Melanoma skin cancer is one of the most dangerous types of skin cancer, which, if not diagnosed early, may lead to death. Therefore, an accurate diagnosis is needed to detect melanoma. Traditionally, a dermatologist utilizes a microscope to inspect and then provide a report on a biopsy for diagnosis; however, this diagnosis process is not easy and requires experience. Hence, there is a need to facilitate the diagnosis process while still yielding an accurate diagnosis. For this purpose, artificial intelligence techniques can assist the dermatologist in carrying out diagnosis. In this study, we considered the detection of melanoma through deep learning based on cutaneous image processing. For this purpose, we tested several convolutional neural network (CNN) architectures, including DenseNet201, MobileNetV2, ResNet50V2, ResNet152V2, Xception, VGG16, VGG19, and GoogleNet, and evaluated the associated deep learning models on graphical processing units (GPUs). A dataset consisting of 7146 images was processed using these models, and we compared the obtained results. The experimental results showed that GoogleNet can obtain the highest performance accuracy on both the training and test sets (74.91% and 76.08%, respectively).

Traditional machine learning approaches to drug sensitivity prediction assume that training data and test data must be in the same feature space and have the same underlying distribution. However, in real-world applications, this assumption does not hold. For example, we sometimes have limited training data for the task of drug sensitivity prediction in multiple myeloma patients (target task), but we have sufficient auxiliary data for the task of drug sensitivity prediction in patients with another cancer type (related task), where the auxiliary data for the related task are in a different feature space or have a different distribution. In such cases, transfer learning, if applied correctly, would improve the performance of prediction algorithms on the test data of the target task via leveraging the auxiliary data from the related task. In this paper, we present two transfer learning approaches that combine the auxiliary data from the related task with the training data of the target task to improve the prediction performance on the test data of the target task. We evaluate the performance of our transfer learning approaches exploiting three auxiliary data sets and compare them against baseline approaches using the area under the receiver operating characteristic curve on the test data of the target task. Experimental results demonstrate the good performance of our approaches and their superiority over the baseline approaches when auxiliary data are incorporated.

Background subtraction, or foreground detection, is a challenging problem in video processing. This problem is mainly concerned with a binary classification task, which designates each pixel in a video sequence as belonging to either the background or foreground scene. Traditional approaches for tackling this problem lack the power of capturing deep information in videos from a dynamic environment encountered in real-world applications, thus often achieving low accuracy and unsatisfactory performance. In this paper, we introduce a new 3-D atrous convolutional neural network, used as a deep visual feature extractor, and stack convolutional long short-term memory (ConvLSTM) networks on top of the feature extractor to capture long-term dependences in video data. This novel architecture is named a 3-D atrous ConvLSTM network. The new network can capture not only deep spatial information but also long-term temporal information in the video data. We train the proposed 3-D atrous ConvLSTM network with focal loss to tackle the class imbalance problem commonly seen in background subtraction. Experimental results on a wide range of videos demonstrate the effectiveness of our approach and its superiority over existing methods.

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

A plethora of negative behavioural activities have recently been found in social media. Incidents such as trolling and hate speech on social media, especially on Twitter, have grown considerably. Therefore, detection of hate speech on Twitter has become an area of interest among many researchers. In this paper, we present a computational framework to (1) examine out the computational challenges behind hate speech detection and (2) generate high performance results. First, we extract features from Twitter data by utilizing a count vectorizer technique. Then, we provide the labeled dataset of constructed features to adopted ensemble methods, including Bagging, AdaBoost, and Random Forest. After training, we classify new tweet examples into one of the two categories, hate speech or non-hate speech. Experimental results show (1) that Random Forest has surpassed other methods by generating 95% using accuracy performance results and (2) word cloud displays the most prominent tweets that are responsible for hateful sentiments.

Background: COVID-19 is a critical pandemic that has affected human communities worldwide, and there is an urgent need to develop effective drugs. Although there are a large number of candidate drug compounds that may be useful for treating COVID-19, the evaluation of these drugs is time-consuming and costly. Thus, screening to identify potentially effective drugs prior to experimental validation is necessary. Method: In this study, we applied the recently proposed method tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected with severe acute respiratory syndrome coronavirus 2. We identified drug candidate compounds that significantly altered the expression of the 163 genes selected by TD-based unsupervised FE. Results: Numerous drugs were successfully screened, including many known antiviral drug compounds such as C646, chelerythrine chloride, canertinib, BX-795, sorafenib, sorafenib, QL-X-138, radicicol, A-443654, CGP-60474, alvocidib, mitoxantrone, QL-XII-47, geldanamycin, fluticasone, atorvastatin, quercetin, motexafin gadolinium, trovafloxacin, doxycycline, meloxicam, gentamicin, and dibromochloromethane. The screen also identified ivermectin, which was first identified as an anti-parasite drug and recently the drug was included in clinical trials for SARS-CoV-2. Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19.