{"title":"An Attentive Convolutional Recurrent Network for Fake News Detection","authors":"A. Sleem, Ibrahim Elhenawy","doi":"10.54216/ijaaci.020101","DOIUrl":"https://doi.org/10.54216/ijaaci.020101","url":null,"abstract":"With the rapid growth of social media and online news platforms, the spread of fake news has become a major problem, leading to misinformation and distrust. In this paper, we propose an attentive convolutional recurrent network (ACRN) for fake news detection, which combines convolutional learning and recurrent learning capabilities to capture both local and global temporal information. Additionally, we incorporate attention mechanisms to focus on important features and reduce noise. We evaluate our model on a publicly available dataset and compare it with state-of-the-art methods. The results show that our ACRN model outperforms the existing methods in terms of accuracy, precision, recall, and F1-score. We also perform an ablation study to demonstrate the effectiveness of our attention mechanisms. Our proposed ACRN model can applied as a reliable computation intelligence tool for detecting fake news and improving the accuracy of news verification.","PeriodicalId":166689,"journal":{"name":"International Journal of Advances in Applied Computational Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116252217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Opinion mining for Arabic dialect in social media: A systematic review","authors":"H. ., Ahmed A. Khamees, S. Salloum","doi":"10.54216/ijaaci.010201","DOIUrl":"https://doi.org/10.54216/ijaaci.010201","url":null,"abstract":"The huge text generated on social media in Arabic, especially the Arabic dialect becomes more attractive for Natural Language Processing (NLP) to extract useful and structured information that benefits many domains. The more challenging point is that this content is mostly written in an Arabic dialect, and the problem with these dialects it has no written rules like Modern Standard Arabic (MSA) or traditional Arabic, and it is changing slowly but unexpectedly. One of the ways to benefit from this huge data is opinion mining, so we introduce this systematic review for opinion mining from Arabic text dialect for the years from 2016 until 2019. We have found that Saudi, Egyptian, Algerian, and Jordanian are the most studied dialects even if it is still under development and need a bit more effort, nevertheless, dialects like Mauritanian, Yemeni, Libyan, and somalin have not been studied in this period; also we have found the main methods that show a good result is the last four years.","PeriodicalId":166689,"journal":{"name":"International Journal of Advances in Applied Computational Intelligence","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121439216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Multi-Layer Perceptron (MLP) Neural Networks for Stellar Classification: A Review of Methods and Results","authors":"A. H. Abdel-aziem, Tamer H. M. Soliman","doi":"10.54216/ijaaci.030203","DOIUrl":"https://doi.org/10.54216/ijaaci.030203","url":null,"abstract":"The remarkable capacity of artificial intelligence (AI) to analyze enormous quantities of information and create precise forecasts has led to its growing prominence in the field of scientific Astrophysics. Stellar categorization is the process by which stars are sorted according to the characteristics revealed by their spectra. To analyze the star's electromagnetic radiation, a diffraction or prism screen separates it into a spectrum with an assortment of hues and spectral lines used to categorize the star. Star wavelengths are an extremely important piece of data for space-based photography studies. Employing data from over 100,000 cases and a variety of AI models, this study demonstrates how to categorize stellar properties as either a Galaxy or a Star. This paper used the multi-layer perceptron (MLP) neural network (NN) for stellar classification. The MLP is applied in 18 features. This paper showed the correlation between these features. This paper achieved 97% accuracy from the MLP model. This study compared various optimizers to show the best optimizer. The Adagrad optimizer is the best optimizer due to getting the highest validation accuracy.","PeriodicalId":166689,"journal":{"name":"International Journal of Advances in Applied Computational Intelligence","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133697563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Digital Revolution in Trade Finance: Exploring The Impact of Smart Blockchain-Based Letters of Credit On E-business Transactions","authors":"Zenat Mohamed, Mahmoud M. Ismail, S. Zaki","doi":"10.54216/ijaaci.030105","DOIUrl":"https://doi.org/10.54216/ijaaci.030105","url":null,"abstract":"This paper aims to explore the impact of Smart Blockchain-based Letters of Credit (BTLOC) on business transactions in the realm of trade finance. The involvement of a third party in business transactions often leads to complications such as process heterogeneity, increased complexity, information security risks, and higher costs. To address these challenges, this research proposes an innovative solution for activities dependent on third-party participation, specifically in the context of global trading. To provide a comprehensive understanding of this solution, the study employs business process modeling in a transaction scenario, offering a deeper insight into its mechanics. The implementation of platforms built upon blockchain technology (BT), and smart contracts has the potential to significantly reshape and streamline business procedures, thereby benefiting participants engaged in global trade. This research primarily focuses on investigating the theoretical aspects and feasibility of incorporating BT into global trade, considering a paradigm shift in the field. A novel BTLOC is introduced as a key element of the research, enabling the examination of its practicality. Additionally, we explore the applications of BTLOC in real case study of international Trading and explore its potential integration into trade finance processes. Through a multi-case analysis, this research contributes to the understanding of the paradigm shift facilitated by BT. The findings shed light on the future potential applications of blockchain in finance and serve as an illustrative example of the extended capabilities associated with financial processes.","PeriodicalId":166689,"journal":{"name":"International Journal of Advances in Applied Computational Intelligence","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124907496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Computational Intelligence for Automatic Detection Cardiac Arrhythmia from ECG Signals: Taxonomy and Open Issues","authors":"Reem Atassi, Fuad Alhosban, Milan Dordevic","doi":"10.54216/ijaaci.020202","DOIUrl":"https://doi.org/10.54216/ijaaci.020202","url":null,"abstract":"Cardiac arrhythmia is a medical disorder, in which the heart beats sporadically or irregularly leading to serious health consequences if left untreated. Early detection of arrhythmias is essential for timely intervention and management of the condition. Recently, there has been a growing interest in using computational intelligence techniques to automatically detect arrhythmias from electrocardiogram (ECG) signals. This approach offers the potential to improve the accuracy and efficiency of arrhythmia detection, as well as reduce the workload on healthcare professionals. This work reviews the current state-of-the-art ML methods for detecting arrhythmias including deep neural networks, support vector machines, and random forests. We will also discuss the challenges associated with using these techniques, such as the need for large and diverse datasets, and the interpretation of model outputs. We also highlight the open research that require further research and development to fully realize the potential of these algorithms in clinical practice.","PeriodicalId":166689,"journal":{"name":"International Journal of Advances in Applied Computational Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132612311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Neutrosophic MCDM Model for Assessment Factors of Wearable Technological Devices to Reduce Risks and Increase Safety: Case Study in Education","authors":"A. Abdelhafeez, Myvizhi. M.","doi":"10.54216/ijaaci.030104","DOIUrl":"https://doi.org/10.54216/ijaaci.030104","url":null,"abstract":"This study investigates the feasibility of using wearable technologies in education to improve safety. This article explores how wearables may be used to improve school safety and wellness, as well as their advantages, disadvantages, and future potential. The article covers a wide range of wearable gadgets and their respective safety-related features, from smartwatches to location trackers to panic buttons and biometric sensors. Privacy issues, data security, user acceptability, and ethical considerations are only some of the problems and hazards discussed in this research on wearables in education. This study the neutrosophic set to deal with uncertain data. The neutrosophic set is integrated with the multi-criteria decision-making (MCDM) CRITIC method. The CRITIC method is used to compute the weights of factors and rank it. There are 15 factors used in this study. The case study is applied in the education field. Educators, technologists, and legislators all need to work together to guarantee the safe and effective use of wearable devices in schools, as shown by the study's findings. The article reiterates the importance of wearables and their potential to enhance safety measures in education before making the case for more studies, pilot programmers, and policy development to fully realize their promise.","PeriodicalId":166689,"journal":{"name":"International Journal of Advances in Applied Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129937894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}