Accurate estimation of fuel consumption and emissions is crucial for assessing the impact of materials and stringent emission control techniques on climate change, particularly in the transportation industry, which accounts for a significant portion of global greenhouse gases and hazardous pollutants emissions. To address these concerns, the government of Canada has collected a large sensor-based dataset containing detailed information on 7384 light-duty vehicles from 2017 to 2021, with the goal of reducing CO₂ emissions by 40–45% by 2030. To this end, various researchers worldwide have developed vehicle emissions and consumption models to comply with these targets and achieve the Canadian government’s ambitious objectives. In this work, we propose the development of boosting and other regression models to predict carbon dioxide emissions for light-duty vehicle designs, with the aim of creating ensemble learning models that leverage vehicle specifications to forecast emissions. Our proposed boosting model is capable of accurately predicting CO₂ emissions, even with only one car attribute as input. Moreover, our regression models, in conjunction with the boosting algorithm, can effectively make predictions from various vehicle inputs. Our proposed technique, categorical boosting (Catboost), provides critical insights into transportation-generated air pollution, offering valuable recommendations for both vehicle users and manufacturers. Importantly, Catboost performs data processing in less time and with less memory than other algorithms proposed in the literature. Future research efforts should focus on developing higher performance models and expanding datasets to further improve the accuracy of predictions.

The wind is a crucial factor in various domains such as weather forecasting, the wind power industry, agriculture, structural health monitoring, and so on. The variability and unpredictable nature of the wind is a challenge faced by most wind-energy-based sectors. Several atmospheric and geographical factors influence wind characteristics. Many wind forecasting methods and tools have been introduced since early times. Wind forecasting can be carried out short-, medium-, and long-term. The uncertainty factors of the wind challenge the accuracy of techniques. This article brings the general background of physical, statistical, and intelligent approaches and their methods used to predict wind characteristics and their challenges—this work’s objective is to improve effective data-driven models for forecasting wind-power production. The investigation and listing of the effectiveness of improved machine learning models to estimate univariate wind-energy time-based data is crucially the prominent focus of this work. The performance of various ML predicting models was examined using ensemble learning (ES) models, such as boosted trees and bagged trees, Support Vector Regression (SVR) with distinctive kernels etc. Numerous neural networks have recently been constructed for forecasting wind speed and power due to artificial intelligence (AI) advancement. Based on the model summary, further directions for research and application developments can be planned.

Ovaries are important part of female reproductive system. The importance of these tiny glands is derived from the production of female sex hormones and female gametes. The location of these ductless almond shaped small glandular organs is on just opposite sides of uterus attached with ovarian ligament. There are many factors due to which ovarian cancer can occur but it can be detected by using various techniques and among them there is one method named as convolutional neural network. This review paper tells us about how we can use Convolutional Neural Network to classify the ovarian cancer tumour and what other ways to deal with it. In this research work we have also discussed about the comparison of various machine learning algorithms like K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network used in detection of ovarian cancer. After comparing the different methods for this cancer detection, it seemed Deep Learning Technique to be the best for yielding results.

Crack detection is essential for observing structural health and guaranteeing structural safety. The manual crack and other damage detection process is time-consuming and subject to surveyors’ biased judgments. The proposed Conv2D ResNet Exponential model for wall quality detection was trained with 5000 wall images, including various imperfections such as cracks, holes, efflorescence, damp patches, and spalls. The model was trained with initial weights to form the trained layers of the base model and was integrated with Xception, VGG19, DenseNet, and ResNet convolutional neural network (CNN) models to retrieve the general high-level features. A transfer deep-learning-based approach was implemented to create a custom layer of CNN models. The base model was combined with custom layers to estimate wall quality. Xception, VGG19, DenseNet, and ResNet models were fitted with different activation layers such as softplus, softsign, tanh, selu, elu, and exponential, along with transfer learning. The performance of Conv2D was evaluated using model loss, precision, accuracy, recall, and F-score measures. The model was validated by comparing the performances of Xception, VGG19, DenseNet, ResNet, and Conv2D ResNet Exponential. The experimental results show that the Conv2D ResNet model with an exponential activation layer outperforms it with an F-score value of 0.9978 and can potentially be a viable substitute for classifying various wall defects.

Most common cancer in females is found to be Breast cancer which is a widespread disease. One out of eight females worldwide are affected by this cancer only. We can detect this cancer by detecting malignancy from breast tissues. There are various types of computer-aided techniques and approaches which are used by doctors for detecting cancer. The major objective of this paper is to build a well-defined model for the recognition of breast cancer by expending various parameters. Different types of machine learning and deep learning methodologies are used for the classification of malignant and benign tissues. In this we are using a dataset that obtains 569 samples with 30 features, this dataset is majorly called the Wisconsin dataset. Many techniques are implemented on this dataset we are using deep convolutional neural network (CNN) and Machine learning methodology (KNN) for the diagnosis and training purpose and then compare the results of both the techniques. Deep convolutional NN is implemented on the google platform called the Google Colab on the other side KNN is implemented on the Anaconda Spyder platform. The best accuracy achieved from KNN is 96.49%. To improve the performance and accuracy we implemented CNN on the same dataset and then achieved 99.41% accuracy. Deep learning is extensively useful in getting the best and optimal results in other performance matrics such as precision, recall, F1-score and AVC-ROC - 98.64%,97.61 %, 98.08%, 97.61% respectively.