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Vehicular Ad-Hoc Networks (VANETs) represent a crucial component of intelligent transportation systems (ITS), enabling vehicles to communicate with each other and with roadside infrastructure. Predicting traffic congestion in VANETs is essential for enhancing road safety, optimizing traffic flow, and improving overall transportation efficiency. Traditional machine learning methods have shown promise in this domain; however, they often fall short in handling the complex, high-dimensional data typical of VANETs. To address these challenges, this study employs Extreme Deep Learning Machines (EDRLM), an advanced deep learning technique, for traffic congestion prediction. The EDRLM framework leverages the strengths of deep neural networks and extreme learning machines, offering a robust and scalable solution for processing the dynamic and heterogeneous data in VANETs. By integrating feature extraction, selection, and prediction into a unified model, EDRLM can capture intricate patterns and temporal dependencies within traffic data. The proposed model is trained and validated using realworld VANET datasets, incorporating various traffic parameters such as vehicle speed, density, and inter-vehicular distances. Our experimental results demonstrate that EDRLM outperforms conventional machine learning algorithms in terms of prediction accuracy, computational efficiency, and robustness to noise and missing data. The model's ability to provide timely and precise congestion predictions can facilitate proactive traffic management strategies, including dynamic routing and adaptive traffic signal control, ultimately leading to reduced travel times and enhanced road safety. This study underscores the potential of EDRLM in transforming traffic management in VANETs, paving the way for more intelligent and adaptive ITS solutions. Future research directions include exploring hybrid models combining EDRLM with other advanced machine learning techniques and expanding the framework to accommodate emerging vehicular communication technologies such as 5G and Internet of Things (IoT) devices. [ABSTRACT FROM AUTHOR]

The major aim of this research is to identify the AML early from the morphological images. The proposed approach detects AML through four major stages, namely pre-processing, segmentation, feature extraction and classification. The pre-processing stage is employed for image enhancement. The segmentation process is utilized for segmenting the nucleus as well as the cell mask. When the human is affected by AML, the nucleus shape is asymmetrical, and texture is altered. The features achieved by the feature extraction process are given as the input of the classifier. The classification is attained by using the hybrid convolutional bidirectional LSTM-based recurrent neural network network. Although there are several optimisation algorithms proposed to solve different segmentation problems in the literature, to achieve the classified result more optimal, the slime mould optimisation algorithm is utilised. The data sets to detect AML were collected from Munich University Hospital. The experimental investigation is conducted, and from the experimental outcomes, it is examined that the proposed AML detection has obtained a sensitivity of 95.12%, a specificity of 93.72%, an accuracy of 97.95%, a precision of 94.64%, Matthews correlation coefficient of 98.2% and recall of 95.78%. This indicates that the proposed technique outperforms other state-of-art methods. [ABSTRACT FROM AUTHOR]