Artificial intelligence and computer vision advancements have revolutionized computer-aided diagnosis (CAD) systems, enabling more accurate breast cancer (BrCan) detection using histopathology images. This study proposes a classification framework that integrates Vision Transformers (ViT), Graph Attention Networks (GAT), and Calibrated Random Forest (CRF) to enhance diagnostic accuracy. ViT effectively captures rich visual representations and helps to form a graph-like structure, while GAT models the structural relationships within histopathology images, providing a more comprehensive understanding of tissue morphology. Extensive experiments were conducted with various model combinations, demonstrating that the ViT + GAT + CRF architecture achieved the highest performance. The experiment is carried out on the BreakHis dataset, and the model acquires an accuracy of 97.33%. These results highlight the effectiveness of incorporating both visual and structural features to improve diagnostic reliability. Our proposed framework represents a significant advancement in digital histopathology-based (BrCan) diagnosis and holds promise for broader applications in medical imaging.

Abstract Automated analysis of skin lesions in dermoscopy images has gained much attention due to its medical importance in the early detection of melanoma. Detection of lesions has become a challenge due to the strong visual similarity between benign and malignant skin lesions. In this research, a customized deep convolutional neural network (CNN) architecture has been designed to discriminate between benign and malignant lesions. The model is designed carefully with lesser convolution layers, fewer filters, and parameters to achieve better classification performance compared to pretrained VGG16, ResNet50, InceptionV3 models and, ensures state‐of‐the‐art performance. The proposed model is composed of nine trainable layers: eight convolution layers and one fully connected layer. The suggested framework is extensively evaluated on the benchmark ISIC 2016 challenge dataset. The effect of different input transformations over the dataset has been studied. For fair comparison, standard deep learning models such as VGG16, ResNet50, and InceptionV3 have been used for lesion classification using transfer learning approach. The memory requirement of the proposed model is reduced by 388, 68, and 63 times and FLOPs needed are lowered by 95%, 85%, and 84% compared to VGG16‐TrL, ResNet50‐TrL, and InceptionV3‐TrL, respectively. Results show that class balancing with external images improves classification performance.

Abstract Traffic sign recognition (TSR) has been a rising and lucrative field for researchers during the last decades. The high improvement of ADAS (autonomic driving autonomous system) has led researchers worldwide to concentrate on the development of TSR systems. As such, a novel automated multiclass TSR system is proposed. The architecture uses wavelet descriptors to extract the high information density and the traffic signs' edges and curves. The LL band image is directly fed into the classifier to avoid normalization. Three classifiers, CNN, CNN ensemble, and LSTM, are deployed for recognition. The architecture is implemented on IRSDBv1.0, the first available Indian traffic sign database. The architecture is also implemented on the standard traffic sign database GTSRB to investigate its effectiveness. An efficiency of 71.57% and 96.76% are recorded on IRSDBv1.0, and GTSRB, respectively. A list of comparative results is also provided to prove the competence of the architecture. The reasons behind the difference in the achieved accuracy are also discussed.

Anomaly activities such as robbery, explosion, accidents, etc. need immediate actions for preventing loss of human life and property in real world surveillance systems. Although the recent automation in surveillance systems are capable of detecting the anomalies, but they still need human efforts for categorizing the anomalies and taking necessary preventive actions. This is due to the lack of methodology performing both anomaly detection and classification for real world scenarios. Thinking of a fully automatized surveillance system, which is capable of both detecting and classifying the anomalies that need immediate actions, a joint anomaly detection and classification method is a pressing need. The task of joint detection and classification of anomalies becomes challenging due to the unavailability of dense annotated videos pertaining to anomalous classes, which is a crucial factor for training modern deep architecture. Furthermore, doing it through manual human effort seems impossible. Thus, we propose a method that jointly handles the anomaly detection and classification in a single framework by adopting a weakly-supervised learning paradigm. In weakly-supervised learning instead of dense temporal annotations, only video-level labels are sufficient for learning. The proposed model is validated on a large-scale publicly available UCF-Crime dataset, achieving state-of-the-art results. The source code and models will be available at https://github.com/snehashismajhi/JointDetectClassify.

Segmentation of handwritten words into isolated characters and their recognition are challenging due to the presence of high variability and cursiveness in Indian scripts. The complex shapes and availability of numerous atomic character classes, compound characters, modifiers, ascendants, and descendants make the recognition task even more difficult. A holistic approach effectively tackles such issues by avoiding the character‐level segmentation and the earlier holistic methods have been mostly developed using multi‐stage machine learning architecture. In this study, a deep convolutional neural network‐based holistic method termed ‘H‐WordNet’ is proposed for handwritten word recognition. The H‐WordNet model includes merely four convolutional layers and one fully connected layer to effectively classify the word images', which lead to a significant reduction in parameters. The efficacy of different pooling operations with the proposed model is investigated. The main purpose of this study is to avoid the need for handcrafted feature extraction and obtain a more stable and generalised system for word recognition. The proposed model is evaluated using a standard handwritten Bangla word database (CMATERdb2.1.2), which contains 18000 Bangla word images of 120 different categories and it obtained a higher recognition accuracy of 96.17% when compared to recent state‐of‐the‐art methods.

Automated diagnosis of pathological brain not only reduces the diagnostic error significantly but also improves the patient's quality of life, thereby addressing the sustainability issues. The last few decades have witnessed an intensive research on binary classification of brain magnetic resonance (MR) images. Multiclass classification of pathological brain MR images is a more challenging task and the literature on this problem is still in its infancy. In this paper, we propose a new automated diagnosis system to classify the brain MR images into five different categories. Texture features within MR images play a significant role in accurate and efficient pathological brain detection. This work presents the extraction of such vital texture features by calculating the entropy over the curvelet subbands. Two faster and simpler strategies of fast curvelet transform are separately employed for feature extraction and the derived features are termed as FCEntF-I and FCEntF-II. The features are finally subjected to kernel extreme learning machine (K-ELM) for classification. The effectiveness of the proposed scheme is evaluated on multiclass as well as binary brain MR datasets. Comparisons with state-of-the-art methods indicate the superiority of the proposed scheme. The discriminatory potential of FCEntF-I and FCEntF-II features is found better than its counterparts.

Chess is an ancient strategy board game that is played on an 8x8 board. Although digital games have become attractive today, chess still retains its popularity in the onscreen version of the game. There has also been considerable development in the chess game engines to play against a human counterpart. The objective of this work is to integrate these chess engines with an actual board game experience and create an autonomous chess player. The system is designed around the use of an open source chess engine and a computer numeric control (CNC) controlled magnetic moving mechanism for moving around the chess pieces. The moves from the human counterpart are taken through an overhead computer vision system. The robot makes the game much more interactive and builds a link between the human and computer system.

The primary innovations behind Software Defined Networks (SDN)are the decoupling of the control plane from the data plane and centralizing the network management through a specialized application running on the controller. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential target. Especially, Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to both control plane and forwarding plane of SDN. Asthe control layer is vulnerable to DDoS attack, the goal of this paper is to provide a defense system which is based on Learning Automata (LA) concepts. It is a self-operating mechanism that responds to a sequence of actions in a certain way to achieve a specific goal. The simulation results show that this scheme effectively reduces the TCP connection setup delay due to DDoS attack.

This paper aims at developing a new method for automated diagnosis of pathological brain using magnetic resonance imaging (MRI). The method derives features using unequally-spaced FFT based fast discrete curvelet transform (FDCT- USFFT). Thereafter, a reduced feature set is obtained using PCA+LDA algorithm. Finally, for classification, we hybridize regularized extreme learning machine and sine cosine algorithm (SCA-RELM) which aims at overcoming the drawbacks of conventional ELM and other classical learning algorithms. We evaluate our proposed scheme on three well-studied datasets and observe that it earns significant improvements over the existing methods. Moreover, the effectiveness of proposed SCA-RELM paradigm is tested against other learning algorithms for single layer feed-forward neural network. Our system will aid the clinicians to effectively diagnose pathological brain.