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Unmanned Aerial Vehicles (UAVs), also known as drones, have advanced greatly in recent years. There are many ways in which drones can be used, including transportation, photography, climate monitoring, and disaster relief. The reason for this is their high level of efficiency and safety in all operations. While the design of drones strives for perfection, it is not yet flawless. When it comes to detecting and preventing collisions, drones still face many challenges. In this context, this paper describes a methodology for developing a drone system that operates autonomously without the need for human intervention. This study applies reinforcement learning algorithms to train a drone to avoid obstacles autonomously in discrete and continuous action spaces based solely on image data. The novelty of this study lies in its comprehensive assessment of the advantages, limitations, and future research directions of obstacle detection and avoidance for drones, using different reinforcement learning techniques. This study compares three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in avoiding obstacles, both stationary and moving; however, these strategies have been more successful in drones. The experiment has been carried out in a virtual environment made available by AirSim. Using Unreal Engine 4, the various training and testing scenarios were created for understanding and analyzing the behavior of RL algorithms for drones. According to the training results, SAC outperformed the other two algorithms. PPO was the least successful among the algorithms, indicating that on-policy algorithms are ineffective in extensive 3D environments with dynamic actors. DQN and SAC, two off-policy algorithms, produced encouraging outcomes. However, due to its constrained discrete action space, DQN may not be as advantageous as SAC in narrow pathways and twists. Concerning further findings, when it comes to autonomous drones, off-policy algorithms, such as DQN and SAC, perform more effectively than on-policy algorithms, such as PPO. The findings could have practical implications for the development of safer and more efficient drones in the future.

Accurate diagnosis of breast cancer using automated algorithms continues to be a challenge in the literature. Although researchers have conducted a great deal of work to address this issue, no definitive answer has yet been discovered. This challenge is aggravated further by the fact that most available datasets have imbalanced class issues, meaning that the number of cases in one class vastly outnumbers those of the others. The goal of this study was to (i) develop a reliable machine-learning-based prediction model for breast cancer based on the combination of the resampling technique and the classifier, which we called a ‘fusion model’; (ii) deal with a typical high-class imbalance problem, which is posed because the breast cancer patients’ class is significantly smaller than the healthy class; and (iii) interpret the model output to understand the decision-making mechanism. In a comparative analysis with three well-known classifiers representing classical learning, ensemble learning, and deep learning, the effectiveness of the proposed machine-learning-based approach was investigated in terms of metrics related to both generalization capability and prediction accuracy. Based on the comparative analysis, the fusion model (random oversampling techniques dataset + extreme gradient boosting classifier) affects the accuracy, precision, recall, and F1-score with the highest value of 99.9%. On the other hand, for ROC evaluation, the oversampling and hybrid sampling techniques dataset combined with extreme gradient boosting achieved 100% performance compared to the models combined with the undersampling techniques dataset. Thus, the proposed predictive model based on the fusion strategy can optimize the performance of breast cancer diagnosis classification.

Although digital changes in power systems have added more ways to monitor and control them, these changes have also led to new cyber-attack risks, mainly from False Data Injection (FDI) attacks. If this happens, the sensors and operations are compromised, which can lead to big problems, disruptions, failures and blackouts. In response to this challenge, this paper presents a reliable and innovative detection framework that leverages Bidirectional Long Short-Term Memory (Bi-LSTM) networks and employs explanatory methods from Artificial Intelligence (AI). Not only does the suggested architecture detect potential fraud with high accuracy, but it also makes its decisions transparent, enabling operators to take appropriate action. The method developed here utilizes model-free, interpretable tools to identify essential input elements, thereby making predictions more understandable and usable. Enhancing detection performance is made possible by correcting class imbalance using Synthetic Minority Over-sampling Technique (SMOTE)-based data balancing. Benchmark power system data confirms that the model functions correctly through detailed experiments. Experimental results showed that Bi-LSTM + Explainable AI (XAI) achieved an average accuracy of 94%, surpassing XGBoost (89%) and Bagging (84%), while ensuring explainability and a high level of robustness across various operating scenarios. By conducting an ablation study, we find that bidirectional recursive modeling and ReLU activation help improve generalization and model predictability. Additionally, examining model decisions through LIME enables us to identify which features are crucial for making smart grid operational decisions in real time. The research offers a practical and flexible approach for detecting FDI attacks, improving the security of cyber-physical systems, and facilitating the deployment of AI in energy infrastructure.

Creating tools, such as a prediction model to assist students in a traditional or virtual setting, is an essential activity in today's educational climate. The early stage towards incorporating these predictive models using techniques of machine learning focused on predicting the achievement of students in terms of the grades obtained. The research aim is to propose a robust hybrid ensemble model (RHEM) that can warn at-risks students (on Cloud Computing course) of their likely outcomes at the early semester assessment. We hybridised four renowned single algorithms – Naïve Bayes, Multilayer Perceptron, k-Nearest Neighbours, and Decision Table – with four well-established ensemble algorithms – Bagging, RandomSubSpace, MultiClassClassifier, and Rotation Forest – which produced 16 new hybrid ensemble classifier models. Hence, we have thoroughly and rigorously built, trained, and tested 24 models all together. The experiment concluded that the Rotation Forest + MultiLayer Perceptron model was the best performing model based on the model evaluation in terms of Accuracy (91.70%), Precision (86.1%), F-Score rate (87.3%), and Receiver Operating Characteristics Area detection (98.6%). Our research will help students identify their likely final grades in terms of whether they are excellent, very good, good, pass, or fail, and, thus, transform their academic conduct to achieve higher grades in the final exam accordingly.

Background: Sepsis, a potentially fatal inflammatory disease triggered by infection, carries significant health implications worldwide. Timely detection is crucial as sepsis can rapidly escalate if left undetected. Recent advancements in deep learning (DL) offer powerful tools to address this challenge. Aim: Thus, this study proposed a hybrid CNNBDLSTM, a combination of a convolutional neural network (CNN) with a bi-directional long short-term memory (BDLSTM) model to predict sepsis onset. Implementing the proposed model provides a robust framework that capitalizes on the complementary strengths of both architectures, resulting in more accurate and timelier predictions. Method: The sepsis prediction method proposed here utilizes temporal feature extraction to delineate six distinct time frames before the onset of sepsis. These time frames adhere to the sepsis-3 standard requirement, which incorporates 12-h observation windows preceding sepsis onset. All models were trained using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, which sourced 61,522 patients with 40 clinical variables obtained from the IoT medical environment. The confusion matrix, the area under the receiver operating characteristic curve (AUCROC) curve, the accuracy, the precision, the F1-score, and the recall were deployed to evaluate the models. Result: The CNNBDLSTM model demonstrated superior performance compared to the benchmark and other models, achieving an AUCROC of 99.74% and an accuracy of 99.15% one hour before sepsis onset. These results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset, particularly within a close proximity of one hour. Implication: The results could assist practitioners in increasing the potential survival of the patient one hour before sepsis onset.

The COVID-19 pandemic has spread globally, resulting in financial instability in many countries and reductions in the per capita gross domestic product. Sentiment analysis is a cost-effective method for acquiring sentiments based on household income loss, as expressed on social media. However, limited research has been conducted in this domain using the LexDeep approach. This study aimed to explore social trend analytics using LexDeep, which is a hybrid sentiment analysis technique, on Twitter to capture the risk of household income loss during the COVID-19 pandemic. First, tweet data were collected using Twint with relevant keywords before (9 March 2019 to 17 March 2020) and during (18 March 2020 to 21 August 2021) the pandemic. Subsequently, the tweets were annotated using VADER (lexicon-based) and fed into deep learning classifiers, and experiments were conducted using several embeddings, namely simple embedding, Global Vectors, and Word2Vec, to classify the sentiments expressed in the tweets. The performance of each LexDeep model was evaluated and compared with that of a support vector machine (SVM). Finally, the unemployment rates before and during COVID-19 were analysed to gain insights into the differences in unemployment percentages through social media input and analysis. The results demonstrated that all LexDeep models with simple embedding outperformed the SVM. This confirmed the superiority of the proposed LexDeep model over a classical machine learning classifier in performing sentiment analysis tasks for domain-specific sentiments. In terms of the risk of income loss, the unemployment issue is highly politicised on both the regional and global scales; thus, if a country cannot combat this issue, the global economy will also be affected. Future research should develop a utility maximisation algorithm for household welfare evaluation, given the percentage risk of income loss owing to COVID-19.

Developing successful software with no defects is one of the main goals of software projects. In order to provide a software project with the anticipated software quality, the prediction of software defects plays a vital role. Machine learning, and particularly deep learning, have been advocated for predicting software defects, however both suffer from inadequate accuracy, overfitting, and complicated structure. In this paper, we aim to address such issues in predicting software defects. We propose a novel structure of 1-Dimensional Convolutional Neural Network (1D-CNN), a deep learning architecture to extract useful knowledge, identifying and modelling the knowledge in the data sequence, reduce overfitting, and finally, predict whether the units of code are defects prone. We design large-scale empirical studies to reveal the proposed model's effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA datasets. The experimental results demonstrate that in terms of f-measure, an optimal and modest 1D-CNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79% and 23.88%, respectively, in ways that minimize overfitting and improving prediction performance for software defects. According to the results, 1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software engineering. This, in turn, could lead to saving software development resources and producing more reliable software.