Journal of Prospective Researches

Hierarchical clustering of social media data is frequent. Data points indicate clusters, and it combines the neighbouring clusters until one remains. Segment photos, analyze social networks, and cluster texts using hierarchical clustering. Hierarchical clustering can group related social media data pieces. Topic-grouping social media communications helps identify patterns. Segmenting images by colour, texture, and form may aid object recognition, face detection, and content-based image retrieval. Social connection hierarchical clustering organises persons or communities in social network analysis. This identifies influential persons and groups and explains social networks. Communities, co-authorship networks, and important actors can be identified on social media. This article analyses social media images ' visual and linguistic information using hierarchical agglomerative clustering. For social media content images from a data set named The experimental results were applied to a dataset named YasminNadiaArabcSocialMediaImages in Kaggle which contains images of famous Arabic social media celebrities, the clustering approach groups comparable pictures using TF-IDF vectorization for textual attributes and PCA and t-SNE for visuals.

This study aims at investigating how Iraqi EFL teachers employ the communication accommodation strategies to develop interaction in EFL classes. observation was used as data collection method and by attending five lectures for five teachers with different specialization and experience at University of Mosul/College of Education for Humanities/ department of English. The results show that accommodation occurs in Iraqi EFL classes and the different accommodation strategies such as interpretability, approximation, discourse management, interpersonal control and emotional expression vary in their implementation. In addition, some factors such as the topic discussed, teacher specialization, looping and the relation between the teacher and his students have a crucial impact on the selection of one strategy over another. Finally code-switching was recorded as additional strategy to develop in-class interaction.

Cancer is a disease characterized by abnormal cell growth that can invade other parts of the body, forming tumors. While some tumors are benign and do not spread, malignant ones can metastasize. Cancer can develop in nearly any body part and exists in various forms, including lymphoma, sarcoma, leukemia, and carcinoma. Symptoms may include weight loss, persistent cough, lumps, unusual bleeding, and altered bowel movements, though these symptoms can have other causes. Over 100 types of cancer exist, often linked to prolonged exposure to carcinogens and the immune system's reparative response. Understanding cancer pathology is vital for its diagnosis, treatment, and prevention.

Knowledge Sharing (KS) has attracted the attention of researchers and is considered an essential element for organizations and universities. Limited studies are conducted in developing countries in general, particularly in Iraq. This study aims to identify the factors influencing KS among academic staff. The study was conducted in Iraq at Cihan University-Erbil. Data were collected by questionnaire, and 135 valid responses were received from the academic staff. Smart-PLS analysed the data, and the results showed that organizational culture (OC), trust, rewards and information communication technology (ICT) positively and significantly impact KS. The study recommended that the university create an environment to enhance and improve KS among staff members and develop a rewards system to increase KS among academic staff.

This in-depth study looks at data on fuel use in two main ways to find trends and make better predictions. One way is to learn how to use machine learning and association rule mining to try to guess what will happen. It uses association rules to show how things in a set are linked and how they do a lot of different things together. We can learn more about the parts that work together to change how much fuel is used. The RNN, TCN, and LSTM machine learning models can all guess how much fuel will be used, but they can do so in different ways. The TCN plan works out the best. The results show how important it is to choose a model design that makes the dataset's features better by putting together numbers and people's ideas about what might be important. We might be able to fully understand how fuel use changes over time if we put together what machine learning and association rule mining tell us. The numbers make it clear that the collection should be used for more research. There are different sets of methods that should be used for machine learning and more general statistical methods. People who give money, make rules, and try to guess how people will use fuel in the future should think about these ideas. It was found that people will be able to make better predictions in the future if they learn more about complicated machine learning design and link rules. The study is a good way to find out how much fuel people use when they switch sources of energy. When people use different types of fuel, we can also see how much they use. We can make the most of what we have this way.