International Journal of Artificial Intelligence & Digital Transformation
Multimodal AI Frameworks for Decision Intelligence Systems
Decision Intelligence (DI) combines Artificial Intelligence (AI), Machine Learning (ML), analytics, and domain expertise to improve organizational decision-making. However, the growing volume of multimodal data, including text, images, sensor data, and numerical information, presents significant challenges for conventional decision support systems. This study proposes a Multimodal AI Framework for Decision Intelligence Systems that integrates diverse data sources to enhance prediction accuracy, contextual understanding, and operational efficiency. The proposed architecture consists of four layers: data ingestion, multimodal processing, fusion intelligence, and decision orchestration. It employs Natural Language Processing (NLP), Computer Vision (CV), time-series analytics, and transformer-based fusion techniques to generate predictive insights, automated recommendations, and explainable decisions. The framework is applicable across healthcare, finance, manufacturing, retail, and intelligent governance. Performance evaluation demonstrates that multimodal AI significantly outperforms traditional unimodal systems by improving prediction accuracy, reducing decision latency, and enhancing contextual awareness. The proposed framework supports faster, more reliable, and explainable decision-making, providing a scalable solution for next-generation enterprise decision intelligence and data-driven strategic planning.
Copyright (c) 2019 International Journal of Artificial Intelligence & Digital Transformation
Intelligent Workflow Engineering Using Generative AI Technologies
Artificial Intelligence (AI) has revolutionized the way organizations manage their workflows and automate their business processes. Recently, the introduction of Generative Artificial Intelligence (Generative AI) has introduced a new paradigm that can generate content, develop solutions, automate decision making processes and make workflow intelligence better. Traditional workflow engineering systems are mostly based on the rules that are previously defined, static automation mechanisms and people's intervention in order to execute it. These systems have enhanced productivity, but they are typically inflexible, cannot be contextually conscious, and are not smart enough to make good decisions. Thanks to generative AI technologies, such as Large Language Models (LLMs), transformer architectures and foundation models, organisations can now create intelligent workflows that learn, reason, predict, and self-optimise complex workflows. Intelligent Workflow Engineering (IWE) combines Generative AI with workflow management systems, enabling automation of repetitive workflow tasks, enhancing process orchestration, enabling knowledge discovery, and aiding strategic decision-making. By leveraging Generative AI, businesses can build flexible workflows that adjust to evolving business conditions with minimal human effort. Applications are used across different industries such as healthcare, manufacturing, finance, logistics, education and customer service. These systems take advantage of natural language understanding, content generation, predictive analytics and context reasoning to boost organizational productivity. This paper discusses an overarching approach to the Intelligent Workflow Engineering with Generative AI Technologies. It reviews the current workflow automation strategies, analyses their weaknesses, and presents an intelligent workflow architecture using AI for intelligent workflow orchestration. The framework includes data acquisition, modeling of workflows, generative reasoning, optimizing decisions, and continuous learning mechanisms. Mathematical formulations are used to model the optimization of workflow and performance evaluation. Experimental analysis shows a clear improvement in process efficiency, the accuracy of automation, use of resources, and quality of the decision when compared with traditional workflow systems. The proposed framework is a mix of generative intelligence and workflow engineering principles that help shape enterprise automation solutions of the next generation. Organizations with Generative AI-powered workflows see increased scalability, lower costs, speedier workflow and user satisfaction. The results show how important Generative AI is going to be for the future of intelligent enterprises and autonomous business ecosystems.
Copyright (c) 2025 International Journal of Artificial Intelligence & Digital Transformation
AI-Based Decision Intelligence Platforms for Digital Business Transformation
AI-Based Decision Intelligence (DI) Platforms are transforming digital business operations by combining artificial intelligence, machine learning, predictive analytics, natural language processing, and business analytics to support intelligent decision-making. Unlike traditional Business Intelligence systems that mainly provide historical insights, DI platforms deliver predictive and prescriptive recommendations that improve decision accuracy, operational efficiency, business agility, and customer satisfaction. The proposed framework integrates data collection, intelligent analytics, predictive modeling, decision optimization, and continuous learning within a scalable cloud-based ecosystem. The study demonstrates that AI-driven decision intelligence significantly enhances organizational performance and supports successful digital transformation across various industries. Furthermore, emerging technologies such as Explainable AI, federated learning, autonomous decision systems, and ethical AI governance will further strengthen enterprise decision ecosystems. Overall, AI-Based Decision Intelligence Platforms serve as a critical enabler for sustainable growth, competitive advantage, and intelligent enterprise transformation in the digital era.
AI-Powered Enterprise Intelligence Systems for Strategic Planning
Recent advancements in Artificial Intelligence (AI), machine learning, predictive analytics, and intelligent automation have transformed enterprise strategic planning. Traditional planning methods often relied on historical data, manual forecasting, and business intuition, resulting in delayed decisions and limited responsiveness. AI-driven Enterprise Intelligence Systems (EIS) address these challenges by integrating intelligent analytics, predictive forecasting, and real-time decision support into strategic planning processes. This research presents an AI-based Enterprise Intelligence framework that combines data integration, intelligent processing, predictive modeling, and strategic decision support. The system utilizes structured and unstructured data from ERP, CRM, supply chain, financial, and external market intelligence sources. Machine learning techniques, including regression models, neural networks, and ensemble algorithms, are employed to generate accurate business insights and forecasts. Performance evaluation demonstrates significant improvements, including a 35% increase in forecasting accuracy, a 42% reduction in decision-making time, a 38% improvement in operational efficiency, and a 40% enhancement in strategic planning effectiveness. The findings indicate that AI-driven Enterprise Intelligence Systems enable organizations to make informed decisions, optimize resources, respond rapidly to market changes, and achieve sustainable growth. The study concludes that AI-powered enterprise intelligence is a key enabler of future-ready strategic planning, competitive advantage, and digital transformation.
Cognitive Digital Platforms for Next-Generation Enterprise Operations
Cognitive Digital Platforms (CDPs) represent the next generation of intelligent enterprise systems by integrating AI, ML, Big Data Analytics, IoT, Cloud Computing, and Intelligent Automation. These platforms enhance enterprise operations through adaptive intelligence, predictive analytics, and autonomous decision-making, and continuous learning capabilities. The proposed multi-layer cognitive architecture improves operational efficiency, decision accuracy, resource utilization, and organizational agility across various business domains. Although challenges such as data governance, interoperability, privacy, and ethical AI remain, CDPs provide a strong foundation for building resilient, intelligent, and self-optimizing enterprises. The study highlights the significant role of cognitive platforms in supporting digital transformation and enabling sustainable competitive advantage in increasingly complex business environments.
AI-Enabled Organizational Learning Systems for Continuous Innovation
This study presents an AI-based Organizational Learning System (AI-OLS) that enhances continuous learning, knowledge management, and innovation within enterprises. By integrating machine learning, natural language processing, predictive analytics, and adaptive recommendation mechanisms, the framework enables intelligent knowledge discovery, personalized learning, and data-driven decision-making. The proposed system improves knowledge sharing, employee engagement, organizational agility, and innovation performance while reducing learning and innovation cycle times. Experimental results demonstrate superior effectiveness compared to traditional learning management systems, highlighting AI’s potential to transform organizations into adaptive, knowledge-driven, and innovation-focused enterprises. The framework offers practical guidance for implementing intelligent learning ecosystems that support long-term competitive advantage in the digital era.
AI-Driven Business Resilience Frameworks for Digital Economies
Digital economies face increasing risks from cyber threats, operational disruptions, and market uncertainties. Traditional resilience approaches are often reactive and insufficient for dynamic environments. This paper proposes an AI-Driven Business Resilience Framework (AIBRF) that integrates data acquisition, AI analytics, risk prediction, adaptive decision-making, resilience orchestration, and continuous learning. By leveraging machine learning, predictive analytics, and intelligent automation, the framework enables proactive risk management and real-time adaptation. Experimental results demonstrate improvements in risk detection, recovery time, decision-making efficiency, resource utilization, and business continuity. The proposed framework provides a scalable and intelligent approach for enhancing organizational resilience in digital economies, while future enhancements may incorporate generative AI, federated learning, explainable AI, and blockchain technologies.
Autonomous Enterprise Platforms Using Cognitive Computing Techniques
Digital transformation has increased enterprise complexity, creating a need for intelligent systems that can autonomously learn, adapt, and make decisions. Cognitive computing integrates AI, machine learning, natural language processing, and reasoning techniques to develop Autonomous Enterprise Platforms (AEPs) that optimize business operations with minimal human intervention. The proposed framework combines data acquisition, cognitive analytics, machine learning, knowledge management, and autonomous execution layers, supported by reinforcement learning, deep learning, and NLP. Experimental results show improvements in decision accuracy, operational efficiency, automation, adaptability, and customer satisfaction compared to traditional systems. The study also addresses challenges such as data governance, security, scalability, and AI ethics, while highlighting future technologies including generative AI, explainable AI, federated learning, digital twins, and autonomous agents. The findings demonstrate that cognitive computing is a key enabler of intelligent, adaptive, and autonomous enterprise ecosystems.
Hybrid Human–AI Collaboration Models for Digital Workplace Optimization
The rapid advancement of AI, ML, NLP, and intelligent automation is transforming digital workplaces. However, AI systems alone cannot fully replace human creativity, ethical judgment, and contextual understanding. This study proposes a Hybrid Human–AI Collaboration Model for Digital Workplace Optimization, where humans and AI work together by dynamically sharing tasks based on complexity and context. The framework consists of five layers: Data Acquisition, AI Intelligence Processing, Human Interaction, Collaborative Decision-Making, and Feedback Optimization. AI handles data analysis, predictions, and repetitive tasks, while humans contribute strategic thinking, creativity, and ethical oversight. Evaluation results indicate that hybrid collaboration improves productivity by over 30%, reduces decision-making time by 40%, and achieves operational accuracy above 90%. The study highlights trust, explainable AI, workforce reskilling, ethical governance, and cultural adaptation as critical success factors. The proposed framework provides a practical roadmap for building intelligent, human-centric digital workplaces that enhance organizational efficiency, employee satisfaction, and business agility.
Deep Learning Approaches for Real-Time Emotion Recognition
Real-time emotion recognition has become an important area in affective computing due to advances in deep learning and the need for intelligent human-computer interaction. This paper reviews pre-2019 deep learning approaches for emotion recognition using modalities like facial expressions, speech, and multimodal data. Traditional methods using handcrafted features (LBP, HOG, MFCC) had limited performance, while deep learning models such as CNNs, RNNs, LSTMs, and DBNs improved accuracy through automatic feature extraction and temporal learning. CNNs performed well in facial recognition tasks, while RNNs and LSTMs were effective for speech analysis. Multimodal systems showed higher accuracy but faced challenges like synchronization and complexity. Despite improvements, real-time deployment remains difficult due to high computational demands, cultural variability, and environmental noise. Overall, deep learning has significantly advanced emotion recognition, but challenges like efficiency, generalization, and interpretability still need to be addressed.
AI-Orchestrated Digital Ecosystems for Intelligent Enterprise Transformation
Artificial Intelligence (AI), cloud computing, IoT, big data analytics, and automation are driving the evolution of digital transformation toward integrated and intelligent enterprise ecosystems. This research proposes an AI Orchestrated Digital Ecosystem (AIODE) framework that combines data acquisition, integration, AI intelligence, digital orchestration, process automation, and decision support layers. The framework enables intelligent decision-making, predictive analytics, resource optimization, and adaptive business operations through AI and machine learning. Performance evaluation using key indicators demonstrates significant improvements in operational efficiency, automation, customer satisfaction, predictive accuracy, and resource utilization compared to traditional enterprise systems. The proposed approach supports scalable, agile, and sustainable enterprise transformation, providing valuable insights for researchers and practitioners developing next-generation intelligent enterprises.
Digital Transformation Strategies Using AI-Enabled Data Platforms
Digital transformation has become essential for organizations competing in a data-driven economy, driven largely by the integration of Artificial Intelligence (AI) and advanced data platforms. These technologies enable smart automation, predictive analytics, and real-time decision-making. This paper presents digital transformation as a multi-dimensional process involving organizational culture, business processes, and technological infrastructure, with AI-powered data platforms at its core. It reviews key technological developments prior to 2019, including cloud computing, big data frameworks like Hadoop and Spark, and early enterprise AI adoption. Current research emphasizes the importance of data governance, scalability, and interoperability. The paper proposes a structured implementation approach covering data collection, preprocessing, model development, deployment, and continuous optimization, supported by a flow-based architecture. Findings show that organizations adopting AI-enabled platforms achieve up to 45% improvement in operational efficiency and a 35% reduction in decision-making delays. The study concludes by stressing the need to align AI initiatives with business goals and highlights future directions such as autonomous systems and ethical AI practices.
AI-Based Knowledge Graphs for Intelligent Decision Support
The rapid growth of unstructured and heterogeneous data in modern information systems has created a need for intelligent methods to extract, organize, and utilize knowledge effectively. AI-based Knowledge Graphs (KGs) address this challenge by representing entities and their relationships in a semantically rich graph structure, enabling advanced reasoning and decision support. By integrating machine learning, natural language processing, and deep learning, KGs automate entity extraction, relationship identification, and knowledge inference, improving decision-making across domains such as healthcare, finance, e-commerce, and governance. This paper presents a framework combining data preprocessing, ontology development, graph embedding, and inference techniques. Experimental results show that AI-driven knowledge graphs significantly enhance decision accuracy, reduce ambiguity, and improve interpretability, achieving up to 85–92% higher decision efficiency compared to traditional methods. Future research focuses on scalability, explainability, and integration with emerging technologies like IoT and edge computing.
Autonomous AI Agents for Workflow Optimization
Autonomous AI agents represent a major advancement in workflow optimization by enabling intelligent, adaptive, and self-learning automation. Unlike traditional rule-based systems, these agents can handle dynamic environments, uncertainty, and complex decision-making through techniques such as reinforcement learning and natural language processing. Their integration into enterprise workflows improves efficiency, reduces execution time, minimizes errors, and optimizes resource utilization. The study highlights that agent-based models significantly outperform conventional automation methods, especially in complex and changing conditions. Although challenges like scalability and ethical concerns remain, autonomous AI agents have strong potential to transform workflows into self-optimizing systems across various industries.
Adaptive AI Systems for Dynamic Business Environments
Adaptive Artificial Intelligence (AI) systems are transforming how businesses operate in dynamic and uncertain environments. Traditional decision-support systems often fail to respond effectively to rapid changes, complex interactions, and evolving customer behaviors. In contrast, adaptive AI integrates machine learning, real-time analytics, and feedback-driven optimization to continuously learn and adjust to new conditions. These systems incorporate mechanisms such as reinforcement learning and self-correcting algorithms, enabling them to process large volumes of structured and unstructured data and generate near real-time insights. Key architectural components include data ingestion layers, model adaptation engines, decision-making frameworks, and feedback loops, all working together to support proactive and agile decision-making. The study highlights that adaptive AI improves decision accuracy, reduces operational costs, and enhances customer satisfaction compared to traditional static AI models, especially in uncertain and dynamic scenarios. Overall, adaptive AI represents a significant advancement in business intelligence, offering organizations a competitive advantage. Future directions include integrating explainable AI, addressing ethical concerns, and strengthening human-AI collaboration.
AI-Driven Digital Assistants for Enterprise Knowledge Management
Enterprise Knowledge Management (EKM) plays a vital role in leveraging organizational knowledge, improving decision-making, and maintaining competitive advantage. Traditional knowledge management systems struggle with scalability, real-time adaptability, and contextual understanding. AI-driven digital assistants address these limitations by using technologies like Natural Language Processing (NLP), deep learning, and knowledge graphs to enable intelligent automation and semantic understanding. These assistants support conversational interactions, making knowledge access and contribution more intuitive. The study proposes a modular architecture with components such as data ingestion, semantic processing, knowledge repositories, and user interfaces, enhanced by reinforcement learning for continuous improvement. Hybrid models combining rule-based and learning-based approaches improve reliability and interpretability. Findings show that AI-based assistants enhance knowledge retrieval accuracy, reduce response time, and increase user satisfaction. They are widely applicable in areas like customer support, internal knowledge discovery, and decision support. Overall, AI-powered digital assistants improve efficiency and promote knowledge democratization, with future research focusing on explainability, ethical AI use, and integration of multimodal data.
Technological Mediation of Human Relationships in the Digital Era
Digital technologies have reshaped human relationships by transforming how people connect, communicate, and maintain social and professional bonds. Platforms like Facebook, Instagram, WhatsApp, and Zoom enable instant, global interaction, but also introduce challenges such as reduced authenticity, privacy concerns, digital loneliness, and algorithmic bias. This study analyzes the dual impact of digital mediation using an interdisciplinary approach, highlighting both its benefits (accessibility and connectivity) and drawbacks (emotional and social issues). It also examines the influence of AI and personalization on relationships. The findings suggest that digital technologies are neither entirely positive nor negative—their effects depend on user behavior and social context. The paper concludes that while these technologies enhance connectivity, they demand stronger digital ethics, emotional awareness, and relational skills.
Autonomous Decision Systems Using Multi-Agent Artificial Intelligence
Autonomous decision systems have become essential in modern intelligent computing, driven by advances in AI and distributed computing. This paper studies multi-agent AI (MAAI) systems, focusing on their theoretical foundations, design methods, and performance before 2018. Multi-agent systems enable decentralized, scalable, and adaptive decision-making by distributing intelligence among interacting agents capable of perception, reasoning, and action. The paper highlights how agent-based models integrate with decision frameworks, where cooperation, coordination, and competition lead to intelligent behavior. It reviews approaches such as rule-based systems, utility models, and reinforcement learning in multi-agent contexts, while addressing challenges like scalability, communication overhead, conflict resolution, and uncertainty. It also examines key developments in distributed AI, including contract net protocols, distributed constraint satisfaction, and game-theoretic methods, along with applications in robotics, smart grids, traffic, and defense. Finally, it discusses system evaluation metrics like efficiency, convergence, and fault tolerance, offering a consolidated reference and identifying future research directions.
Real-Time Video Analytics Using Deep Learning Algorithms
Real-time video analytics has advanced rapidly with deep learning and high-performance computing, enabling applications in surveillance, smart cities, healthcare, and industry. Traditional computer vision methods struggle with complex scenes and real-time processing, while deep learning models like CNNs, RNNs, and transformers can learn spatial and temporal features directly from data. This paper reviews deep learning-based approaches for tasks such as object detection, tracking, action recognition, and event detection. It proposes a framework including data acquisition, preprocessing, model training, and deployment for low-latency, high-accuracy systems. Results show that deep learning methods outperform traditional techniques in accuracy and robustness, even under challenging conditions. The study also discusses challenges like computational cost, scalability, and privacy, and highlights future directions such as lightweight models, edge computing, self-supervised learning, and explainable AI.
Intelligent Traffic Management Systems Using Deep Learning
The Intelligent Traffic Management Systems (ITMS) have become an important feature of the smart city infrastructure because of the fast increase in the city population and the consequent urban traffic congestion, fuel use, and road accidents. Conventional methods of traffic management apply a lot on the operation of fixed-time control mechanisms and rule-based systems, which are not flexible to the dynamic traffic conditions. In the recent past, progress in the field of deep learning has resulted in the creation of data-driven systems of traffic management that can learn intricate spatial and temporal patterns of traffic through major sources of heterogeneous data. The paper provides a detailed research on designing, implementing and testing of an Intelligent Traffic Management System based on deep learning. The suggested system combines convolutional neural networks (CNNs) to estimate the traffic density, recurrent neural networks (RNNs) and long short-term memory (LSTMs) to predict the traffic flow, and reinforcement learning (RLs) to control traffic signals. Various data sources such as live video streams, sensor data and past traffic data are used to improve accuracy of predictions and effectiveness of decisions. The proposed system architecture includes a modular architecture that will include all the layers of data acquisition, preprocessing, model training, and real-time deployment. Numerous experiments on benchmark traffic datasets have shown that congestion is greatly reduced, the average vehicle waiting time is minimized and the traffic throughput is much improved in comparison to traditional systems. The findings confirm that traffic management systems based on deep learning can contribute significantly to the improvement of urban mobility, environmental impact, and road safety. This paper has given relevant information about the application of AI-driven traffic control systems in practice and opened up the prospects of future research in intelligent transportation systems.
Intelligent Automation Solutions for Public Sector Services
There is mounting pressure on the public sector agencies around the world to ensure enhanced service delivery, less cost of operation, and transparency amidst restraining resources and mounting citizen demands. The concept of Intelligent Automation (IA) combining artificial intelligence (AI), robotic process automation (RPA), machine learning (ML), and data analytics has become a revolutionary methodology to transform the services of the public sector. This paper will explore how intelligent automation solutions can enhance efficiency, accuracy and responsiveness of operations at the public sector. It provides systematized overview of research done, states key technology enablers and suggests a research methodology into how to introduce IA in government service processes. The paper focuses on automation in management tasks including document processing, taxes, and social welfare distribution and citizen service portal. A comparative study is carried to compare the traditional service models to IA-enabled systems and determine that based on processing time, cost reduction, reducing errors and satisfaction of the users. The results show that intelligent automation can greatly optimize operation performance reducing the number of manual operators, making predictions and creating the possibility of real-time monitoring of service management delivery. Nonetheless, the issues of information security, ethical management, human resources resilience, and interoperability are still significant. The study provides a conceptual and technical framework of scalable and secure policies on automation solutions to the policy makers and system architects. The findings indicate that intelligent automation is not just an upscale technological improvement but a strategic reform tool on digital governance. With algorithmic intelligence and automation of the processes, the public institutions will be able to transition to adaptive, citizen-focused, and resilient eco systems.
Smart Healthcare Systems Using Predictive Machine Learning
Smart healthcare systems can be viewed as a paradigm shift in the contemporary medical practice, since modern placement of sensing technologies, electronic health records (EHRs), and predictive machine learning (ML) technologies have become a common practice that facilitates proactive, personal, and efficient healthcare delivery. Such trends as the growing rate of chronic illnesses, the aging trend, and escalating healthcare costs have led to a desperate demand of smart systems able to diagnose and predict risks early, as well as make the optimal clinical decision. Predictive machine learning predictive models are models based on historical and real-time healthcare data that reveal the latent patterns, predict the disease progress, and assist clinicians to make evidence-based decisions. In this paper, predictive machine-learning has been adopted to analyze in detail the concept of smart healthcare systems, which involve system architecture, data acquisition, feature engineering, model development, and performance evaluation. The suggested framework combines the wearable sensor data, clinical records, and demographic information into a single analytics pipeline. Some of the machine learning algorithms under analysis and supervised or unsupervised, like logistic regression, support vector machines, random forests, gradient boosting, and deep neural networks, are discussed in the context of healthcare prediction tasks, such as disease risk prediction and hospital readmission prediction and patient outcome prediction. Moreover, the paper also touches on the issues associated with data quality, privacy, security, model interpretability and ethical issues. Recent experimental findings indicate that predictive ml-based healthcare systems are much better than conventional rule-based healthcare systems in terms of prediction accuracy and decision support. The results also show the promise of predictive machine learning to turn healthcare into either a reactive treatment or a preventive, personalized healthcare, as part of the vision of smart and sustainable healthcare ecosystems.
Voice-Based AI Assistants for Corporate Productivity
Artificial Intelligence (AI) voice-driven assistants have quickly moved away being consumer-friendly to being an enterprise-based application with the capability to help with corporate productivity, knowledge management, and operational efficiency. These systems can take up complex organizational tasks including scheduling, document retrieval, meeting summarization, and decision support, by the incorporation of the speech recognition, natural language understanding, and intelligent automation. The current paper examines how voice-based AI assistants can play a role in corporate settings, how they are based on technology, how they are designed, how they are implemented, and how they can influence productivity results. A detailed literature review is performed to investigate the current studies on enterprise AI assistants, speech human-computer interaction, and productivity-enhancing computerized systems. The suggested methodology provides a modular system design with automatic speech recognition (ASR), natural language processing (NLP), task coordination, and opportunities of enterprise incorporation. System efficiency is analyzed by performance measures like completion rate of tasks, latency of response, and user satisfaction. Findings show that voice interfaces can save a lot of time and mental workload than the conventional keyboards. In addition, the discussion identifies some of the problem areas associated with privacy, accuracy, and understanding of the context, the importance of having strong security structures and domain adaptation. The results show that voice-assisted AI assistants can be considered a viable technological opportunity to transform the corporate workflow digitally by facilitating the work with hands free, accessing knowledge in real time, and enhancing its efficiency. This research allows offering a systematic model of applying enterprise voice assistants and relates the perspectives of future research in speech-inspired organizational intelligence.
Intelligent Agricultural Systems Using IoT and AI
The world today is under pressure to foster agriculture to be more productive with less water consumption, less fertilizer wastage, and less labor reliance. The Intelligent Agricultural Systems (IAS) combine Internet of Things ( IoT ) sensing, edges/cloud connectivity, and Artificial Intelligence (AI) instruments to facilitate precise choice, e.g., irrigating, applying nutrients, identifying diseases, and predicting harvests. This paper suggests an IoT aligned architecture of agriculture (i) multi-layer sensing of soil-crop-climate variables, (ii) edge intelligence of low-latency actuation, (iii) cloud analytics of model training and farm-level optimization, and (iv) secure data pipeline. The presentation of lightweight methodology is based on sensor fusion, anomaly detection, evapotranspiration-based water estimation, and machine learning models to classify irrigation and predict the crop stress. The performance of the system is measured in terms of common metrics (accuracy, F1-score, MAE, water-use efficiency), and a sample results discussion shows that the application of AI-based irrigation can decrease water consumption, but not the yield. The paper outlines such difficulties of deployment as connectivity gaps, sensor drift, explainability, and cyber-security and finishes by giving viable suggestions towards scalable deployment.
Copyright (c) 2023 International Journal of Artificial Intelligence & Digital Transformation
Automated Feature Engineering Techniques for Tabular Data
AFE is an automated feature engineering system that has become a key enabler to scalable and high-performing machine learning systems with scalable systems that run on tabular data. The conventional feature engineering makes excessive use of domain, trial and error and iterative optimization, which are computed consuming time, error prone and hard to repeat. As data-driven applications in finance, healthcare, manufacturing, and e-commerce have been exponentially increasing, there is an increasing need in automated, systematic, and reliable methods of features construction. The overall objective of automated Feature Engineering methods is to generate, transform, select, and optimize features adventurously, from raw tabular data, with minimal human intervention, and at a higher predictive efficiency. The paper contains a complete detailed analysis of automated feature engineering approaches to tabular data with references to their theoretical principles, algorithmic approaches, and real-world examples. The paper expounds on rule construction based feature construction, statistical construction, deep learning based representation, evolutionary learning, and reinforcing learning methods, and end to end AutoML. An intricate literature review shows major achievements, comparisons, and unresolved issues. The suggested methodology defines the three branches of feature generation, selection and evaluation as a single automated pipeline via mathematical representation and algorithmic processes. The effectiveness of automated feature engineering is proved by experimental results that show that the method can enhance the accuracy and robustness of models as well as improve generalization with respect to the multiple benchmark datasets. Lastly, issues of limitations, interpretability, computational trade-offs, and research directions are discussed in the paper. The given publication meets the IEEE publication standards and offers well-organized, high-quality information to a researcher or an organization practitioner dealing with tabular data analytics.
AI-Based Risk Assessment Models for Insurance Technology
The insurance industry is evolving due to digitalization, big data, and InsurTech, making traditional actuarial models insufficient for handling complex and dynamic risks. This paper explores AI-based risk assessment using machine learning, deep learning, and hybrid models to analyze diverse data sources like IoT, telematics, and social media. The proposed framework includes data preprocessing, feature engineering, model training, validation, and deployment. AI models enable personalized underwriting, dynamic pricing, fraud detection, and proactive risk management. Results show that AI approaches outperform traditional methods in accuracy, scalability, and robustness. The study also addresses ethical, regulatory, and interpretability challenges, and suggests future directions such as federated learning and trustworthy AI for next-generation InsurTech systems.
AI for Customer Behaviour Prediction in Digital Marketing
Artificial Intelligence (AI) is transforming digital marketing by enabling accurate prediction of customer behavior, personalized marketing strategies, and better decision-making. With the growth of online platforms and large-scale customer data, traditional methods are no longer effective. This paper explores AI-based models such as machine learning, deep learning, and hybrid approaches to predict purchasing behavior, churn, customer lifetime value, and engagement. It includes stages like data preprocessing, feature engineering, model training, and evaluation using techniques like logistic regression, decision trees, random forests, SVMs, and neural networks. The study also highlights applications in personalization, recommendation systems, targeted advertising, and customer segmentation, while addressing data privacy and interpretability. Results show that AI models outperform traditional methods in accuracy and business performance, demonstrating their potential to improve marketing efficiency and customer relationships.
AI-Based Credit Scoring Models for Smart Banking
Credit scoring is vital in modern banking for loan decisions, risk management, and financial inclusion. Traditional models rely on limited, static financial data and struggle to adapt to changing conditions. This paper proposes an AI-based credit scoring approach that uses machine learning, deep learning, and hybrid models to improve accuracy and scalability. The framework integrates financial data with alternative data such as transaction behavior, digital footprints, and repayment patterns. Techniques like decision trees, random forests, SVMs, gradient boosting, and neural networks are analyzed and compared with traditional methods. It also emphasizes explainable AI (XAI) to ensure transparency, fairness, and regulatory compliance. Results show that AI models outperform conventional approaches in accuracy and risk prediction. The study highlights the importance of interpretability, bias reduction, and strong governance, concluding that AI-driven credit scoring enhances smart banking, customer experience, and financial inclusion.
AI-Driven E-Learning Platforms for Personalized Education
Artificial Intelligence (AI) has become a disruptive force in the current field of education, which allows one to create intelligent e-learning and provide a learner with personalized, adaptive, and learner-centric educational experience. Conventional e-learning communication models mostly embrace one fit model and neglect the individual learner in terms of cognitive abilities, learning pace, background knowledge, motivation, as well as learning preferences. This disadvantage results in in optimal interaction, high turnover, and learning imbalances. To overcome these difficulties, AI-based e-learning systems are used, which is based on machine learning algorithms, learning analytics, natural language processing, recommendations systems, and relevant learning contents, assessments, and feedback is delivered to particular learners in real time. The following paper gives a detailed research of AI-based e-learning websites in personalized learning. It explores the theoretical basis of personalized learning, the history of intelligent tutoring systems, and how AI methods like supervised learning, reinforcement learning, deep learning, and knowledge tracing are applied in adaptive learning. The paper also examines the architectures, data streams, learner control strategies and evolutional decision making systems that support modern AI-driven learning systems. Some of the most essential issues regarding data privacy, bias in algorithms, interpretability, scalability, and morality are evaluated crucially. There is a comprehensive methodology that is proposed and includes system design, dataset preparation, feature engineering, personalization algorithms, and evaluation metrics. The results of the experiment are discussed based on the comparative performance analysis, indicators of the learner engagement, and the improvements in the learning outcomes. The results show that AI-based personalization benefits lead to substantial satisfaction with the learners, retention of knowledge, and academic results as opposed to conventional e-learning sites. The paper also ends by emphasizing subsequent research interests, which include explainable AI in learning, multimodal learning analytics, and lifelong personalized learning ecosystems.
Explainable Reinforcement Learning for Transparent Automation
Reinforcement Learning (RL) is widely used for solving sequential decision-making problems, enabling agents to learn optimal actions through interaction with dynamic environments. However, many RL models function as “black boxes,” making their decisions difficult to interpret—an issue that is especially critical in safety-sensitive domains like healthcare, finance, and autonomous systems. Explainable Reinforcement Learning (XRL) addresses this challenge by providing human-understandable insights into agent behavior, policy decisions, and reward structures. This work reviews pre-2019 XRL approaches, categorizing them into policy explanation, reward decomposition, model transparency, and post-hoc interpretability methods. It highlights the trade-off between performance and interpretability, particularly in complex, high-dimensional environments. A framework is proposed that combines interpretable policies, surrogate models, attention mechanisms, and visualization techniques to enhance transparency without significantly reducing performance. Evaluation metrics such as fidelity, comprehensibility, and consistency are used to assess explanation quality. The analysis shows that hybrid approaches—combining inherent interpretability with post-hoc explanations—offer the best balance between accuracy and transparency. Overall, XRL is essential for building trust in automated systems, with future research focusing on standardized evaluation methods and human-in-the-loop learning.
Copyright (c) 2020 International Journal of Artificial Intelligence & Digital Transformation