Search

Breast cancer is one the most critical disease and suffered many people around the world. The efficient and correct detection of breast cancer is still needed to ensure this medical issue although the researchers around the world are proposed different diagnostic methods for detection of this disease, however these existing methods still needed further improvement to correct and efficient detection of this disease. In this study, we proposed a new breast cancer identification method by using machine learning algorithms and clinical data. In the proposed method supervised (Relief algorithm) and unsupervised (Autoencoder, PCA algorithms) techniques have been used for related features selection from data set and then these selected features have been used for training and testing of classifier support vector machine for accurate and on time detection of breast cancer. Additionally, in the proposed approach k fold cross validation method has been used for model validation and best hyperparameters selection. The model performance evaluation metrics have been used for model performance evaluation. The BC data sets have been used for testing of the proposed method. The analysis of experimental results has been demonstrated that the features selected by Relief algorithm are more related for accurate detection of Breast cancer instead of features selected by Auotencoder and PCA algorithms. The proposed method has been attained high results in terms of accuracy on selected feature selected by Relief algorithm and achieved 99.91% accuracy. We have been employed McNemar's statistical test for performance comparison of our different models. Further, the proposed method performance has been compared with baseline methods in the literature and the proposed method performance is high as compared to base line methods. Due to the high performance of the proposed method (Relief-Support vector machine) we highly recommended it for the diagnosis of breast cancer. In addition, the proposed method can be easily incorporated into the healthcare system for reliable diagnosis of Breast cancer.

Current object detection techniques have difficulties in detecting small objects and a low level of accuracy in detecting occluded objects. To solve these problems, this paper proposes an object detection framework named FFAN which is based on Faster R-CNN that introduces a feature fusion network and an adversary occlusion network into the structure. The feature fusion network combines a feature map of low resolution and high semantic information with a feature map of high resolution and low semantic information using the deconvolution operation to increase the ability to extract low-level features in the network. FFAN then generates a single advanced feature map with high resolution and high semantic information which is used to predict the detection of small objects in the image more effectively. The adversary occlusion network creates occlusion on a deep feature map of the object, and generates an adversary training sample that is difficult for the detector to discriminate. At the same time, the detector classifies accurately the generated occluded adversary samples by self-learning. The two compete with and learn from each other to further improve the performance of the algorithm. We train FFAN on the PASCAL VOC 2007, PASCAL VOC 2012, MS COCO and KITTI datasets. A number of quantitative and qualitative experiments show that FFAN achieves a state-of-the-art detection accuracy.

Recommender systems (RS) currently play a crucial role in information filtering and retrieval, and have been ubiquitously applied in many domains, although suffering from such data sparsity and cold start problems. There are plenty of studies that try to make efforts to improve the performance of RS through different aspects, such as traditional matrix factorization technique and deep learning methods in recent years, however, it's still a challenging issue under research. In this paper, motivated by this, a two-stage deep learning-based model for top-N recommendation with interests exploring (DLMR) is proposed: 1) DLMR explores latent interests for each user, captures factors from reviews and contextual information via convolutional neural network, and performs convolutional matrix factorization to generate the candidates list; 2) In order to enhance the recommendation performance, DLMR further conducts candidates ranking through a three-layer denoising autoencoder, with taking account of heterogeneous side information. The DLMR provides a flexible scheme to leverage the available resources for recommendation, which is able to explore user's latent interests, capture the intricate interactions between users and items, and provide accurate and personalized recommendations. Experimental analysis over real world data sets demonstrates that DLMR could provide high performance top-N recommendation in sparse settings and outperform state-of-the-art recommender approaches significantly.

Recently, the rapidly developing Internet of Things (IoT) needs to support massive connectivity with high transmission data rate, which requires IoT transmitters with high speed and low power consumption. In this work, non-polar micro-LED operating at low current density of 10 A/cm $^{2}$ can still maintain a high bandwidth of 508 MHz and near-peak external quantum efficiency (EQE), making it promising for low-power, energy-efficient visible light communication (VLC) to address the challenge. At 10 A/cm $^{2}$ , non-polar micro-LEDs can achieve a low power consumption and high energy efficiency, still maintaining the data rate of 0.25 Gbps. In addition, data rates of 2 Gbps and 1.1 Gbps were also successfully achieved for non-polar micro-LED operating at 320 and 50 A/cm $^{2}$ , respectively, to evaluate the relationship between power consumption and achievable data rate. The capability of non-polar micro-LED that can realize low-power consumption and high bandwidth at low current density is expected to play a great role in the field of energy-efficient VLC, multi-function display and IoT in the future. [ABSTRACT FROM AUTHOR]

Hierarchical structures of labels usually exist in large-scale classification tasks, where labels can be organized into a tree-shaped structure. The nodes near the root stand for coarser labels, while the nodes close to leaves mean the finer labels. We label unseen samples from the root node to a leaf node, and obtain multigranularity predictions in the hierarchical classification. Sometimes, we cannot obtain a leaf decision due to uncertainty or incomplete information. In this case, we should stop at an internal node, rather than going ahead rashly. However, most existing hierarchical classification models aim at maximizing the percentage of correct predictions, and do not take the risk of misclassifications into account. Such risk is critically important in some real-world applications, and can be measured by the distance between the ground truth and the predicted classes in the class hierarchy. In this work, we utilize the semantic hierarchy to define the classification risk and design an optimization technique to reduce such risk. By defining the conservative risk and the precipitant risk as two competing risk factors, we construct the balanced conservative/precipitant semantic (BCPS) risk matrix across all nodes in the semantic hierarchy with user-defined weights to adjust the tradeoff between two kinds of risks. We then model the classification process on the semantic hierarchy as a sequential decision-making task. We design an algorithm to derive the risk-minimized predictions. There are two modules in this model: 1) multitask hierarchical learning and 2) deep reinforce multigranularity learning. The first one learns classification confidence scores of multiple levels. These scores are then fed into deep reinforced multigranularity learning for obtaining a global risk-minimized prediction with flexible granularity. Experimental results show that the proposed model outperforms state-of-the-art methods on seven large-scale classification datasets with the semantic tree. [ABSTRACT FROM AUTHOR]

InGaN-based micro light-emitting diodes (micro-LEDs) with 5, 10, and 13 nm quantum barrier (QB) thickness were fabricated by metal-organic chemical vapor deposition (MOCVD) to investigate the influence of quantum-confined Stark effect (QCSE) on modulation bandwidth and luminous performance of devices. The room-temperature photoluminescence (PL), low-temperature time-resolved PL (TRPL), and electroluminescence (EL) results show that the decrease of QB thickness is beneficial to reduce QCSE of the device. The thinner QB thickness is good for improving the modulation bandwidth because the thinner QB is more beneficial to increase the total carrier recombination rate, but it is not conducive to the improvement of external quantum efficiency (EQE) due to degraded crystal quality. In addition, the modulation bandwidth of the device was calculated by using ABC model in combination with simulation. The calculation is in good agreement with the measured value. What is more, an optical link using an orthogonal-frequency division multiplexing (OFDM) modulation scheme was demonstrated. The transmission data rate increases with the increase of QB thickness from 1.309 to 1.773 Gbps, even though the modulation bandwidth decreases from 245 to 169 MHz. This work provides a way to balance the quantum efficiency and communication performance of green micro-LEDs. [ABSTRACT FROM AUTHOR]

GaN micro-LEDs have been spotlighted as promising candidates for high-speed optical communication, which may become important components in 6G communication blueprint. To achieve a superior network covering sea, land and air for 6G communication, we demonstrated cross-medium communication based on an 80 $\mu$ m double-sided emission micro-LED array. The high enough light output power radiated from both sides of the green micro-LED enabled interference-free signal transmission in different media. Data rates of 1.55 Gbps over the 3 m free-space link and 1.11 Gbps over the 3.4 m underwater link could be achieved simultaneously. Furthermore, parallel-connected micro-LEDs were utilized to demonstrate the feasibility of pattern display and communication, and expected to achieve longer communication distances in media such as underwater channels. [ABSTRACT FROM AUTHOR]