IMAGE BASED MERN STACK FOR SERVICE DISCOVERY USING METHOD WEB SERVICE

Server consolidation based on virtualization technology will simplify system administration, reduce the cost of power and physical infrastructure, and improve utilization in today's Internet-service-oriented enterprise data centers. How much power and how many servers for the underlying physical infrastructure are saved via server consolidation in VM-based data centers is of great interest to administrators and designers of those data centers. Various workload consolidations differ in saving power and physical servers for the infrastructure. The impacts caused by virtualization to those concurrent services are fluctuating considerably which may have a great effect on server consolidation. This paper proposes a utility analytic model for Internet-oriented server consolidation in VM-based data centers, modelling the interaction between server arrival requests with several QoS requirements, and capability flowing amongst concurrent services, based on the queuing theory. According to features of those services' workloads, this model can provide the upper bound of consolidated physical servers needed to guarantee QoS with the same loss probability of requests as in dedicated servers. At the same time, it can also evaluate the server consolidation in terms of power and utility of physical servers. Finally, we verify the model via a case study comprised of one e-book database service and one e-commerce Web service, simulated respectively by TPC-W and SPECweb2005 benchmarks. Our experiments show that the model is simple but accurate enough. The VM-based server consolidation saves up to 50% physical infrastructure, up to 53% power, and improves 1.7 times in CPU resource utilization, without any degradation of concurrent services' performance, running on Rainbow - our virtual computing platform.

Abstract: The Tax Wizard project is a comprehensive web-based application aimed at modernizing and simplifying the delivery of professional tax education and services. The platform is designed with two core interfaces: the Admin Panel, which facilitates backend management of content and users, and the Client Panel, which provides an interactive environment for learners and general users. This dual-structured architecture enhances both administrative control and user engagement. On the Admin Side, authorized users can manage essential platform components, including courses, student records, customer data, blog posts, advertisements, FAQs, and general website content such as the About Us section. This enables real-time updates and efficient content delivery, which are crucial for maintaining the relevance and quality of educational offerings. The Client Side serves as the user-facing component where students and visitors can explore available courses, read tax-related blogs, submit enquiries, view ads, and browse frequently asked questions. Registered users can enroll in courses, monitor their academic progress, and download certificates upon successful course completion. This structure promotes independent learning and easy access to professional-level tax knowledge. The system is built using PHP and MySQL, with RESTful APIs acting as a bridge between the frontend and backend. The application is designed to be modular, scalable, secure, and responsive—ensuring a seamless user experience across different devices. Emphasizing user-friendliness and real-time interactivity, Tax Wizard stands as a digital solution to bridge the gap between tax educators and learners. This project addresses the lack of structured, accessible tax education online and brings a unified solution for both educational delivery and administrative efficiency in the digital age. Objectives: To provide a centralized digital platform for tax education and services Create a unified web- based system that delivers structured tax learning resources and related services to students and To design a user-friendly and responsive web application Ensure smooth navigation and accessibility across all devices, enhancing the overall user experience for both administrators and learners. To enable efficient management of educational content and user data Allow admins to easily create, update, and manage courses, blogs, FAQs, advertisements, and student records through the Admin Panel. To support self-paced learning and certification Empower students to enroll in courses, track their learning progress, and download certificates upon completion, promoting flexible, independent learning. To implement secure and scalable backend architecture Use PHP, MySQL, and RESTful APIs to maintain data security, ensure system stability, and allow for future expansion. Keywords: Tax Education, Web-based Application, Admin Panel, ClientPanel, Course Management, Student Dashboard,Online Learning, PHP and MySQL, RESTful APIs, Responsive Design, Certification System, Content Management System, Enquiry Management, Blog Integration, Digital Learning Platform

The modern business system increasingly integrates artificial intelligence to improve decision quality, operational efficiency, and the human experience. The rapid growth of digital platforms, customer data, and real-time services creates an environment in which traditional rule-based systems fail to respond adaptively. The business organizations therefore require intelligent systems that align technological efficiency with human needs, trust, and satisfaction. Despite widespread adoption, many AI-driven business systems focus primarily on automation and cost reduction, which has resulted in fragmented user experiences, reduced transparency, and limited human engagement. The lack of alignment between AI outputs and human expectations reduces customer satisfaction and employee acceptance. This gap highlights the need for a structured AI-enabled business framework that prioritizes human-centered outcomes while maintaining measurable business performance. This study proposes an AI-integrated modern business system that combines predictive analytics, natural language processing, and adaptive decision support. The system architecture includes data ingestion layers, an AI reasoning module, and a human-interaction layer that emphasizes explainability. A mixed-method evaluation approach has been adopted that combines quantitative performance analysis with user-experience assessment. The model has been validated using a simulated retail and service dataset consisting of 50,000 transactions and 12,000 user interaction logs. The proposed system has achieved a 28.6% improvement in customer satisfaction scores and a 21.4% reduction in service response time when compared with conventional business intelligence systems. Decision accuracy has improved from 76.2% to 89.7%, while employee task efficiency has increased by 18.9%. The explainable AI module has improved user trust ratings from 3.1 to 4.2 on a five-point scale. These results indicate that AI that has been aligned with human-centric design significantly enhances both business performance and human experience.

A sales analysis is a detailed report that tells about more profound understanding of a business’s sales performance, customer data, and the revenue. This tells you which deals are worth chasing and which are better left behind. Also, for the deals your sales team does decide to pursue, they’ll have a good approach ready to make the lead or customer more receptive to the sale. Using Sales Analysis helps to take retailers towards profit in this world of competition. Nowadays shopping malls keep the track of their sales data of each and every individual item for predicting future demand of the customer and update the inventory management as well. These data stores basically contain a large number of customer data and individual item attributes in a data warehouse. Further, anomalies and frequent patterns are detected by mining the data store from the data warehouse. The resultant data can be used for predicting future sales volume with the help of different machine learning techniques for the retailers like Big Mart. A predictive model is build using different algorithms. In this paper, we investigate forecasting sales for a Big Mart, with four machine learning algorithms (Random Forest, Linear Regression, Decision Tree and XG Booster Algorithms). The results show that the Random Forest algorithm performs better than the other two models.

Open banking is revolutionizing the financial landscape, presenting fintech companies with a host of opportunities and challenges. This new paradigm, driven by regulations and the implementation of APIs, is reshaping how banking services are offered and consumed. By mandating banks to share customer data securely with third-party providers, open banking aims to foster innovation, enhance competition, and promote collaboration in the digital banking sector. For fintech companies, this represents a golden opportunity to innovate. With access to a wealth of customer data, these companies can create personalized financial products and services, improving user experiences and meeting the evolving needs of consumers. Moreover, open banking levels the playing field, allowing smaller fintech firms to compete with established banks by offering unique and tailored solutions. However, the journey is not without its hurdles. Regulatory compliance remains a significant challenge. Fintech companies must navigate a complex landscape of regulations designed to protect consumer data and ensure security. This requires substantial investment in technology and legal expertise, potentially straining resources, especially for smaller firms. Security is another critical concern. The increased data sharing inherent in open banking can expose companies to cyber threats. Ensuring robust security measures and maintaining consumer trust is paramount. Fintech companies must continually innovate their security protocols to safeguard sensitive information. Collaboration between banks and fintech firms is essential for the success of open banking. While this partnership can drive innovation and improve customer offerings, it also requires overcoming cultural and operational differences between traditional banks and agile fintech startups. Effective collaboration strategies and mutual trust are crucial for leveraging the full potential of open banking.

Direct to consumer (D2C) business models, characterized by firms that design, source, and sell products directly to consumers bypassing traditional retail channels, are a relatively new phenomenon. One aspect we know little about is under what conditions consumers are willing to utilize a D2C channel, specifically, the degree to which the logistic service quality and information quality of user experience matter in choice of channel. Moreover, as these firms directly compete with Amazon’s delivery capability, we need to ask to what extent the so called “Amazon effect” impacts customer expectations. This study addresses these questions by conducting a scenario-based experiment which evaluates the effect of logistics service quality (LSQ) and online user experience (OUX) on customer satisfaction (CS), as moderated by price relative to Amazon (PRA). We find that LSQ has the largest positive effect on customer satisfaction, followed by OUX. Price moderates negatively (lessens) the effect of LSQ on customer satisfaction. Paradoxically, price also moderates the effect of OUX on CS, but counter-intuitively, strengthens it. The findings are reviewed in light of the so-called “Amazon effect”, the tendency of consumers to set their expectations for logistics at the levels of performance consistently provided by Amazon.com. Potential implications of the findings for the further evolution and growth of the D2C industry are presented.

Purpose Basic capturing of emotion on user experience of web applications and browsing is important in many ways. Quite often, online user experience is studied via tangible measures such as task completion time, surveys and comprehensive tests from which data attributes are generated. Prediction of users’ emotion and behaviour in some of these cases depends mostly on task completion time and number of clicks per given time interval. However, such approaches are generally subjective and rely heavily on distributional assumptions making the results prone to recording errors. This paper aims to propose a novel method – a window dynamic control system – that addresses the foregoing issues. Design/methodology/approach Primary data were obtained from laboratory experiments during which 44 volunteers had their synchronized physiological readings – skin conductance response, skin temperature, eye movement behaviour and users activity attributes taken by biosensors. The window-based dynamic control system (PHYCOB I) is integrated to the biosensor which collects secondary data attributes from these synchronized physiological readings and uses them for two purposes: for detection of both optimal emotional responses and users’ stress levels. The method’s novelty derives from its ability to integrate physiological readings and eye movement records to identify hidden correlates on a webpage. Findings The results from the analyses show that the control system detects basic emotions and outperforms other conventional models in terms of both accuracy and reliability, when subjected to model comparison – that is, the average recoverable natural structures for the three models with respect to accuracy and reliability are more consistent within the window-based control system environment than with the conventional methods. Research limitations/implications Graphical simulation and an example scenario are only provided for the control’s system design. Originality/value The novelty of the proposed model is its strained resistance to overfitting and its ability to automatically assess user emotion while dealing with specific web contents. The procedure can be used to predict which contents of webpages cause stress-induced emotions to users.

Escalating proliferation of inorganic accounts, commonly known as bots, within the digital ecosystem represents an ongoing and multifaceted challenge to online security, trustworthiness, and user experience. These bots, often employed for the dissemination of malicious propaganda and manipulation of public opinion, wield significant influence in social media spheres with far-reaching implications for electoral processes, political campaigns and international conflicts. Swift and accurate identification of inorganic accounts is of paramount importance in mitigating their detrimental effects. This research paper focuses on the identification of such accounts and explores various effective methods for their detection through machine learning techniques. In response to the pervasive presence of bots in the contemporary digital landscape, this study extracts temporal and semantic features from tweet behaviors and proposes a bot detection algorithm utilizing fundamental machine learning approaches, including Support Vector Machines (SVM) and k-means clustering. Furthermore, the research ranks the importance of these extracted features for each detection technique and also provides uncertainty quantification using a distribution free method, called the conformal prediction, thereby contributing to the development of effective strategies for combating the prevalence of inorganic accounts in social media platforms.

This research presents a false comment identification method based on rolling-type collaborative training. False comments pose a significant challenge in online platforms, impacting credibility and user experiences. The proposed method effectively utilizes unlabeled samples to assist model learning and integrates multiple characteristics, including emotion and text representation, to enhance the identification performance. The method involves obtaining comment text and determining its content characteristics, as well as obtaining reviewer information and determining their behavior characteristics. By combining these characteristics, the method performs false comment identification and outputs the identification result. Experimental results show that the proposed method achieves a 3.5% improvement in accuracy compared to traditional methods. The rolling-type collaborative training approach demonstrates the potential to enhance the reliability of comment evaluation systems and combat the spread of false information in online platforms.

A method for adaptive usability evaluation of B2C eCo mmerce web services is proposed.For measuring eCo mmerce usability a checklist integrating eCo mmerce quality and usability is developed.By a Glo wworm swarm optimizat ion (GSO) neural networks-based model the usability dimensions and their checklist items are adaptively selected.A case study for usability evaluation of an eCo mmerce anthurium retail website is carried out.The experimental results show that GSO with neural networks supports the allocation of usability problems and the defining of relevant improvement measures.The main advantage of the approach is the adaptive selection of most significant checklist dimensions and items and thus significant reduction of the time for usability evaluation and design.

Several research studies have recently explored user experience, emphasizing its expanding importance. Due to technology's rapid advancement and new platforms, online user experience management and improvement are becoming more critical. User experience is crucial in the metaverse as it helps build a devoted user following. In the metaverse, gamification, sociability, and personalization can all improve user experience. However, only some studies have integrated and synthesized existing works using the user experience perspective to make metaverse user experience research more consolidated, relevant, and impactful. Content analysis was used on the selected articles to combine the outcomes into five themes: Metaverse as a New Transformational Technology and User Experience, Metaverse Interactivity and User Experience, Metaverse Immersiveness and User Experience, Integrative Flow in Metaverse and User Experience and Metaverse Environment Interface and User Experience. Researchers, practitioners, and policymakers will find this study helpful for those interested in user experience and the metaverse. In order to guide future empirical research in the field, the study concludes with a framework consolidating the findings. Keywords: Metaverse; User Experience; Immersiveness; Environment interface; Transformational technology; Interactivity

With increasing scrutiny of museum and gallery public access and dissemination policies, heritage organisations are investing heavily in the online user experience. This includes allowing users to browse and search for images and other media of objects and artefacts held by the organisation. While public interface websites are attracting a lot of attention, their creators often forget that the media on them — generally images at present — can be viewed and used in environments other than the originating website. Heritage organisations encourage the public to download images and use them for private non-commercial purposes. This is where the use of embedded data becomes important. This paper details some of the ways embedded data may be used in practice, both in internal workflows and for images on publicly accessible websites.

Across many scientific domains, the ability to aggregate disparate datasets enables more meaningful global analyses. Within marine biology, the Census of Marine Life served as the catalyst for such a global data aggregation effort. Under the Census framework, the Ocean Biogeographic Information System was established to coordinate an unprecedented aggregation of global marine biogeography data. The OBIS data system now contains 31.3 million observations, freely accessible through a geospatial portal. The challenges of storing, querying, disseminating, and mapping a global data collection of this complexity and magnitude are significant. In the face of declining performance and expanding feature requests, a redevelopment of the OBIS data system was undertaken. Following an Open Source philosophy, the OBIS technology stack was rebuilt using PostgreSQL, PostGIS, GeoServer and OpenLayers. This approach has markedly improved the performance and online user experience while maintaining a standards‐compliant and interoperable framework. Due to the distributed nature of the project and increasing needs for storage, scalability and deployment flexibility, the entire hardware and software stack was built on a Cloud Computing environment. The flexibility of the platform, combined with the power of the application stack, enabled rapid re‐development of the OBIS infrastructure, and ensured complete standards‐compliance.

Text based social media has become one of important communication tools between customers and enterprises. In social media, users can easily express their opinions and evaluation regarding products or services. These online user experiences, especially negative evaluations indeed affect other consumers’ behaviors. Consequently, to effectively identify customers’ sentiments and avoid these negative comments to bring a great damage to enterprisers has become one of critical issues. In recent years, machine learning algorithms were viewed as one of effective solutions for sentiment classification. But, when the amount of the online reviews arises, the dimensionality of text data rises remarkably. The performances of machine learning methods have been degraded due to the dimensionality problem. But, conventional feature selection methods tend to select attributes from the majority sentiments, which usually cannot improve classification performance. Therefore, this study attempt to present two feature selection methods called modified categorical proportional difference (MCPD) approach that improves conventional CPD method, and balance category feature (BCF) strategy that equally selects attributes from both positive and negative examples, to improve sentiment classification performances. Finally, several real sentiment cases of text reviews will be provided to demonstrate the effectiveness of our proposed methods. Results showed that the combination of proposed BCF strategy and MCPD method can not only remarkably reduce feature space, but also improve the sentiment classification performance.

The research investigates consumer behavior within e-commerce, focusing on the factors influencing online purchasing decisions and strategies to enhance customer engagement and loyalty. The study explores psychological, social, and technological elements that shape online behaviors, including personalization, trust-building mechanisms, and user experience optimization. Employing a mixed-methods approach, it examines how e-commerce businesses leverage data analytics, marketing strategies, and customer feedback to adapt to evolving consumer preferences. Special attention is given to the impact of COVID-19 on accelerating digital shopping trends. Findings reveal critical insights into trust-building, customer retention, and the effectiveness of personalization and loyalty programs. This study provides a comprehensive framework for e-commerce businesses to refine their strategies, ensuring competitiveness and sustained consumer satisfaction in a rapidly evolving digital marketplace.