In artificial intelligence, deep learning (DL) is a process that replicates the working mechanism of the human brain in data processing, and it also creates patterns for decision making. Deep learning or neural networks have been deployed in several fields, such as computer vision, natural language processing, and speech recognition. It has been used in many healthcare applications for the diagnosis and treatment of many chronic diseases. These algorithms have the power to avoid outbreaks of illness, recognize and diagnose illnesses, minimize running expenses for hospital management and patients. This paper discusses the deep learning methods used in different healthcare fields, i.e., identifying depression, heart diseases, physiological signals, lymph node metastases from breast cancer, etc. These diseases are categorized into the central nervous system, cardiovascular system, and respiratory system. For each category, after summarizing the studies, comparison tables are laid down using some important factors. Different applications, tools, methods, and data sets used for DL models are leveraged. Finally, research opportunities and challenges being faced for deep learning models are discussed.

Cloud multi-factor authentication is a critical security measure that helps strengthen cloud security from unauthorized access and data breaches. Multi-factor authentication verifies that authentic cloud users are only authorized to access cloud apps, data, services, and resources, making it more secure for enterprises and less inconvenient for users. The number of authentication factors varies based on the security framework’s architecture and the required security level. Therefore, implementing a secured multi-factor authentication framework in a cloud platform is a challenging process. In this paper, we developed an adaptive multi-factor multi-layer authentication framework that embeds an access control and intrusion detection mechanisms with an automated selection of authentication methods. The core objective is to enhance a secured cloud platform with low false positive alarms that makes it more difficult for intruders to access the cloud system. To enhance the authentication mechanism and reduce false alarms, multiple authentication factors that include the length, validity, and value of the user factor is implemented with a user’s geolocation and user’s browser confirmation method that increase the identity verification of cloud users. An additional AES-based encryption component is applied to data, which are protected from being disclosed. The AES encryption mechanism is implemented to conceal the login information on the directory provider of the cloud. The proposed framework demonstrated excellent performance in identifying potentially malicious users and intruders, thereby effectively preventing any intentional attacks on the cloud services and data.

A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The stroke prediction dataset was pre-processed by handling missing values using the KNN imputer technique, eliminating outliers, applying the one-hot encoding method, and normalizing the features with different ranges of values. After data splitting, synthetic minority oversampling (SMO) was applied to balance the stroke samples and no-stroke classes. Furthermore, to fine-tune the hyper-parameters of the ML algorithm, we employed a random search technique that could achieve the best parameter values. After applying the tuning process, we stacked the parameters to a tuning ensemble RXLM that was analyzed and compared with traditional classifiers. The performance metrics after tuning the hyper-parameters achieved promising results with all ML algorithms.

One of the most prevalent diseases affecting women in recent years is breast cancer. Early breast cancer detection can help in the treatment, lower the infection risk, and worsen the results. This paper presents a hybrid approach for augmentation and segmenting breast cancer. The framework contains two main stages: augmentation and segmentation of ultrasound images. The augmentation of the ultrasounds is applied using generative adversarial networks (GAN) with nonlinear identity block, label smoothing, and a new loss function. The segmentation of the ultrasounds applied a modified U-Net 3+. The hybrid approach achieves efficient results in the segmentation and augmentation steps compared with the other available methods for the same task. The modified version of the GAN with the nonlinear identity block overcomes different types of modified GAN in the ultrasound augmentation process, such as speckle GAN, UltraGAN, and deep convolutional GAN. The modified U-Net 3+ also overcomes the different architectures of U-Nets in the segmentation process. The GAN with nonlinear identity blocks achieved an inception score of 14.32 and a Fréchet inception distance of 41.86 in the augmenting process. The GAN with identity achieves a smaller value in Fréchet inception distance (FID) and a bigger value in inception score; these results prove the model’s efficiency compared with other versions of GAN in the augmentation process. The modified U-Net 3+ architecture achieved a Dice Score of 95.49% and an Accuracy of 95.67%.

Text classification is a prominent research area, gaining more interest in academia, industry and social media. Arabic is one of the world’s most famous languages and it had a significant role in science, mathematics and philosophy in Europe in the middle ages. During the Arab Spring, social media, that is, Facebook, Twitter and Instagram, played an essential role in establishing, running, and spreading these movements. Arabic Sentiment Analysis (ASA) and Arabic Text Classification (ATC) for these social media tools are hot topics, aiming to obtain valuable Arabic text insights. Although some surveys are available on this topic, the studies and research on Arabic Tweets need to be classified on the basis of machine learning algorithms. Machine learning algorithms and lexicon-based classifications are considered essential tools for text processing. In this paper, a comparison of previous surveys is presented, elaborating the need for a comprehensive study on Arabic Tweets. Research studies are classified according to machine learning algorithms, supervised learning, unsupervised learning, hybrid, and lexicon-based classifications, and their advantages/disadvantages are discussed comprehensively. We pose different challenges and future research directions.

Breast cancer is ranked as the second most common cancer among women globally, highlighting the critical need for precise and early detection methods. Our research introduces a novel approach for classifying benign and malignant breast ultrasound images. We leverage advanced deep learning methodologies, mainly focusing on the vision transformer (ViT) model. Our method distinctively features progressive fine‐tuning, a tailored process that incrementally adapts the model to the nuances of breast tissue classification. Ultrasound imaging was chosen for its distinct benefits in medical diagnostics. This modality is noninvasive and cost‐effective and demonstrates enhanced specificity, especially in dense breast tissues where traditional methods may struggle. Such characteristics make it an ideal choice for the sensitive task of breast cancer detection. Our extensive experiments utilized the breast ultrasound images dataset, comprising 780 images of both benign and malignant breast tissues. The dataset underwent a comprehensive analysis using several pretrained deep learning models, including VGG16, VGG19, DenseNet121, Inception, ResNet152V2, DenseNet169, DenseNet201, and the ViT. The results presented were achieved without employing data augmentation techniques. The ViT model demonstrated robust accuracy and generalization capabilities with the original dataset size, which consisted of 637 images. Each model’s performance was meticulously evaluated through a robust 10‐fold cross‐validation technique, ensuring a thorough and unbiased comparison. Our findings are significant, demonstrating that the progressive fine‐tuning substantially enhances the ViT model’s capability. This resulted in a remarkable accuracy of 94.49% and an AUC score of 0.921, significantly higher than models without fine‐tuning. These results affirm the efficacy of the ViT model and highlight the transformative potential of integrating progressive fine‐tuning with transformer models in medical image classification tasks. The study solidifies the role of such advanced methodologies in improving early breast cancer detection and diagnosis, especially when coupled with the unique advantages of ultrasound imaging.