Pneumonia, an acute respiratory infection, causes serious breathing hindrance by damaging lung/s. Recovery of pneumonia patients depends on the early diagnosis of the disease and proper treatment. This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. The deep Convolutional Neural Networks (CNNs)—CheXNet and VGG-19 are trained and used to extract features from given X-ray images. These features are then ensembled for classification. To overcome data irregularity problem, Random Under Sampler (RUS), Random Over Sampler (ROS) and Synthetic Minority Oversampling Technique (SMOTE) are applied on the ensembled feature vector. The ensembled feature vector is then classified using several Machine Learning (ML) classification techniques (Random Forest, Adaptive Boosting, K-Nearest Neighbors). Among these methods, Random Forest got better performance metrics than others on the available standard dataset. Comparison with existing methods shows that the proposed method attains improved classification accuracy, AUC values and outperforms all other models providing 98.93% accurate prediction. The model also exhibits potential generalization capacity when tested on different dataset. Outcomes of this study can be great to use for pneumonia diagnosis from chest X-ray images.

The COVID-19 pandemic wreaks havoc on healthcare systems all across the world. In pandemic scenarios like COVID-19, the applicability of diagnostic modalities is crucial in medical diagnosis, where non-invasive ultrasound imaging has the potential to be a useful biomarker. This research develops a computer-assisted intelligent methodology for ultrasound lung image classification by utilizing a fuzzy pooling-based convolutional neural network FP-CNN with underlying evidence of particular decisions. The fuzzy-pooling method finds better representative features for ultrasound image classification. The FPCNN model categorizes ultrasound images into one of three classes: covid, disease-free (normal), and pneumonia. Explanations of diagnostic decisions are crucial to ensure the fairness of an intelligent system. This research has used Shapley Additive Explanation (SHAP) to explain the prediction of the FP-CNN models. The prediction of the black-box model is illustrated using the SHAP explanation of the intermediate layers of the black-box model. To determine the most effective model, we have tested different state-of-the-art convolutional neural network architectures with various training strategies, including fine-tuned models, single-layer fuzzy pooling models, and fuzzy pooling at all pooling layers. Among different architectures, the Xception model with all pooling layers having fuzzy pooling achieves the best classification results of 97.2% accuracy. We hope our proposed method will be helpful for the clinical diagnosis of covid-19 from lung ultrasound (LUS) images.

Natural Language Processing (NLP) deals with analysing, understanding and generating languages likes human. One of the challenges of NLP is training computers to understand the way of learning and using a language as human. Every training session consists of several types of sentences with different context and linguistic structures. Meaning of a sentence depends on actual meaning of main words with their correct positions. Same word can be used as a noun or adjective or others based on their position. In NLP, Word Embedding is a powerful method which is trained on large collection of texts and encoded general semantic and syntactic information of words. Choosing a right word embedding generates more efficient result than others. Most of the papers used pretrained word embedding vector in deep learning for NLP processing. But, the major issue of pretrained word embedding vector is that it can’t use for all types of NLP processing. In this paper, a local word embedding vector formation process have been proposed and shown a comparison between pretrained and local word embedding vectors for Bengali language. The Keras framework is used in Python for local word embedding implementation and analysis section of this paper shows proposed model produced 87.84% accuracy result which is better than fastText pretrained word embedding vectors accuracy 86.75%. Using this proposed method NLP researchers of Bengali language can easily build the specific word embedding vectors for word representation in Natural Language Processing.

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a realtime framework that incorporates the Internet of Things (IoT), Blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the Internet of Things (IoT) and Blockchain technologies facilitates establishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the Blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 seconds after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms.

Scientific knowledge of image-based crack detection methods is limited in understanding their performance across diverse crack sizes, types, and environmental conditions. Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments. Hence, this research aims to generate an intelligent scheme that can recognize the presence of cracks and visualize the percentage of cracks from an image along with an explanation. The proposed method fuses features from concrete surface images through a ResNet-50 convolutional neural network (CNN) and curvelet transform handcrafted (HC) method, optimized by linear discriminant analysis (LDA), and the eXtreme gradient boosting (XGB) classifier then uses these features to recognize cracks. This study evaluates several CNN models, including VGG-16, VGG-19, Inception-V3, and ResNet-50, and various HC techniques, such as wavelet transform, counterlet transform, and curvelet transform for feature extraction. Principal component analysis (PCA) and LDA are assessed for feature optimization. For classification, XGB, random forest (RF), adaptive boosting (AdaBoost), and category boosting (CatBoost) are tested. To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. Two explainable AI (XAI) tools, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping++ (Grad-CAM++) are integrated with the proposed method to enhance result clarity. This research introduces a novel feature fusion approach that enhances crack detection accuracy and interpretability. The method demonstrates superior performance by achieving 99.93% and 99.69% accuracy on two existing datasets, outperforming state-of-the-art methods. Additionally, the development of an algorithm for isolating and quantifying crack regions represents a significant advancement in image processing for structural analysis. The proposed approach provides a robust and reliable tool for real-time crack detection and assessment in concrete structures, facilitating timely maintenance and improving structural safety. By offering detailed explanations of the model’s decisions, the research addresses the critical need for transparency in AI applications, thus increasing trust and adoption in engineering practice.