Video pornography and nudity detection aim to detect and classify people in videos into nude or normal for censorship purposes. Recent literature has demonstrated pornography detection utilising the convolutional neural network (CNN) to extract features directly from the whole frames and support vector machine (SVM) to classify the extracted features into two categories. However, existing methods were not able to detect the small-scale content of pornography and nudity in frames with diverse backgrounds. This limitation has led to a high false-negative rate (FNR) and misclassification of nude frames as normal ones. In order to address this matter, this paper explores the limitation of the existing convolutional-only approaches focusing the visual attention of CNN on the expected nude regions inside the frames to reduce the FNR. The You Only Look Once (YOLO) object detector was transferred to the pornography and nudity detection application to detect persons as regions of interest (ROIs), which were applied to CNN and SVM for nude/normal classification. Several experiments were conducted to compare the performance of various CNNs and classifiers using our proposed dataset. It was found that ResNet101 with random forest outperformed other models concerning the F1-score of 90.03% and accuracy of 87.75%. Furthermore, an ablation study was performed to demonstrate the impact of adding the YOLO before the CNN. YOLO–CNN was shown to outperform CNN-only in terms of accuracy, which was increased from 85.5% to 89.5%. Additionally, a new benchmark dataset with challenging content, including various human sizes and backgrounds, was proposed.

Given the excessive foul language identified in audio and video files and the detrimental consequences to an individual’s character and behaviour, content censorship is crucial to filter profanities from young viewers with higher exposure to uncensored content. Although manual detection and censorship were implemented, the methods proved tedious. Inevitably, misidentifications involving foul language owing to human weariness and the low performance in human visual systems concerning long screening time occurred. As such, this paper proposed an intelligent system for foul language censorship through a mechanized and strong detection method using advanced deep Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) through Long Short-Term Memory (LSTM) cells. Data on foul language were collected, annotated, augmented, and analysed for the development and evaluation of both CNN and RNN configurations. Hence, the results indicated the feasibility of the suggested systems by reporting a high volume of curse word identifications with only 2.53% to 5.92% of False Negative Rate (FNR). The proposed system outperformed state-of-the-art pre-trained neural networks on the novel foul language dataset and proved to reduce the computational cost with minimal trainable parameters.

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Inappropriate visual content on the internet has spread everywhere, and thus children are exposed unintentionally to sexually explicit visual content. Animated cartoon movies sometimes have sensitive content such as pornography and sex. Usually, video sharing platforms take children's e-safety into consideration through manual censorship, which is both time-consuming and expensive. Therefore, automated cartoon censorship is highly recommended to be integrated into media platforms. In this paper, various methods and approaches were explored to detect inappropriate visual content in cartoon animation. First, state-of-the-art conventional feature techniques were utilised and evaluated. In addition, a simple endto-end convolutional neural network (CNN) was used and was found to outperform conventional techniques in terms of accuracy (85.33%) and F1 score (83.46%). Additionally, to target the deeper version of CNNs, ResNet, and EfficientNet were demonstrated and compared. The CNN-based extracted features were mapped into two classes: normal and porn. To improve the model's performance, we utilised feature and decision fusion approaches which were found to outperform state-of-the-art techniques in terms of accuracy (87.87%), F1 score (87.87%), and AUC (94.40%). To validate the domain generalisation performance of the proposed methods, CNNs, pre-trained on the cartoon dataset were evaluated on public NPDI-800 natural videos and found to provide an accuracy of 79.92%, and F1 score of 80.58%. Similarly, CNNs, pretrained on the public NPDI-800 natural videos, were evaluated on cartoon dataset and found to give an accuracy of 82.666%, and F1 score of 81.588%. Finally, a novel cartoon pornography dataset, with various characters, skin colours, positions, viewpoints, and scales, was proposed.

Recent discovered technologies have exposed many new theories and possibilities to improve our standard of living. Medical assistance has been a major research topic in the past, many efforts were put in to simplify the process of following treatment prescriptions. This paper summarizes the work done in developing LoRa driven medical adherence system in order to improve medicine adherence for elderlies. The designed system is composed of two sections; embedded hardware device for the use of patients at home and Web application to manage all patients along with their medicines and keep track of their medicine intake history. LoRa wireless communication technology is used for connecting all embedded devices with a central gateway that manages the network. Hardware and software tests have been conducted and showed great performance in terms of LoRa network range and latency. In short, the proposed system shows promising method of improving medicine adherence.