Student Academic Performance is a great concern for academic institutions in all levels of academic years. Techniques like classification, clustering and association are provided by Data Mining. In this paper, two classification techniques, Decision Tree (J48) and Artificial Neural Network (ANN), are used to build a classification model, that can predict the academic performance of university students in Jordan, expected GPA in precise. A dataset has been gathered using online questionnaire, and certain attributes were selected to test their relevance to the academic performance of a Jordanian university students. The paper describes the methodology conducted to apply the J48 and ANN, using a special tool (WEKA), and the results are discussed in details, showing a better performance for ANN in some cases, and a better performance for Decision Tree in others.

Privacy-preserving of medical information (such as medical records and images) is an essential right for patients to ensure security against undesired access parties. This right is typically protected by law through firm regulations set by healthcare authorities. However, sensitive-private data usually requires the application of further security and privacy mechanisms such as encipherment (encryption) techniques. ’Medical images’ is one such example of highly demanding security and privacy standards. This is due to the quality and nature of the information carried among these images, which are usually sensitive-private information with few features and tonal variety. Hence, several state-of-the-art encryption mechanisms for medical images have been proposed and developed; however, only a few were efficient and promising. This paper presents a hybrid crypto-algorithm, MID-Crypt, to secure the medical image communicated between medical laboratories and doctors’ accounts. MID-Crypt is designed to efficiently hide medical image features and provide high-security standards. Specifically, MID-Crypt uses a mix of Elliptic-curve Diffie–Hellman (ECDH) for image masking and Advanced Encryption Standard (AES) with updatable keys for image encryption. Besides, a key management module is used to organize the public and private keys, the patient’s digital signature provides authenticity, and integrity is guaranteed by using the Merkle tree. Also, we evaluated our proposed algorithm in terms of several performance indicators including, peak signal-to-noise ratio (PSNR) analysis, correlation analysis, entropy analysis, histogram analysis, and timing analysis. Consequently, our empirical results revealed the superiority of MID-Crypt scoring the best performance values for PSNR, correlation, entropy, and encryption overhead. Finally, we compared the security measures for the MID-Crypt algorithm with other studies, the comparison revealed the distinguishable security against several common attacks such as side-channel attacks (SCA), differential attacks, man-in-the-middle attacks (MITM), and algebraic attacks.

Recently, medical image encryption has gained special attention due to the nature and sensitivity of medical data and the lack of effective image encryption using innovative encryption techniques. Several encryption schemes have been recommended and developed in an attempt to improve medical image encryption. The majority of these studies rely on conventional encryption techniques. However, such improvements have come with increased computational complexity and slower processing for encryption and decryption processes. Alternatively, the engagement of intelligent models such as deep learning along with encryption schemes exhibited more effective outcomes, especially when used with digital images. This paper aims to reduce and change the transferred data between interested parties and overcome the problem of building negative conclusions from encrypted medical images. In order to do so, the target was to transfer from the domain of encrypting an image to encrypting features of an image, which are extracted as float number values. Therefore, we propose a deep learning-based image encryption scheme using the autoencoder (AE) technique and the advanced encryption standard (AES). Specifically, the proposed encryption scheme is supposed to encrypt the digest of the medical image prepared by the encoder from the autoencoder model on the encryption side. On the decryption side, the analogous decoder from the auto-decoder is used after decrypting the carried data. The autoencoder was used to enhance the quality of corrupted medical images with different types of noise. In addition, we investigated the scores of structure similarity (SSIM) and mean square error (MSE) for the proposed model by applying four different types of noise: salt and pepper, speckle, Poisson, and Gaussian. It has been noticed that for all types of noise added, the decoder reduced this noise in the resulting images. Finally, the performance evaluation demonstrated that our proposed system improved the encryption/decryption overhead by 50–75% over other existing models.

Data Mining techniques have become widely used for several types of tasks, such as data classification. Data classification aims to classify data into set of classes based on trained model called classifier. Such techniques are used to extract important information needed to make decisions in different scopes, one of them is Education. This paper, applies several classification techniques on an academic dataset for online courses, to find the best classifier to predict the student performance, using certain features that might affect this performance. Classifiers used are Decision Tree, Artificial Neural Network, Support Vector Machine, and K-Nearest Neighbors. Experiments used real data and evaluated the models based on four performance measures: accuracy, precision, recall, and F-measure. Results show that Decision Tree and Artificial Neural Network achieved best results among other classifiers.

As a significant natural language processing task (NLP), Arabic text classification is essential for efficiently processing and analyzing Arabic language content in various digital forms, such as information retrieval, sentiment analysis, and topic modeling. Deep Learning architectures, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been widely utilized to categorize and organize language contents accurately to improve the autonomy and perception of NLP tasks. In this paper, we develop a hybrid deep learning framework for Arabic text classification, using the Inception-CNN (introduced in the GoogleNet architecture) and the LSTM (variation of the Recurrent Neural Network). Specifically, the proposed system has been trained and evaluated on two datasets of an Arabic articles dataset, viz. SANAD and NADiA datasets. Consequently, several variations of the model architecture have been configured, trained, evaluated, and compared, with the aim of obtaining the best model architecture and hyperparameters. Our best experimental evaluation showed that the proposed hybrid system (Inception CNN with and LSTM) yielded an accuracy of 92% and 96% for the Akhbarona and AlKhaleej datasets, respectively. At the same time, the entire SANAD data set also yielded a high accuracy of 92%. Lastly, comparing with the state-of-the-art models revealed the superiority of our hybrid model, which outperformed the other architectures in the same area of study, the accuracies have been improved by 1% to 30% for the different datasets.