Software Engineer& AI Integrator

Agricultural world is largely worried about reduced crop yields due to infestation by pests and diseases. The challenge that is facing the farmers is that they have had a hard time trying to detect and diagnose plant diseases especially at the early stages on the leaves before they appear in the fruits. To meet this requirement, the new product PaudhRog, relies on artificial intelligence (AI) to recognize the illness of plant leaves at an early age with special attention to crops like wheat, tomato, apple, and several tomato and other plants. The research will be able to surpass the admirable 99.5 percent accuracy rate using the potent InceptionResNetV2 model. To achieve this, such strategic measures as reducing the imbalances of datasets through oversampling and under sampling, integrating transfer learning with InceptionResNetV2, and systematic application of TensorFlow and Flask in Python have been used. It enhances its ability to identify the disease using high quality photos in the huge plant village collection that has a broad selection of plant leaves. Since the contribution of the meteorological conditions to the crop productivity is enormous, the weather widget on the website is not only usable but also provides the farmers with the necessary information about environmental conditions that are critical. PaudhRog aims to become an automated disease detector that seeks to be a proactive partner in safeguarding agricultural output and a major stakeholder in the global food security.

Prostate cancer is the second most common disease in men in the United States and touches the lives of millions of men every year, thereby becoming an important global health issue. The glandular prostate (PG), which plays a pivotal role in seminal fluid production, is the main organ that gets targeted by this disease. Since magnetic resonance imaging (MRI) is not invasive and gives accurate soft-tissue contrast, it has become an essential diagnostic tool for prostate cancer. To enhance the accuracy and efficacy of prostate cancer detection from MRI images, we present in this paper an improved deep learning approach that employs the InceptionResNetV2 model and exploits transfer learning. Compared with traditional diagnosis approaches, our model is effective in extracting intricate features and enhancing sensitivity. We also explore the viability of few-shot learning in medical imaging using prototypical networks with a minor portion of the dataset and reveal the potential of few-shot learning strategies in cases with limited annotated medical images and lay the groundwork for future studies. Large-scale validation shows strong and stable results, endorsing the applicability of our method in clinical environments for early detection and tailored treatment planning, eventually leading to enhanced patient outcomes.

It presents a groundbreaking approach for lung cancer detection utilizing Convolutional Neural Networks. Cancer remains a leading cause of global morbidity and mortality, underscoring the urgency for accurate and timely diagnostics. Leveraging the power of deep learning, our proposed CNN-based model demonstrates exceptional efficacy in analyzing medical imaging data, particularly chest radiographs, to identify subtle patterns indicative of lung malignancies. The model's architecture integrates advanced features for feature extraction and classification, enhancing sensitivity and specificity in comparison to conventional methods. Through extensive validation on diverse datasets, our CNN exhibits robust performance, showcasing its potential as a reliable tool for early and precise lung cancer detection. The implementation of this technology has significant implications for improving patient outcomes by facilitating earlier interventions and personalized treatment strategies in the fight against lung cancer.

Artificial intelligence has a lot of potential to boost crop output and promote food security by identifying plant diseases and pests. Tomato is one of the most essential and widely consumed vegetables globally, but it is susceptible to various diseases that can cause severe yield loss. Early detection of these diseases is critical for effective management and control. In this study, we propose a deep learning-based approach for the automatic detection of tomato crop diseases using Effective Convolutional Neural Networks (CNN). We trained a CNN model on a large dataset of tomato crop images with modified layers to classify them into healthy or diseased plants. The proposed approach achieved an accuracy of over 91.66% in detecting three common tomato diseases, namely Early Blight, Late Blight, and Septoria Leaf Spot, demonstrating its effectiveness for practical use. The results show that the proposed method can help farmers and researchers to detect crop diseases at an early stage, which can prevent yield losses and improve overall crop productivity.