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This exploratory research of data lakes in big data times is a prominent topic for both academia and industry. One of the main motivations behind is that companies need to cope with more data than ever before, and the problems of how to analyze even how to store data are becoming more and more challenging in many industries. The occurrence of the concept of a data lake to meet such big data problems is enlightening and will most likely be considered in any relevant big data strategy. This review paper presents a summary of some popular data lake concepts at present, followed by its advantages, potential risks and criticism from some professionals as well. Additionally, a general process in a data lake is described.

This research focused on Walton, Bangladesh's largest manufacturer in the electrical, electronics, automobile, and home appliance sectors. Walton mainly focused on its innovative and sustainable business practices. Its strategic emphasis on advanced R&D and Advanced manufacturing system has positioned it in leading position locally and helps it to compete global market. The company’s commitment to quality, affordability, and sustainability has made it competitive internationally. This research highlights Walton's technological, organizational, environmental approach to demonstrating how these elements contribute to its economic success and social impact. By exploring Walton's role in job creation, skill development, and export growth, the study offers insights into how emerging market firms can achieve global competitiveness. Walton's practices provide insights for integrating technological advancement and environmental responsibility, setting new standards in the industry and offering a model for other corporations in developing economies.

Internet of Things (IoT) is supposed to revolutionize the future extended smart applications and also helped to create the smart environments such as smart cities, smart buildings, smart kitchen, smart vehicles and also smart hospitals for every moment monitoring of the MAPE 15.15, VAF 96.16% and R2 0.92) is better than normal IoT based kitchen. Therefore, our proposed model is Patients. Artificial Intelligence (AI) plays a spreading role in IoT development and applications. Till date, most of the major companies integrated their IoT platforms with AI capabilities such as analytics based on Deep and Machine learning based. Synergy of IoT and AI is the key to unlock the potential of IoT. This paper proposed a smart kitchen system in this regard. An approach of prediction and tracing has been adopted with various prediction-based algorithms while using the devices (components of kitchen). Predictions are calculated to achieve subsequent next acknowledgment focusing on specific occasions for mechanization. Experimental results show that our proposed model of Smart kitchen (RMSE 0.0561, better and promising way for research and development engineers in the task of implementing smart kitchen.

The global business landscape is undergoing a significant transformation driven by the rapid growth of electronic business (e-business). This study investigates the e-business evolution stages in the Philippines and South Korea through a quantitative research design, employing a survey questionnaire based on the e-business evolution model by Bradley et al. (2015). The findings indicate that the Philippines is predominantly in the Growth stage, whereas South Korea is in the Transformation stage, reflecting varying levels of e-business maturity. Both countries demonstrate positive perceptions of social, economic, and technical factors, although concerns remain regarding perceived risks and transaction costs. This comparative analysis provides valuable strategic insights for policymakers, industry leaders, and stakeholders aiming to navigate and leverage emerging e-business opportunities across Asia.

Late blight, caused by Phytophthora infestans, threatens tomato and potato crops in Nepal. This study explores developing and deploying a mobile application powered by a graph neural network (GNN) to predict late blight risk for Nepali farmers. The GNN trained on 43 years of NASA satellite weather data can generate 5-days forecast for 320 weather stations in Sudurpashim and Karnali Province, Nepal. The mobile application offers user-friendly forecasts and visualizes late blight risk through clear graphical interfaces. In the visited sites, 30% of tomato and potato crops were found infected with late blight, which the app had identified as high-risk. Samples infected with late blight were collected and analyzed in a wet lab setting. All infected samples tested positive for P. infestans, confirming the app's ability to predict real-world late blight outbreaks. This study showcases the successful development and deployment of a GNN-powered mobile application for assessing late blight risk in Nepal. The application disseminates critical weather information and localized risk assessments, potentially enhancing late blight management in tomato and potato crops. Further research, including extensive field trials comparing with farmers' practices, could increase the application's usability in Nepali fields.

Digital convergence and its importance to contemporary economies is undeniable. What does it mean to be a digital economy? This question is a big challenge to answer digital convergence disseminates new business models and brings about substantial changes across industrial activities worldwide. Convergence is becoming as a hot issue within the Information Technology (IT) industries. Several benefits come from converging communication networks, like increased productivity, reduction of transaction costs and a valuable impact on economic growth. Digital convergence of various industries has created new business opportunities. The net benefit of digitalization on a global basis could surpass $100 trillion by 2030. Industrial digitalization and internet access are projected to experience exponential growth. The digital economy is expected to grow from 15.5% to 24.3% of the global GDP by 2025. Digital technologies' long-term return on investments is 6.7 times more than that of non-digital investments. This study provides a look at digital convergence and its economic implications.

In today's digital age where health data is increasingly stored, accessed, and shared electronically, ensuring the privacy and security of patient information has become a major concern. The potential risks associated with unauthorized access, data breaches, and privacy violations require innovative approaches to protect sensitive health data. This is where the power of artificial intelligence (AI) comes into play. AI innovations have emerged as a transformative force in healthcare and offer promising solutions to enhance privacy protection. This paper discusses how healthcare organizations can use artificial intelligence (AI) to protect patient privacy while extracting valuable insights from their data. It also expresses how AI techniques such as differential privacy and fused learning can help organizations collaborate, aggregate their data, and train machine learning models securely without compromising patient privacy. It emphasizes the importance of understanding the challenges and potential limitations of privacy-preserving techniques in this context and encourages stakeholders to collaborate on developing industry standards and best practices for privacy protection.

Network analysis is one of the hottest areas of research in biotechnology and biomedical research. It is a straight forward method of representing local and global characteristics of biological nodes representing the various biological elements. In network analysis, we use mathematical graph theory to show the interaction among the genes and its product. This represents the various biological activities in the cell. In order to predict the certain activity and interaction among the biological elements, it is necessary to find the optimal path in the network. This optimal path is usually the shortest path, which clearly depicts the key elements involved for the particular reaction. It is also necessary to count the number of shortest paths between the given pairs of genes in the network. This paper describes the need for finding shortest path in the biological network and illustrate the usage of Bellman-Ford algorithm to find the shortest path in Hutchinson-Gilford Progeria Syndrome (HGPS) data sets.

Higher education in India was not always confined to a learning process in four walls. Our ancient learning methodology included conversations, debates, life studies, and learning through observations. With the passage of time learning was confined to the study of written texts and prescribed syllabi. Though it has contributed remarkably in the last two centuries towards the establishment of new world order, its relevance is now often questioned due to the pace of technological changes and industries’ expectations from young graduates. In the last decade, there is an evident shift towards Digital Learning platforms as a mandatory part of the standard curriculum and as a source of obtaining extra knowledge with flexibility of choosing subjects and content for learning. Platforms like Coursera, EDX, NPTEL, STP, and Udemy are ready to efficiently fill the gap between standard university curriculum and industry requirement. In this paper, authors have identified and recognized the role of digital learning platforms as a mandatory part of higher education and have studied the impact on young learners. This paper presents an analysis based on the opinions of first-time users of such platforms. Through this study, authors have tried to identify the persisting shortcomings and effectiveness in the current scenario and have also recommended suitable measures to be integrated for the effective utilization of this resource.

The global population is increasing rapidly because of increasing urbanization and such increasing urbanization directs the up-growing need of urban safety and preventions. This urbanization is also responsible for two things that is increased job opportunities and increased the crime rates. In this era technology has gone far more forward in a positive way. By making use of these technologies such as machine learning, artificial intelligence and big data we presented an approach through which crime pattern analysis is done. We have used apache spark (scala-programming) and machine learning algorithm for predictive crime pattern analysis. The data that we have used is a real-world data set based on Chicago city of United State of America. Our main goal of work is to define a predictive crime analysis which shows top crime patterns related to the top community areas of Chicago city.