Location analytics is key to get into the current market. It deals with movements data of customer which tells about the customer’s lifestyle, their behavior through understanding the visiting pattern. A geo-fence is a virtual boundary for a real-world geographic area. A geo-fence can be generated as per user’s need by considering different radius, or a geo-fence can be a predefined set of boundaries. Geo-fencing is use of geo-fence. Geo fencing is a feature that uses the global positioning system (GPS) (...) to define geographical boundaries. It has widely increasing scope. Very few applications are dealing with geo-notifications that are intended to inform the mobile/stationary user proactively about location specific information. The technology behind proactive location-based services is called Geo-fencing. (shrink)

The management of waste has become an increasingly critical issue as urbanization and industrial activities surge globally. Traditional waste management practices are struggling to keep pace with the rising quantities and complexities of waste. This paper delves into the integration of intelligent technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), and data analytics, into waste management systems. By examining current practices, methodologies, experiments, and results, this study aims to highlight the transformative potential of intelligent technologies in optimizing (...) waste management processes, enhancing efficiency, and reducing environmental impact. (shrink)

The intersection of intelligent computing and linguistics has emerged as a vibrant field of study, offering innovative solutions and applications that transform how we understand and interact with language. This paper explores the diverse applications of intelligent computing in linguistics, encompassing natural language processing (NLP), computational linguistics, language modeling, speech recognition, and more. It delves into the underlying technologies, methodologies, and the impact of these advancements on various linguistic subfields. Through an extensive review of recent literature, case studies, and practical (...) examples, this paper aims to provide a comprehensive overview of the current state and future prospects of intelligent computing in linguistics. (shrink)

The rapid advancement of intelligent computing has revolutionized the field of economic forecasting, providing unprecedented capabilities for developing, implementing, and analyzing advanced prediction models. This paper explores the comprehensive process of harnessing intelligent computing for economic forecasting, emphasizing the critical stages of model development, integration, and evaluation. Initially, it discusses data collection and preprocessing techniques essential for building robust models, followed by the selection of suitable statistical, machine learning, and deep learning algorithms. The paper then outlines the practical aspects of (...) model implementation, including real-time data integration, deployment strategies, and scalability considerations. Further, it delves into the continuous monitoring and maintenance required to ensure model accuracy and reliability over time. The analysis section evaluates model performance using various accuracy metrics, scenario analyses, and visualization techniques to provide actionable insights. Through case studies across the financial sector, government policy, and business planning, the paper illustrates the transformative impact of intelligent computing on economic forecasting. The findings underscore the potential of advanced prediction models to enhance decision-making, anticipate economic trends, and respond proactively to dynamic economic environments. (shrink)

This work focuses on enhancing security for transmitted e-documents, such as e-stamp papers, within wireless environments through dynamic authentication using variable circular concealment of multiple invisible signature images. This method is driven by encrypted secret keys and session-based random numbers, alongside a region-specific bit casting strategy and dynamic bit sequencing for multiple signatures to bolster security and robustness. Multi-copy signature marking on non-overlapping document areas, coupled with varied bit encoding strategies for each modified pixel byte coefficient in cover image sub-blocks, (...) further enhances protection and resilience against attacks. The reverse transformation of bit-coded pixel byte components in spatial domain ranges is preserved, allowing threshold-based bit creation to perform effectively even under attack scenarios. With secret information and random numbers transmitted to the receiver, the securely embedded signature bits can be reliably identified in their original sequence from the forward-transformed sub-block pixel elements. Experimental results validate that this approach significantly improves authentication and confidentiality, surpassing prior methods in terms of security and reliability. (shrink)

We present the scheduling algorithms of Distributed Real-time Systems in this analysis paper. A real-time system is a type of software where, under a given time period, we have to execute the process with a specific result. Whereas, in the general context, there is no fixed deadline. In reality, scheduling implies performing the operation according to its characteristics and scheduling is done on different processors, one is Uni-processor and one is Multiprocessor and can be performed on the distributed machine as (...) well. Make span length, processor utilization, cache performance, power consumption, task balance, and scheduling penalties are the criteria typically used to evaluate the various scheduling algorithms. The scheduling of real-time assignments consists of 2 sub-problems in the distributed and multiprocessor network: CPU role distribution and scheduling tasks on single processors. The allocation of tasks may be static or interactive. For successful task execution, we'll discuss numerous task allocation algorithms. (shrink)