Intelligent transportation systems (ITS) are pivotal in modern urban development by enhancing mobility and transit efficiency. However, the challenges of accurate short-term traffic prediction persist due to real-time traffic data’s dynamic, nonlinear, and non-stationary nature. In this study, the author addresses these challenges and proposes a novel approach to improve traffic flow prediction. Our research introduces a hybrid model, combining Long-Short Term Memory (LSTM) and the Kalman filter-based Rauch-Tung-Striebel (RTS) noise reduction technique, tailored to mitigate the limitations of low market penetration of connected vehicles and data availability. This paper aims to provide a robust traffic prediction solution with direct applications in smart cities and real-time traffic management. To evaluate our model’s efficacy, The author conducted an empirical case study using the Enhanced Next Generation Simulation (NGSIM) dataset, which offers highly granular vehicle trajectory data. The author rigorously analyses our methodology and algorithms to assess the quality of traffic predictions. Our results show that the proposed model outperforms traditional LSTM models. In particular, the Bi-LSTM/RTS model achieved a remarkable accuracy of 99% in predicting traffic patterns during sunny weather conditions, signifying a significant advancement in short-term traffic prediction accuracy. The evaluation metrics demonstrate that Bi-LSTM outperforms LSTM by a wide margin, with a coefficient of determination value of 0.99 against 0.97. The trend anticipated by Bi-LSTM (8.6%) is more in line with the actual trend than that projected by LSTM (8.9%), as seen by the smaller MAPE. In conclusion, Bi-LSTM outperforms LSTM at predicting traffic patterns.

The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.

Image captioning is a popular topic in the domains of computer vision and natural language processing (NLP). Recent advancements in deep learning (DL) models have enabled the improvement of the overall performance of the image captioning approach. This study develops a metaheuristic optimization with a deep learning-enabled automated image captioning technique (MODLE-AICT). The proposed MODLE-AICT model focuses on the generation of effective captions to the input images by using two processes involving encoding unit and decoding unit. Initially, at the encoding part, the salp swarm algorithm (SSA), with a HybridNet model, is utilized to generate effectual input image representation using fixed-length vectors, showing the novelty of the work. Moreover, the decoding part includes a bidirectional gated recurrent unit (BiGRU) model used to generate descriptive sentences. The inclusion of an SSA-based hyperparameter optimizer helps in attaining effectual performance. For inspecting the enhanced performance of the MODLE-AICT model, a series of simulations were carried out, and the results are examined under several aspects. The experimental values suggested the betterment of the MODLE-AICT model over recent approaches.