Ansuman Samal

Culture, morals, and religion have always been a part of parenting, and certain cultural, moral, and religious beliefs shape parents' approach to nurturing the emotional, intellectual, social, and spiritual growth of their children. Still, the role and context in which parents carry out their responsibilities have dramatically changed in recent years with the rise of digital technologies such as the smartphone, social media, artificial intelligence, online games, streaming services, virtual learning environments and internet-based communications. In today's world, religious parents are tasked with taking both risks and benefits of the new technologies which offer learning and development opportunities to children, while at the same time safeguarding them from the dangers the Internet holds that might undermine their religious beliefs, morals, and ethics. In this way, religion-based parenting has become a dynamic process that combines traditional religious values and teachings.

The study proposes a smart food supply chain monitoring system using Internet of Things (IoT) to detect food adulteration in real time, predict the freshness of food and ensure the food safety in a distributed food supply chain. The framework relies on the multimodal IoT sensing, edge computing, hybrid CNN–LSTM deep learning, blockchain-based traceability, and XAI, which can be implemented for continuous monitoring of food quality. The experiments were conducted with milk, edible oils, fruits, vegetables and packaged perishable products with dynamically changing storage and transportation conditions and the results were validated. The success rate of the proposed framework was able to detect the adulteration successfully with the high accuracy (97.14%) and low latency edge assisted monitoring. The multimodal sensor fusion system greatly reduced the number of false-positive alerts and increased the stability of the contamination prediction system as compared to traditional single-sensor systems.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in the built environment is transforming how buildings are designed, constructed, operated, and maintained, with far-reaching implications for sustainability, occupant comfort, operational efficiency, and cost-effectiveness. In the context of building performance, AI and ML act as pivotal technologies that enable data-driven decision-making and proactive control strategies. As buildings become more complex and demand greater energy efficiency and user responsiveness, traditional rule-based systems often fall short in handling the volume, velocity, and variety of data generated by modern sensors and control devices. AI and ML offer powerful alternatives, with the ability to learn from historical and real-time data, recognize patterns, forecast performance, and autonomously adapt to changing conditions. One of the most critical areas where AI and ML contribute to building performance is energy management and optimization.

The proliferation of the Internet of Things (IoT) has fundamentally transformed the landscape of distributed computing, necessitating robust architectural frameworks capable of dynamic service orchestration. IoT environments are inherently heterogeneous, encompassing diverse devices, communication protocols, and service paradigms that must interoperate seamlessly to deliver real-time, context-aware functionalities. Dynamic service orchestration refers to the automated composition, coordination, and adaptation of distributed services in response to evolving system states, user demands, and environmental conditions. Modern architectural frameworks leverage a combination of layered, modular, and microservice-based designs to achieve scalability, flexibility, and resilience in complex IoT ecosystems. At the core of these frameworks are service registries, semantic service descriptions, and orchestration engines that employ rule-based, event-driven, or AI-assisted mechanisms to manage service workflows.

AI-powered market research leverages diverse sources—transactions, surveys, browsing behavior, and more—to derive actionable insights that guide strategic decision-making. However, this data-intensive approach raises significant concerns regarding data privacy, a fundamental issue that must be rigorously addressed to maintain trust, ensure compliance with legal frameworks, and protect individual rights. Data privacy, broadly defined, refers to the handling, processing, storage, and sharing in ways safeguard prevent unauthorized access or misuse. In AI-driven market research, data privacy involves a complex interplay of ethical, technical, considerations influence, anonymized, encrypted, ultimately utilized. ensuring data privacy -powered market research need for detailed, high-quality data against privacy safeguards. AI algorithms thrive on detailed, granular data to detect subtle patterns and provide nuanced insights; however, this often involves processing sensitive or personally identifiable information.

Volunteer tourism, often referred to as “voluntourism,” has emerged as a significant and rapidly expanding sector within the global travel industry over the past two decades. This form of tourism combines the traditional elements of travel and leisure with purposeful volunteer work, where travelers contribute their time, skills, and energy to various community projects, environmental conservation efforts, and social development initiatives across the world. Unlike conventional tourism, which primarily focuses on sightseeing and relaxation, volunteer tourism aims to foster meaningful connections between travelers and host communities while addressing pressing social, economic, and environmental challenges. The rise of volunteer tourism reflects broader shifts in global travel patterns, increased awareness of global inequalities, and the growing desire among travelers, especially younger generations, to seek experiences that are both enriching and impactful.

Agricultural enterprises have financial uncertainties due to market volatility, climate disruptions, and changes in policies; therefore, farming operations must use timely and accurate forecasts, as they are particularly vulnerable to external economic shocks and environmental variability. Standard forecasting methods usually cannot capture nonlinear dependencies and dynamic shifts in risk profiles; therefore, there is a need to consider intelligent, adaptive systems. Research proposes a novel financial risk prediction model using the Sooty Tern Optimization Algorithm Attention-Based Long Short-Term Memory (STOA-Att-LSTM). Financial risk data were collected, which included agricultural enterprise financial records, national weather databases, and commodity market indices. To ensure data integrity and modelling efficiency, two essential pre-processing techniques were employed. Handling missing values was performed using linear interpolation to reconstruct incomplete sequences, particularly in time-series financial and climatic data, to standardize variables, facilitating efficient model training and convergence. The STOA algorithm was used to optimize the hyper-parameters of the Att-LSTM model, enhancing its generalization and predictive accuracy. The attention mechanism enabled the model to dynamically focus on critical time-dependent features influencing financial risk. Dynamic state analysis further strengthened the framework by capturing temporal shifts in enterprise conditions. Model evaluation using Python-based implementation of error metrics and classification accuracy (0.9899) showed better results compared to traditional and baseline deep learning (DL) models. The proposed framework offers a robust, adaptive tool for proactive financial risk assessment in agricultural enterprises, supporting sustainable decision-making in uncertain environments.

Musealization refers to the process by which objects, artifacts, and cultural practices are transformed into museum-worthy exhibits, thereby gaining new significance within a broader social and cultural context. This concept is rooted in the recognition that the representation of history and culture in museums goes beyond mere display; it involves a complex interplay of selection, interpretation, and meaning-making. The evolution of musealization is intertwined with the development of museums themselves, which have evolved from private collections and cabinets of curiosities in the Renaissance to dynamic institutions that aim to educate, engage, and reflect contemporary societal values.Historically, musealization began in the 17th and 18th centuries with the establishment of early museums that collected natural history specimens and artistic works. As the concept of the museum evolved, especially during the 19th century, the democratization of access became a driving force.

This approach transcends traditional metrics, offering a more nuanced understanding of consumer behavior, brand impact, and market dynamics.Traditional methods often rely on periodic surveys and historical data, which can be limited in scope and slow to capture emerging trends. In contrast, AI systems can continuously ingest data from diverse sources, including social media, online reviews, and sales transactions, enabling brands to gain immediate and comprehensive insights into their performance. This involves natural language processing (NLP) techniques that interpret the emotional tone and context of written content. For example, an AI system might detect a surge in positive sentiment following a successful marketing campaign or identify emerging issues from negative feedback before they escalate. This real-time analysis allows brands to respond more swiftly and effectively to changes in public perception. This enables brands to tailor their strategies with greater precision, targeting specific segments with personalized messages and offers.

People are getting long-term illnesses like diabetes, heart disease, and high blood pressure more and more often. Because of this, it's even more important to find better ways to handle these situations and move quickly when they happen. Using AI-powered health informatics in predictive analytics seems like a good way to improve the quality of care and patient outcomes when dealing with long-term illnesses. This study looks at how AI models, like machine learning algorithms, predictive modelling, and data-driven analytics, can change how long-term illnesses are watched, identified, and treated. By looking at a lot of data from smart tech, medical pictures, and electronic health records (EHRs), AI systems can find patterns and guess how a disease will get worse before the symptoms show up. By finding high-risk patients early on, these insights can help healthcare workers make the best use of resources, give more personalised care, and cut costs. Using AI in health technology also makes it easier to make systems that can keep an eye on people with long-term illnesses in real time. These systems can keep an eye on vital signs, living factors, and drug compliance all the time. This can help people get help right away, which can cut down on problems and hospital stays. AI technologies can also help automate repetitive chores like data filing, medical support, and decision-making, which frees up healthcare workers to spend more time caring for patients directly. However, using AI to handle chronic diseases can be hard because of issues with data protection, the need for uniform data forms, and making sure that AI models can be understood and held accountable. At the end of the paper, the future uses of AI in managing chronic diseases are talked about. It is emphasized that healthcare workers, data scientists, and lawmakers need to keep researching and working together to get the most out of AI-driven health informatics.