Image classification is a fundamental task in computer vision, with wide applications including autonomous navigation, content recommendation, and environmental monitoring. This paper offers a comprehensive comparative analysis of deep learning techniques using the Intel Natural Scenes Image dataset, which features diverse scenes such as forests, mountains, seas, streets, buildings, and glaciers. Our study evaluates the performance of various Convolutional Neural Network (CNN) architectures, focusing on aspects such as network design, hyperparameters, data augmentation, and transfer learning strategies. We describe our experimental setup in detail, including the CNN models used, preprocessing techniques applied, and evaluation metrics employed. The results and discussions present key findings, highlighting the strengths and limitations of the approaches studied and providing guidance for future research and practical applications. Our systematic analysis yields valuable insights into effective strategies for recognizing natural scenes.

White Blood Cell (WBC) image classification is pivotal for early disease detection and diagnosis. Convolutional Neural Networks (CNNs) have emerged as potent tools for such tasks due to their ability to learn intricate features from raw pixel data. In this study, we present a CNN-based approach for automated WBC classification. Our methodology encompasses image preprocessing to enhance contrast and normalize color, succeeded by CNN training with multiple convolutional and pooling layers, thereby enabling feature acquisition from diverse WBC classes. We evaluate our approach using a publicly accessible WBC image dataset, comparing our results against other contemporary methods. Our proposed method achieves an impressive 96.2% accuracy for six distinct WBC classes, surpassing prior techniques by a considerable margin. This showcases CNNs’ potential in automated WBC classification, underscoring its significance in medical diagnosis and research. In summary, we introduce a CNN-based approach for automated WBC classification that attains state-of-the-art performance on a publicly available dataset. Our methodology encompasses image preprocessing, contrast enhancement, color normalization, and CNN training to capture distinctive features of diverse WBC classes. Our findings underscore CNNs’ promise in this domain and propose its deployment as a valuable tool in medical research and diagnosis. Subsequent efforts will explore advanced techniques like transfer learning to further elevate our method’s performance.

Privacy preserving analysis of a social network aims at a better understanding of the network and its behavior, while at the same time protecting the privacy of its individuals. We propose an anonymization method for weighted graphs, i.e., for social networks where the strengths of links are important. This is in contrast with many previous studies which only consider unweighted graphs. Weights can be essential for social network analysis, but they pose new challenges to privacy preserving network analysis. In this paper, we mainly consider prevention of identity disclosure, but we also touch on edge and edge weight disclosure in weighted graphs. We propose a method that provides k-anonymity of nodes against attacks where the adversary has information about the structure of the network, including its edge weights. The method is efficient, and it has been evaluated in terms of privacy and utility on real word datasets.

This paper deals with the use of Bayesian Belief Networks in order to improve the accuracy and training time of character segmentation for unconstrained handwritten text. Comparative experimental results have been evaluated against Naive Bayes classification, which is based on the assumption of the independence of the parameters and two additional previous commonly used methods. Results have depicted that obtaining the inferential dependencies of the training data, could lead to the reduction of the required training time and size by a factor of 55%. Moreover, the achieved accuracy in detecting segment boundaries exceeds 86% whereas limited training data are proved to endow with very satisfactory results.

Learning Bayesian belief networks (BBN) from corpora and support vector machines (SVM) have been applied to the automatic acquisition of verb subcategorization frames for Modern Greek. We are incorporating minimal linguistic resources, i.e. basic morphological tagging and phrase chunking, to demonstrate that verb subcategorization, which is of great significance for developing robust natural language human computer interaction systems, could be achieved using large corpora, without having any general-purpose, syntactic parser at all. In addition, apart from BBN and SVM, which have not previously used for this task, we have experimented with three well-known machine learning methods (feedforward backpropagation neural networks, learning vector quantization and decision tables), which are also being applied to the task of verb subcategorization frame defection for the first time. We argue that both BBN and SVM are well suited for learning to identify verb subcategorization frames. Empirical results will support this claim. Performance has been methodically evaluated using two different corpora types, one balanced and one domain-specific in order to determine the unbiased behaviour of the trained models. Limited training data are proved to endow with satisfactory results. We have been able to achieve precision exceeding 80% on the identification of subcategorization frames which were not known beforehand.