News’ Credibility Detection on Social Media Using Machine Learning Algorithms

Social media is essential in many aspects of our lives. Social media allows us to find news for free. anyone can access it easily at any time. However, social media may also facilitate the rapid spread of misleading news. As a result, there is a probability that low-quality news, including incorrect and fake information, will spread over social media. As well as detecting news credibility on social media becomes essential because fake news can affect society negatively, and the spread of false news has a considerable impact on personal reputation and public trust. In this research, we conducted a model that detects the credibility of Arabic news from social media; particularly Arabic tweets. The content of the tweets revolves around the COVID-19 pandemic. The proposed model applied to detect news credibility using text mining techniques and one of the well-known machine learning classifiers, Decision tree which has the best accuracy equal to 86.6

A Literature Review on Agile Methodologies Quality, eXtreme Programming and SCRUM

most applied methods in the software development industry. However, agile methodologies face some challenges such as less documentation and wasting time considering changes. This review presents how the previous studies attempted to cover issues of agile methodologies and the modifications in the performance of agile methodologies. The paper also highlights unresolved issues to get the attention of developers, researchers, and software practitioners

Risk Assessment Approaches in Banking Sector –A Survey

Prediction analysis is a method that makes predictions based on the data currently available. Bank loans come with a lot of risks to both the bank and the borrowers. One of the most exciting and important areas of research is data mining, which aims to extract information from vast amounts of accumulated data sets. The loan process is one of the key processes for the banking industry, and this paper examines various prior studies that used data mining techniques to extract all served entities and attributes necessary for analytical purposes, categorize these attributes, and forecast the future of their business using historical data, using a model, banks\u27 business, and strategic goals

Key Performance Indicators Detection Based Data Mining

One of the most prosperous domains that Data mining accomplished a great progress is Food Security and safety. Some of Data mining techniques studies applied several machine learning algorithms to enhance and traceability of food supply chain safety procedures and some of them applying machine learning methodologies with several feature selection methods for detecting and predicting the most significant key performance indicators affect food safety. In this research we proposed an adaptive data mining model applying nine machine learning algorithms (Naive Bayes, Bayes Net Key -Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), J48, Hoeffding tree, Logistic Model Tree) with feature selection wrapper methods (forward and backward techniques) for detecting food deterioration’s key performance indicators. In conclusion the proposed model applied effectively and successfully detected the most significant indicators for meat safety and quality with the aim of helping farmers and suppliers for being sure of delivering safety meat for consumer and diminishing the cost of monitoring meat safety

Credit Card Fraud Detection Using Machine Learning Techniques

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today\u27s banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary losses, not just for financial institutions but also for individuals. as technology and usage patterns evolve, making credit card fraud detection a particularly difficult task. Traditional statistical approaches for identifying credit card fraud take much more time, and the result accuracy cannot be guaranteed. Machine learning algorithms have been widely employed in the detection of credit card fraud. The main goal of this review intends to present the previous research studies accomplished on Credit Card Fraud Detection (CCFD), and how they dealt with this problem by using different machine learning techniques

The Impact of Applying ISO Standards Systems on Improving the Quality of the Performance in Higher Educational Institutions in Egypt

Applying ISO 21001:2018 standard ensures that universities have a competitive advantage as well as the achievement of their objectives. This study aims to identify the impact of implementing ISO 21001: 2018 management systems standards on the performance quality of higher education institutions. The study investigates the reasons why private higher education institutions seek ISO standards certificates in general and the specifications of management systems for educational institutions in particular. The study applied a set of statistical testing methods on paired samples as well as independent samples to ensure quality assurance. The study also proposes the required prerequisites that should be considered. The study investigated a hypothesis stating that "there are no statistically significant differences before and after applying the ISO 21001:2018 management systems specification for educational institutions in improving the quality of performance in higher education institutions" which was rejected by conducting an experiment in Future University in Egypt and accepting the alternative hypothesis. The study confirmed the impact of quality which was previously investigated by prior research that has been discussed in this study. The study further presented the need to apply quality based on determined criteria which were not considered in prior studies. Moreover, the study proposed the impact of ISO standards in educational institutions in general and in Egypt in specific. This recommendation is proved by this study to enhance the quality level in educational institutions

Arabic Documents classification method a Step towards Efficient Documents Summarization

The massive growth of online information obliged the availability of a thorough research in the domain of automatic text summarization within the Natural Language Processing (NLP) community. To reach this goal, different approaches should be integrated and collaborated. One of these approaches is the classification od documents. Therefore, the aim of this paper is to propose a successful framework for agricultural documents classification as a step forward for a language independent automatic summarization approach. The main target of our serial research is to propose a complete novel framework which not only responses to the question, but also gives the user an opportunity to find additional information that is related to the question. We implemented the proposed method. As a case study, the implemented method is applied on Arabic text in the agriculture field. The implemented approach succeeded in classifying the documents submitted by the user. The approach results have been evaluated using Recall, Precision and F-score measures. DOI: 10.17762/ijritcc2321-8169.15017

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

A Statistical-Mining Techniques’ Collaboration for Minimizing Dimensionality in Ovarian Cancer Data

A feature is a single measurable criterion to an observation of a process. While knowledge discovery techniques successfully contribute in many fields, however, the extensive required data processing could hinder the performance of these techniques. One of the main issues in processing data is the dimensionality of the data. Therefore, focusing on reducing the data dimensionality through eliminating the insignificant attributes could be considered one of the successful steps for raising the applied techniques’ performance. On the other hand, focusing on the applied field, ovarian cancer patients continuously suffer from the extensive analysis requirements for detecting the disease as well as monitoring the treatment progress. Therefore, identifying the most significant required analysis could be a positive step to reduce the emotional and financial suffering. This research aims to reduce the data dimensionality of the ovarian cancer disease and highlight the most significant analysis using the collaboration of clustering techniques and statistical techniques. The research succeeded to identify twelve significant analysis out of forty-four with a total of fourteen significant attributes for ovarian cancer data

A Proposed Model for Improving the Reliability of Online Exam Results Using Blockchain

In recent times, Learning Management Systems (LMS) have gained significant popularity, particularly due to the COVID-19 pandemic, offering improved effectiveness and efficiency. Within LMS, online exams have emerged as a critical tool for assessing students’ performance and understanding of course material, playing a vital role in determining their progression. Ensuring the reliability and transparency of online exam results is imperative. Any vulnerability, such as hacking, can adversely impact students’ grades. Conventional online exam systems often store data centrally in databases like MySQL, making them susceptible to unauthorized access and manipulation. This paper presents a blockchain-based framework to enable secure and peer-to-peer conduction and evaluation of academic exams. The framework uses hashing techniques to ensure data integrity and employs proof of stake mechanisms for enhanced security. Blockchain’s decentralized data storage and cryptographic hashing for each block make it effective in safeguarding data integrity. The paper demonstrates the use of blockchain for developing online exams, storing each question and answer directly on the blockchain. To achieve this, we have created a module that integrates with the Moodle learning management system. Through a comparative analysis with Moodle’s default centralized storage, our module modifies the exam result storage, ensuring secure and tamper-proof data storage on the blockchain. By leveraging the blockchain, exam data is reliably secured, maintaining integrity, and resisting manipulation. Our results show that data stored on the blockchain is entirely accurate, with no discrepancies compared to Moodle’s standard approach. The blockchain network provides a reliable and immutable platform, preventing unauthorized changes to student data. In conclusion, our blockchain-based framework offers a robust solution for enhancing the security and reliability of online exam results. By harnessing blockchain’s decentralized and tamper-proof nature, we ensure data integrity and transparency, providing a more trustworthy assessment of academic performance