copyright (c) 2026 Muhammad Shahrukh Aslam, Mehtab Jamal (Author) https://creativecommons.org/licenses/by-nc/4.0
Recent Advances in Healthcare Informatics: A Review of AI and Machine Learning for Predictive Analytics, Data Analytics, and Healthcare Supply Chain Systems
The combination of Artificial Intelligence (AI) and Machine Learning (ML) has changed healthcare informatics, allowing predictive analytics, data-driven decision-making, and effective management of the supply chain. The review dwells upon the new developments in the AI and ML usage, such as disease forecast, patient risk categorization, personal treatment, and optimization of operations. Intelligent supply chain systems can be used to improve the allocation of resources and minimize wastage, whereas healthcare data analytics can help with evidence-based care and managing population health. New technologies, including IoT, blockchain, and cloud computing, hold the promise of a more proactive, efficient and patient-centered healthcare ecosystem despite data privacy, iSnteroperability and ethical issues.
Copyright (c) 2026 Alexandra Harry (Author) https://creativecommons.org/licenses/by-nc/4.0
Healthcare Informatics and Cybersecurity: A Review of AI and Machine Learning for Predictive Analytics, Data Analytics, and Supply Chain Systems
The high pace of digitalization in the sphere of healthcare resulted in the emergence of large volumes of multifaceted information, which requires sophisticated methods of computation to enhance patient care, efficiency, and system safety. This paper examines the use of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare informatics, predictive analytics, data analytics, and cybersecurity and supply chain management. Models based on AI can be used to predict diseases in advance, plan treatment by case, and optimize the management of hospital resources. Also, AI and ML can be used to improve cybersecurity by identifying and preventing any threats to sensitive patient information. The problems of data quality, integration, ethical issues and compliance are outlined and future research prospects linked to develop secure, efficient, intelligent healthcare systems are discussed. This paper demonstrates that AI-based solutions have the potential to transform healthcare practices to create resilient, data-driven, and patient-centered healthcare settings.
Securing, Optimizing, and Innovating: A Comprehensive Review of AI in Healthcare Cybersecurity, Analytics, and Supply Chain Ecosystems
Artificial Intelligence (AI) has become an influential movement in the fields of healthcare, cybersecurity, data analytics, product management, and supply chain ecosystems. With the help of machine learning, deep learning, and predictive analytics, AI allows making decisions proactively, being efficient in their operations, and improving service delivery. In the medical field, AI assists in diagnostic, patient surveillance, and individual treatment, and also enhances cybersecurity by detecting anomalies and eliminating threats. AI is used in supply chains to optimize demand forecasting, inventory management, and logistics and make supply chains resilient and economical. This review examines the uses of AI, methods of its integration, issues, and the future direction of AI, which is used to develop secure, adaptive, and intelligent systems to help in driving innovation in various areas that are critical.
Healthcare Transformation through AI, Machine Learning, Cybersecurity, Analytics, and Supply Chains
The development of Artificial Intelligence (AI), Machine Learning (ML), data analytics, cybersecurity, and intelligent supply chains systems is proceeding at a rapid pace and transforms the entire modern healthcare in a holistic manner. In this review, we have examined how these technologies have been integrated to improve clinical decision-making, operational efficiency, data security, and resilience of supply ns. It also aids in predictive diagnostics, personalized medicine and clinical decision support with the help of AI and ML applications, making interventions timely and more accurate. Data analytics allows drawing actionable insights using big and complex data on health care, supporting real-time monitoring, population health management, and strategic planning. Other challenges in integration identified in the review are interoperability barriers, data quality, and biases in algorithms, privacy protection, and the adaptation of the workforce. The future of intelligent healthcare ecosystems is determined by the emerging trends like federated learning, explainable AI, edge computing, and autonomous supply networks. In general, the integration of these technologies provides a route to safe, evidence-based, effective, and patient-centered healthcare systems.
Secure and Intelligent Healthcare: Applications of Machine Learning and Data Analytics
The high-speed digitalization of the medical field has brought the incorporation of the latest computational technologies that should enhance clinical quality, efficiency, and system safety. The given review examines the ways in which machine learning and data analytics may be utilized to create intelligent healthcare systems that can be used to facilitate predictive diagnostics, clinical decision-making that will be and resource management that will be optimized. It demonstrates that artificial intelligence helps to improve the accuracy of diagnosis and the efficiency of business processes and that smart supply chain models improve inventory prediction and distribution resilience. The article compares the application of AI in precision agriculture, but focuses more on overall implications of predictive analytics and data-driven logistics. Moreover, the paper explains the rising significance of cybersecurity systems to the safety of sensitive patient information, and the integrity of systems in progressively interconnected digital ecosystems. The issues of regulations, ethics and implementation are discussed to highlight the necessity of transparent, secure and compliant technology adoption. On the whole, the review shows that machine learning, data analytics, artificial intelligence, secure supply chains, and robust cybersecurity coming together will be the basis of ensuring resilient, patient-centered, and future-ready healthcare ecosystems.
Artificial Intelligence at the Intersection of Computer Science and Healthcare: A Comprehensive Review
AI has become a disruptive technology in healthcare that is using the breakthroughs in computer science to improve the fields of diagnostics, treatment planning, patient monitoring, and medical research. In this review, the author discusses the historical development of AI starting with the early rule-based systems about modern machine learning, deep learning, and natural language processing applications. The most important areas of healthcare use are medical imaging, predictive analytics, personalized medicine and robotic-assisted surgery. Issues of data quality, interpretability, ethical issues, and regulation are mentioned, and novel trends like federated learning, generative AI, and human-AI collaboration are stated, and AI has potential to enhance patient outcomes and healthcare efficiency.
Copyright (c) 2026 Shah Zeb (Author) https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence in Data Analytics and Product Lifecycle Management: Current Trends and Future Directions
By utilizing Artificial Intelligence (AI), data analytics and Product Lifecycle Management (PLM) are currently undergoing a revolution as it allows making intelligent, data-driven decisions at every step of the product development process. This review addresses the background of AI in analytics, its use in design, manufacturing, maintenance, and sustainability, emerging trends, including the use of AI in creating digital twins, generative design, and integrating Internet of Things. Some of the main challenges such as quality of data, bias in algorithms, interpretability, technological integration, and skills gaps are discussed and strategies to address them are provided. Lastly, future directions show a way to autonomous design, customized products, and optimization of lifecycle sustainability. In general, AI is an innovation, efficiency and competitiveness driver as a strategic enabler in the contemporary product development.
Copyright (c) 2026 Muhammad Mohsin Kabeer (Author) https://creativecommons.org/licenses/by-nc/4.0
AI in Healthcare, Data Analytics, Block chain, Cybersecurity, and Machine Learning
The introduction of Artificial Intelligence (AI), machine learning (ML), Block chain, and cybersecurity is revolutionizing the current healthcare provision system through provision of predictive, personalized, and secure medical solutions. AI and ML improve diagnostics, patient monitoring, and treatment planning with the help of information-based visions, whereas Block chain secures transparent, reliable, and interoperative management of medical records and supply chains. Innovative cybersecurity protects sensitive health information against the changing threats. This review examines recent developments, uses and limitations of these technologies and the opportunities to enhance patient outcomes, operational efficiency and confidence in digital healthcare systems, as well as taking into account cross-industry applications and prospects.
Copyright (c) 2026 Ibrar Hussain (Author) https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence in Data Analytics, Product Management, and Cybersecurity: A Comprehensive Review of Machine Learning Applications
The use of Artificial Intelligence (AI) and Machine Learning (ML) has emerged as the key technologies that revolutionize data analytics, products management, and cybersecurity. The fast development of digital data, the growth of system complexity and the development of cyber threats led to the introduction of AI-solutions in intelligent decision-making and automation in organizations. The review is a thorough analysis of the basics of AI and ML, as well as their usage in data analytics, product management, and cybersecurity. It underscores the role that machine learning methods play in making predictive and prescriptive analytics more useful, assisting with product prescriptions, and providing the ability to detect and respond to threats proactively. Another aspect of cross-domain integration, ethical and legal issues, and the main challenges of data quality, model interpretability, and adversarial risk are also discussed in the article. Moreover, the upcoming research trends and direction such as generative AI and autonomous systems are studied. In general, the review provides useful information on the potential and constraints of AI-based methods and highlights the necessity of responsible, secure, and scalable application of AI in the contemporary digital ecosystems.
Copyright (c) 2026 Nahid Neoaz (Author) https://creativecommons.org/licenses/by-nc/4.0
AI Enhanced Organic Chemistry: Emerging Tools and Computational Strategies
Artificial intelligence (AI) is transforming organic chemistry by making it possible to make predictive and data-driven contributions to the design of molecules, synthesis of molecules, and optimization of reactions. Machine learning, deep learning and cheminformatics AI applications are used to predict reactions, plan retrosynthetic, and analyze computations, and save time and resources on experimentation. New instruments and platforms combine AI and automation, high-throughput experimentation and molecular modeling, which is speeding up the discovery in pharmaceuticals, materials and green chemistry. Even though some of the challenges to AI have been the data quality, interpretability, and experimental translation, the opportunities that AI presents are enormous and unexplored. This review also mentions existing applications, case studies, computational strategies, and future views and demonstrates the transformative influence of AI in the field of modern organic chemistry.
Copyright (c) 2026 Javeriya Sayed (Author) https://creativecommons.org/licenses/by-nc/4.0
Harnessing Artificial Intelligence in Product Management and Data Analytics: Trends, Applications, and Future Directions
Product management and data analytics are changing as a result of Artificial Intelligence (AI) which facilitates data-driven decision-making and predictive insights as well as automating workflows. AI can be used in product management to improve strategic planning, customer behavior analysis, and product lifecycle optimization so that organizations can identify trends in the market, make decisions about priority features, and increase customer experiences. In data analytics, AI is used to automate the processing of data, unearth concealed patterns, and assist in predictive and prescriptive analytical processes which allow real-time, actionable insights. AI integration in these areas makes it a synergy between analytics and product strategy to encourage innovation and efficiency in operations. The new trends, including AI-driven personalization, augmented analytics, generative AI, and ethical AI models, show a huge potential of the future, providing organizations with competitive edge and long-term development in the market that becomes more data-driven.
Integrating Big Data, AI, and Deep Learning for Intelligent Supply Chain Decisions
Big data, artificial intelligence (AI), and deep learning are converting supply chain management into an intelligent data-driven system. This review discusses the role of these technologies in increasing visibility, predictive and prescriptive decision-making and efficiency in operations feasible at the strategic, tactical and operational levels. Big data offers all the available information in different forms, whereas AI and deep learning generate insights in a complex dataset to make demand forecasting, inventory management, logistics, and risk mitigation optimally. The recent trends, such as IoT, automation, and explainable AI, also contribute to real-time, adaptive, and sustainable supply chains. The paper presents prospects, threats and future of smart supply chain analytics.
Copyright (c) 2026 Ali Husnain (Author) https://creativecommons.org/licenses/by-nc/4.0