Lung and colon cancers are two of the most common causes of death and morbidity in humans. One of the most important aspects of appropriate treatment is the histopathological diagnosis of such cancers. As a result, the main goal of this study is to use a multi-input capsule network and digital histopathology images to build an enhanced computerized diagnosis system for detecting squamous cell carcinomas and adenocarcinomas of the lungs, as well as adenocarcinomas of the colon. Two convolutional layer blocks are used in the proposed multi-input capsule network. The CLB (Convolutional Layers Block) employs traditional convolutional layers, whereas the SCLB (Separable Convolutional Layers Block) employs separable convolutional layers. The CLB block takes unprocessed histopathology images as input, whereas the SCLB block takes uniquely pre-processed histopathological images. The pre-processing method uses color balancing, gamma correction, image sharpening, and multi-scale fusion as the major processes because histopathology slide images are typically red blue. All three channels (Red, Green, and Blue) are adequately compensated during the color balancing phase. The dual-input technique aids the model’s ability to learn features more effectively. On the benchmark LC25000 dataset, the empirical analysis indicates a significant improvement in classification results. The proposed model provides cutting-edge performance in all classes, with 99.58% overall accuracy for lung and colon abnormalities based on histopathological images.

Artificial Intelligence (AI) has emerged as a transformative force in modern agriculture, revolutionizing traditional farming practices into smart agriculture ecosystems. This paper presents the ideas and uses of AI in smart agriculture, therefore highlighting its great influence on improving farming efficiency, sustainability, and production. Consisting of several layers that enable data collecting, analysis, and decision-making in farming operations, we suggest in this paper an AI-enabled Internet of Things (IoT) architecture for smart agriculture. This paper also investigates several AI-driven technologies like Machine Learning (ML), computer vision, and IoT integration, which enable farmers with real-time data insights, predictive analytics, and autonomous decision-making capability. We also look at how AI might solve important agricultural problems, including resource optimization, climate resilience, insect control, and crop monitoring. This paper clarifies the bright future of smart agriculture driven by AI in guaranteeing sustainable farming and world food security.

Conventionally, convolutional neural networks (CNNs) have been used to identify and detect thorax diseases on chest x-ray images. To identify thorax diseases, CNNs typically learn two types of information: disease-specific features and generic anatomical features. CNNs focus on disease-specific features while ignoring the rest of the anatomical features during their operation. There is no evidence that generic anatomical features improve or worsen the performance of convolutional neural networks for thorax disease classification in the current research. As a consequence, the relevance of general anatomical features in boosting the performance of CNNs for thorax disease classification is investigated in this study. We employ a dual-stream CNN model to learn anatomical features before training the model for thorax disease classification. The dual-stream technique is used to compel the model to learn structural information because initial layers of CNNs often learn features of edges and boundaries. As a result, a dual-stream model with minimal layers learns structural and anatomical features as a priority. To make the technique more comprehensive, we first train the model to identify gender and age and then classify thorax diseases using the information acquired. Only when the model learns the anatomical features can it detect gender and age. We also use Non-negative Matrix Factorization (NMF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to pre-process the training data, which suppresses disease-related information while amplifying general anatomical features, allowing the model to acquire anatomical features considerably faster. Finally, the model that was earlier trained for gender and age detection is retrained for thorax disease classification using original data. The proposed technique increases the performance of convolutional neural networks for thorax disease classification, as per experiments on the Chest X-ray14 dataset. We can also see the significant parts of the image that contribute more for gender, age, and a certain thorax disease by visualizing the features. The proposed study achieves two goals: first, it produces novel gender and age identification results on chest X-ray images that may be used in biometrics, forensics, and anthropology, and second, it highlights the importance of general anatomical features in thorax disease classification. In comparison to state-of-the-art results, the proposed work also produces competitive results.

The monolithic Eulerian formulation has widely been employed for solving numerically fluid–structure interaction (FSI) problems of finite structural displacement using the same mathematical variational formulation for fluid and structural dynamics. Recently, different physical features of fluid flow have been analyzed using this approach to such coupling problems by computing the classical benchmark solutions in a non-classical framework. Despite producing decent results, the analysis of micro-structural characteristics of fluid flow by applying the classical benchmark solutions still needs to be enhanced and extended further for such coupling problems. In this paper, the classical benchmark solutions have been enhanced and extended further for analyzing the combined micro-structural effects of linearly increasing Reynolds number $R_e$ and mean inflow velocity $\overline{U}$ on flow fields with mesh independence analysis by employing a monolithic Eulerian formulation in a non-classical framework. To this aim, the Cosserat fluids theory is taken into account for the present coupling problem by considering three micro-rotational degrees of freedom (dof) of fluid particles. The model equations of the proposed Cosserat fluid–structure interaction (CFSI) problem are derived using underlying laws of continuum mechanics. A numerical section presents the implementation of the benchmark problem with test examples followed by a detailed evaluation of the obtained results. The results obtained indicate that a uniform linear increase in Reynolds number $R_e$ and mean inflow velocity $\overline{U}$ produce the significant combined micro-structural effects on the micro-rotation velocity field $\mathsf{\omega }$, and this effect is found increasing on the increase of both parameters. This combined effect of increasing $R_e$ and $\overline{U}$ on the velocity field $u$ is also observed to be very significant in a sense that horizontal and vertical flow velocity profiles experience great variation by maintaining the same pattern on each increasing value of $R_e$ and $\overline{U}$ at any particular location in the computational domain. Further, the mesh independence analysis is employed to verify the convergence of obtained results. The study concludes that the linear increase in Reynolds number and mean inflow velocity affect micro-rotational velocity field significantly at the micro-structural level with mesh independence analysis. Finally, some future recommendations to enhance and extend the study with some of its limitations are presented.

Brain tumor being one of the major health hazards always needs a prompt diagnosis for the early treatment options to improve the chances of the survival of the patients. Traditional manual assessment of MRI (magnetic resonance imaging) to identify these conditions is a common practice. Hence, automation of the diagnosis practices can considerably improve the quality of the treatment procedures. In recent years almost every method for the automated diagnosis of brain tumor detection uses a deep learning technique. Despite decent works current techniques need a significant improvement to produce better results. Along with failure to produce decent results, the current deep learning models present very complex architectures which sometimes require huge computational resources. Therefore, in this paper we present a novel method that uses a lightweight dual-stream model with dual-input to detect brain tumors on MRI images. Both the inputs use different pre-processing mechanisms based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and White Patch Retinex algorithm in order to enhance the feature learning capabilities of the proposed model. In addition, the proposed method uses a two-fold margin loss to boost training process for better feature learning. The proposed method produces state-of-the-art results for the detection of Glioma, Meningioma and Pituitary tumors. The model presents accuracy of 99%, 98% and 99% respectively for Glioma, Meningioma and Pituitary tumor detection.