International Journal of Modern Innovations and Emerging Trends
AI-Based Predictive Models for Urban Air Quality Management
Rapid urbanization, industrialization, increasing vehicle emissions, fossil fuel consumption, and construction activities have significantly deteriorated urban air quality, posing serious risks to public health, the environment, and the economy. Traditional air quality monitoring systems, which rely on fixed monitoring stations and statistical forecasting methods, often lack adequate spatial coverage and fail to capture the complex relationships among environmental factors. Artificial Intelligence (AI) offers an effective alternative by integrating data from IoT sensors, satellite observations, meteorological stations, traffic systems, and historical pollution records to generate accurate real-time air quality predictions. This paper presents an AI-based predictive framework for urban air quality management that combines data preprocessing, feature engineering, machine learning, deep learning, and ensemble models. The framework analyzes key environmental parameters, including particulate matter (PM₂.₅ and PM₁₀), gaseous pollutants, weather conditions, traffic density, and industrial emissions. Advanced preprocessing techniques improve data quality, while algorithms such as Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gradient Boosting enhance forecasting accuracy. The proposed model supports intelligent decision-making by enabling early pollution warnings, optimized traffic management, industrial emission control, and sustainable urban planning. Experimental results demonstrate that AI-based models outperform conventional statistical approaches in prediction accuracy and computational efficiency. Overall, the framework provides a scalable and reliable solution for smart city applications, contributing to healthier, more sustainable, and resilient urban environments.
Quantum Computing Applications in Optimization and Data Analytics
Quantum computing leverages quantum phenomena such as superposition and entanglement to solve complex optimization and data analytics problems more efficiently than classical computing. Recent advances in quantum algorithms, including QAOA, Quantum Annealing, VQE, and hybrid quantum-classical models, have enabled applications in finance, healthcare, logistics, manufacturing, cybersecurity, and artificial intelligence. This paper surveys quantum computing applications, proposes a taxonomy, and presents a hybrid quantum-classical framework integrating quantum optimization with quantum-enhanced machine learning. Mathematical formulation, algorithmic representation, and experimental evaluation demonstrate improved optimization accuracy, computational efficiency, predictive performance, and solution quality over conventional approaches, highlighting quantum computing's potential for next-generation intelligent decision-making systems.
Quantum Approximate Optimization Algorithm (QAOA)
AI-Integrated Smart Farming Solutions for Crop Enhancement
With the speed of AI development, the agriculture sector has been revolutionized, with intelligent, data-driven farming practices enhancing productivity and sustainability while optimizing the use of resources. Smart farming with AI technology involves the integration of machine learning, deep learning, computer vision, Internet of Things (IoT), unmanned aerial vehicles (UAVs), remote sensing, and cloud computing, all working together to track crop conditions, forecast weather, manage water usage, identify plant diseases, and improve agricultural decision-making. In traditional farming, making important observations and decisions is usually done by hand and based on general knowledge, which results in waste of resources and poor crop results. AI-driven precision agriculture, on the other hand, enables farmers to track crop health in real time and make predictions that help them take proactive measures to optimise quality and reduce expenses in their operations.Precision agriculture, on the other hand, powered by AI allows farmers to monitor and predict crop health, enabling them to take proactive steps to maximise crop quality while minimising operational costs. This study reviewed the state-of-the-art smart farming solutions integrated with AI, specifically aimed at crop improvement. The paper outlines the history of smart agriculture, examines the latest AI-driven innovations in agriculture, offers insights into ongoing research and development challenges, and recommends potential future research opportunities. In addition, it highlights the importance of data analytics, sensor technologies, and autonomous agricultural systems in the context of sustainable food production in a world of growing population and climate change. Research objectives are to develop a conceptual framework based on multiple AI technologies that can be combined in a single smart farming ecosystem to boost productivity, minimize environmental impacts, increase decision making accuracy and to support sustainable agricultural development. The results have shown that smart farming with the use of artificial intelligence is a paradigm.
AI-Powered Digital Twins for Predictive Infrastructure Management
Smart infrastructure increasingly requires predictive maintenance to improve asset reliability and reduce operational risks. AI-powered Digital Twins integrate IoT, cloud computing, big data, and machine learning to enable real-time monitoring, anomaly detection, asset health assessment, and failure prediction. This paper reviews their architecture, enabling technologies, applications, current research trends, and key challenges, including data quality, interoperability, cybersecurity, and explainability. The study highlights that AI-driven Digital Twins significantly improve maintenance efficiency, reduce costs, enhance safety, and support sustainable, intelligent infrastructure management.
Green AI: Energy-Efficient Machine Learning Models for Sustainable Computing
Artificial Intelligence (AI) has emerged as one of the most game-changers in today's world of innovation, revolutionizing the healthcare, finance, manufacturing, transportation, education, and smart city sectors. However, the exponential expansion of machine learning (ML) models, particularly deep neural networks and large language models, has led to significant rise in both computational complexity, electricity consumption and carbon emissions during training and deployment of these models. Due to this concern, there is a new paradigm of research on energy-efficient model design, computational optimization, and environmentally sustainable AI development, known as Green AI. In this paper, the authors provide an overview and a framework for designing energy-efficient machine learning models for sustainable computing infrastructures. The proposed Green AI framework aims to lower the computational overhead by utilizing efficient data preprocessing, light hardware, compression and pruning of neural networks, knowledge distillation, efficient hardware utilization, renewable energy-aware scheduling, and carbon-aware optimization to preserve high accuracy of learning. The study also covers metrics for evaluating model performance, energy consumption models, and indicators of sustainability that allow researchers to quantify environmental impacts throughout the creation of AI models. In addition, the paper outlines current research challenges such as the need for accuracy and efficiency, energy-evaluation approaches, and environmentally friendly AI systems. The overall framework outlines the potential impact of Green AI in areas such as cost reduction, carbon footprint minimization, resource efficiency, and sustainable digital transformation, making it a promising avenue for advancing the sustainability of computing systems.
AI-Driven Climate Analysis for Sustainable Urban Planning
Rapid urbanization, climate change, and environmental degradation have increased the need for intelligent urban planning approaches that address complex sustainability challenges. Traditional climate assessment methods, which rely mainly on statistical models and historical observations, often fail to capture nonlinear environmental interactions and dynamic urban growth patterns. Artificial Intelligence (AI) has emerged as a transformative solution by integrating machine learning, deep learning, remote sensing, Internet of Things (IoT) sensors, and geographic information systems (GIS) to deliver accurate climate predictions and data-driven decision support. AI-based climate analysis enables continuous monitoring of key environmental parameters, including temperature, humidity, precipitation, air quality, land surface characteristics, and greenhouse gas emissions, thereby supporting resilient urban planning. This study reviews recent AI-driven climate analysis techniques and proposes an intelligent framework that integrates diverse environmental datasets with advanced predictive models. The framework aims to improve urban resilience, optimize land-use planning, strengthen disaster preparedness, promote green infrastructure, and reduce carbon emissions through informed decision-making. Furthermore, the paper examines the role of machine learning, deep neural networks, GIS, and explainable AI in urban climate applications. The findings indicate that AI-powered climate analysis significantly enhances prediction accuracy, resource optimization, and environmental sustainability while supporting adaptive strategies for the development of smart, sustainable, and climate-resilient cities.
AI-Assisted Drug Discovery: Emerging Technologies and Challenges
Artificial Intelligence (AI) is transforming drug discovery by making the process faster, more cost-effective, and more accurate than traditional methods, which often require 10–15 years and billions of dollars to develop a new drug. AI techniques such as machine learning, deep learning, natural language processing, reinforcement learning, and generative AI are widely used for drug target identification, biomarker discovery, molecular screening, toxicity prediction, lead optimization, and clinical trial support. Advanced models including Support Vector Machines (SVM), Random Forests (RF), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Transformers improve the prediction of molecular properties and drug-target interactions, while generative AI enables the design of novel therapeutic molecules. This study reviews AI-driven drug discovery methods, presents a structured AI pipeline from data collection to candidate selection, and evaluates performance using metrics such as prediction accuracy, screening efficiency, lead optimization success, and toxicity reduction. Despite its advantages, AI faces challenges including limited high-quality datasets, model bias, interpretability, regulatory uncertainty, computational complexity, and integration with conventional laboratory workflows. The findings indicate that AI significantly improves drug discovery efficiency, reduces research costs, and accelerates pharmaceutical innovation. Future advancements will rely on explainable AI, multimodal biological data integration, federated learning, and stronger regulatory frameworks.
Large Language Models in Healthcare: Opportunities and Ethical Challenges
The Large Language Models (LLMs) are one of the most impactful technologies in artificial intelligence, showcasing astonishing natural language understanding, medical knowledge representation, clinical decision support and healthcare communication. The incorporation of LLMs into healthcare systems can have a profound impact on enhancing diagnostic precision, patient engagement, clinical documentation, medical research, and tailored treatment planning. LLMs can be utilized to analyze vast biomedical datasets, extract key information, aid clinical decision-making, and summarize complex medical records, due to their ability to process large amounts of information and understand complex patterns using advanced deep learning architectures. However, the use of LLMs in healthcare brings significant ethical, legal, technical and regulatory issues. Many challenges still stand in the way of large-scale implementation, such as patient privacy concerns, data security, algorithmic bias, explainability, misinformation, accountability, transparency, and regulatory compliance. In addition, there is a need for careful evaluation and ongoing monitoring of the performance of the models to guarantee fairness and reliability for different patient groups. The present paper is a thorough review of the opportunities and ethical concerns related to LLM in healthcare. It reviews the developments in LLM technologies, their uses in clinical and administrative settings, as well as their ethical considerations. In addition, the paper suggests a conceptual structure for responsible implementation that will ensure both technological innovation and patient safety, as well as regulatory compliance and ethical health care practices. The results offer insights that can guide and inform researchers, healthcare practitioners, policy makers, and AI developers in the design and development of trustworthy, transparent, and human-centered intelligent healthcare systems.
Explainable Artificial Intelligence (XAI)
Edge Computing Architectures for Ultra-Low Latency Applications
As enterprise data grows across cloud, edge, and geographically distributed environments, traditional Edge computing has emerged as a transformative extension of cloud computing by addressing the limitations of latency, bandwidth, and scalability in real-time applications. With the increasing demand for ultra-low latency in autonomous vehicles, industrial automation, telemedicine, smart cities, and augmented reality, traditional cloud architectures face challenges due to centralized processing and network delays. Edge computing overcomes these issues by processing data closer to end devices, enabling faster decision-making and reduced communication overhead. This paper presents a comprehensive survey of edge computing architectures for ultra-low latency applications, covering key technologies such as 5G, Software-Defined Networking (SDN), Network Function Virtualization (NFV), Artificial Intelligence (AI), microservices, and container orchestration. It also examines major challenges, including resource management, interoperability, security, and energy efficiency. A multi-layer edge computing framework with intelligent task scheduling and dynamic resource allocation is proposed to optimize latency and resource utilization. Experimental findings demonstrate significant improvements over conventional cloud architectures, achieving over 70% reduction in end-to-end latency and 65% improvement in resource efficiency. The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.
Digital Twin Systems for Next-Generation Product Engineering
Digital Twin (DT) technology enables real-time virtual representations of physical systems, transforming modern product engineering. By integrating Artificial Intelligence (AI), the Internet of Things (IoT), cloud computing, and big data analytics, Digital Twins enhance product design, manufacturing, predictive maintenance, and lifecycle management. This paper reviews recent advancements, industrial applications, and implementation approaches of Digital Twin systems while proposing an AI-enabled framework for real-time monitoring, simulation, and optimization. The findings demonstrate improvements in product quality, operational efficiency, maintenance accuracy, and cost reduction, highlighting Digital Twins as a key enabler of smart manufacturing and Industry 4.0. Future developments in edge computing and generative AI are expected to further expand their capabilities.
AI-Enabled Threat Detection in Network Security
Modern networks face increasing cyber threats such as malware, ransomware, phishing, DDoS, insider attacks, and advanced persistent threats, making traditional signature-based security systems less effective. Artificial Intelligence (AI), through machine learning and deep learning, enables intelligent threat detection by identifying known and unknown attacks in real time. This study proposes an AI-based threat detection framework that integrates network traffic analysis, preprocessing, feature engineering, threat classification, and automated response. Experimental evaluation using metrics such as accuracy, precision, recall, F1-score, false positive rate, and detection latency demonstrates that the proposed framework outperforms conventional methods by providing higher detection accuracy, lower false alarms, and faster response. Despite challenges related to data quality, model interpretability, and computational cost, AI-driven cybersecurity offers a scalable and effective solution for modern network security.
Intelligent Transportation Systems Using Vehicle-to-Everything (V2X) Communication
Intelligent Transportation Systems (ITS) have become a transformative solution to address the emerging challenges such as traffic congestion, road accidents, environmental pollution and inefficient mobility management in urban transportation. The Vehicle-to-Everything (V2X) communication has greatly improved the potential of ITS by allowing for the seamless and real-time exchange of information between vehicles, road-side infrastructure, pedestrians, cloud platforms and communication networks. V2X communication is defined as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), Vehicle-to-Network (V2N) and Vehicle-to-Grid (V2G) communication, all of which will result in an intelligent transportation ecosystem that will support autonomous driving, cooperative traffic management and smart mobility services. The fifth-generation (5G) and emerging sixth-generation (6G) wireless communication technologies, along with Artificial Intelligence (AI), Edge Computing, IoT and Cloud Computing, have driven the deployment of very reliable, low latency V2X-enabled transportation systems even further. This paper provides a thorough review of current Intelligent Transportation Systems (ITS) technologies, highlights research gaps and introduces an artificial intelligence (AI) based communication framework to enhance transportation systems and road safety. The proposed framework brings in intelligent decision-making algorithms and edge-enabled V2X communication to enable dynamic traffic control, accident prevention, route optimization, and emergency response coordination. The study also covers communication protocols, system architecture, performance metrics, and challenges in implementing V2X. The results show that intelligent V2X communication greatly contributes to traffic efficiency, communication latency, and driving safety, as well as sustainable smart city transportation infrastructures.
Intelligent Transportation Systems (ITS)
Blockchain-Based Digital Identity Framework for Trusted e-Governance
The digitalization of government services has increased the efficiency, accessibility and transparency of public administration, with an impact on the speed of change. Government service digitalization has increased the efficiency, accessibility and transparency of public administration, which affects the speed of change. But traditional digital identity management systems still have a lot of issues to overcome, such as centralised data storage, identity theft, unauthorised access, data tampering, privacy breach, and inadequate interoperability between government departments. These issues erode citizens' confidence and affect the safe and secure service delivery of e-Governance. A decentralized, immutable, transparent and cryptographically secure platform for identity management has emerged in the form of blockchain technology. Blockchain-based digital identities give citizens more control over their personal data and allow existing governments to provide secure digital identities and trusted authentication for information sharing between governments and individuals. The paper provides a detailed research on Blockchain-Enabled Identity Systems for Secure e-Governance by exploring various aspects of blockchain-based identity systems, including technologies, components, authentication mechanisms, and security features, which contribute to secure e-governance. The proposed framework combines Distributed Ledger Technology (DLT), smart contracts, cryptographic verification, permissioned blockchain networks, and decentralized identity management to create a secure identity infrastructure for public service delivery. Additionally, the paper explores the opportunities and prospects of blockchain for improving data integrity, transparency, accountability, and efficiency, and curbing fraud and administrative burden. The proposed framework clearly shows how a blockchain-based identity system can play a significant role in strengthening citizen-centric governance, especially when it comes to improving authentication accuracy, safeguarding sensitive information, simplifying the cross-departmental verification of citizens' identities, and enabling secure digital transformation initiatives within modern governments.
Synthetic Data Generation Models for Privacy-Safe AI
The increasing demand for high-quality datasets for AI development has raised significant privacy, security, and regulatory concerns, particularly in sensitive domains such as healthcare, finance, and government. Synthetic data generation addresses these challenges by creating artificial datasets that preserve the statistical characteristics of real data while protecting individual privacy. Recent advances in generative AI, including GANs, VAEs, diffusion models, and transformer-based models, have significantly improved the realism and utility of synthetic data. This paper surveys synthetic data generation techniques, privacy-preserving methods, applications, research challenges, and future directions for developing trustworthy AI systems.
AI-Driven Decision Systems for Real-Time Disaster Prediction
Natural and human-induced disasters are increasing in frequency and severity due to climate change, rapid urbanization, environmental degradation, and population growth. Conventional disaster prediction methods often lack the speed and accuracy needed for real-time emergency response. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Big Data Analytics enable intelligent systems to analyze diverse real-time data from satellites, IoT sensors, weather stations, seismic networks, GIS, and social media for accurate disaster forecasting. This paper presents an AI-based decision support framework integrating data acquisition, preprocessing, feature engineering, machine learning, deep learning, and automated decision-making within a scalable cloud-edge architecture. The study also reviews existing AI-based disaster prediction approaches, identifies their limitations, and compares their performance. The findings demonstrate that AI-driven disaster prediction systems significantly improve early warning capabilities, situational awareness, resource allocation, infrastructure protection, and emergency response, ultimately reducing disaster impacts and saving lives.
Agentic AI Architectures for Autonomous Business Applications
Agentic Artificial Intelligence (Agentic AI) represents the next generation of intelligent systems capable of autonomous sensing, reasoning, planning, and action with minimal human intervention. Unlike traditional AI, Agentic AI integrates large language models, reinforcement learning, multi-agent systems, planning mechanisms, orchestration layers, and memory modules to enable adaptive and goal-oriented decision-making. This paper explores Agentic AI architectures for autonomous business applications, highlighting their role in finance, healthcare, supply chain, enterprise resource planning, customer relationship management, and industrial operations. A layered architecture comprising perception, reasoning, orchestration, and execution layers is proposed to support autonomous analysis, strategic planning, and optimized action execution. The framework also incorporates memory, monitoring, and governance modules to enhance transparency, reliability, and explainability. Experimental evaluation demonstrates that the proposed architecture improves workflow automation, decision accuracy, operational efficiency, resource utilization, and response time while reducing manual intervention. The findings indicate that Agentic AI provides a scalable and robust foundation for future autonomous enterprise systems. The study also discusses key challenges, including explainability, governance, ethics, and trust, emphasizing their importance for successful enterprise adoption. Overall, Agentic AI architectures offer significant potential to accelerate intelligent automation and drive the next generation of business transformation.
The Emergence of Explainable AI in Modern Decision Systems
Artificial Intelligence (AI) has revolutionized decision-making systems of today, allowing automated data analysis, intelligent prediction, and real-time decision-making in a variety of application areas, including healthcare, finance, transportation, manufacturing, cybersecurity, and public administration. While deep learning and other advanced machine learning techniques have been able to deliver impressive results, numerous AI models can be considered as ‘black-box’ models, meaning that they give very accurate predictions without actually offering understandable explanations for their decisions. This lack of transparency has generated a number of concerns about trust, accountability, fairness, ethical compliance, and regulatory acceptance. Explainable Artificial Intelligence (XAI) is thus becoming an indispensable research field which aims to reconcile the predictive power and human interpretability. By explaining the reasoning behind AI system output, model importance, feature impact, and confidence scores, XAI helps users gain insights into how the system is working. This is done to build trust among stakeholders and promote responsible AI governance and decision-making. This paper offers a detailed overview of the concept of Explainable AI in contemporary decision-making processes, covering its theoretical underpinnings, its development, prominent explainability methods, implementation in practice, hurdles, and prospects. A methodology is advanced to embed explainability in the AI decision-making process, starting from data preprocessing to generating explanations and human evaluation. The paper also delves into the implications of explainability on decision quality, user trust, model reliability, and regulatory compliance. The results highlight the potential of explainability to enhance human comprehension and foster responsible use of AI systems in high-stakes decision-making scenarios.
Federated Learning Frameworks for Privacy-Preserving Smart Applications
Internet of Things (IoT), edge computing, and cloud computing has transformed data-driven services across healthcare, transportation, manufacturing, finance, agriculture, and smart cities. These applications generate large volumes of sensitive distributed data, making traditional centralized machine learning unsuitable due to privacy, security, communication, and regulatory challenges. Federated Learning (FL) addresses these issues by enabling collaborative model training without transferring raw data, thereby preserving user privacy.This paper presents a privacy-preserving federated learning framework that integrates secure aggregation, differential privacy, encryption, adaptive communication, and federated optimization for large-scale heterogeneous environments. The framework incorporates edge computing, blockchain, Trusted Execution Environments (TEE), and Explainable AI (XAI) to improve security, transparency, and trust. A multi-layer architecture consisting of client devices, edge servers, federated coordinators, cloud services, and security modules is proposed. Adaptive client selection, weighted federated averaging, dynamic privacy allocation, and asynchronous synchronization are employed to improve learning under Non-IID data distributions. Experimental results demonstrate high model accuracy, reduced privacy leakage, lower communication overhead, faster convergence, enhanced scalability, and strong resilience against security attacks, making the proposed framework suitable for next-generation privacy-preserving smart applications.
Retrieval-Augmented Generation (RAG) Systems for Knowledge Management
Artificial Intelligence (AI) and Large Language Models (LLMs) have significantly transformed knowledge management by enabling intelligent, context-aware, and automated information access. However, standalone LLMs often suffer from limitations such as outdated knowledge, hallucinated responses, lack of domain-specific expertise, and limited transparency, reducing their reliability in enterprise and research applications. Retrieval-Augmented Generation (RAG) has emerged as an effective solution by combining language models with external knowledge retrieval, allowing responses to be generated using up-to-date and relevant information. This study proposes a comprehensive Retrieval-Augmented Generation framework for intelligent knowledge management systems. The framework integrates document acquisition, preprocessing, semantic embedding generation, vector database indexing, document retrieval, prompt augmentation, LLM-based response generation, response validation, and continuous knowledge base updates. It supports diverse knowledge sources, including enterprise databases, technical documents, digital libraries, and research repositories, while incorporating sparse, dense, hybrid retrieval, and neural reranking techniques to improve retrieval accuracy. The proposed framework is evaluated using retrieval precision, recall, F1-score, response relevance, latency, grounding accuracy, and user satisfaction. Results demonstrate improved semantic understanding, reduced hallucinations, enhanced factual correctness, and real-time knowledge updates compared with conventional keyword-based knowledge management systems. The study also discusses future directions, including multimodal RAG, graph-enhanced retrieval, federated knowledge management, continual learning, and autonomous enterprise knowledge assistants, establishing RAG as a robust foundation for trustworthy and intelligent knowledge-driven AI systems.
Smart Healthcare Monitoring through Edge Intelligence
Digital healthcare technologies have significantly improved patient monitoring and disease prediction; however, conventional cloud-based healthcare systems face challenges such as communication latency, bandwidth consumption, privacy concerns, and delayed clinical responses. This study proposes a Smart Healthcare Monitoring through Edge Intelligence framework that integrates Artificial Intelligence (AI), Edge Computing, the Internet of Things (IoT), and Machine Learning (ML) to enable real-time and secure healthcare services. The proposed architecture employs wearable sensors to continuously monitor vital physiological parameters, including heart rate, ECG, blood oxygen saturation (SpO₂), body temperature, blood pressure, respiratory rate, glucose level, and physical activity. Medical data is processed locally at edge devices for noise removal, anomaly detection, risk assessment, and selective cloud synchronization. AI-based edge analytics support early disease prediction, personalized healthcare recommendations, and rapid emergency alerts while reducing communication overhead and protecting patient privacy. The proposed framework demonstrates improved monitoring accuracy, lower response latency, enhanced network efficiency, better data security, and reliable healthcare decision-making. It provides a scalable and intelligent solution for telemedicine, remote patient monitoring, and next-generation smart healthcare systems.
Predictive Analytics for Real-Time Supply Chain Optimization
Recent trends such as uncertainty, shorter product life cycles, globalization, and rising customer expectations have made real-time supply chain optimization increasingly important. Traditional planning methods based on periodic forecasts and static optimization struggle to respond to disruptions like demand spikes, supplier failures, shipment delays, and price fluctuations. However, advances in predictive analytics, machine learning, deep learning, and streaming data platforms enable organizations to anticipate future conditions and make proactive decisions. This article proposes a framework for applying predictive analytics to real-time supply chain optimization across demand forecasting, inventory management, transportation routing, production scheduling, and risk mitigation. The framework integrates three main components: real-time data ingestion from systems such as ERP, WMS, TMS, IoT sensors, and external sources; predictive models that generate probabilistic forecasts; and prescriptive optimization modules that update operational decisions in real time. A key feature is a multi-layer architecture separating prediction and decision-making while maintaining continuous feedback. The prediction layer uses time-series models and machine learning techniques to generate forecasts with uncertainty estimates. The decision layer applies these predictions within stochastic and robust optimization models to improve inventory control, replenishment, and routing decisions. Real-time responsiveness is achieved through rolling-horizon planning and event-driven re-optimization. The paper also reviews existing research and identifies challenges such as model drift, integration of external data, computational efficiency, and ensuring interpretability. To address these issues, it proposes a deployment approach involving offline training, online inference, periodic model updates, anomaly detection, and MLOps-based monitoring. Finally, the article emphasizes that predictive accuracy alone does not guarantee supply chain improvement. Real value arises when predictions are integrated into operational decision-making. By enabling proactive responses to disruptions, predictive analytics can reduce stockouts, improve service levels, lower costs, and enhance supply chain resilience.
Copyright (c) 2023 Dr. Rakesh Chandra (Author)
The sphere of agriculture is currently experiencing an enormous change fueled by the incorporation of the Artificial Intelligence (AI), Internet of Things (IoT), big data analytics, and new sensing technologies. The challenges to farming by the traditional practices usually include; poor use of resources, unpredictable weather, pests, and poor production. AI-based smart farming will provide data-driven, demonstration-free, and predictive solutions that will improve crop yielding, optimize resource use and enable sustainable and agricultural development. Within this paper, a detailed analysis of AI-based smart farming technologies to improve the crop condition is provided, including smart decision-making, precision agriculture, and real-time detection. The suggested framework is an integration of machine learning algorithms, computer vision, remote sensing, and internet of things with devices that will be used to touch soil, weather condition, and crop growth phases and pests. The prediction of yields, detection of diseases and the optimization of irrigation are reviewed using various AI methodologies including supervised learning, deep learning, and reinforcement learning. A comparative analysis shows that AI-based methods outperform the traditional farming methods in the aspects of productivity, economic efficiency, and environmental sustainability. The implementation challenges and scalability issues, as well as research directions are also addressed in the study. The results show that AI-combinations with smart farming can transform the agriculture sector as the application can lead to better quality of crops, higher yield and sustaining food security the world over due to climatic change and population explosion.
Copyright (c) 2023 Thomas Fischer, Anna Schmidt (Author)
Both natural and man-made disasters are very impactful to the surroundings, infrastructure, and human life. Proper and timely forecasting of disasters is essential in the reduction of the disaster. The conventional disaster prediction models are based on analysis of previous history and simple statistical methods which could be not capable of offering real-time and adaptive decision-making options. The present paper includes an in-depth research concerning AI-based decision systems of real-time disaster predictions that combine the latest machine learning (ML), deep learning (DL), and real-time sensor networks. We suggest an approach based on multi-layered which involves combining real-time data collection, AI-enhanced predictive analytics, and automated decision-making to improve disaster preparedness and response. The system resorts to ensemble learning, recurrent neural networks (RNNs), and spatiotemporal modeling and manages to predict the occurrence of a flood, earthquake, wildfire, and storm with high accuracy. The system is shown in one of the case studies that use actual real-time sensor data on the environment and satellite images to prove the efficiency of the system. It is shown that there is substantial increase in accuracy of prediction and response time over traditional systems. The given strategy is focused on scalability, flexibility, and resilience to different disaster risks. In addition, the combination of AI and Internet of Things (IoT) and Geographic Information System (GIS) allows developing a real-time decision support system that can support the government agencies, emergency responders, and communities with making proactive and data-driven decisions. The study indicates the possibilities of AI-powered systems in shifting disaster management to a predictive instead of a reactive and prevention system.
Copyright (c) 2023 Dr. Pooja Agarwal (Author)
A Cross-Sector Analysis of Modern Trends in Digital Transformation
Digital transformation (DX) has matured to be a sector-neutralized technological uptake operation to a everywhere strategic urgency within industries. Rapid development of cloud computing, artificial intelligence (AI), edge computing, blockchain infrastructures, Internet of Things (IoT), robotic automation, and data-driven decision-ecosystems have transformed conventional business strategies and business environments. This paper provides a comprehensive cross-sector discussion on digital transformation currently and specifically regarding technology convergence, enterprise integration practices, socio-technical outcomes and performance. The research applies the multi approach method, involving literature synthesis, framework modelling, enterprise case study and qualitative analysis of sectors in healthcare, manufacturing, finance, transportation, education and the government sectors. The evaluation shows that pressure to digital transformation is ever more being mounting on strategic innovation, cybersecurity governance, customer centric service model and sustainability requirements. Synergy of clouds and AI, hyper-automation, intelligent platform, and predictive analytics are the most powerful technology enablers. The paper has pinpointed several significant issues such as data fragmentation, inability to achieve interoperability, skills mismatch in the workforce, constraint of the legacy infrastructure and ethical issues in AI-managed decision systems. A research methodology has been suggested as a model of systematic evaluation of technology preparedness, process reengineering, and digital maturity measurement. Outcomes indicate that there are strong relations between transformation success and the organizational capabilities, cultural preparedness, and technological agility. The synthesis is giving rise to a better comprehension of the cross-sector transformation mechanisms which should be taken into the consideration of policy makers, digital strategists as well as the enterprise architects or industry leaders. The paper ends with recommendations on the future research directions of sectoral convergence, responsible AI governance, and next generation transformation architecture that includes quantum computing systems, 6G-enabled intelligent ecosystems, and autonomous cyber-physical enterprises.
Copyright (c) 2023 Dr. Matteo Rossi, Dr. Giulia Romano (Author)
Robotic Process Automation for Modern Enterprise Workflows
Robotic Process Automation (RPA) has been a transformative technology that allows companies to automate plastic-based tasks that are repetitive and basis rule-based in heterogeneous information systems without having to change the underlying infrastructure. Since organizations are under growing pressure to enhance both operational efficiency, accuracy, and scalability, RPA provides a cost-effective solution to the digital transformation process since it simulates human interactions with programs. The following paper is a detailed analysis of the concept of RPA within the current enterprise processes in terms of its architecture, deployment patterns, and quantifiable business outcomes. The paper is meticulously conducting a scientific review of literature to determine existing trends, advantages, challenges, and gaps in the present research on RPA. It suggests using the structured methodology that encompasses the process discovery, bot design, orchestration, and governance mechanisms according to the enterprise standards. Measures of performance like reduction in execution time, minimization in error rate, cost savings, and return on investment are measured to check effectiveness. The convergence of RPA and artificial intelligence and machine learning is also discussed and results in intelligent automation that has the potential to process semi-structured and unstructured information. At the end of the paper, the authors establish the main issues associated with scalability, security, and maintainability and show future research perspectives of sustainable adoption of RPA in large-scale enterprise settings.
Copyright (c) 2023 Mr. Kenji Sato, Ms. Aiko Yamamoto (Author)
The Role of Quantum-Safe Cryptography in Next-Generation Security
Quantum computing offers major computational advances but threatens modern public-key cryptography. Classical algorithms such as RSA, Diffie–Hellman (DH), and Elliptic Curve Cryptography (ECC) are vulnerable to quantum attacks, particularly Shor’s algorithm. As large-scale quantum capabilities emerge, post-quantum cryptography (PQC) has become essential to ensure future data confidentiality, integrity, and authentication. This paper discusses the need to replace classical cryptography, explores quantum-safe solutions, and examines challenges in large-scale migration. PQC is critical across government, critical infrastructure, finance, healthcare, telecommunications, IoT, autonomous vehicles, and 6G networks. A key concern is “harvest-now, decrypt-later” attacks, where encrypted data is stored today for future quantum decryption. The study analyzes classical cryptographic vulnerabilities and reviews major PQC families: lattice-based, hash-based, code-based, multivariate-based, and isogeny-based schemes, highlighting the ongoing NIST standardization efforts. It proposes a migration framework including quantum-readiness assessment, algorithm selection, hybrid implementation, and performance evaluation. Results show that although PQC introduces higher computational complexity, optimized implementations can support real-time applications with reasonable overhead. Among PQC approaches, lattice-based schemes appear most mature and balanced in terms of security and key size. The paper concludes that quantum-safe cryptography is a necessary evolution requiring continuous monitoring, adaptable systems, and alignment with emerging standards.
Copyright (c) 2023 Noah Wright, Isabella Moore (Author)
The Impact of Resource Management on Client Satisfaction in the Hospitality Industry
This paper examines the critical role of resource management in influencing client satisfaction within the hospitality industry. By analyzing various types of resources including human, physical, financial, and technological resources—the study explores how their efficient allocation directly impacts the quality of service and the overall guest experience. It highlights the challenges faced by hospitality providers in balancing resource constraints with the need for high-quality service delivery. Furthermore, the paper provides an overview of best practices and strategies in resource management, such as forecasting, staff training, and the integration of technology, which contribute to enhancing client satisfaction. Through case studies and industry examples, this research underscores the importance of effective resource management in creating memorable and satisfying experiences for guests, thereby improving client retention and business success.
Copyright (c) 2023 Dr. Chen Wei, Dr. Liu Fang (Author)
Utilizing Resource Management Tools for Improving Client Relationship Management (CRM) Systems
This paper explores the integration of resource management tools within Customer Relationship Management (CRM) systems to enhance client relationship strategies. By examining the functionalities of resource management tools and their impact on CRM processes, the study aims to identify how these integrations can lead to improved client engagement, operational efficiency, and data-driven decision-making. Through a review of existing literature and case studies, the paper highlights the benefits and challenges associated with such integrations, providing insights into best practices for businesses seeking to optimize their CRM systems.
Copyright (c) 2023 Dr. Lakshmi Narayanan (Author)
The Influence of Green Technology Adoption on Manufacturing Efficiency
Green technology is increasingly recognized as a strategic solution for improving manufacturing efficiency, reducing environmental impact, and sustaining global competitiveness. Rapid industrialization has intensified energy consumption, greenhouse gas emissions, and waste generation, prompting manufacturers to adopt environmentally friendly technologies due to regulations, international agreements, and consumer expectations. This paper examines how green technologies—such as energy-efficient equipment, renewable energy systems, waste optimization methods, and digital innovations—enhance overall manufacturing performance. Modern efficiency includes productivity, energy and material efficiency, cost-effectiveness, sustainability, and regulatory compliance. These technologies optimize resources, reduce costs, improve equipment performance, minimize pollution, and extend asset life cycles. Using multidimensional indicators like energy intensity, production throughput, carbon footprint reduction, and ROI, the study evaluates eco-technology impacts. Although SMEs face financial and technological barriers, long-term benefits outweigh short-term challenges. Integration with Industry 4.0 technologies—smart sensors, AI monitoring, and IoT—further accelerates green transformation through real-time analytics and predictive maintenance. Through literature review, case studies, KPI evaluation, and statistical analysis, the study compares traditional and green systems. Results show strong positive correlations, including 12–35% energy reduction, 8–20% better material utilization, and up to 50% waste reduction, along with improved corporate image and regulatory compliance. The study concludes that green technology adoption drives industrial competitiveness and recommends supportive policies, financial incentives, and strategic frameworks to advance global green manufacturing.
Copyright (c) 2023 Dr. Arvind Kumar Singh (Author)
The Emergence of Explainable Artificial Intelligence in Modern Decision Systems
Through the application of Artificial intelligence (AI), there has been the establishment of the technology as a cornerstone in current decision systems in essential fields like health, finance, transport, industrial automation, and the governance of citizens. Although traditional AI and machine learning frameworks, specifically deep learning models, have shown outstanding predictive performance, their nature is not enlightened by default, and as such, they have cast considerable doubt on the issues of trust, accountability, fairness, and regulatory compliance. It is thanks to this limitation that Explainable Artificial Intelligence (XAI) as a paradigm has emerged, aimed at ensuring that AI-driven decisions are easy to understand and interpret by the human stakeholders without major performance reduction. This paper is a thorough and stepwise analysis of how XAI was created in current decision systems. It starts with the historical contextualization of the development of AI, as an expert system driven by rules, to a data-driven black-box model and the increasing demand to explain decision-making processes. The paper provides a critical literature review of the current research on XAI methods classifying them into model-intrinsic and post-hoc methods of explanation, and discussing their relevance to various fields. An elaborate methodology is suggested, that incorporates explainability protocols into the AI choice channel, such as information pre-processing, model order, explanation creation and human-centered assessment. Additionally, the paper evaluates the experimental findings and case-based debates on how XAI enhances transparency, end-user trust, compliance with regulations, and system resilience. Popular explainability methods are also compared and evaluated including SHAP, LIME, saliency maps, and rule extraction. The results indicate that explainable models, in addition to increasing interpretability, can also help to improve debugging, bias detection and ethical AI deployment. The paper ends by recommending the current challenges, areas of open research, and future roles of XAI in the development of responsible and human-centered intelligent decision systems.
Copyright (c) 2023 Dr. Meena Krishnan (Author)