Nowadays, the modern management is promoted to resolve the issue of unreliable information transmission and to provide work efficiency. The basic aim of the modern management is to be more effective in the role of the school to train talents and serve the society. This article focuses on the application of data mining (DM) in the development of information management system (IMS) in universities and colleges. DM provides powerful approaches for a variety of educational areas. Due to the large amount of student information that can be used to design valuable patterns relevant to student learning behavior, research in the field of education is continuously expanding. Educational data mining can be used by educational institutions to assess student performance, assisting the institution in recognizing the student’s accomplishments. In DM, classification is a well-known technique that has been regularly used to determine student achievement. In this study, the process of DM and the application research of association rules is introduced in the development of IMS in universities and colleges. The results show that the curriculum covers the whole field and the minimum transaction support count be 2, min _conf = 70%. The results also suggested that students who choose one course also tend to choose the other course. The application of DM theory in university information will greatly upsurge the data analysis capability of administrators and improve the management level.

Diabetic retinopathy (DR) is on the increase nowadays due to the high sugar level in the blood, and it is the reason for blindness that mainly occurs in middle-aged people. Furthermore, the Internet of Medical Things (IoMT) enabled computer-aided diagnostic (CAD) systems record DR-related data online and give patients with reassuring information. The internet allows for the interconnection of a variety of smart devices, enabling remote healthcare systems based on the IoMT to link patients with medical professionals. The proper diagnosis of diabetic patients and detection of DR severity in earlier stages help in preventing blindness. Therefore, the basic aim of this study is to prevent the diabetic patient from losing vision by detecting and classifying the severity of DR fundus images using the IoMT-enabled CAD system. This article designed a novel diabetic retinopathy classification (DRC) system by hybridizing the DL model with optimization algorithms to classify the DR images based on severity. This system begins with preprocessing phase for removing the noise from edges. Next, the proposed K-mean cluster-based growing region segmentation is employed to extract the useful region from the images. Then, pretrained convolutional neural network (CNN) model, i.e., RESnet with the proposed hybrid genetic and ant colony optimization (HGACO) algorithm, is applied to extract the features from the region of interest (ROI) and classify them into four severity levels. Performance indices such as AUC, $F$ -measure, accuracy, sensitivity, and specificity are analyzed to evaluate the performance on MESSIDOR dataset. The performance of the proposed DRC system is compared with state-of-the-art classification systems. The proposed HGACO algorithm is also compared with Adam and gradient descent (GD) optimizers. The system is also evaluated by employing different parameters of CNN and HGACO. Additionally, for the determination of the classification accuracy of the DRC system, the confidence interval statistical test is implemented considering various parameters and configurations of the neural network. The results revealed that the proposed DR system provides higher classification results by achieving 95.78%, 91.98%, 97.7%, and 94.56% AUC, sensitivity, accuracy, and specificity rate, respectively. CNN alleviates the difficulty of developing image features, whereas the HGACO algorithm-based technique automates CNN hyperparameter design.

With the increasing use of Virtual Learning Environments (VLEs), student performance prediction has become a necessary task to better academic outcomes, especially for disabled students who experience specific constraints. Machine learning (ML) models like Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN) have shown high predictive accuracy. However, these models often lack interpretability and making it difficult for educators to understand the key factors influencing student success. In addition, studies only concentrate on general student populations and do not incorporate complete demographic and behavioural data in their analyses, making it difficult to address the needs of disabled students. To address these challenges, this study proposed a novel approach by combining Long Short-Term Memory (LSTM) networks with SHapley Additive exPlanations (SHAP). LSTM captured the temporal dependencies in student interaction data, and SHAP make the model interpretable by identifying the most influential features responsible for achieving good learning performance. The proposed system allows for prediction accuracy and transparent decision making, thereby facilitating targeted interventions for disabled students. The proposed model is tested on the OULAD dataset that contains demographic, engagement, and assessment-related information. The pre-processing techniques are applied to handle missing values, normalize numerical attributes, and balance class distributions. The experimental results demonstrate that the proposed LSTM-SHAP model outperforms traditional machine learning methods in predicting student performance, achieving an accuracy of 92.88%, precision of 92.96%, recall of 92.80%, and F1-score of 92.79% on the OULAD dataset. In a cross-domain evaluation, it maintains strong generalizability with 85.0% accuracy, 98.4% precision, 78.5% recall, and an F1-score of 87.2% when tested on the xAPI dataset. Additionally, SHAP analysis reveals that assessment scores, time spent on course materials, and forum interactions are the most important factors behind academic success. The statistical tests, such as ANOVA, Friedman tests and post hoc Nemenyi, are applied to assess the statistical significance of the differences in performance between the models, and results suggest that LSTM outperforms other models in each performance metric.

Machines are continually being channelized in the current era of automation to deliver accurate interpretations of what people communicate on social media. The human species is today engulfed in the concept of what and how people believe, and the decisions made as a result are mostly dependent on the sway of the masses on social media platforms. The usage of internet as well as social media is booming day by day. Today, this ocean of data can be used for the fruitful purposes. Analysis of social media sentiment textual posts can supply knowledge and information that can be used in citizen opinion polling, business intelligence, social contexts, and Internet of Things (IOT)-mood triggered devices. In this manuscript, the main focus is the sentiment analysis based on Emotional Recognition (ER). The proposed system highlights the process of gaining actual sentiment or mood of a person. The key idea to this system is posed by the fact that if smile and laughter can be two different categories of being happy, then why not happpyyyyyy and happy. A novel lexicon-based system is proposed that considers the lengthened word as it is, instead of being omitted or normalized. The aggregated intensified senti-scores of lengthened words are calculated using framed lexicon rules. After that, these senti-scores of lengthened words are used to calculate the level of sentiment of the person. The dataset used in this paper is the informal chats happened among different friend groups over Facebook, Tweets and personal chat. The performance of proposed system is compared with traditional systems that ignore lengthened words and proposed system outperform tradition systems by achieving 81% to 96% F-measure rate for all datasets.