The user is prompted to insert text that they wish to summarize. - The user can submit their text with a "summarize" button and receive an AI-powered summary in an output textarea.

This is a Project-Based learning tutorial from Postman.The app will have a client side (website), server side, and external API integration to Hugging Face. Hugging Face allows you to try out many models for free with the Hugging Face Inference API. I used this API to access Facebook"s bart-large-cnn model, a Large Language Model (LLM) specially trained for text summarization.

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This App integrates Amadeus Self-Service APIs to build a simple flight search form to receive your users' travel information and display the cheapest flights matching their criteria.

The app uses the Amadeus Airport & City Search API to create a rich origin/destination autocomplete system for your search and the Amadeus Flight Offers Search API to retrieve the cheapest flight deals matching your user's criteria.

In this first part of the project, I have built the form"s frontend with React, while in part 2 I will build an Express backend powered by Amadeus APIs and connect it with the frontend to display the search results.

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Django LMS is a Learning Management System using the framework, you guessed it right, Django. The system gives students and parents a simple platform to learn online through tutorial videos. They can download presentations, lecture notes and videos if they want to.

The project started as a challenge to improve and test my knowledge of Django. The initial version of this project was created with vanilla JavaScript, PHP, HTML, CSS and Bootstrap. In the future, I would like to add the instructors panel to deal with their respective subjects.

This platform is fairly simple yet provides most of the necessary features required in a Learning Management System. It uses Django's MTV architecture.

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This system allows students to select courses and gives students access to course offerings via online as well as the ability to complete various administrative functions allowing for a better management of curriculum decisions in the context of academic objectives. This Course Selection Portal will be operated by three users, the administrator, students and lecturers.

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This system gives customers the opportunity to book a slot for salon appointment. Admin (Owner) can check comments made on the page for feedback and suggestions; check log; and verify booked appointments. There is also a live chat system embeded for communication between customers and owner.

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Abstract

The Covid-19 pandemic has resulted in significant and far-reaching economic impacts on a global scale. This paper presents a comprehensive analysis of the effects of Covid-19 on the global financial crisis from multiple perspectives. To do so, we review the existing literature on the crisis and its impact on the global economy and present a preliminary analysis of its causes and effects. We propose a set of methods for studying the crisis in greater detail and present the results of our experiments. Our findings indicate that Covid-19 has led to macroeconomic impacts such as decreased global GDP, higher unemployment rates, and a decline in international trade. At the industry-specific level, the pandemic has had varying impacts across different sectors, with some industries, such as travel and tourism, being hit particularly hard. Government responses to the pandemic have also varied, with some countries implementing more effective policies than others. Supply chain disruptions have affected industries globally, and long-term effects of the pandemic are expected to be far-reaching. Our analysis reveals the complex and evolving impact of Covid-19 on the global financial crisis, highlighting the need for interdisciplinary perspectives to fully understand the issue. We also discuss the implications of our findings, including the potential for future research to inform policy decisions aimed at mitigating the impact of Covid-19 on the global economy. In general, our study provides a comprehensive perspective on the impact of Covid-19 on the global financial crisis, highlighting the need for continued research and collaboration across disciplines to fully understand the extent of the pandemic's economic impacts and inform effective policy responses.

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Abstract

The study aims to present an architecture for a recommendation system based on user items that are transformed into narrow categories. In particular, to identify the movies a user will likely watch based on their favorite items. The recommendation system focuses on the shortest connections between item correlations. The degree of attention paid to user-group relationships provides another valuable piece of information obtained by joining the sub-groups. Various relationships have been used to reduce the data sparsity problem. We reformulate the existing data into several groups of items and users. As part of the calculations and containment of activities, we consider Pearson similarity, cosine similarity, Euclidean distance, the Gaussian distribution rule, matrix factorization, EM algorithm, and k-nearest neighbors (KNN). It is also demonstrated that the proposed methods could moderate possible recommendations from diverse perspectives.

Keywords: recommendation system; collaborative filtering; movie; multi-clustering; k-nearest neighbors (KNN)

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Abstract

Due to greater accessibility, healthcare databases have grown over the years. In this paper, we practice locating and associating data points or observations that pertain to similar entities across several datasets in public healthcare. Based on the methods proposed in this study, all sources are allocated using AI-based approaches to consider non-unique features and calculate similarity indices. Critical components discussed include accuracy assessment, blocking criteria, and linkage processes. Accurate measurements develop methods for manually evaluating and validating matched pairs to purify connecting parameters and boost the process efficacy. This study aims to assess and raise the standard of healthcare datasets that aid doctors’ comprehension of patients’ physical characteristics by using NARX to detect errors and machine learning models for the decision-making process. Consequently, our findings on the mortality rate of patients with COVID-19 revealed a gender bias: female 15.91% and male 22.73%. We also found a gender bias with mild symptoms such as shortness of breath: female 31.82% and male 32.87%. With congestive heart disease symptoms, the bias was as follows: female 5.07% and male 7.58%. Finally, with typical symptoms, the overall mortality rate for both males and females was 13.2%.

Keywords: healthcare; machine learning; NARX; logistic regression; data matching

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Abstract

Renewable Energy (RE) decentralisation has become a means towards energy sustainability due to the revolution in blockchain technology. RE sources have undergone remarkable growth as a result of the privatisation of the energy industry and the boost in incentives and policy initiatives on energy. As supply and demand are affected by a sudden change in weather conditions, a new set of challenges in how electricity systems are managed and operated have emerged. Therefore, a level of flexibility measures for safety and stability is required. Blockchain as an emerging technology is regarded as an essential technological landscape affecting nearly every transaction done over the internet. However, its role in RE is still unclear. Therefore, this study comprehensively explores the theory of blockchain and its status in the RE space. The review revealed that the integration of blockchain technology into RE has received significant attention over the years, which is an indication that existing blockchain research on energy is more focused on RE, as a way of addressing the many challenges in its evolution process, and providing sustainable solutions to replace energy from fossil fuels.

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Abstract

This paper proposes an approach which safeguards confidentiality to avoid disclosures of features within a multiple IoT situation, that is, a setup of objects in networks that communicate with each other. Two ideas derived from the theory of databases, namely k-anonymity and t-certitude, form our basis. They are used to cluster the objects to provide a unitary view of them and their characteristics. In fact, the use of anonymity and t-closeness robustly ensures privacy for derived groups. Furthermore, description of the object grouping scheme that preserves privacy, which represents the core of our approach was studied. Eventually, we illustrated the corresponding security model and analyzed the associated properties. The study also provided important advantages for the protection of user privacy in all those situations where knowledge of object features may help an attacker to obtain information about user habits and behavior. This study prevents not only the disclosure of information but also the divulgation of features. This is a major strength of our approach as malicious analyzes of the characteristics of objects can interfere with the privacy of people.

Keywords: Confidentiality, Preserves Privacy, Networks, Internet Of Things, User Privacy, Derived Group

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