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Nowadays there is growing demand for surveillance applications due to the safety and security from anomalous events. An anomaly in the video is referred to as an event that has some unusual behavior. Although time is required for the recognition of these anomalous events, computerized methods might help to decrease it and perform efficient prediction. However, accurate anomaly detection is still a challenge due to complex background, illumination, variations, and occlusion. To handle these challenges a method is proposed for a vision transformer convolutional recurrent neural network named ViT-CNN-RCNN model for the classification of suspicious activities based on frames and videos. The proposed pre-trained ViT-base-patch16-224-in21k model contains 224 × 224 × 3 video frames as input and converts into a 16 × 16 patch size. The ViT-base-patch16-224-in21k has a patch embedding layer, ViT encoder, and ViT transformer layer having 11 blocks, layer-norm, and ViT pooler. The ViT model is trained on selected learning parameters such as 20 training epochs, and 10 batch-size to categorize the input frames into thirteen different classes such as robbery, fighting, shooting, stealing, shoplifting, Arrest, Arson, Abuse, exploiting, Road Accident, Burglary, and Vandalism. The CNN-RNN sequential model is designed to process sequential data, that contains an input layer, GRU layer, GRU-1 Layer and Dense Layer. This model is trained on optimal hyperparameters such as 32 video frame sizes, 30 training epochs, and 16 batch-size for classification into corresponding class labels. The proposed model is evaluated on UNI-crime and UCF-crime datasets. The experimental outcomes conclude that the proposed approach better performed as compared to recently published works.

White blood cells (WBCs) are a portion of the immune system which fights against germs. Leukemia is the most common blood cancer which may lead to death. It occurs due to the production of a large number of immature WBCs in the bone marrow that destroy healthy cells. To overcome the severity of this disease, it is necessary to diagnose the shapes of immature cells at an early stage that ultimately reduces the modality rate of the patients. Recently different types of segmentation and classification methods are presented based upon deep-learning (DL) models but still have some limitations. This research aims to propose a modified DL approach for the accurate segmentation of leukocytes and their classification. The proposed technique includes two core steps: preprocessing-based classification and segmentation. In preprocessing, synthetic images are generated using a generative adversarial network (GAN) and normalized by color transformation. The optimal deep features are extracted from each blood smear image using pretrained deep models i.e., DarkNet-53 and ShuffleNet. More informative features are selected by principal component analysis (PCA) and fused serially for classification. The morphological operations based on color thresholding with the deep semantic method are utilized for leukemia segmentation of classified cells. The classification accuracy achieved with ALL-IDB and LISC dataset is 100% and 99.70% for the classification of leukocytes i.e., blast, no blast, basophils, neutrophils, eosinophils, lymphocytes, and monocytes, respectively. Whereas semantic segmentation achieved 99.10% and 98.60% for average and global accuracy, respectively. The proposed method achieved outstanding outcomes as compared to the latest existing research works.

Concerns related to current harmonics have gained increased attention in recent years due to technological advancement in power electronics and the proliferation of non-linear loads in power distribution system. It has become challenging to limit current total harmonic distortion (THD) at consumer end mainly due to increased penetration of intermittent switching based non-linear loads. Shunt active power filter (SAPF) is considered a marvelous power electronics device to tackle consumer side current harmonics. This paper presents a synchronous reference frame (SRF) based improved control method for compliant working of SAPF to reduce current harmonics in the distribution network with same and different THD levels in three phases. This research work investigates an effectual DC link voltage control technique linked with SRF based control of SAPF to effectively mitigate current harmonics. A fuzzy logic-based automatic switch (FLBAS) is designed according to the THD standards of IET and IEEE for real-time controlling of SRF based control of SAPF. In addition, S-plane stability analysis of the proposed control scheme is performed using the model of SAPF's inverter. It is established that the closed-loop system is asymptotically stable with the proposed control scheme. The presented simulation results validate the effectiveness of the proposed control technique for SAPF robustly.