Cancer is one of the most feared and aggressive diseases in the world and is responsible for more than 9 million deaths universally. Staging cancer early increases the chances of recovery. One staging technique is RNA sequence analysis. Recent advances in the efficiency and accuracy of artificial intelligence techniques and optimization algorithms have facilitated the analysis of human genomics. This paper introduces a novel optimized deep learning approach based on binary particle swarm optimization with decision tree (BPSO-DT) and convolutional neural network (CNN) to classify different types of cancer based on tumor RNA sequence (RNA-Seq) gene expression data. The cancer types that will be investigated in this research are kidney renal clear cell carcinoma (KIRC), breast invasive carcinoma (BRCA), lung squamous cell carcinoma (LUSC), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC). The proposed approach consists of three phases. The first phase is preprocessing, which at first optimize the high-dimensional RNA-seq to select only optimal features using BPSO-DT and then, converts the optimized RNA-Seq to 2D images. The second phase is augmentation, which increases the original dataset of 2086 samples to be 5 times larger. The selection of the augmentations techniques was based achieving the least impact on manipulating the features of the images. This phase helps to overcome the overfitting problem and trains the model to achieve better accuracy. The third phase is deep CNN architecture. In this phase, an architecture of two main convolutional layers for featured extraction and two fully connected layers is introduced to classify the 5 different types of cancer according to the availability of images on the dataset. The results and the performance metrics such as recall, precision and F1 score show that the proposed approach achieved an overall testing accuracy of 96.90%. The comparative results are introduced, and the proposed method outperforms those in related works in terms of testing accuracy for 5 classes of cancer. Moreover, the proposed approach is less complex and consume less memory.

Water, sanitation, and hygiene are essential components of the 2030 agenda for sustainable development. Goal 6 is dedicated to guarantee all societies have access to water and sanitation. Water quality (WQ) assessment is crucial to ensure the availability of clean water. This paper presents an approach called AHA–XDNN for predicting WQ. The proposed approach is based on three pillars to predict WQ with high accuracy and confidence, namely, deep neural networks (DNN), artificial hummingbird algorithm (AHA), and explainable artificial intelligence. The proposed approach involves five phases: data preprocessing, optimization, training, and evaluation. In the first phase, problems such as unwanted noise and imbalance are addressed. In the second phase, AHA is applied to optimize the DNN model’s hyper-parameters. In the third phase, the DNN model is trained on the dataset processed in the first phase. The performance of the optimized DNN model is evaluated using four measurements, and the results are explained and interpreted using SHapley additive exPlanations. The proposed approach achieved an accuracy, average precision, average recall, average F1-score of 91%, 91%, 91.5%, and 91% on the test set, respectively. By comparing the proposed approach with existing models based on artificial neural network (ANN), the proposed approach was able to outperform its counterparts in terms of average recall and average F1-score.