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The most prevalent form of malignant tumors that originate in the brain are known as gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and resilient segmentation of the tumors, along with an estimation of the patients’ overall survival rate. Therefore, we have introduced a deep learning approach that employs a combination of MRI scans to accurately segment brain tumors and predict survival in patients with gliomas. To ensure strong and reliable tumor segmentation, we employ 2D volumetric convolution neural network architectures that utilize a majority rule. This method helps to significantly decrease model bias and improve performance. Additionally, in order to predict survival rates, we extract radiomic features from the tumor regions that have been segmented, and then use a Deep Learning Inspired 3D replicator neural network to identify the most effective features. The model presented in this study was successful in segmenting brain tumors and predicting the outcome of enhancing tumor and real enhancing tumor. The model was evaluated using the BRATS2020 benchmarks dataset, and the obtained results are quite satisfactory and promising.

With the rapid development of Information Technology (IT) industries, data or information security has become one of the critical issues. Nowadays, Blockchain technology is widely using for improving data security. It is a tool for the individual and organization to interchange the digital asset without the intervention of a trusted third party i.e. a central administrator. This technology has given the ability to create digital tokens for representing assets, innovation and likely reshaping the scenery of entrepreneurship. Blockchain has several key properties, such as decentralization, immutability and transparency without using a trusted third party. It can be used in several fields, such as healthcare, digital voting, Internet of Things (IoT) and many more. This study aims to discuss the fundamentals of Blockchain. In this paper, the technology or working procedure of Blockchain including many applications in several fields are discussed. Finally, future work directions and open research challenges in the domain of Blockchain have been also discussed in detail.

Lymphoma histopathological diagnosis is complex due to rare subtypes, morphological overlaps, and poor tumor differentiation. In this paper, an AI-based system using deep transfer learning and simulated federated learning is developed to classify two lymphoma types i.e. Chronic Lymphocytic Leukemia (CLL) and Follicular Lymphoma (FL) from a dataset of 4500 histopathological images. Six models (VGG-16, VGG-19, MobileNetV2, ResNet50, DenseNet161, and Inception V3) were evaluated across four data thresholds (0.05 to 0.2). These models used fine-tuned convolutional layers to automatically extract high-level image features relevant to tissue morphology; the extracted features were processed internally through each model’s classifier, forming an end-to-end classification pipeline. DenseNet161 achieved the best classification performance across thresholds, while Inception V3 showed the highest accuracy (97.5%) and lowest RMSE (0.393) in the testing phase using deep learning. A simulated federated learning setup was also explored, where Inception V3 again outperformed other models, indicating its robustness in decentralized learning scenarios. The reported evaluation metrics loss, accuracy, precision, RMSE, F1 score, and recall, are derived from the testing phase, ensuring an accurate assessment of generalization performance. The findings highlight the efficacy of deep transfer learning in early and accurate lymphoma detection, with Inception V3 and DenseNet161 demonstrating strong performance across both learning paradigms. However, since federated learning was not fully deployed in a real-world distributed environment, its broader applicability remains a subject for future exploration.

The agriculture industry has an enormous influence on a nation's economy. Loss of yield due to plant diseases remains a reason, reducing crop quantity and quality. Incorrect diagnosis of crop diseases can result in improper application of chemical pesticides, which promotes immune microbial strains, raises expenses, and triggers fresh outbreaks that are harmful to the economy and the ecosystem. Despite the potential of Machine Learning (ML) and Deep Learning (DL) approaches in plant disease detection, their limited effectiveness results in poor or late disease detection. Resolving this issue is critical, requiring the development of more accurate disease detection methods. This research introduces an innovative approach for the detection of apple leaf diseases utilizing the CNN-based Inception-v3 model. The dataset comprises images taken on location without having any control over the image-capturing settings may provide better relevance to real-world scenarios. The proposed method integrates canny edge detection and watershed transformation to achieve accurate image segmentation, thereby enhancing the identification of disease regions. Additionally, exploratory data analysis was performed, and channel distributions were visualized to understand the dataset's characteristics. To ensure robust evaluation, the model's performance underwent stratified 5-fold cross-validation. The model classified plant images with 84.60% precision, 87.40% recall, 85.00% F1-score, and 94.76% accuracy. Experimental results substantiate the efficacy of the proposed approach, surpassing existing methods in disease classification.

Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstanding potential for image classification. This study presents Maize Net, a convolutional neural network model that precisely identifies diseases in maize leaves. Maize Net uses an attention mechanism to increase the model’s efficiency by focusing on the relevant features and residual learning to improve the gradient flow. This also addresses the vanishing gradient problem while training deeper neural networks. A five-fold cross-validation test is conducted for generalization across the dataset, generating five models based on distinct training and testing sets. The macro-average of all evaluation metrics is considered to address the dataset’s class imbalance problem. Maize Net achieved an average F1-score of 0.9509, recall of 0.9497, precision of 0.9525, and classification accuracy of 0.9595. These outcomes demonstrate MaizeNet’s robustness and reliability in automated plant disease classification.

Many software reliability growth models (SRGMs) have been proposed by researchers within the context of probability theory to estimate software reliability, remaining number of faults and optimal release time. The Fault Detection Rate (FDR) may vary because of changes in testing strategies. Due to lack of knowledge of software code, the testing team might be unable to rectify the detected faults thereby introducing new faults during the fault correction process. The debugging process is imperfect due to factors like human error, insufficient testing and complex codes resulting in epistemic uncertainty. In this paper, we have proposed a new software belief reliability growth model (SBRGM) using uncertain differential equations to deal with epistemic uncertainty effectively. We have incorporated imperfect debugging and change point based on the approach of belief reliability theory, making this model more accurate as compared to some of the previously developed models. Model parameters estimation methodology is derived using the least square method and Python version 3.10. Calculation of change point is done using empirical data analysis based on the First principle of Derivatives. Three real data sets have been used to validate the proposed model. This research contributes to being more flexible and realistic in dealing with epistemic uncertainty effectively as compared to conventional approaches.

Cotton, being a crucial cash crop globally, faces significant challenges due to multiple diseases that adversely affect its quality and yield. To identify such diseases is very important for the implementation of effective management strategies for sustainable agriculture. Image recognition plays an important role for the timely and accurate identification of diseases in cotton plants as it allows farmers to implement effective interventions and optimize resource allocation. Additionally, deep learning has begun as a powerful technique for to detect diseases in crops using images. Hence, the significance of this work lies in its potential to mitigate the impact of these diseases, which cause significant damage to the cotton and decrease fibre quality and promote sustainable agricultural practices. This paper investigates the role of deep transfer learning techniques such as EfficientNet models, Xception, ResNet models, Inception, VGG, DenseNet, MobileNet, and InceptionResNet for cotton plant disease detection. A complete dataset of infected cotton plants having diseases like Bacterial Blight, Target Spot, Powdery Mildew, Aphids, and Army Worm along with the healthy ones is used. After pre-processing the images of the dataset, their region of interest is obtained by applying feature extraction techniques such as the generation of the biggest contour, identification of extreme points, cropping of relevant regions, and segmenting the objects using adaptive thresholding. During experimentation, it is found that the EfficientNetB3 model outperforms in accuracy, loss, as well as root mean square error by obtaining 99.96%, 0.149, and 0.386 respectively. However, other models also show the good performance in terms of precision, recall, and F1 score, with high scores close to 0.98 or 1.00, except for VGG19. The findings of the paper emphasize the prospective of deep transfer learning as a viable technique for cotton plant disease diagnosis by providing a cost-effective and efficient solution for crop disease monitoring and management. This strategy can also help to improve agricultural practices by ensuring sustainable cotton farming and increased crop output.

Outlier detection is essential for identifying unusual patterns or observations that significantly deviate from the normal behavior of a dataset. With the rapid growth of data science, the prevalence of anomalies and outliers has increased, which can disrupt system modeling and parameter estimation, leading to inaccurate results. Recently, deep learning-based outlier detection methods have gained significant attention, but their performance is often limited by challenges in parameter selection and the nearest neighbor search. To overcome these limitations, we propose a three-stage Efficient Outlier Detection Approach (named EODA), that not only detects outliers with high accuracy but also emphasizes dataset characteristics. In the first stage, we apply a feature selection algorithm based on the Boruta method and Random Forest to reduce the data size by selecting the most relevant attributes and calculating the highest Z-score of shadow features. In the second stage, we improve the K-nearest neighbors algorithm to enhance the accuracy of nearest neighbor identification in the clustering phase. Finally, the third stage efficiently identifies the most significant outliers within clustered datasets. We evaluate the proposed EODA algorithm across eight UCI machine-learning repository datasets. The results demonstrate the effectiveness of our EODA approach, achieving a Precision of 63.07%, Recall of 82.49%, and an F1-Score of 64.53%, outperforming the existing techniques in the field.

Brain tumor diagnosis is a complex task due to the intricate anatomy of the brain and the heterogeneity of tumors. While magnetic resonance imaging (MRI) is commonly used for brain imaging, accurately detecting brain tumors remains challenging. This study aims to enhance brain tumor classification via deep transfer learning architectures using fine-tuned transfer learning, an advanced approach within artificial intelligence. Deep learning methods facilitate the analysis of high-dimensional MRI data, automating the feature extraction process crucial for precise diagnoses. In this research, several transfer learning models, including InceptionResNetV2, VGG19, Xception, and MobileNetV2, were employed to improve the accuracy of tumor detection. The dataset, sourced from Kaggle, contains tumor and non-tumor images. To mitigate class imbalance, image augmentation techniques were applied. The models were pre-trained on extensive datasets and fine-tuned to recognize specific features in MRI brain images, allowing for improved classification of tumor versus non-tumor images. The experimental results show that the Xception model outperformed other architectures, achieving an accuracy of 96.11%. This result underscores its capability in high-precision brain tumor detection. The study concludes that fine-tuned deep transfer learning architectures, particularly Xception, significantly improve the accuracy and efficiency of brain tumor diagnosis. These findings demonstrate the potential of using advanced AI models to support clinical decision making, leading to more reliable diagnoses and improved patient outcomes.

We are grateful to Dr. Pastore for his thoughtful comments [...]