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Predicting the severity of crashes has become a significant issue in research on road accidents. Traffic accident severity prediction is essential for protecting vulnerable road users and preventing traffic accidents. For practitioners to identify significant risk variables and set appropriate countermeasures in place, explainability of the forecast is also essential. Most previous research ignores the severity of property loss caused by traffic accidents and cannot differentiate between different levels of fatalities and property loss severity. Additionally, while an understandable structure of deep neural networks (DNN) is significantly lacking in existing works, understanding traditional systems is quite simple. An inability to use structural data when describing forecasting and the many attempts to incorporate neural networks afflict the absence of hidden layers. We propose a Deep Learning (DL) framework for forecasting traffic crash severity to overcome the accident severity prediction. It has three steps to process. Initially, collected input data are cleaned. Data cleaning is performed in a preprocessing step. We conduct experiments on two datasets, A Countrywide (US) Traffic Accident Dataset and UK Road Accident Dataset. The outcomes of the experiments demonstrate that the proposed technique outperformed other approaches and produced the best accuracy. [ABSTRACT FROM AUTHOR]

There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits. Dermoscopy and other advanced imaging techniques have enhanced early detection by providing detailed images of lesions. However, accurately interpreting these images to distinguish between benign and malignant tumors remains a difficult task. Improved predictive modeling techniques are necessary due to the frequent occurrence of erroneous and inconsistent outcomes in the present diagnostic processes. Machine learning (ML) models have become essential in the field of dermatology for the automated identification and categorization of skin cancer lesions using image data. The aim of this work is to develop improved skin cancer predictions by using ensemble models, which combine numerous machine learning approaches to maximize their combined strengths and reduce their individual shortcomings. This paper proposes a fresh and special approach for ensemble model optimization for skin cancer classification: the Max Voting method. We trained and assessed five different ensemble models using the ISIC 2018 and HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, and Extra Trees. Their combined predictions enhance the overall performance with the Max Voting method. Moreover, the ensemble models were fed with feature vectors that were optimally generated from the image data by a genetic algorithm (GA). We show that, with an accuracy of 95.80%, the Max Voting approach significantly improves the predictive performance when compared to the five ensemble models individually. Obtaining the best results for F1-measure, recall, and precision, the Max Voting method turned out to be the most dependable and robust. The novel aspect of this work is that skin cancer lesions are more robustly and reliably classified using the Max Voting technique. Several pre-trained machine learning models' benefits are combined in this approach. [ABSTRACT FROM AUTHOR]

Recently, target coverage and node connectivity have been playing a significant role in various wireless sensor network applications. Various target coverage algorithms are widely used to monitor the target point by dividing sensor nodes into a set of cover groups where, each sensor cover group contains the target points. Optimal sensor node placement imposes a critical task when the number of sensors is limited. The quality of maximum target coverage and node connectivity can be improved by deploying sensors in the optimal location. In this paper, a novel genetic algorithm with 2-D discrete Daubechies 4 (db4) lifting wavelet transform is proposed for identifying the optimal position of each sensor. Initially, the genetic algorithm identifies the population-based sensor location and 2-D discrete db4 lifting adjusts the sensor location into an optimal position where each sensor can cover a maximum number of targets that are connected to another sensor. The potential position of the sensor is examined while the sensor covers the target points, and the node can communicate to another node. To further prove that the proposed model is better than the existing algorithm, a set of experiments are carried out with different scenarios by achieving maximum target points covers, node connectivity, and network lifetime with a limited number of sensor nodes. [ABSTRACT FROM AUTHOR]