Brain tumor detection from MRI images plays a critical role in early diagnosis and effective treatment planning. Deep learning methods have shown promising results in medical image analysis, including brain tumor detection. In this paper, we propose a deep learning pipeline for accurate brain tumor detection in MRI images using the VGG-16 model. The proposed pipeline consists of multiple stages, starting with preprocessing to enhance image quality and reduce noise. The preprocessed images are then fed into the VGG-16 model, which has been pretrained on a large dataset of natural images, and fine-tuned on a specialized dataset of brain MRI images. The model leverages its deep architecture to automatically learn intricate features representative of tumor regions. To evaluate the effectiveness of the pipeline, extensive experiments were conducted on a diverse dataset of brain MRI scans. The results demonstrate that our approach achieved an impressive accuracy of 99.99

Handwritten digit recognition is used in computer vision and pattern recognition. This research categorizes MNIST digits using multiple neural network architectures. A typical machine learning benchmark is the MNIST dataset, which contains 28 × 28 grayscale photos of the numbers 0–9. Our major objective is to develop and test digit recognition models. Five network models, including an FCN and an ANN, were built, deployed, and assessed in this research. CNN Sequence with Dense Layers Sequential CNN with Convolution and MaxPooling, Flatten, and Dense Layers are alternative methods. These models extract characteristics from pictures using fully connected, convolutional, and pooling layers. We discuss loading, normalizing, and rearranging the dataset. The layers, activation functions, and output requirements of each model are specified in its design. Compilation involves selecting loss functions and optimizers. The validation set evaluates model performance, whereas the training set fine-tunes them. The results suggest that our models recognize digits. Other models have accuracy ranges from 93.70

Recently, the concept of transfer learning has been extensively utilized, demonstrating its efficacy across various tasks. Motivated by its success in image classification, this research presents a comparative analysis of road crack detection using transfer learning and Artificial Neural Networks (ANNs). Three different architecture for road crack detection were explored. First two were based on transfer learning and third one was based on Artificial Neural Network (ANN). Transfer learning with VGG16, VGG19 for road crack detection was employed and the performance of these models was evaluated and compared with the performance of ANN based model. Before exploring these model’s architecture, this work also involves other steps: collecting crack and non-crack images from three different sources and preprocessed them. The dataset consists of 42,266 crack and non-crack images. Then, the model based on VGG16 was trained on this dataset and achieved 98.55% validation accuracy. VGG19 and ANN based models were also trained on same dataset and achieved 99.01% and 98.94% validation accuracy respectively. The learning rate of these three models was set to 0.001.

This paper illustrates the pivotal role of K-means clustering in shaping effective e-commerce strategies by identifying customer segments with similar buying behaviors. This leads to targeted marketing, personalized product recommendations, and optimized pricing. The elbow method is employed to enhance cluster count determination. Through K-Means clustering analysis, color-coded points highlight distinct customer groups, aiding strategic decision-making for price and location predictions. Logistic regression, a vital supervised learning algorithm, accurately predicts categorical outcomes like customer purchasing behavior, achieving a 97

Alzheimer’s disease is a significant public health concern, and early detection is crucial for effective intervention. In this paper, we explore the application of ensemble learning approaches to classify Alzheimer’s disease in brain imaging data (MRI images). We employed several pre-trained deep learning models, including VGG-19, ResNet-152, EfficientNetB1, and EfficientNetB2, to extract valuable features from the imaging data. These models were individually trained for ten epochs, resulting in impressive training and validation accuracies. Specifically, VGG-19 achieved 99.22 and 93.88

Rice leaf disease poses a significant challenge to Jhum cultivation, making early and accurate detection vital for effective disease management. This study examines two cutting-edge deep learning models, VGG19 and DenseNet201, to identify and classify diseases in rice leaves. Our collection includes images of rice leaves that have been meticulously classed as bacterial blight, blast, brown spot, and the tungro. The diagnostic efficacy of each model was assessed after training with this dataset. Our findings reveal that the DenseNet201 model outperforms with a test accuracy of 99.63% and outstanding ROC-AUC scores across all disease categories. While the VGG19 model also demonstrates commendable performance with a test accuracy of 89.53%, it falls short of the DenseNet201 model. The findings highlight the potential of utilizing deep learning to transform disease detection in Jhum rice farming, providing valuable advantages for managing crops and optimizing yield.

Fake news detection is a critical challenge in the digital age, where misinformation spreads rapidly, causing real-world harm. In the context of the Bangla language, this problem is exacerbated by the scarcity of labeled data for model training. This paper introduces an enriched dataset of Bangla fake news, containing 7000 authentic and 1000 fake news texts, meticulously labeled for comprehensive analysis. To tackle this issue, we explore state-of-the-art language models such as DistilBERT and RoBERTa. Our experiments with these models achieve remarkable accuracies of 94.5

The rapid dissemination of information across social networks, online news portals, and digital platforms has given rise to a pervasive issue—fake news. This misinformation, deliberately propagated by groups with ulterior motives, has the potential to divert societies in alarming directions. In a digital landscape inundated with millions of daily stories, distinguishing fact from fabrication has become a formidable challenge, casting a dark cloud of uncertainty over the information age. In response to this critical issue, this paper presents an innovative approach for enhanced fake news detection through the fusion of deep learning and Repeat Vector representations. In an era where individuals of all ages increasingly rely on online news sources, the dissemination of misinformation has become an alarming norm. It is a challenge to discern deceptive narratives that aim to sow discord and create undue anxiety.To confront this challenge head-on, we propose a deep neural network model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architectures using the Repeat Vector method. This hybrid model excels in deciphering complex patterns within 1D data, effectively tackling the menace of fake news. Our rigorous evaluation demonstrates that our proposed model outperforms contemporary methods, achieving an impressive validation accuracy of up to 98.94% on our dataset.In a world increasingly plagued by misinformation, our approach represents a significant stride toward safeguarding the integrity of information in the digital age.