Mohammad Irfan

Artificial Intelligence (AI) plays a transformative role in enhancing predictive accuracy and operational performance in renewable energy systems. As the global shift toward sustainable energy intensifies, AI enables improved efficiency, reduced costs, and greater reliability across energy infrastructures. This study investigates AI's potential to optimize energy production forecasting, consumption patterns, and smart grid operations. Using a methodological approach that combines a comprehensive review of AI applications and real-world case studies, the research evaluates techniques such as predictive analytics, deep learning, and real-time grid optimization. Findings reveal that AI significantly enhances forecasting accuracy, system adaptability, and real-time energy management. By integrating AI, renewable energy systems become more resilient and efficient, supporting long-term sustainability goals. The study offers strategic insights into how AI technologies can be effectively leveraged to advance renewable energy development and intelligent grid performance.

Delivering quality service is crucial for achieving patient satisfaction in healthcare. In the digital era and growing competition, high-quality service is essential for healthcare providers to attract both in-service and out-service users. This paper evaluates the service performance of private hospitals in Dimapur, Nagaland, using the SERVQUAL tool and a sample of 400 respondents. The findings reveal significant gaps in reliability and assurance among private healthcare providers in the district. The results indicate that private hospitals in Dimapur have not met patient expectations, highlighting the need for improved service standards and performance to address these shortcomings.

Access to safe and sustainable water and sanitation continues to be a significant global concern. Many regions still struggle to provide reliable services because of limited financial resources, institutional weaknesses, and growing climate pressures on water supplies. This paper examines how financing approaches, including blended finance, microcredit, and public–private partnerships, can be strengthened through the practical application of technology. Tools such as remote sensing improve understanding of water availability and quality, enabling more informed planning and allocation of resources. In addition, advanced data analysis supports better risk evaluation, operational efficiency, and long-term infrastructure management. The study concludes that when financial strategies are supported by accurate data and sound governance, they become more transparent, accountable, and capable of attracting sustainable investment. Such an integrated approach supports universal access to water and sanitation while promoting public health, social equity, and long-term economic resilience.

AI-powered teaching support has reshaped financial market prediction by capturing non-linear relationships, complex features, and hidden structures in data. Improved computing power and advanced algorithms have raised hopes of overcoming earlier forecasting limits. Yet, financial markets tend to absorb information quickly, reducing the scope for consistent predictability. Although these models can detect patterns, their stability over time remains doubtful. Market data often show non-stationarity, volatility clustering, and structural changes, making predictions less reliable. In addition, as market participants adapt, widely known patterns lose their advantage. Challenges such as overfitting, data leakage, and weak signal strength further affect model outcomes. Studies also show that better statistical results do not always lead to real economic benefits once costs and risks are considered. Therefore, the limits of prediction are largely economic in nature, and machine learning is better viewed as a support tool for decision-making rather than a guaranteed source of excess returns.

The integration of quantum computing into financial systems marks a transformative leap in risk management, security, and market operations. Unlike classical computing, quantum algorithms can process vast datasets and complex models at unprecedented speeds, enabling real-time risk analytics, rapid fraud detection, and optimized trading strategies. This study explores the emerging field of quantum-driven finance, focusing on its potential to enhance predictive accuracy, reduce systemic vulnerabilities, and accelerate decision-making in volatile markets. Applications such as quantum-enhanced Monte Carlo simulations, quantum machine learning for anomaly detection, and optimization in algorithmic trading are examined alongside challenges in scalability, cybersecurity, and regulatory adaptation. By evaluating current advancements and future prospects, this research highlights quantum computing's role in shaping a secure, intelligent, and high-velocity global financial ecosystem.

The growing challenges of food insecurity, climate change, biodiversity decline, and waste mismanagement require practical and regenerative solutions. This paper proposes an integrated bio-cluster model built around three insect systems: Black Soldier Fly (Hermetia illucens), silkworm (Bombyx mori), and bees (Apis species and other pollinators). Each contributes unique ecological and economic value. Black Soldier Fly supports organic waste conversion into protein and compost; silkworms generate high-value silk and biomaterials; bees enhance pollination and agricultural productivity. When combined within a localized bio-cluster, these systems create a circular framework that transforms waste into resources, improves soil fertility, and diversifies rural income. The study presents a holistic approach linking biology, ecology, and economics to demonstrate how insect-based clusters can reduce emissions, lower landfill use, support carbon balance, and strengthen climate-resilient rural development through integrated, multi-stream livelihoods.

This study explores the integration of Lean Manufacturing tools—Value Stream Mapping (VSM), Kanban, and Just-in-Time (JIT)—to improve process efficiency and reduce waste in cement production. Conducted at a plant processing 11,000 tons daily, data from 340 working days revealed significant inefficiencies in material flow and inventory management. Implementing Lean strategies reduced non-value-added time from 35 days to 8 and improved cumulative value-added time to 42 seconds, achieving 90% operational uptime. Key stages—Crushing, Milling, Rotary Kiln, Processing, and Dispatching—were optimized, resulting in reduced lead times, enhanced productivity, and lower operational costs. The findings confirm that Lean Sigma offers a structured and sustainable approach to minimizing waste and maximizing efficiency in cement manufacturing operations.

In the Industry 4.0 era, organizations face challenges in sustaining employee engagement while enhancing productivity through data-driven strategies. This study explores the link between employee engagement and performance, emphasizing the mediating role of HR analytical strategies. A quantitative approach was used, with data from 125 professionals across sectors. Regression, correlation, and collinearity diagnostics revealed that physical, emotional, and cognitive engagement significantly influence task and contextual performance. HR analytics emerged as a key mediator, strengthening this relationship. Furthermore, organizational factors like compatibility, relative advantage, and top management support positively impacted engagement-performance outcomes. The study underscores how HR analytics can transform engagement into a strategic lever for sustainable performance. Practical implications, limitations, and future research avenues are discussed.

This paper explores how AI is reshaping business transformation in Islamic finance, emphasizing its role in enhancing transparency, improving risk-sharing mechanisms, and promoting ethical investments. Through the integration of AI, particularly machine learning algorithms such as NGBoost, Islamic financial institutions are optimizing financing assessments, detecting fraud, and personalizing customer services. AI's ability to process vast amounts of data enables Islamic finance to overcome traditional limitations in scalability, efficiency, and market resilience. The paper presents qualitative research, including case studies of Islamic financial institutions that have adopted AI. The study provides valuable insights for policymakers, financial institutions, and technology developers aiming to drive sustainable and ethical growth in the Islamic finance industry.

The rapid advancement of artificial intelligence has transformed the financial landscape, allowing innovative services and personalized experiences. However, this sets in motion new innovations regarding dimensions of financial fraud, which leverage AI tools to exploit vulnerabilities in digital systems. This chapter explores the regulatory challenges posed by AI-driven financial fraud in the digital economy. The authors consider the frauds that are being supported through AI, namely, synthetic identity, deepfakes, and manipulations in the realm of algorithmic trading, and the extent to which these challenges have undermined existing regulatory approaches. Further, they investigate the role of cross-border complexities and the ethical concerns surrounding AI governance. Based on the findings about gaps in policy and enforcement, this chapter serves as the basis for an anticipatory regulation framework specifically aimed at emerging threats of AI-based fraud.