Kidney diseases such as cysts, stones, and tumors are among the most prevalent health issues worldwide. Early and accurate diagnosis is critical to preventing severe complications, and computed tomography (CT) imaging plays a key role in this process. However, manual interpretation of CT scans is time-consuming and prone to subjectivity. To address this, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) units for the automated multiclass classification of kidney diseases using CT images. The CNN component extracts spatial features from the input scans, while the LSTM layers model spatial dependencies and enhance the learning of complex patterns. The model was trained and evaluated on a curated dataset consisting of four kidney conditions: Cyst, Normal, Stone, and Tumor. Extensive experimentation demonstrates that the proposed CNN- LSTM model achieves a classification accuracy of 99.6%, with precision, recall, and F1-score values exceeding 99% across all classes. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed for model interpretability, enabling visualization of discriminative regions in the images responsible for predictions. The results indicate the potential of the model to serve as a reliable decision-support tool for radiologists and clinicians. This framework paves the way for enhanced diagnostic accuracy and faster clinical workflows in nephrological imaging.

Keywords : Kidney disease classification, CT images, CNN- LSTM, Deep learning, Grad-CAM, Medical imaging

Author : Bhuvannagari Dillibabu , M. Sreevani

Title : Automated and Explainable Kidney Abnormality Detection from CT Images Using CNN-LSTM Architecture

Volume/Issue : 2025;2(3)

Page No : 1 - 8

The increasing number of fake social media posts together with fraudulent content has become a major factor in online fraud growth which causes people to doubt trustworthiness and security. The authentication of post authenticity has evolved into a vital operation because user-generated content increases dynamically every day. The research investigates the effectiveness of XGBoost and Random Forest as well as Logistic Regression for classifying posts into real or fake categories. A total of 18,000 different online scam-related posts comprised the Employment Scam Aegean Dataset (EMSCAD). These algorithms show high success rates in detecting genuine content because they use their gained knowledge from previous data analysis. The study delivers important findings to automate scam detection systems which lead to better security measures and lower online fraudulent risks on different platforms.

Keywords : Data Mining, Classification, XGBoost, Catboost, Light Gradient Boosting, Random Forest

Author : Vijay Bhaskar Reddy , Bhuvana Reddy , Naga Shanthi , Isharth Aliya MS , Deepthi A

Title : Fake Job Recruitment Detection Using Machine Learning

Volume/Issue : 2025;2(3)

Page No : 9 - 14

The identification of medicinal plants is essential for traditional medicine and botanical research, but it often requires specialized knowledge and considerable time. To address these challenges, this study utilizes advanced deep learning models to automate the classification of 40 distinct medicinal plant species. The research explores the performance of Convolutional Neural Networks (CNN), MobileNet, and a hybrid model combining MobileNet with Recurrent Neural Networks (RNN). These models are trained on a comprehensive set of plant images and evaluated for their effectiveness in classification tasks, with metrics including accuracy, precision, and recall. The CNN serves as a strong baseline for image classification, while MobileNet is employed for its computational efficiency, making it suitable for environments with limited resources. The hybrid MobileNet and RNN model is assessed for its potential to capture sequential and contextual patterns within the image data. The results of this study provide insights into the development of more efficient and accessible automated plant identification systems with practical applications for researchers, herbalists, and other practitioners. These advancements have the potential to enhance the speed, accuracy, and reliability of medicinal plant classification, supporting the growth of traditional medicine and botanical research

Keywords : Medicinal Plants, DL, CNN, MobileNet, RNN, Plant Species.

Author : Kamakshamma Vasepalli , Nandini Ramanujam , Gowthami Ponnuru , Bhavya Nemakal

Title : Utilizing Deep Learning Techniques for the Identification of Medicinal Plants

Volume/Issue : 2025;2(3)

Page No : 15 - 19