Groundwater level (GWL) refers to the depth of the water table or the level of water below the Earth’s surface in underground formations. It is an important factor in managing and sustaining the groundwater resources that are used for drinking water, irrigation, and other purposes. Groundwater level prediction is a critical aspect of water resource management and requires accurate and efficient modelling techniques. This study reviews the most commonly used conventional numerical, machine learning, and deep learning models for predicting GWL. Significant advancements have been made in terms of prediction efficiency over the last two decades. However, while researchers have primarily focused on predicting monthly, weekly, daily, and hourly GWL, water managers and strategists require multi-year GWL simulations to take effective steps towards ensuring the sustainable supply of groundwater. In this paper, we consider a collection of state-of-the-art theories to develop and design a novel methodology and improve modelling efficiency in this field of evaluation. We examined 109 research articles published from 2008 to 2022 that investigated different modelling techniques. Finally, we concluded that machine learning and deep learning approaches are efficient for modelling GWL. Moreover, we provide possible future research directions and recommendations to enhance the accuracy of GWL prediction models and improve relevant understanding.

The alpha–beta filter algorithm has been widely researched for various applications, for example, navigation and target tracking systems. To improve the dynamic performance of the alpha–beta filter algorithm, a new prediction learning model is proposed in this study. The proposed model has two main components: (1) the alpha–beta filter algorithm is the main prediction module, and (2) the learning module is a feedforward artificial neural network (FF‐ANN). Furthermore, the model uses two inputs, temperature sensor and humidity sensor data, and a prediction algorithm is used to predict actual sensor readings from noisy sensor readings. Using the novel proposed technique, prediction accuracy is significantly improved while adding the feed‐forward backpropagation neural network, and also reduces the root mean square error (RMSE) and mean absolute error (MAE). We carried out different experiments with different experimental setups. The proposed model performance was evaluated with the traditional alpha–beta filter algorithm and other algorithms such as the Kalman filter. A higher prediction accuracy was achieved, and the MAE and RMSE were 35.1%–38.2% respectively. The final proposed model results show increased performance when compared to traditional methods.

In the past few decades, the transmission of data over an unsecure channel has resulted in an increased rate of hacking. The requirement to make multimedia data more secure is increasing day by day. Numerous algorithms have been developed to improve efficiency and robustness in the encryption process. In this article, a novel and secure image encryption algorithm is presented. It is based on a modified chaotic logistic map (CLM) that provides the advantage of taking less computational time to encrypt an input image. The encryption algorithm is based on Shannon’s idea of using a substitution–permutation and one-time pad network to achieve ideal secrecy. The CLM is used for substitution and permutation to improve randomness and increase dependency on the encryption key. Various statistical tests are conducted, such as keyspace analysis, complexity analysis, sensitivity analysis, strict avalanche criteria (SAC), histogram analysis, entropy analysis, mean of absolute deviation (MAD) analysis, correlation analysis, contrast analysis and homogeneity, to give a comparative analysis of the proposed algorithm and verify its security. As a result of various statistical tests, it is evident that the proposed algorithm is more efficient and robust as compared to previous ones.

The recent emergence of futuristic ships is the result of advances in information and communication technology, big data, and artificial intelligence. They are generally autonomous, which has the potential to significantly improve safety and drastically reduce operating costs. However, the commercialization of Maritime Autonomous Surface Ships requires the development of appropriate technologies, including intelligent navigation systems, which involves the identification of the current maritime traffic conditions and the prediction of future maritime traffic conditions. This study aims to develop an algorithm that predicts future maritime traffic conditions using historical data, with the goal of enhancing the performance of autonomous ships. Using several datasets, we trained and validated an artificial intelligence model using long short-term memory and evaluated the performance by considering several features such as the maritime traffic volume, maritime traffic congestion fluctuation range, fluctuation rate, etc. The algorithm was able to identify features for predicting maritime traffic conditions. The obtained results indicated that the highest performance of the model with a valid loss of 0.0835 was observed under the scenario with all trends and predictions. The maximum values for 3, 6, 12, and 24 days and the congestion of the gate lines around the analysis point showed a significant effect on performance. The results of this study can be used to improve the performance of situation recognition systems in autonomous ships and can be applied to maritime traffic condition recognition technology for coastal ships that navigate more complex sea routes compared to ships navigating the ocean.

This paper introduces new learning to the prediction model to enhance the prediction algorithms' performance in dynamic circumstances. We have proposed a novel technique based on the alpha-beta filter and deep extreme learning machine (DELM) algorithm named as learning to alpha-beta filter. The proposed method has two main components, namely the prediction unit and the learning unit. We have used the alpha-beta filter in the prediction unit, and the learning unit uses a DELM. The main problem with the conventional alpha-beta filter is that the values are generally selected via the trial-and-error technique. Once the alpha-beta values are chosen for a specific problem, they remain fixed for the entire data. It has been observed that different alpha-beta values for the same problem give different results. Hence it is essential to tune the alpha-beta values according to their historical behavior for certain values. Therefore, in the proposed method, we have addressed this problem and added the learning module to the conventional α- β filter to improve the α- β filter's performance. The DELM algorithm has been used to enhance the conventional alpha-beta filter algorithm's performance in dynamically changing conditions. The model performance has been measured using indoor environmental values of temperature and humidity. The relative improvement in the proposed learning prediction model's accuracy was 7.72% and 16.47% in RMSE and MSE metrics. The results show that the proposed model outperforms in terms of the result as compared to the conventional alpha-beta filter.

Leukemia involves the abnormal growth of white blood cells and poses diagnostic difficulties due to its diverse symptoms and swift progression. Timely and precise detection is vital for effective treatment and good patient outcomes. This paper introduces a novel lightweight YOLOv8 model, integrated with a residual attention mechanism, to improve leukemia detection and classification. The enhancements to the YOLOv8n architecture include depthwise separable convolution network (DWSCNN) and residual convolution block attention mechanism (RCBAM) layers, which improve its feature extraction and contextual information gathering abilities. The model was trained on a comprehensive dataset of blood cell images annotated according to various stages of leukemia: benign, malignant-early, malignant-pre, and malignant-pro. It achieved noteworthy results, with a mAP of 98.4%, F1-score of 96.2%, and inference speed of 3.5 milliseconds, significantly surpassing traditional YOLOv8 variants and other leading techniques. The proposed model not only improves diagnostic precision but also minimizes computational requirements, making it suitable for use in clinical settings, especially where resources are limited. By enabling the early and precise detection of leukemia, this model holds promise for advancing treatment strategies and improving patient outcomes, paving the way for future innovations in medical imaging and automated disease diagnosis.