Disease prediction of a human means predicting the probability of a patient’s disease after examining the combinations of the patient’s symptoms. Monitoring a patient's condition and health information at the initial examination can help doctors to treat a patient's condition effectively. This analysis in the medical industry would lead to a streamlined and expedited treatment of patients. The previous researchers have primarily emphasized machine learning models mainly Support Vector Machine (SVM), K-nearest neighbors (KNN), and RUSboost for the detection of diseases with the symptoms as parameters. However, the data used by the prior researchers for training the model is not transformed and the model is completely dependent on the symptoms, while their accuracy is poor. Nevertheless, there is a need to design a modified model for better accuracy and early prediction of human disease. The proposed model has improved the efficacy and accuracy model, by resolving the issue of the earlier researcher’s models. The proposed model is using the medical dataset from Kaggle and transforms the data by assigning the weights based on their rarity. This dataset is then trained using a combination of machine learning algorithms: Random Forest, Long Short-Term Memory (LSTM), and SVM. Parallel to this, the history of the patient can be analyzed using LSTM Algorithm. SVM is then used to conclude, the possible disease. The proposed model has achieved better accuracy and reliability as compared to state-of-the-art methods. The proposed model is useful to contribute towards development in the automation of the healthcare industries.

Facial emotions are prime characteristics of humans that reflect the attributes of a human emotional condition. Facial Emotional Recognition (FER) is a significant research area since it has several significant applications such as crime detection, security, government sectors, surveillance, etc. Many researchers have addressed FER and put efforts into improving recognition using various methods such as Deep Convolutional Neural Networks (DCNN), Local Binary Pattern Convolutional Neural Networks (LBPCNN) and Micro Expression Recognition (MER). Nevertheless, there is a dearth of better Convolutional Neural Networks (CNN) for better accuracy of FER. Various methods have already proven their accuracies on datasets such as Fer_2013, CK48 and Legend. There are many challenges such as varying positions of images, illumination and noise etc. are not resolved yet. In this direction, the proposed modified CNN model has a combination of CNN layers to train the model and achieved better training and validation accuracy on datasets. The proposed method has three-fold contributions (1) to propose an efficient CNN model for FER and (2) to train the proposed model on three standard datasets 3) to test on real-time images. The proposed model is trained on three standard datasets (Fer_2013, CK48, and Legend) and achieves significantly higher training accuracy 92.34%, 99.84%, and 89.86%, respectively as compared to previous methods. Furthermore, the model demonstrates impressive generalization ability by achieving even higher test accuracy on real-world images 91.54%, 96.27%, and 87.47%, respectively. Also, the proposed novel CNN model has addressed all current challenges of FER with low computational complexity and prediction of facial expression more efficiently.

Indian bay leaf is a crucial spice in Indian cuisine. However, its quality and authenticity are often compromised. To address this, we introduce The Digital Indian Bay leaf dataset, a comprehensive collection of high-resolution 5696 images capturing diverse bay leaf samples under controlled conditions. The dataset encompasses variations in leaf conditions such as fresh leaf, dried leaf and diseased prone leaf. The dataset is meticulously curated to support research in condition analysis and machine learning applications for leaf quality assessment. To ensure data diversity, each category includes a wide range of images captured under controlled conditions with varying lighting, background, and leaf orientation. By providing a standardized and accessible resource, this dataset aims to accelerate research in this domain and contribute to the improvement of the Indian spice industry.

Sign language is a very important mode of communication among deaf and hard-of-hearing populations. Automatic sign language detection based on deep learning model is the theme of this study. Hand gestures are classified by the Convolutional Neural Network (CNN) model to different signs. For training purposes, there are 26,000 images available with 3000 images for every alphabet letter such that there is complete representation of sign language gesture. Photos were taken in controlled lighting with a consistent black background to facilitate better feature extraction. The data contains varied participants of various age groups, skin types, and hand shapes to enhance generalization. Data collection was standardized through iPhone 15 Pro Max, black background cloth, tripod stand, and remote-controlled Drodcam app to maintain consistency in image quality and framing. For diversity and realism, three participants were involved in data collection, each providing 1000 images per sign, resulting in a rich and diverse dataset. Preprocessing of data methods were used for achieving the best quality of data, such as resizing, conversion to grayscale, normalization, and augmentation. Different techniques of data augmentation like rotation, flipping, scaling, brightness change, and addition of Gaussian noise were used to introduce variations in hand gestures and make the model robust against various environmental conditions. The dataset was then partitioned into 70 % training, 15 % validation, and 15 % test sets for maximizing model performance and ensuring good generalization. The dataset show high accuracy, reflecting the potential of the model for real-world usage, such as accessibility tools for the deaf community, educational tools, and real-time sign language recognition systems.