Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.

The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals’ complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given the association between EEG signals and particular demographics, such as gender and age, and the influences of these demographic characteristics on the incidence of depression, it would be preferable to include demographic factors during EEG modeling and depression detection. The main objective of this work is to develop an algorithm that can recognize depression patterns by studying EEG data. Following a multiband analysis of such signals, machine learning and deep learning techniques were used to detect depression patients automatically. EEG signal data are collected from the multi-modal open dataset MODMA and employed in studying mental diseases. The EEG dataset contains information from a traditional 128-electrode elastic cap and a cutting-edge wearable 3-electrode EEG collector for widespread applications. In this project, resting EEG readings of 128 channels are considered. According to CNN, training with 25 epoch iterations had a 97% accuracy rate. The patient’s status has to be divided into two basic categories: major depressive disorder (MDD) and healthy control. Additional MDD include the following six classes: obsessive-compulsive disorders, addiction disorders, conditions brought on by trauma and stress, mood disorders, schizophrenia, and the anxiety disorders discussed in this paper are a few examples of mental illnesses. According to the study, a natural combination of EEG signals and demographic data is promising for the diagnosis of depression.

AI-driven precision agriculture applications can benefit from the large data source that remote sensing provides, as it can gather agricultural monitoring data at various scales throughout the year. Numerous advantages for sustainable agricultural applications, including yield prediction, crop monitoring, and climate change adaptation, can be obtained from remote sensing and artificial intelligence. In this work, we proposed a fully automated Optimized Self-Attention Fused Convolutional Neural Network (CNN) architecture for land use and land cover classification using remote sensing (RS) data. A new contrast enhancement equation has been proposed and utilized in the proposed architecture for the data augmentation. After that, a fused Self-Attention CNN architecture was proposed. The proposed architecture initially consists of two custom models named IBNR-65 and Densenet-64. Both models have been designed based on the inverted bottleneck residual mechanism and Dense Blocks. After that, both models were fused using a depth-wise concatenation and append a Self-Attention layer for deep features extraction. After that, we trained the model and performed classification using neural network (NN) classifiers. The results obtained from the NN classifiers are insufficient; therefore, we implemented a Bayesian Optimization and fine-tuned the hyperparameters of NN. In addition, we proposed a Quantum Hippopotamus Optimization Algorithm for the best feature selection. The selected features are finally classified using fine-tuned NN classifiers and obtained improved accuracy of 98.20, 89.50, and 91.70%, and the highest precision rate is 98.23, recall is 98.20, and f1-score is 98.21 respectively, for SIRI-WHU, EuroSAT, and NWPU datasets. Moreover, a detailed ablation study was conducted, and the performance was compared with SOTA. The proposed architecture shows improved accuracy, sensitivity, precision and computational time performance.

A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances in the field of explainable artificial intelligence have contributed to the development of robust intrusion detection modes for CPS environments. This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS). The proposed XAIID-SCPS technique mainly concentrates on the detection and classification of intrusions in the CPS platform. In the XAIID-SCPS technique, a Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm is applied for feature selection purposes. For intrusion detection, the Improved Elman Neural Network (IENN) model was utilized with an Enhanced Fruitfly Optimization (EFFO) algorithm for parameter optimization. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better understanding and explainability of the black-box method for accurate classification of intrusions. The simulation values demonstrate the promising performance of the XAIID-SCPS technique over other approaches with maximum accuracy of 98.87%.

Botnet detection in a cloud-aided Internet of Things (IoT) environment is a tedious process, meanwhile, IoT gadgets are extremely vulnerable to attacks due to poor security practices and limited computing resources. In the cloud-aided IoT environment, Botnet can be identified by monitoring network traffic and analyzing it for signs of malicious activity. It can be performed by using intrusion detection systems, machine learning (ML) algorithms, and other security tools that are devised for identifying known botnet behaviors and signatures. Therefore, this study presents a Hybrid Metaheuristics with Machine Learning based Botnet Detection (HMMLB-BND) method in the Cloud Aided IoT environment. The projected HMMLB-BND technique focuses on the detection and classification of Botnet attacks in the cloud-based IoT environment. In the presented HMMLB-BND technique, modified firefly optimization (MFFO) algorithm for feature selection purposes. The HMMLB-BND algorithm uses a hybrid convolutional neural network (CNN)-quasi-recurrent neural network (QRNN) module for botnet detection. For the optimal hyperparameter tuning process, the chaotic butterfly optimization algorithm (CBOA) is employed. A series of simulations were made on the N-BaIoT dataset and the experimental outcomes stated the significance of the HMMLB-BND technique over other existing approaches.

Comparing Arabic to other languages, Arabic lacks large corpora for Natural Language Processing (Assiri, Emam & Al-Dossari, 2018; Gamal et al., 2019). A number of scholars depended on translation from one language to another to construct their corpus (Rushdi-Saleh et al., 2011). This paper presents how we have constructed, cleaned, pre-processed, and annotated our 20,0000 Gold Standard Corpus (GSC) AraCust, the first Telecom GSC for Arabic Sentiment Analysis (ASA) for Dialectal Arabic (DA). AraCust contains Saudi dialect tweets, processed from a self-collected Arabic tweets dataset and has been annotated for sentiment analysis, i.e.,manually labelled (k=0.60). In addition, we have illustrated AraCust’s power, by performing an exploratory data analysis, to analyse the features that were sourced from the nature of our corpus, to assist with choosing the right ASA methods for it. To evaluate our Golden Standard corpus AraCust, we have first applied a simple experiment, using a supervised classifier, to offer benchmark outcomes for forthcoming works. In addition, we have applied the same supervised classifier on a publicly available Arabic dataset created from Twitter, ASTD (Nabil, Aly & Atiya, 2015). The result shows that our dataset AraCust outperforms the ASTD result with 91% accuracy and 89% F1avg score. The AraCust corpus will be released, together with code useful for its exploration, via GitHub as a part of this submission.