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The advent of deep learning has significantly propelled the capabilities of automated medical image diagnosis, providing valuable tools and resources in the realm of healthcare and medical diagnostics. This research delves into the…

Crops hold paramount significance as they serve as the primary provider of energy, nutrition, and medicinal benefits for the human population. Plant diseases, however, can negatively affect leaves during agricultural cultivation, resulting…

India is an agriculture-dependent country. As we all know that farming is the backbone of our country it is our responsibility to preserve the crops. However, we cannot stop the destruction of crops by natural calamities at least we have to…

We report applications of Convolutional Neural Networks (CNN) to multi-classification classification of a large medical data set. We discuss in detail how changes in the CNN model and the data pre-processing impact the classification…

In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications. In this paper, we present a light-weight neural network for Parkinson's disease diagnostics, in…

For a global breeding organization, identifying the next generation of superior crops is vital for its success. Recognizing new genetic varieties requires years of in-field testing to gather data about the crop's yield, pest resistance,…

Crop failure owing to pests & diseases are inherent within Indian agriculture, leading to annual losses of 15 to 25% of productivity, resulting in a huge economic loss. This research analyzes the performance of various optimizers for…

Recently, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system to medical imaging has become one of the important methods which are assisting researchers to have…

CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable…

The use of deep learning (DL) in medical image analysis has significantly improved the ability to predict lung cancer. In this study, we introduce a novel deep convolutional neural network (CNN) model, named ResNet+, which is based on the…

Deep learning (DL) techniques have shown unprecedented success when applied to images, waveforms, and text. Generally, when the sample size ($N$) is much bigger than the number of features ($d$), DL often outperforms other machine learning…

Crop and weed monitoring is an important challenge for agriculture and food production nowadays. Thanks to recent advances in data acquisition and computation technologies, agriculture is evolving to a more smart and precision farming to…

The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an…

Rice plays a vital role as a primary food source for over half of the world's population, and its production is critical for global food security. Nevertheless, rice cultivation is frequently affected by various diseases that can severely…

Deep learning (DL) has gained popularity in recent years as an effective tool for classifying the current health and predicting the future of industrial equipment. However, most DL models have black-box components with an underlying…

Skin cancer, the most common human malignancy, is primarily diagnosed visually by physicians [1]. Classification with an automated method like CNN [2, 3] shows potential for challenging tasks [1]. By now, the deep convolutional neural…

Skin cancer detection is challenging since different types of skin lesions share high similarities. This paper proposes a computer-based deep learning approach that will accurately identify different kinds of skin lesions. Deep learning…

There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been performed using Object-Based Image Analysis (OBIA) methods, which…

We have developed a comprehensive computer system to assist farmers who practice traditional farming methods and have limited access to agricultural experts for addressing crop diseases. Our system utilizes artificial intelligence (AI) to…

The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge extracted by Deep Convolutional and Recurrent Neural Networks…