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Urban air mobility, also known as UAM, is currently being researched in a variety of metropolitan regions throughout the world as a potential new mode of transport for travelling shorter distances inside a territory. In this article, we investigate whether or not the market can back the necessary financial commitments to deploy UAM. A challenge in defining and addressing a critical phase of such guidance is called a demand forecast problem. To achieve this goal, a deep learning model for forecasting temporal data is proposed. This model is used to find and study the scientific issues involved. A benchmark dataset of 150,000 records was used for this purpose. Our experiments used different state-of-the-art DL models: LSTM, GRU, and Transformer for UAM demand prediction. The transformer showed a high performance with an RMSE of 0.64, allowing decision-makers to analyze the feasibility and viability of their investments. [ABSTRACT FROM AUTHOR]

Software vulnerabilities pose a significant threat to system security, necessitating effective automatic detection methods. Current techniques face challenges such as dependency issues, language bias, and coarse detection granularity. This study presents a novel deep learning-based vulnerability detection system for Java code. Leveraging hybrid feature extraction through graph and sequence-based techniques enhances semantic and syntactic understanding. The system utilizes control flow graphs (CFG), abstract syntax trees (AST), program dependencies (PD), and greedy longest-match first vectorization for graph representation. A hybrid neural network (GCN-RFEMLP) and the pre-trained CodeBERT model extract features, feeding them into a quantum convolutional neural network with self-attentive pooling. The system addresses issues like long-term information dependency and coarse detection granularity, employing intermediate code representation and inter-procedural slice code. To mitigate language bias, a benchmark software assurance reference dataset is employed. Evaluations demonstrate the system's superiority, achieving 99.2% accuracy in detecting vulnerabilities, outperforming benchmark methods. The proposed approach comprehensively addresses vulnerabilities, including improper input validation, missing authorizations, buffer overflow, cross-site scripting, and SQL injection attacks listed by common weakness enumeration (CWE). [ABSTRACT FROM AUTHOR]

Forecasting rainfall is crucial to the well-being of individuals and is significant everywhere in the world. It contributes to reducing the disastrous effects of floods on agriculture, human life, and socioeconomic systems. This study discusses the challenges of effectively forecasting rainfall and floods and the necessity of combining data with flood channel mathematical modelling to forecast floodwater levels and velocities. This research focuses on leveraging historical meteorological data to find trends using machine learning and deep learning approaches to estimate rainfall. The Bangladesh Meteorological Department provided the data for the study, which also uses eight machine learning algorithms. The performance of the machine learning models is examined using evaluation measures like the R2 score, root mean squared error and validation loss. According to this research's findings, polynomial regression, random forest regression, and long short-term memory (LSTM) had the highest performance levels. Random forest and polynomial regression have an R2 value of 0.76, while LSTM has a loss value of 0.09, respectively. [ABSTRACT FROM AUTHOR]

Sentiment analysis taskhas widely been studied for various languages such as English and French. However, Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf Natural Language Processing (NLP) solutions. The primary objective of this study is to investigate the diverse machine learning methods for the sentiment analysis of Roman Urdu data which is very informal in nature and needs to be lexically normalized. To mitigate this challenge, we propose a fine-tuned Support Vector Machine (SVM) powered by Roman Urdu Stemmer. In our proposed scheme, the corpus data is initially cleaned to remove the anomalies from the text. After initial pre-processing, each user review is being stemmed. The input text is transformed into a feature vector using the bag-of-word model. Subsequently, the SVM is used to classify and detect user sentiment. Our proposed scheme is based on a dictionary based Roman Urdu stemmer. The creation of the Roman Urdu stemmer is aimed at standardizing the text so as to minimize the level of complexity. The efficacy of our proposed model is also empirically evaluated with diverse experimental configurations, so as to fine-tune the hyper-parameters and achieve superior performance. Moreover, a series of experiments are conducted on diverse machine learning and deep learning models to compare the performance with our proposed model. We also introduced the largest dataset on Roman Urdu, i.e., Roman Urdu e-commerce dataset (RUECD), which contains 26K+ user reviews annotated by the group of experts. The RUECD is challenging and the largest dataset available of Roman Urdu. The experiments show that the newly generated dataset is quite challenging and requires more attention from the peer researchers for Roman Urdu sentiment analysis. [ABSTRACT FROM AUTHOR]

High-dimensional medical data makes prediction a complex and difficult task. This study aims at modeling predictive models for medical data. Two datasets of medical data are applied in the study — one online available dataset (Heart Disease data) and another real clinical dataset (Eye Infection Data). A wide range of machine learning algorithms are applied in the modeling stage: Decision Tree, Multilayer Perceptron, Naive Bayesian, Random Forest, and Support Vector Machine. Furthermore, bagging and voting ensemble methods have also been applied with base learners. Both split and cross-validation methods are adopted for the model validation, and well-established evaluation metrics such as accuracy, precision, recall, and F-measure have been considered as evaluation metrics for the predictive models. The method applied for the modeling is comprised of two stages. The first stage uses available features for the predictions. In the second stage, selected features based on positive correlation are used. The adopted method is also for deep learning, especially Convolutional Neural Network (CNN) is applied to analyze the outcomes compared to conventional machine learning algorithms. The experimental results reveal that better predictions are achieved in the second stage. Besides, experiments also indicate split percentage produces better predictive models, and marginally better outcomes are observed in the presence of ensemble methods in comparison with base models. NB outperformed other algorithms with the highest accuracy rate as 88.90%, and MLP obtained 97.50% accuracy for Heart Disease and Eye Infection data, respectively, using 80–20 splits in the second stage. However, the CNN model performed poorly due to the size of the considered datasets. • Two medical datasets are applied in the study: one online available and the other real. • Machine learning algorithms including DT, MLP, NB and RF are applied in the modeling. • The method applied for the modeling is comprised of two stages. • The experimental results reveal that better predictions are achieved in the second stage. • NB outperformed for an online dataset with 88.90% accuracy and MLP with 97.50% for real dataset. [ABSTRACT FROM AUTHOR]