Applying image-based processing methods to original videos on a framewise level breaks the temporal consistency between consecutive frames. Traditional video temporal consistency methods reconstruct an original frame containing flickers from corresponding nonflickering frames, but the inaccurate correspondence realized by optical flow restricts their practical use. In this article, we propose a temporally broad learning system (TBLS), an approach that enforces temporal consistency between frames. We establish the TBLS as a flat network comprising the input data, consisting of an original frame in an original video, a corresponding frame in the temporally inconsistent video on which the image-based technique was applied, and an output frame of the last original frame, as mapped features in feature nodes. Then, we refine extracted features by enhancing the mapped features as enhancement nodes with randomly generated weights. We then connect all extracted features to the output layer with a target weight vector. With the target weight vector, we can minimize the temporal information loss between consecutive frames and the video fidelity loss in the output videos. Finally, we remove the temporal inconsistency in the processed video and output a temporally consistent video. Besides, we propose an alternative incremental learning algorithm based on the increment of the mapped feature nodes, enhancement nodes, or input data to improve learning accuracy by a broad expansion. We demonstrate the superiority of our proposed TBLS by conducting extensive experiments.

Lung and colon cancers are two of the most common causes of death and morbidity in humans. One of the most important aspects of appropriate treatment is the histopathological diagnosis of such cancers. As a result, the main goal of this study is to use a multi-input capsule network and digital histopathology images to build an enhanced computerized diagnosis system for detecting squamous cell carcinomas and adenocarcinomas of the lungs, as well as adenocarcinomas of the colon. Two convolutional layer blocks are used in the proposed multi-input capsule network. The CLB (Convolutional Layers Block) employs traditional convolutional layers, whereas the SCLB (Separable Convolutional Layers Block) employs separable convolutional layers. The CLB block takes unprocessed histopathology images as input, whereas the SCLB block takes uniquely pre-processed histopathological images. The pre-processing method uses color balancing, gamma correction, image sharpening, and multi-scale fusion as the major processes because histopathology slide images are typically red blue. All three channels (Red, Green, and Blue) are adequately compensated during the color balancing phase. The dual-input technique aids the model’s ability to learn features more effectively. On the benchmark LC25000 dataset, the empirical analysis indicates a significant improvement in classification results. The proposed model provides cutting-edge performance in all classes, with 99.58% overall accuracy for lung and colon abnormalities based on histopathological images.

Given a target grayscale image and a reference color image, exemplar-based image colorization aims to generate a visually natural-looking color image by transforming meaningful color information from the reference image to the target image. It remains a challenging problem due to the differences in semantic content between the target image and the reference image. In this paper, we present a novel globally and locally semantic colorization method called exemplar-based conditional broad-GAN, a broad generative adversarial network (GAN) framework, to deal with this limitation. Our colorization framework is composed of two sub-networks: the match sub-net and the colorization sub-net. We reconstruct the target image with a dictionary-based sparse representation in the match sub-net, where the dictionary consists of features extracted from the reference image. To enforce global-semantic and local-structure self-similarity constraints, global-local affinity energy is explored to constrain the sparse representation for matching consistency. Then, the matching information of the match sub-net is fed into the colorization sub-net as the perceptual information of the conditional broad-GAN to facilitate the personalized results. Finally, inspired by the observation that a broad learning system is able to extract semantic features efficiently, we further introduce a broad learning system into the conditional GAN and propose a novel loss, which substantially improves the training stability and the semantic similarity between the target image and the ground truth. Extensive experiments have shown that our colorization approach outperforms the state-of-the-art methods, both perceptually and semantically.

Virtualization is an important feature for cloud computing which has recently attracted considerable attention. In this work, we de-sign an energy efficient approach which aims to minimize the energy consumption without reducing the Quality of Service (QoS) for user's deadline requirements in cloud data centers. We also propose a VM re-placement algorithm for QoS and energy-awareness in cloud data cen-ters. We experimentally compare the performance of our algorithm with other the state-of-the-art algorithms (i.e., the random algorithm and the first fit algorithm). The experimental results show that our solution im-proves first fit and random algorithms by twice and four times respec-tively, and is also very energy efficient with reliable QoS.

Angle closure glaucoma leads to fluid deposition in eye, and intraocular pressure occurs that damage the optic nerve, causes blindness and vision loss. Anterior chamber (AC) evaluation is imperative for determining the risk of angle‐closure. Previously, techniques were dependent on either Pentacam–Scheimpflug that interprets poor visual information, anterior segment optical coherence tomography is injurious to intercede opaque optical structures. Therefore, in this paper, an experimental protocol is designed for detailed disease analysis based on IBM SPSS statistics via ultrasound biomicroscopy which is superior in evaluating deep structures; first, the affected parameter for AC is analysed, and afterwards the direction that needs laser surgery is explored. Experiments are conducted on large‐scale clinical studies from an affiliated hospital in Shanghai, China. The dataset comprised 600 AC images in five directions of 60 subjects. The mean with standard deviation for anterior open distance is 0.158790.096779 mm, 0.158630.081435 mm, and anterior chamber angle is 18.74908.0315, 18.74108.3889 for left and right eye respectively. It is found that anterior chamber angle in the downside of the AC is wider than the upside. However, this decision is partly based on the narrowest part of the angle to widen the depth of the direction and eliminate pupil block.