The majority of the people in India dependent on farming to earn a living. As a due to climate change, farmers face various challenges. One of them is a reduction in yield, and one of the causes of that is the development of diseases in the plant. The main economic agricultural activity is a banana plantation, particularly in Asian and African nations. Feature extraction using CNN and SVM was used to identify and classify the banana fruit leaf diseases. The dataset was initially improved, precompiled using Matlab code, and then divided into training and testing sections. During the conduct of this research, the ratio employed to divide the data into training and validation was 80:20. After the CNN was implemented successfully, and the SVM models, the maximum average accuracy measured was 94%. According to this study, the suggested model achieves the automatic right diagnosis of banana leaf diseases and gives a workable method for the detection of crop leaf diseases with high recognition accuracy.

Kiwi leaf disease manual detection is a time-consuming method that typically calls for the understanding of actual professionals. This study presents a machine learning approach for the automated detection of kiwi leaf diseases. The proposed method makes use of a dataset of annotated photos to differentiate healthy and sick kiwi leaves. The proposed approach has the potential to greatly reduce the amount of time and money required for kiwi leaf disease detection, enabling more rapid and efficient management of kiwi agricultural production. In this study, the kiwi fruit leaf illnesses were detected using a machine learning CNN & SVM approach. This is one of the key research areas in education. The proportion used to split the information into training and validation was 80:20 during the execution of this research. Our experimental results demonstrate that, with an overall accuracy of 83.34%, the proposed approach successfully detected 5 different forms of kiwi leaf diseases, namely Healthy leaf, Anthracnose, Brown spot, Bacterial canker, and Mosaic. The suggested method can be used for other types of plant disease detection as well, and it can significantly reduce the time and money associated with the manual detection of kiwi leaf diseases.

The process of reviewing resumes takes time. The unstructured written language can be understood and parsed by natural language processing and machine learning, which can then extract the required information. The goal is to teach the computer to analyze written papers similarly to a human. For assessing and analyzing the unstructured data, various methods including named entity recognition, tokenization, text classification, and approaches of other natural language processing techniques have been explored. A user uploads a resume into the program, which takes it and converts it into a standard text format before parsing it for the necessary information and organizing the extracted data in a standard defined format. After extraction and preprocessing, a predictive model is trained is better and categorize resumes for use in various applications. The k-fold cross validation with k=5 outcome in this case reveals that SVM(0.58 - 0.65) and XG Boost(0.65 - 0.73) have higher ranges of training performance than all other models. Here, the XG Boost outperforms previous trained models, accurately classifying the newly created resume into the target category. The proposed work is novel and presents one of its kind methodology.

Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.