Business Applications Research Articles

Sometimes simple is good enough: An analysis of methods for residential building population estimation

AbstractResidential building population data can be useful in a breadth of urban planning, health, transportation, and business applications. Unfortunately, complete datasets of residential building populations are not widely available for use in Canada, and therefore either larger census geographies are used or residential building populations must be estimated. This research explores four different methods of estimating residential building populations, including: an equal allocation method, two measures based on building volume, and a novel method that integrates census data at the dissemination area level to calibrate a population estimation model. This work comprises three parts: 1) a description of these approaches, 2) an evaluation of their validity in a case study in Hamilton, Ontario, and 3) an application of these methods in measuring spatial accessibility to schools. Our results show that most methods yield very similar results, and most provide reasonable estimates of building populations that could be useful for some analytical tasks. However, all methods resulted in instances of error, particularly for the largest population buildings. We conclude that while more complex methods do not significantly outperform simpler methods based on building volume alone, the blend of these methods could yield more accurate population predictions.

Taxonomy of theories for blockchain applications in business and management

The rising prominence of blockchain technology has spurred researchers to delve deeper into its applications through the lens of organizational and management theories. Our thematic analysis of blockchain literature reveals examination across five key domains: economic, strategic, operational, behavioral, and implementation considerations, each viewed through various theoretical perspectives. The multifaceted nature of blockchain phenomena necessitates diverse types of theory and research questions. Thus, the unique characteristics within each domain stimulate the formulation of inquiries that address a system of research questions about what, how, why, when, where, what will be, and how to do. Existing literature predominantly emphasizes explanatory and predictive theories, notably expanding research topics such as blockchain adoption behavior. However, this singular focus limits the exploration of diverse reasoning approaches suited to blockchain's complexity. Our study illustrates how different types of theory contribute to understanding each of the identified blockchain application areas. Based on our findings, we propose pertinent questions that highlight the need for varied theoretical frameworks and address specific issues within each application domain. Furthermore, we recommend appropriate theories to address these questions, fostering a more multi-perspective investigation across the breadth of blockchain-related research topics and areas. By embracing diverse theoretical perspectives, we aim to create a more balanced research landscape, facilitating deeper insights into blockchain phenomena.

An efficient test suit reduction methodology for regression testing

<span lang="EN-US">This paper's goal is to provide a more effective algorithm for reducing the amount of test cases in a test suit during the regression testing phase. This algorithm divide the entire test suit into equivalence classes in first step and then apply boundary value coverage to select test case out of repeated test cases which has same importance in test suit. This algorithm is based on the concept that before selecting best test cases out of repeated test case in test suit to prepare reduced test suit we can divide all test cases in number of equivalence classes so number of test case under consideration reduced by great extend. This paper proposed a method of experimentation involving test cases from different software application areas; minimization algorithms and the maths and algorithms of minimization algorithms in details. Test case techniques are equivalence portioning and boundary value analysis. Along with this concept, I also discuss a case study to verify and check new algorithm for its efficiency, for that I apply my algorithm on one of the program or group of program. This complete proposed methodology shall be applied to different software applications belonging to soft computing, Engineering software, Financial Software, Cloud Applications, Business Applications, AI and Machine Learning, Data Analytics are being identified. This selection has been made keeping in mind the trends, industrial importance, economic values and research challenges. Minimization in test cases would lead to lesser testing effort and desirable test completion.</span>

Blockchain based Decentralized Application

This research article provides an overview of business applications (DApps) in the context of blockchain innovation. It includes the definition, structure and classification of DApps, highlighting their focus and challenges. It also examines the importance of distribution, security, user participation, flexibility, collaboration, and governance in DApp development. The article also examines smart contracts, protocols, APIs, and independent independent organizations (DAOs) when implementing DApps. Overall, it highlights the importance of blockchain innovation in this field, offering some information and advice to designers and partners interested in creating DApps.

Online Auction System in E-commerce Products using Deep Learning and Data Mining

Abstract: Aims: In today’s world everything is going online which effects market that are offline. Auction of any rare products are bid offline, but it’s time to push this auction system online so that more buyer’s will connect online and more bid options will available for them. Online auctions are the most influential e-commerce business applications. Although there have been considerable efforts in setting up market places, online trading still lays in its early stages. Online auction system is the best way to bid such a products who are rare and can’t find easily anywhere. This is mostly work for archaeological old products that are rare. Methods: The most impressing concept of online auction system is you don’t need to anywhere offline. It’s the system where you can bid online without investing your time and bid for particular products. Results: Using this system buyers, sellers come online and connect on web-portal using this system. Conclusion: In this system using this web- portal registered user can propose or bid for new auctions, purchase and place bid product in order to buy that item.

Artificial intelligence in restaurant businesses: a systematic review on service robots

PurposeWithin the scope of the research, articles about service robots were examined by the systematic review method.Design/methodology/approachThe research aims to evaluate the articles on service robots, an artificial intelligence (AI) application in restaurant businesses, using a systematic review method. In systematic reviews, the data obtained as a result of scanning databases to find an answer to a research question are synthesized and reported. The criterion sampling technique, one of the purposeful sampling methods, was used for the sample of the research. Inclusion and exclusion criteria were applied within the scope of screening.FindingsThe articles on service robots were carried out between 2018 and 2023. In terms of research methods, most of the articles are quantitative, while there are studies on mixed and qualitative methods. In studies, data were generally collected by survey technique. The keywords of the studies on service robots are examined; the most commonly used words were service robot and AI, technology, restaurant, satisfaction, revisit intention, consumer behavior, intention, preference, hospitality and foods. The objectives of the articles pertinent to service robots are mostly to determine people's attitudes and acceptance toward these services focuses.Originality/valueThe studies seem to focus more on customer acceptance, trust, expectations, risks, adaptation, reasons for preference, impact on creative services, emotional and cognitive effects and human–robot interaction. Despite this, it is observed that there are fewer studies on topics such as the development of service robots in restaurant businesses, their reflections on the future, future opportunities and the quality of chef service robots. Based on this, it is recommended to consider studies that will serve as a reference for revealing innovative opportunities that can meet future expectations in order to increase the quality of service robots in restaurant businesses.

Fintech's Transformative Influence on Traditional Banking Strategies and its Role in Enhancing Financial Inclusion

In the previous researches of authors, dynamics of cryptocurrency with use of blockchain technology has been studied and legal challenges in India as related with the applicability of cryptocurrency has been critically reviewed. Also, growing impact of blockchain technology on trading and businesses has been worked out whereby challenges in using the blockchain application in business has been figured out. This paper further tries to explore and examine the current state of using financial technology in the banking sector. Fintech has rapidly transformed traditional banking services, presenting both challenges and opportunities for financial institutions. The paper investigates into the key aspects of the role of fintech in the banking sector, examining the challenges faced by traditional banks and the opportunities that arise from embracing technological advancements. By conducting a comprehensive literature review, the authors aim to explore the utilization of technology across diverse facets of banking transactions, identifying challenges in its implementation, facts about enhanced role of technology in financial inclusion and more related data. The study also delves into future trends and the regulatory landscape of fintech within India's banking industry along with its scope around financial inclusivity. This paper holds significance for scholars, researchers, and banking professionals alike, providing insights into the methodology of employing technology for secure transactions in banks. The work introduces a meticulously reviewed and integrated framework, offering practitioners an understanding of the current scenario and potential future implications.

Ai-Powered Customer Experience: Personalization, Engagement, and Intelligent Decision-Making in Crm

There has been a recent increase in interest regarding the remarkable potential of artificial intelligence (AI) to profoundly transform online advertising. The purpose of this research is to critically assess how AI can enhance customer experience (CX) in various business applications. We aim to identify important concepts, evaluate the impact of AI-powered CX initiatives, and offer suggestions for future research. By conducting a thorough analysis of academic publications, industry reports, and case studies, this study extracts theoretical frameworks, empirical findings, and practical insights. The results highlight the significant changes that occur with the integration of AI into Customer Relationship Management (CRM). AI enables personalized interactions, strengthens customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four key themes emerge from research findings: personalized service, improved engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article makes a valuable contribution to the AI in CRM discourse by summarizing the current state, exploring key themes, and suggesting future research opportunities. It is strongly advocated for responsible AI implementation, emphasizing ethical considerations and providing guidance to organizations as they navigate the opportunities and challenges presented by AI.

Business chatbots with deep learning technologies: state-of-the-art, taxonomies, and future research directions

With the support of advanced hardware and software technology, Artificial Intelligence (AI) techniques, especially the increasing number of deep learning algorithms, have spawned the popularization of online intelligent services and accelerated the contemporary development and applications of chatbot systems. The promise of providing 24/7 uninterrupted business services and minimizing workforce costs has made business chatbots a hot topic due to the impact of the pandemic. It has attracted considerable attention from academic researchers and business practitioners. However, a thorough technical review of advanced chatbot technologies and their relevance and applications to various business domains is rare in the literature. The main contribution of this review article is the critical analysis of various chatbot development approaches and the underlying deep learning computational methods in the context of some business applications. We first conceptualize current business chatbot architectures and illustrate the technical characteristics of two common structures. Next, we explore the mainstream deep learning technologies in chatbot design from the perspective of computational methods and usages. Then, we propose a new framework to classify chatbot construction architectures and differentiate the traditional retrieval-based and generation-based chatbots in terms of the modern pipeline and end-to-end structures. Finally, we highlight future research directions for business chatbots to enable researchers to devote their efforts to the most promising research topics and commercial scenarios and for practitioners to benefit from realizing the trend in business chatbot development and applications.

Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business

In this paper we offer a comprehensive review of Sociophysics, focusing on relevant models as well as selected applications in social trading, behavioral finance and business. We discuss three key aspects of social diffusion dynamics, namely Opinion Dynamics (OD), Group Decision-Making (GDM) and Knowledge Dynamics (KD). In the OD case, we highlight special classes of social agents, such as informed agents, contrarians and extremists. As regards GDM, we present state-of-the-art models on various kinds of decision-making processes. In the KD case, we discuss processes of knowledge diffusion and creation via the presence of self-innovating agents. The primary question we wish to address is: to what extent does Sociophysics correspond to social reality? For that purpose, for each social diffusion model category, we present notable Sociophysics applications for real-world socioeconomic phenomena and, additionally, we provide a much-needed critique of the existing Sociophysics literature, so as to raise awareness of certain issues that currently undermine the effective application of Sociophysics, mainly in terms of modelling assumptions and mathematical formulation, on the investigation of key social processes.

The Application of Artificial Intelligence (Ai) and Internet of Things (Iot) Middleware in Agriculture

Artificial intelligence (AI) devices like master frameworks, regular language handling, discourse acknowledgment, and machine vision have modified both the amount and nature of work in the rural area because of the developing worldwide populace, the rising interest for food, as well as changes in climate and the availability of water. Specialists and researchers are presently attempting to involve new IoT advances in savvy cultivating to help ranchers in making better seeds, crop assurance, and manures utilizing AI technology. Both the nation's overall economy and the profitability of ranchers will be benefited from this. Farming robots, prescient investigation, and soil and yield observing  are the three main regions where AI is beginning to appear. In this sense, ranchers are utilizing sensors and soil test all the more often to gather information that the executives frameworks might use for more examination. Through an outline of AI applications in the farming business, this paper makes a commitment to the area. It starts with a prologue to AI and an investigation of all AI strategies utilized in the horticultural area, including AI, the Internet of Things using Universal Middleware, master frameworks, picture handling, and PC vision. This study incorporates a complete examination of the literature to research how artificial intelligence (AI) and the Internet of Things (IoT) are utilized in farming.

Revolutionazing Software Development: the Rise of No Code/low Code Development Solutions in Digital Era

Low/No-code improvement is a program development method that gives clients with a stage for outwardly creating applications with small or no coding. Companies and organizations require program applications and information systems for different commerce purposes like administration in the technology period. Low/No-code advancement gives non-IT professionals a helpful device for quickly building simple business applications they require without or with small coding. In this paper, we investigated the benefits & confinements of Low/No Code advancement and advanced Low/No Code development platforms in the industry. In expansion, we analyzed how it can be improved and prospected the impacts of Low/No Code development on society and related businesses in the future. In conclusion, we discover that Low/No code improvement is a promising trend that can essentially affect future program development and computerized change

Roadmap to Smart Cities in Saudi Arabia: Key Challenges and Opportunities

Technologies are constantly evolving and finding applications in both business and government organizations for various purposes such as improving operational efficiency, increasing customer satisfaction, and reducing the cost of transactions. Technology adoption is a well-researched phenomenon in the academic world. Based on the current state of e-governance in Saudi cities and the potential of Artificial Intelligence (AI), Internet of Things and Blockchain in e-governance, this research study aims to study factors influencing the adoption intention of AI and Blockchain technologies in citizen services delivery within a smart city in Saudi. This article explores AI, IoT and Blockchain technologies adoption intention in the context of smart cities within a developing country like Saudi. Finally, the study provides a set of practical recommendations for smart city officials while planning to launch service applications using AI and Blockchain technologies. More specifically, the study emphasizes the importance of ethics, data privacy and security, and transparency in building trust between smart city systems.

Research on a Critical Link Discovery Method for Network Security Situational Awareness.

Network security situational awareness (NSSA) aims to capture, understand, and display security elements in large-scale network environments in order to predict security trends in the relevant network environment. With the internet's increasingly large scale, increasingly complex structure, and gradual diversification of components, the traditional single-layer network topology model can no longer meet the needs of network security analysis. Therefore, we conduct research based on a multi-layer network model for network security situational awareness, which is characterized by the three-layer network structure of a physical device network, a business application network, and a user role network. Its network characteristics require new assessment methods, so we propose a multi-layer network link importance assessment metric: the multi-layer-dependent link entropy (MDLE). On the one hand, the MDLE comprehensively evaluates the connectivity importance of links by fitting the link-local betweenness centrality and mapping entropy. On the other hand, it relies on the link-dependent mechanism to better aggregate the link importance contributions in each network layer. The experimental results show that the MDLE has better ordering monotonicity during critical link discovery and a higher destruction efficacy in destruction simulations compared to classical link importance metrics, thus better adapting to the critical link discovery requirements of a multi-layer network topology.

Optimizing Task Scheduling in Cloud Data Centres with Dynamic Resource Allocation Using Genetic Algorithm (TSOGA)

Nowadays, Massive business applications are increasingly giving attention to cloud computing data centres because of its high potential, adaptability, and efficiency in supplying several sources of both software and hardware to support networked consumers. The criteria for autonomy of virtual machines necessitate a flexible resource allocation strategy for Virtual Machines (VMs) .The majority of resource utilization models were inaccurate, making it impossible to determine the virtual machine's energy usage directly from the hardware. Due to the size of modern data centres and the constantly changing character of their resource supply, efficient scheduling solutions must be developed to oversee these resources and meet the objectives of both cloud service providers and cloud customers. Hence an algorithm called Task Scheduling Optimization based Genetic Algorithm (TSOGA) has been proposed to dynamically allocate the resources in pursuit of scheduling the tasks in cloud data centers. The proposed module initially focuses on task scheduling process, followed by optimized running time of task execution. For data centres with dynamic resource allocation, the goal of TSOGA is to efficiently assign jobs to resources while minimizing execution time and optimizing resource utilization. Thus, to manage the data centres while achieving high levels of efficiency in resource allocation, we constructed a virtual node for our research. Incorporation of Genetic Algorithm is to determine an ideal or nearly ideal schedule for carrying out tasks using the available resources while taking into account a variety of restrictions and goals, such as minimizing execution and waiting time of task during dynamic scheduling process and efficient resource utilization.

From HAL to GenAI: Optimize chatbot impacts with CARE

This article explores the evolution and prospects of conversational chatbots, specifically the latest generation referred to as Generative Artificial Intelligence (GenAI) chatbots. This article comprehensively examines GenAI chatbots’ business applications and impact across macro, meso, and micro levels of organizations. At the Macro level, this article explores how GenAI chatbots reshapes industry dynamics. The Meso perspective delves into organizational changes, while the Micro lens focuses on enhancing individual productivity, learning, and creativity. However, GenAI chatbots’ immense potential is accompanied by risks in four META areas – Matching, Ethics, Technology, and Adaptability. In response to these challenges, the article introduces a human-centric CARE framework – Collaboration, Accountability, Responsiveness, and Empowerment – to mitigate the risks and optimize the impacts brought by GenAI chatbots. This work provides practical guidelines to navigate the complex landscape of GenAI implementation.

Blockchain for Distributed Systems Security in Cloud Computing: A Review of Applications and Challenges

The blockchain is a technology that utilizes a decentralized and distributed ledger system to enhance security in cloud computing for distributed systems. It has gained significant attention in various applications, including the Internet of Things (IoT) and cloud computing. However, the blockchain has scalability limitations that restrict its ability to handle different types of transactions effectively. On the other hand, cloud computing provides the availability of shared computer system resources on demand, but it faces challenges related to automation, process management, policy, and others. By combining blockchain technology with cloud computing in a unified system, it is possible to improve data integrity, resource management, pricing, fair compensation, and resource allocation. This article examines the applications and challenges of blockchain, emphasizing how it ensures data integrity, transparency, and resistance to tampering. It also explores various use cases to address obstacles like scalability issues and interoperability concerns, providing a comprehensive overview of the intersection between blockchain, distributed systems, and cloud computing security. The integration of cloud computing and blockchain is important for business applications because it offers advantages in terms of privacy, security, and service support. This review provides an extensive and up-to-date summary of the integration of cloud computing and blockchain, highlighting its significance in business contexts.

The Advancement оf Modern Chatbots аnd Their Applications

Artificial Intelligence (AI) is on the rise, with applications which use it developing very rapidly in recent years. This is very much characteristic of one of its subclasses, Generative Artificial Intelligence, which underpins many of today’s most popular AI applications. Challenges in developing Large language models (LLMs) are discussed, which include difficulties such as language ambiguity, common sense, and understanding complex language constructs and queries. The key innovations that led to the development of LLMs are described, namely the creation of transformers, self-supervised learning and large datasets. Next, the most popular applications of LLM today, chatbots, are discussed. The capabilities, advantages, and disadvantages of some of the most popular chatbots, such as ChatGPT, Copilot, Bard, YouChat, HuggingChat, and Jasper, are shown, and an example of wrong answers from them is given. The advantages of chatbots – answering questions asked in natural language, generating creative content, solving problems – are summarized, and a number of their applications in business and economic fields are shown. Disadvantages are pointed out, including the provision of sometimes inaccurate or misleading information. Finally, expectations for the future development of LLMs and chatbots and the dangers they may bring to people are presented.

Advancements in predictive analytics: A philosophical and practical overview

Advancements in predictive analytics: A philosophical and practical overview

Discovery of Actionable Patterns Through Scalable Vertical Data Split Method with Meta Actions and Information Granules

Action Rules are rule based systems that extract actionable patterns which are hidden in big volumes of data. Users need recommendations on actions they can undertake to increase their profit or accomplish their goals, this recommendations are provided by Actionable patterns. In the technological world of big data, massive amounts of data are collected by organizations, including in major domains like financial, medical, social media and Internet of Things(IoT). To analyze and store such a massive amount of data, distributed computing frameworks like Hadoop and Spark are introduced to store the big data in a distributed fashion which manage and analyze them in parallel. The traditional Action Rules extraction models, which analyze the data in a nondistributed fashion, do not perform well when dealing larger datasets. Serious complications of discovering Action Rules with such distributed environments are - data distribution among computing nodes and calculation of major parameters including : support, confidence, utility, and coverage, that represent the whole data. Information granules form basic entities in the world of Granular Computing(GrC), which represents meaningful smaller units derived from a larger complex information system. In this research, we focus on the data distribution phase of the distributed Actionable Pattern Mining problem. To handle the data distribution task by splitting the big data in both horizontal and vertical fashions - we propose partition threshold rho. In this work, we concentrate on using information granules to implement a vertical data splitting strategy with Meta Actions. Hence our results discover valuable Actionable Knowledge with application in Business and Education domains.