Punam Rattan

There are numerous aspects of human life in which knowing how much rain to expect might be beneficial. Heavy rainfall events such as flash floods and landslides, as well as droughts, can be predicted with effective rainfall forecasting. Because of reliable weather forecasts, the infrastructure required to capture rainwater and cultivate crops may be planned ahead of time. A variety of machine learning (ML) and deep learning (DL) algorithms enable accurate weather forecasting. This work seeks to provide a full overview of the numerous ML algorithms used for rainfall prediction by focusing on the technique, input parameters, and several performance measures. The review consists of 51 works divided into three sections. It is found that long short-term memory (LSTM), one of the DL algorithms, is mostly used by researchers for developing the model, but in recent years, ensemble learning and hybrid learning have also gained popularity among researchers as they give more accurate results. These methods need to be explored further.

Skin cancer is caused by aberrant cells that proliferate uncontrollably after unrepaired DNA damage results in mutations in the epidermis. The majority of skin cancer is caused by high UV exposure from the sun, tanning beds, or sunlamps. Due to sociocultural hurdles, limited access to specialized dermatological care, and low public knowledge, many nations, including India, have higher mortality rates and late-stage presentations. The unequal distribution of specialized dermatological treatments, particularly in rural and underdeveloped areas, makes detection and treatment more difficult. For skin cancer, one of the most prevalent malignancies with a high death rate, early detection is crucial. This study gathered 1200 dermoscopic images from two clinics in Himachal Pradesh in order to solve these problems. In order to automatically classify dermoscopic clinical images into melanoma and non-melanoma skin cancer categories, this study compares VGG16 with ResNet-50. Preprocessing, lesion segmentation, and classification are all part of the suggested approach. A collection of 1200 dermoscopic images with clinical annotations was used to improve the models. ResNet-50 outperformed VGG16 in tests, with 93% accuracy and 96% AUC-ROC as opposed to 89% and 94%, respectively. These results emphasize how crucial model selection and preprocessing are to diagnostic performance. Ensemble methods, multi-class classification, explainability integration, and clinical validation will be investigated in order to facilitate the implementation of AI-assisted dermatological diagnostic tools.

Higher education is essential because it exposes students to a variety of areas. The academic performance of IT students is crucial and might fail if it isn’t documented to identify the features influencing them, as well as their strengths and shortcomings. The student academic prediction system needs to be enhanced so that teachers can forecast their students’ performance. Numerous studies have been conducted to increase the prediction accuracy of IT students, but they encountered difficulties with unbalanced data and algorithm tuning. To address these issues, the study proposed different machine learning (ML) algorithms that handled imbalanced data by applying the synthetic minority oversampling technique (SMOTE) and employing hyperparameter tuning algorithms to enhance prediction during the training process. The ML models we used were decision tree (DT), k-nearest neighbor, and XGBoost. The models were fine-tuned by applying Ant colony optimization (ACO) and artificial bee colony optimization techniques. Subsequently, these optimization techniques further enhanced the performance of the models. After comparing them, the results showed that SMOTE and ACO combined with the DT model outperformed other models for academic prediction. Additionally, the study utilized the Kendall Tau correlation coefficient technique to analyze the correlation between features and identify factors that positively or negatively impact student success.

Several studies have shown that Artificial Intelligence (AI) algorithms can accurately predict crop yields based on data from various sources, such as weather conditions, soil moisture levels, and crop growth rates. Integration of AI in agriculture has the potential to revolutionize the way we grow and produce food. By collecting and analysing data, optimizing resource utilization, and increasing sustainability, AI can help farmers improve crop yields and reduce waste, leading to a more efficient and sustainable agricultural sector. However, there are also concerns about the impact of AI on employment and the potential for unintended consequences, so it will be important to proceed with caution and continue to monitor the effects of AI on agriculture. The use of (AI) in agriculture has been the subject of numerous studies and research papers in recent years. In this chapter, authors examine some of the key findings and insights from the agriculture sector. Major contributions by a number of start-ups have been considered for explanation purpose.

The main idea behind this work is to present three-dimensional (3D) image visualization through two-dimensional (2D) images that comprise various images. 3D image visualization is one of the essential methods for excerpting data from given pieces. The main goal of this work is to figure out the outlines of the given 3D geometric primitives in each part, and then integrate these outlines or frames to reconstruct 3D geometric primitives. The proposed technique is very useful and can be applied to many kinds of images. The experimental results showed a very good determination of the reconstructing process of 2D images. Request access from your librarian to read this chapter's full text.