This research paper investigates the efficacy of various machine learning models, including deep learning and hybrid models, for text classification in the English and Bangla languages. The study focuses on sentiment analysis of comments from a popular Bengali e-commerce site, "DARAZ," which comprises both Bangla and translated English reviews. The primary objective of this study is to conduct a comparative analysis of various models, evaluating their efficacy in the domain of sentiment analysis. The research methodology includes implementing seven machine learning models and deep learning models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional 1D (Conv1D), and a combined Conv1D-LSTM. Preprocessing techniques are applied to a modified text set to enhance model accuracy. The major conclusion of the study is that Support Vector Machine (SVM) models exhibit superior performance compared to other models, achieving an accuracy of 82.56% for English text sentiment analysis and 86.43% for Bangla text sentiment analysis using the porter stemming algorithm. Additionally, the Bi-LSTM Based Model demonstrates the best performance among the deep learning models, achieving an accuracy of 78.10% for English text and 83.72% for Bangla text using porter stemming. This study signifies significant progress in natural language processing research, particularly for Bangla, by enhancing improved text classification models and methodologies. The results of this research make a significant contribution to the field of sentiment analysis and offer valuable insights for future research and practical applications.

Hair symbolizes the beauty of women and men. All of us are jealous of our hair. We lose hair at a young age due to some mistakes or irregularities. Lots of men and women all over the world are suffering from hair falling and the number of females is suffering growing per year. Genetically, dandruff, allergy and stress are the major problems for falling hair. We are doing this research survey for helping people. This study is representing two things. First of all, we are findings how many reasons are involved in hair fall. Another thing is we train our dataset with machine learning algorithms to find out the accuracy. Machine learning technologies have rapidly evolved to analyze survey datasets. SVM, Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-nearest Neighbor and XGBoost algorithms for performance comparison. The experimental results indicated that XGBoost had the best performance, with an accuracy of 92.62%.

Polycystic Ovary Syndrome is one of the familiar hormonal diseases which occurs during reproductive age of women life. It is increasing rapidly among women due to a destructive lifestyle. About 1 in 10 women of childbearing age are affected by PCOS. Polycystic Ovary Syndrome is mainly occurring of having cysts in the ovary. PCOS is caused in women because of hormonal imbalance. PCOS is a combination of lots of various symptoms. There are many symptoms of PCOS: hormonal imbalance, ovary in cysts, irregular period, hormonal acne in face, insulin resistance, infertility, excess hair in body, excess hair falls etc. There is no proper cure for PCOS. According to doctors women can reverse this by modifying their lifestyle. So, awareness about the disorder and finding the common symptoms based on our survey is our main goal for doing this research. For doing this research, initially collecting real data is very difficult. We collected data from various PCOS related Facebook groups. The survey is to identify the most common symptoms that PCOS women have and their diagnosis age. Identification of age and symptoms which will create awareness among future generations of girls. Also the process of collecting data in this field will give clear guidelines for future researchers.

Insect identification is crucial for agriculture, entomology, and ecological monitoring, where accurate pest detection can avoid crop damage and reduce pesticide use. To assure model transparency and dependability, this work suggests an improved method for automated insect categorization that combines explainable artificial intelligence (XAI) techniques with transfer learning. The main goal is to create a high-accuracy, easily deployable classification system with excellent interpretability by utilizing the ResNet152v2 architecture. Nine different classes of insects totaling 4509 photos were gathered and pre-processed for noise reduction, resolution standardization, and data normalization. The ResNet152v2 model was trained, and Grad-CAM (gradient-weighted class activation mapping) was applied to illustrate significant characteristics driving model decisions. With a 96% classification accuracy, the model proved useful in practical applications, minimizing the need for big labeled datasets while preserving transparency. Using this model in agricultural contexts can help farmers protect crops from pests, use less pesticides, and improve farmland fertility, among other practical uses. This system, which uses a web-based application, is accessible, inexpensive, and simple to use. It provides farmers with timely information so they may take preventive measures against pest infestations. This work contributes significantly to the field by providing a scalable and trustworthy solution for real-time insect identification, laying the groundwork for future AI-driven innovations in sustainable agriculture and ecological monitoring.