Predicting protein-protein interactions as a one-class classification problem

Protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been used to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. While it is easy to get a dataset of interacting protein as positive example, there is no experimentally confirmed non-interacting protein to be considered as a negative set. Therefore, in this paper we solve this problem as a one-class classification problem using One-Class SVM (OCSVM). Using only positive examples (interacting protein pairs) for training, the OCSVM achieves accuracy of 80%. These results imply that protein-protein interaction can be predicted using one-class classifier with reliable accuracy

EMPOWERING EDUCATION THROUGH AI: POTENTIAL BENEFITS AND FUTURE IMPLICATIONS FOR INSTRUCTIONAL PEDAGOGY

This study explores the transformative potential of Artificial Intelligence (AI) in education. AI-powered systems offer a paradigm shift from traditional methods, fostering personalized learning experiences. The paper examines various AI applications including intelligent tutoring systems, virtual reality environments, and advanced data analysis. Machine learning algorithms personalize learning journeys by analyzing student data and preferences. Learner models track progress and adapt instruction based on strengths and weaknesses. The research identifies potential benefits such as improved access to education, enhanced student engagement, and streamlined administrative tasks. Additionally, the paper explores the future implications of AI in education, including adaptive assessments, virtual teaching assistants, and increased parental involvement. Recommendations for further research emphasize exploring AI's role in instructional pedagogy, integrating AI concepts into the curriculum, and providing hands-on learning opportunities through AI projects. Overall, the study highlights AI's potential to revolutionize education by creating a more individualized and effective learning environment for all students

Quality Education Factor Effects on Student Satisfaction in Saudi Arabian Universities

This research examines the level of student satisfaction in Saudi Arabian universities. The research studied factors such as classroom facilities, available courses, learning environment, e-learning methods, type of leadership, and instructor's expertise. Four-hundred students responded to a questionnaire on three different campuses with a 5-point Likert scale. Regression analysis results of student responses showed a significant impact on students’ satisfaction with varying degrees of strength. Most importantly, classroom facilities and instructor expertise had a more substantial effect among all variables, whereas learning environment and type of leadership had the least impact. Hence, university policymakers should focus more on classroom facilities and instructor expertise to increase students’ satisfaction in their universities. Keywords: Classroom facilities, Available courses, Learning environment, E-learning methods, Type of leadership, Instructor's expertise, Education Quality DOI: 10.7176/JEP/11-9-10 Publication date:March 31st 202

Using Self-labeling and Co-Training to Enhance Bots Labeling in Twitter

The rapid evolution in social bots have required efficient solutions to detect them in real-time. In fact, obtaining labeled stream datasets that contains variety of bots is essential for this classification task. Despite that, it is one of the challenging issues for this problem. Accordingly, finding appropriate techniques to label unlabeled data is vital to enhance bot detection. In this paper, we investigate two labeling techniques for semi-supervised learning to evaluate different performances for bot detection. We examine self-training and co-training. Our results show that self-training with maximum confidence outperformed by achieving a score of 0.856 for F1 measure and 0.84 for AUC. Random Forest classifier in both techniques performed better compared to other classifiers. In co-training, using single view approach with random forest classifier using less features achieved slightly better compared to single view with more features. Using multi-view of features in co-training in general achieved similar results for different splits

Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter

The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector machines, and neural networks. We found that the cross-validation attribute evaluation performance was the best when compared to other FS methods. Our results show that the random forest classifier with six optimal features achieved the best score of 94.3% for the area under the curve. The results maintained overall 89% accuracy, 83.8% precision, and 83.3% recall for the bot class. We found that using four features: favorites_count, verified, statuses_count, and average_tweets_per_day, achieves good performance metrics for bot detection (84.1% precision, 81.2% recall)

Characteristics of Similar-Context Trending Hashtags in Twitter: A Case Study

© 2020, Springer Nature Switzerland AG. Twitter is a popular social networking platform that is widely used in discussing and spreading information on global events. Twitter trending hashtags have been one of the topics for researcher to study and analyze. Understanding the posting behavior patterns as the information flows increase by rapid events can help in predicting future events or detection manipulation. In this paper, we investigate similar-context trending hashtags to characterize general behavior of specific-trend and generic-trend within same context. We demonstrate an analysis to study and compare such trends based on spatial, temporal, content, and user activity. We found that the characteristics of similar-context trends can be used to predict future generic trends with analogous spatiotemporal, content, and user features. Our results show that more than 70% users participate in location-based hashtag belongs to the location of the hashtag. Generic trends aim to have more influence in users to participate than specific trends with geographical context. The retweet ratio in specific trends is higher than generic trends with more than 79%

Integration of genome-wide expression and methylation data: Relevance to aging and Alzheimer\u27s disease

The progressive and latent nature of neurodegenerative diseases, such as Alzheimer\u27s disease (AD) indicates the role of epigenetic modification in disease susceptibility. Previous studies from our lab show that developmental exposure to lead (Pb) perturbs the expression of AD-associated proteins. In order to better understand the role of DNA methylation as an epigenetic modifications mechanism in gene expression regulation, an integrative study of global gene expression and methylation profiles is essential. Given the different formats of gene expression and methylation data, combining these data for integrative analysis can be challenging. In this paper we describe a method to integrate and analyze gene expression and methylation arrays. Methylation array raw data contain the signal intensities of each probe of CpG sites, whereas gene expression data measure the signal intensity values of genes. In order to combine these data, methylation data of CpG sites have to be associated with genes

Developing a framework for the success of international development projects in the Maldives / Mohamed Yamin… [et.al]

The paper concentrates on International Development (ID) projects implemented in the Maldives, an island nation in the Indian Ocean. Some of the critical issues reported on these projects include failure of contractors and consultants to deliver goods and services, non-compliance issues on financial management / reporting practices, and project delays. The overarching central question guiding the study is “How can project success be achieved in ID projects implemented in the Maldives?” This paper seeks to assess the challenges facing projects, explore the critical success factors, and project success criteria of ID projects in the Maldives. Furthermore, the study will look into developing a framework for the success of ID projects in the Maldives. It is intended to be carried out based on a qualitative case study approach. The study hopes to capture the views of beneficiaries, project teams, and donors, and thus, help resolve the misalignment between theoretical frameworks and practice

Bot-Mgat: A Transfer Learning Model Based On A Multi-View Graph Attention Network To Detect Social Bots

Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approaches to solving this problem. Due to the scarcity of recently updated labeled data, the performance of detection systems degrades when exposed to a new dataset. Therefore, semi-supervised learning (SSL) techniques can improve performance, using both labeled and unlabeled examples. In this paper, we propose a framework based on the multi-view graph attention mechanism using a transfer learning (TL) approach, to predict social bots. We called the framework \u27Bot-MGAT\u27, which stands for bot multi-view graph attention network. The framework used both labeled and unlabeled data. We used profile features to reduce the overheads of the feature engineering. We executed our experiments on a recent benchmark dataset that included representative samples of social bots with graph structural information and profile features only. We applied cross-validation to avoid uncertainty in the model\u27s performance. Bot-MGAT was evaluated using graph SSL techniques: single graph attention networks (GAT), graph convolutional networks (GCN), and relational graph convolutional networks (RGCN). We compared Bot-MGAT to related work in the field of bot detection. The results of Bot-MGAT with TL outperformed, with an accuracy score of 97.8%, an F1 score of 0.9842, and an MCC score of 0.9481