This page has only limited features, please log in for full access.
The Internet of Things (IoT) has developed a well-defined infrastructure due to commercializing novel technologies. IoT networks enable smart devices to compile environmental information and transmit it to demanding users through an IoT gateway. The explosive increase of IoT users and sensors causes network bottlenecks, leading to significant energy depletion in IoT devices. The wireless network is a robust, empirically significant, and IoT layer based on progressive characteristics. The development of energy-efficient routing protocols for learning purposes is critical due to environmental volatility, unpredictability, and randomness in the wireless network’s weight distribution. To achieve this critical need, learning-based routing systems are emerging as potential candidates due to their high degree of flexibility and accuracy. However, routing becomes more challenging in dynamic IoT networks due to the time-varying characteristics of link connections and access status. Hence, modern learning-based routing systems must be capable of adapting in real-time to network changes. This research presents an intelligent fault detection, energy-efficient, quality-of-service routing technique based on reinforcement learning to find the optimum route with the least amount of end-to-end latency. However, the cluster head selection is dependent on residual energy from the cluster nodes that reduce the entire network’s existence. Consequently, it extends the network’s lifetime, overcomes the data transmission’s energy usage, and improves network robustness. The experimental results indicate that network efficiency has been successfully enhanced by fault-tolerance strategies that include highly trusted computing capabilities, thus decreasing the risk of network failure.
Tariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Suhail Ashfaq Butt; Allah Ditta; Sirajuddin Qureshi. An intelligent fault detection approach based on reinforcement learning system in wireless sensor network. The Journal of Supercomputing 2021, 1 -30.
AMA StyleTariq Mahmood, Jianqiang Li, Yan Pei, Faheem Akhtar, Suhail Ashfaq Butt, Allah Ditta, Sirajuddin Qureshi. An intelligent fault detection approach based on reinforcement learning system in wireless sensor network. The Journal of Supercomputing. 2021; ():1-30.
Chicago/Turabian StyleTariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Suhail Ashfaq Butt; Allah Ditta; Sirajuddin Qureshi. 2021. "An intelligent fault detection approach based on reinforcement learning system in wireless sensor network." The Journal of Supercomputing , no. : 1-30.
Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.
Khalil Rehman; Jianqiang Li; Yan Pei; Anaa Yasin; Saqib Ali; Tariq Mahmood. Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network. Sensors 2021, 21, 4854 .
AMA StyleKhalil Rehman, Jianqiang Li, Yan Pei, Anaa Yasin, Saqib Ali, Tariq Mahmood. Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network. Sensors. 2021; 21 (14):4854.
Chicago/Turabian StyleKhalil Rehman; Jianqiang Li; Yan Pei; Anaa Yasin; Saqib Ali; Tariq Mahmood. 2021. "Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network." Sensors 21, no. 14: 4854.
Both telomere length and alcohol consumption have an important impact on biological age and carcinogenesis. Researchers have conducted many efforts to study the relationship between alcohol consumption and telomere length yet reached no consensus. In this paper, a meta-analysis is performed and relevant investigation results from previous literature are integrated. Twenty-one works of literature published between 2000 and 2016, which comprise 27 analyses with a total samples' size of 35,891, meet our screening conditions. Whether the relationship between alcohol consumption and telomere length is significant, this issue varies with study type (cohort, case-control, or cross-sectional) and study population (Europe, Asia, American, or Australia). It is deduced by combined evidence that alcohol consumption is associated with telomere length (with Fisher's combined p-value = 3.52E-8 and Liptak's weighted p-value = 8.24E-3). In the future, the consistent standardised quantifications of alcohol consumption and telomere length will avail further aggregation of the evidence from various studies.
Jianqiang Li; Yu Guan; Xi Xu; Yan Pei; Jason C. Hung; Weiliang Qiu. Association between alcohol consumption and telomere length. International Journal of Web and Grid Services 2021, 17, 1 .
AMA StyleJianqiang Li, Yu Guan, Xi Xu, Yan Pei, Jason C. Hung, Weiliang Qiu. Association between alcohol consumption and telomere length. International Journal of Web and Grid Services. 2021; 17 (1):1.
Chicago/Turabian StyleJianqiang Li; Yu Guan; Xi Xu; Yan Pei; Jason C. Hung; Weiliang Qiu. 2021. "Association between alcohol consumption and telomere length." International Journal of Web and Grid Services 17, no. 1: 1.
This research proposes to use ensemble learning methods to diagnose and predict Turner syndrome using facial images. Turner syndrome, also known as congenital ovarian hypoplasia syndrome, is a common clinical chromosomal disorder. Without the aid of cytogenetic diagnostic results, the accuracy of diagnosis made by the paediatrician is unsatisfactory. Early diagnosis of the Turner syndrome requires the expertise of well-trained medical professionals, which may hinder early intervention due to a high potential cost. So far, most of the studies have reported the use of clinical chromosome detection to diagnose Turner syndrome. In this research, we are the first to use facial recognition technology to diagnose Turner syndrome using ensemble learning techniques. First, the features from each of the facial image are extracted by principal component analysis, kernel-based principal component analysis, and others. Second, we randomly selected samples and features to establish a basic learning model. Finally, we developed a combination of multiple basic learning models using majority voting and stacking for the facial image classification task. Experimental results show that the correct classification rate of the Turner syndrome detection was elevated up to 88.1%. The proposed method can be implemented to automatically diagnosis Turner syndrome patients that can facilitate clinicians during the prognosis process.
Qing Zhao; Guohong Yao; Faheem Akhtar; Jianqiang Li; Yan Pei. An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods. IEEE Access 2020, 8, 223335 -223345.
AMA StyleQing Zhao, Guohong Yao, Faheem Akhtar, Jianqiang Li, Yan Pei. An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods. IEEE Access. 2020; 8 (99):223335-223345.
Chicago/Turabian StyleQing Zhao; Guohong Yao; Faheem Akhtar; Jianqiang Li; Yan Pei. 2020. "An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods." IEEE Access 8, no. 99: 223335-223345.
Background Screening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications. Results In this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934. Conclusion The proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify the brain images at the slice level. It has great potential for application to multi-label detection problems, especially with regard to the brain CT images.
Jianqiang Li; Guanghui Fu; Yueda Chen; Pengzhi Li; Bo Liu; Yan Pei; Hui Feng. A multi-label classification model for full slice brain computerised tomography image. BMC Bioinformatics 2020, 21, 200 .
AMA StyleJianqiang Li, Guanghui Fu, Yueda Chen, Pengzhi Li, Bo Liu, Yan Pei, Hui Feng. A multi-label classification model for full slice brain computerised tomography image. BMC Bioinformatics. 2020; 21 (S6):200.
Chicago/Turabian StyleJianqiang Li; Guanghui Fu; Yueda Chen; Pengzhi Li; Bo Liu; Yan Pei; Hui Feng. 2020. "A multi-label classification model for full slice brain computerised tomography image." BMC Bioinformatics 21, no. S6: 200.
Jianqiang Li; Lu Liu; Jingchao Sun; Yan Pei; Jijiang Yang; Hui Pan; Shi Chen; Qing Wang. Diagnosis and Knowledge Discovery of Turner Syndrome Based on Facial Images Using Machine Learning Methods. IEEE Access 2020, 8, 214866 -214881.
AMA StyleJianqiang Li, Lu Liu, Jingchao Sun, Yan Pei, Jijiang Yang, Hui Pan, Shi Chen, Qing Wang. Diagnosis and Knowledge Discovery of Turner Syndrome Based on Facial Images Using Machine Learning Methods. IEEE Access. 2020; 8 ():214866-214881.
Chicago/Turabian StyleJianqiang Li; Lu Liu; Jingchao Sun; Yan Pei; Jijiang Yang; Hui Pan; Shi Chen; Qing Wang. 2020. "Diagnosis and Knowledge Discovery of Turner Syndrome Based on Facial Images Using Machine Learning Methods." IEEE Access 8, no. : 214866-214881.
Cataract is the most prevailing reason for blindness across the globe, which occupies about 4.2% population of the world. Even with the developments in visual sciences, fundus image-based diagnosis is deemed as a gold standard for cataract detection and grading. Though the increase in the workload of ophthalmologists and complexity of fundus images, the results may be subject to intelligence. Therefore, the development of an automatic method for cataract detection is necessary to prevent visual impairment and save medical resources. This paper aims to provide a novel hybrid convolutional and recurrent neural network (CRNN) for fundus image-based cataract classification. The proposed CRNN fuses the advantages of convolution neural network and recurrent neural network to preserve long- and short-term spatial correlation between the patches. Coupled with transfer learning, we adopt AlexNet, GoogLeNet, ResNet and VGGNet to extract multilevel feature representation and to analyse how well these models perform cataract classification. The results demonstrate that the proposed method outperforms state-of-the-art methods with an average accuracy of 0.9739 for four-class cataract classification and provides a compelling reason to be applied for other retinal diseases.
Azhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Tariq Mahmood; Li Zhang. Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network. The Visual Computer 2020, 1 -11.
AMA StyleAzhar Imran, Jianqiang Li, Yan Pei, Faheem Akhtar, Tariq Mahmood, Li Zhang. Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network. The Visual Computer. 2020; ():1-11.
Chicago/Turabian StyleAzhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Tariq Mahmood; Li Zhang. 2020. "Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network." The Visual Computer , no. : 1-11.
Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still a need to develop a robust model that can fetch useful information and investigate self-build handwritten digit data efficiently and effectively. The convolutional neural network (CNN) models incorporating a sigmoid activation function with a large number of derivatives have low efficiency in terms of feature extraction. Here, we designed a novel CNN model integrated with the extreme learning machine (ELM) algorithm. In this model, the sigmoid activation function is upgraded as the rectified linear unit (ReLU) activation function, and the CNN unit along with the ReLU activation function are used as a feature extractor. The ELM unit works as the image classifier, which makes the perfect symmetry for handwritten digit recognition. A deeplearning4j (DL4J) framework-based CNN-ELM model was developed and trained using the Modified National Institute of Standards and Technology (MNIST) database. Validation of the model was performed through self-build handwritten digits and USPS test datasets. Furthermore, we observed the variation of accuracies by adding various hidden layers in the architecture. Results reveal that the CNN-ELM-DL4J approach outperforms the conventional CNN models in terms of accuracy and computational time.
Saqib Ali; Jianqiang Li; Yan Pei; Muhammad Saqlain Aslam; Zeeshan Shaukat; Muhammad Azeem. An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification. Symmetry 2020, 12, 1742 .
AMA StyleSaqib Ali, Jianqiang Li, Yan Pei, Muhammad Saqlain Aslam, Zeeshan Shaukat, Muhammad Azeem. An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification. Symmetry. 2020; 12 (10):1742.
Chicago/Turabian StyleSaqib Ali; Jianqiang Li; Yan Pei; Muhammad Saqlain Aslam; Zeeshan Shaukat; Muhammad Azeem. 2020. "An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification." Symmetry 12, no. 10: 1742.
We propose a chaotic evolution algorithm with elite strategy. The conventional chaotic evolution algorithm uses each individual to search in its local area. The proposed algorithm searches the parameter space always around the elite individual from the last generation. We evaluate the proposed algorithm in both single-objective and multi-objective optimization problems. In the single objective optimization problem, the elite is the individual has the best fitness value, and in the multi-objective optimization problem, the elites are the individuals in the first Pareto front. We design and evaluate these two algorithms with elite strategy using single- and multi-objective benchmark functions. We design a jump strategy to avoid searching within a local optima areas by applying elite strategy several generations one time. The numerical evaluation results demonstrate the proposed algorithm has strong local exploitation capability in the early generations. The optimization performance of chaotic evolution algorithm has a potential possibility to apply in high dimensional and more complex optimization problems.
Yan Pei. Chaotic Evolution Algorithm with Elite Strategy in Single-objective and Multi-objective Optimization. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2020, 579 -584.
AMA StyleYan Pei. Chaotic Evolution Algorithm with Elite Strategy in Single-objective and Multi-objective Optimization. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2020; ():579-584.
Chicago/Turabian StyleYan Pei. 2020. "Chaotic Evolution Algorithm with Elite Strategy in Single-objective and Multi-objective Optimization." 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , no. : 579-584.
Recently, the automatic diagnosis of Turner syndrome (TS) has been paid more attention. However, existing methods relied on handcrafted image features. Therefore, we propose a TS classification method using unsupervised feature learning. Specifically, first, the TS facial images are preprocessed including aligning faces, facial area recognition and processing of image intensities. Second, pre-trained convolution filters are obtained by K-means based on image patches from TS facial images, which are used in a convolutional neural network (CNN); then, multiple recursive neural networks are applied to process the feature maps from the CNN to generate image features. Finally, with the extracted features, support vector machine is trained to classify TS facial images. The results demonstrate the proposed method is more effective for the classification of TS facial images, which achieves the highest accuracy of 84.95%.
Lu Liu; Jingchao Sun; Jianqiang Li; Yan Pei. Automatic Classification of Turner Syndrome Using Unsupervised Feature Learning. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2020, 1578 -1583.
AMA StyleLu Liu, Jingchao Sun, Jianqiang Li, Yan Pei. Automatic Classification of Turner Syndrome Using Unsupervised Feature Learning. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2020; ():1578-1583.
Chicago/Turabian StyleLu Liu; Jingchao Sun; Jianqiang Li; Yan Pei. 2020. "Automatic Classification of Turner Syndrome Using Unsupervised Feature Learning." 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , no. : 1578-1583.
Colorectal cancer is a type of malignant from the intestinal tract. The accurate diagnosis of colorectal polyps can effectively guarantee the life safety of potential patients. There are supervised radionics methods and deep learning methods when determining whether polyps exist. This paper proposes to obtain global features set from computed tomographic colonography (CTC) images by radionics methods and the local features set using deep convolutional neural network simultaneously. Specifically, we use the chaotic evolution algorithm to optimize the parameters in the support vector machine classifier and random forest classifier. Finally, our hybrid method achieved better classification result by random forest classifier on combinational features in which accuracy is 91.318% from the experiment.
Xiaoyu Zhan; Jianqiang Li; Yan Pei. A Computer-Aided Diagnostic System to Detect Polyp in Computed Tomographic Colonography Images. Lecture Notes in Electrical Engineering 2020, 1 -9.
AMA StyleXiaoyu Zhan, Jianqiang Li, Yan Pei. A Computer-Aided Diagnostic System to Detect Polyp in Computed Tomographic Colonography Images. Lecture Notes in Electrical Engineering. 2020; ():1-9.
Chicago/Turabian StyleXiaoyu Zhan; Jianqiang Li; Yan Pei. 2020. "A Computer-Aided Diagnostic System to Detect Polyp in Computed Tomographic Colonography Images." Lecture Notes in Electrical Engineering , no. : 1-9.
Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false- (positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent featureextraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using "Breast Cancer" keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.
Tariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Azhar Imran; Khalil Ur Rehman. A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities. IEEE Access 2020, 8, 165779 -165809.
AMA StyleTariq Mahmood, Jianqiang Li, Yan Pei, Faheem Akhtar, Azhar Imran, Khalil Ur Rehman. A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities. IEEE Access. 2020; 8 (99):165779-165809.
Chicago/Turabian StyleTariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Azhar Imran; Khalil Ur Rehman. 2020. "A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities." IEEE Access 8, no. 99: 165779-165809.
Cataract is the most prevalent cause of blindness worldwide, which accounts for more than 51% of overall blindness. The early detection of cataract can salvage impaired vision leading to blindness. Most of the existing cataract classification systems are based on traditional machine learning methods with hand-engineered features. The manual extraction of retinal features is generally a time-taking process and requires professional ophthalmologists. Convolutional neural network (CNN) is a widely accepted model for automatic feature extraction, but it necessitates a larger dataset to evade overfitting problems. Contrarily, classification using SVM has great generalisation power to elucidate small-sample dataset. Therefore, we proposed a hybrid model by integrating deep learning models and SVM for 4-class cataract classification. The transfer learning-based models (AlexNet, VGGNet, ResNet) are employed for automatic feature extraction and SVM performs as a recogniser. The proposed architecture evaluated on 8030 retinal images with strong feature extraction and classification techniques has achieved 95.65% of accuracy. The results of this study have verified that the proposed method outperforms conventional methods and can provide a reference for other retinal diseases.
Azhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Ji-Jiang Yang; Yanping Dang. Automated identification of cataract severity using retinal fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2020, 8, 691 -698.
AMA StyleAzhar Imran, Jianqiang Li, Yan Pei, Faheem Akhtar, Ji-Jiang Yang, Yanping Dang. Automated identification of cataract severity using retinal fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2020; 8 (6):691-698.
Chicago/Turabian StyleAzhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Ji-Jiang Yang; Yanping Dang. 2020. "Automated identification of cataract severity using retinal fundus images." Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 8, no. 6: 691-698.
In this study, we analyse auditory perception and sound aesthetic characteristic using a sound composition optimizations system, which is designed and implemented using a revised interactive differential evolution algorithm. We revise the population initialization and crossover operation of a conventional interactive differential evolution algorithm. The initialization population of the sound composition system uses a set of well-designed sounds as individuals rather than these randomly generated in a search space. The crossover operation of a conventional interactive differential evolution algorithm is revised in order to maintain the search space within a feasible range. The differential mechanism of interactive differential evolution provides a more feasible combination of these well-designed sounds to create a new type of sounds in the timbre domain. The subjective evaluation of revised interactive differential evolution algorithm leads to the sound composition to satisfy the preference of a subject as much as possible. We invite five subjects to conduct the experimental evaluation to create sounds with their personal preferences. We analyse the sound aesthetic characteristic of these five subjects using principal component analysis and k-means clustering methods with the sound data selected in the optimization process. The characteristics of selected sound help to explain optimization process and convergence of the algorithm, and assist to better understand the aesthetic judgement of each subject. We found that the proposed system and method can generate or create a sound satisfying a subject's preference. The proposal is an efficient method to analyse the characteristics of auditory perception and sound aesthetic of the subjects as well.
Hayato Shindo; Yan Pei. Characteristic analysis of auditory perception and aesthetics in sound composition optimization using revised interactive differential evolution. Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion 2020, 1 .
AMA StyleHayato Shindo, Yan Pei. Characteristic analysis of auditory perception and aesthetics in sound composition optimization using revised interactive differential evolution. Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. 2020; ():1.
Chicago/Turabian StyleHayato Shindo; Yan Pei. 2020. "Characteristic analysis of auditory perception and aesthetics in sound composition optimization using revised interactive differential evolution." Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion , no. : 1.
In this paper, we propose a diagnosis and classification method of hydrocephalus computed tomography (CT) images using deep learning and image reconstruction methods. The proposed method constructs pathological features differing from the other healthy tissues. This method tries to improve the accuracy of pathological images identification and diagnosis. Identification of pathological features from CT images is an essential subject for the diagnosis and treatment of diseases. However, it is difficult to accurately distinguish pathological features owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions, etc. Some study results reported that the ResNet network has a better classification and diagnosis performance than other methods, and it has broad application prospectives in the identification of CT images. We use an improved ResNet network as a classification model with our proposed image reconstruction and information fusion methods. First, we evaluate a classification experiment using the hydrocephalus CT image datasets. Through the comparative experiments, we found that gradient features play an important role in the classification of hydrocephalus CT images. The classification effect of CT images with small information entropy is excellent in the evaluation of hydrocephalus CT images. A reconstructed image containing two channels of gradient features and one channel of LBP features is very effective in classification. Second, we apply our proposed method in classification experiments on CT images of colonography polyps for an evaluation. The experimental results have consistency with the hydrocephalus classification evaluation. It shows that the method is universal and suitable for classification of CT images in these two applications for the diagnosis of diseases. The original features of CT images are not ideal characteristics in classification, and the reconstructed image and information fusion methods have a great effect on CT images classification for pathological diagnosis.
Pengzhi Li; Jianqiang Li; Yueda Chen; Yan Pei; Guanghui Fu; Haihua Xie. Classification and recognition of computed tomography images using image reconstruction and information fusion methods. The Journal of Supercomputing 2020, 77, 2645 -2666.
AMA StylePengzhi Li, Jianqiang Li, Yueda Chen, Yan Pei, Guanghui Fu, Haihua Xie. Classification and recognition of computed tomography images using image reconstruction and information fusion methods. The Journal of Supercomputing. 2020; 77 (3):2645-2666.
Chicago/Turabian StylePengzhi Li; Jianqiang Li; Yueda Chen; Yan Pei; Guanghui Fu; Haihua Xie. 2020. "Classification and recognition of computed tomography images using image reconstruction and information fusion methods." The Journal of Supercomputing 77, no. 3: 2645-2666.
In recent years, a rapid rise in the incidence of Large for gestational age (LGA) neonate is reported, and health care professionals employed themselves to discover the cause. Utmost of the previous studies were cohort or observational studies that employed simple linear or multivariate regression models, and very few of them employed machine learning (ML) schemes. Therefore, this research proposes to use 1 expert-driven and 7 automated feature selection schemes with well-known ML classifiers using 10 and 30 folds cross-validation. The induced results were compared with existing baselines, and Wilcoxon signed-rank test and the Friedman test were also introduced to verify the results. The ranked 20 features of the proposed expert-driven feature selection scheme outperformed amongst 7 automated feature selection schemes with a prediction precision, accuracy, and AUC scores of 0.94606, 0.84529, and 0.86492, respectively. Out of twenty features, eleven features were found similar to twenty ranked features of the automated feature selection schemes subsets. The classification results of the extracted features were utmost identical to the results of twenty features subset proposed by the expert-driven feature selection scheme. Therefore, we suggest pediatricians to refresh LGA diagnosis process with the proposed scheme because of its practical usage and maximum expert involvement.
Faheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Bo Liu. Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data. Multimedia Tools and Applications 2020, 79, 34047 -34077.
AMA StyleFaheem Akhtar, Jianqiang Li, Yan Pei, Azhar Imran, Asif Rajput, Muhammad Azeem, Bo Liu. Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data. Multimedia Tools and Applications. 2020; 79 (45-46):34047-34077.
Chicago/Turabian StyleFaheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Bo Liu. 2020. "Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data." Multimedia Tools and Applications 79, no. 45-46: 34047-34077.
Thalassemia is an inherited blood disorder caused by abnormal production of hemoglobin. In order to establish an efficient thalassemia prognosis process, the practitioners suggest using complete blood count (CBC) report. Based on CBC report, practitioners often use professional experience and domain knowledge to discover the cause and relevant risk factors. Thus, to the best of our knowledge, this research is the first that uses machine learning techniques to accurately classify and predict thalassemia patients using the parameters of CBC report. WBC, RBC, HB, HCT, Platelets and an additional parameter Ferritin (Iron) are the selected parameters for the experimentations. The experimental analysis of the results show that RBC, HB, and Ferritin (Iron) plays a vital role in the establishment of an efficient thalassemia prognosis process.
Faheem Akhtar; Anum Shakeel; Jianqiang Li; Yan Pei; Yanping Dang. Risk Factors Selection for Predicting Thalassemia Patients using Linear Discriminant Analysis. 2020 Prognostics and Health Management Conference (PHM-Besançon) 2020, 1 -7.
AMA StyleFaheem Akhtar, Anum Shakeel, Jianqiang Li, Yan Pei, Yanping Dang. Risk Factors Selection for Predicting Thalassemia Patients using Linear Discriminant Analysis. 2020 Prognostics and Health Management Conference (PHM-Besançon). 2020; ():1-7.
Chicago/Turabian StyleFaheem Akhtar; Anum Shakeel; Jianqiang Li; Yan Pei; Yanping Dang. 2020. "Risk Factors Selection for Predicting Thalassemia Patients using Linear Discriminant Analysis." 2020 Prognostics and Health Management Conference (PHM-Besançon) , no. : 1-7.
In recent years, increasingly complex business applications, IP communications systems and cloud services all place great demands on network architectures that weren’t originally designed to handle such workloads. As networks grow in complexity, network performance problems increase as well, increasingly inadequate. Network functions virtualization (NFV) enabled the implementation of these network functions using software and general computing equipment, rather than dedicated hardware. The deployment and management of NFV are facilitated by the use of software-defined networking (SDN). Therefore, In this paper, we design a practical access control mechanism for software defined network (SDN). We first introduce the current problem of end-to-end authentication problem in SDN, and then claim that both network and routing layers protocol will require data authentication improvement. Hence, this paper proposes a hash flow tagging mechanism, which combines hash function and flow tag advantages to protect end-to-end network intrusion and achieve high security in SDN. We sketch out the changes and extensions of a SDN network controller and switches to enable high security flow data. The complete design of a SDN architecture using hash table is an exciting avenue for end-to-end authentication. The evaluation result shows the proposed architecture can achieve better protection and remain high data throughput.
Shih-Hao Chang; Yan Pei; Ping-Tsai Chung. Hash Flow: An Access Control Mechanism for Software Defined Network. Advances in Intelligent Systems and Computing 2020, 554 -565.
AMA StyleShih-Hao Chang, Yan Pei, Ping-Tsai Chung. Hash Flow: An Access Control Mechanism for Software Defined Network. Advances in Intelligent Systems and Computing. 2020; ():554-565.
Chicago/Turabian StyleShih-Hao Chang; Yan Pei; Ping-Tsai Chung. 2020. "Hash Flow: An Access Control Mechanism for Software Defined Network." Advances in Intelligent Systems and Computing , no. : 554-565.
Gestational weight is an essential parameter for the pediatrician to clinically evaluate the health of both neonate and the mother. During the last several decades, an increase in the prevalence of Large for Gestational Age (LGA) neonate is reported and several researchers engaged themselves to discover the cause. Most of them conducted observational or retrospective studies that used simple statistical test (i.e. univariate/multivariate logistic regression etc.,). However, machine learning schemes are rarely been employed to discover the cause. In this research, one proposed expert-driven and seven automated feature selection schemes with five well-known machine learning classifiers using (10 & 30)-fold cross-validations are employed for the establishment of an efficient and accurate LGA classification model. Accuracy, precision, and AUC scores are selected for the evaluation of the proposed scheme. Wilcoxon signed rank, friedman, and bonferroni-dunn tests are used to observe the variations among (10 & 30)-fold cross validation results and to rank various feature selection and classification schemes. Two baseline methods are also used to compare the results of the proposed expert-driven feature selection scheme. The top 20 features selected by the proposed expert-driven feature selection scheme outperformed among seven automated feature selection schemes. A comparison analysis is also performed between expert-driven and data-driven feature subsets. Furthermore, with the intersection of proposed expert-driven and data-driven feature subsets, it is foreseen that out of 20 features, 11 features are found similar, which authenticates the proposed scheme. The classification performance of the 11 extracted features is almost similar to the proposed expert-driven feature selection scheme. Ensemble technique is also exploited to build the better and effective LGA classification model.
Guohong Yao; Jianqiang Li; Yan Pei; Faheem Akhtar; Bo Liu. An Automatic Turner Syndrome Identification System with Facial Images. Lecture Notes in Electrical Engineering 2020, 105 -112.
AMA StyleGuohong Yao, Jianqiang Li, Yan Pei, Faheem Akhtar, Bo Liu. An Automatic Turner Syndrome Identification System with Facial Images. Lecture Notes in Electrical Engineering. 2020; ():105-112.
Chicago/Turabian StyleGuohong Yao; Jianqiang Li; Yan Pei; Faheem Akhtar; Bo Liu. 2020. "An Automatic Turner Syndrome Identification System with Facial Images." Lecture Notes in Electrical Engineering , no. : 105-112.
Turner syndrome adheres serious health-related complications with a tendency to affect various organs during different stages of life which includes hypertension, infertility, and retarded growth. The proper diagnosis of TS requires an expensive test named karyotype test which is not easily available in remote health care units in the countryside. Therefore, we proposed to use facial images to detect TS to pursue a higher accuracy of recognition. The proposed scheme achieved the accuracy of 91.3% with mixed feature extraction schemes using thirty principle components selected with criteria that retained 95% of the information from the turner dataset. Moreover, this research is the first that uses facial features to accurately diagnose TS patients and has the capability to help doctors to establish a cost-effective TS prognosis process in remote health care units that lack required health care facilities.
Xiang Gao; Jianqiang Li; Yan Pei; Faheem Akhtar; Qing Wang; Ting Yang; Ke Huang; Jun Li; Ji-Jiang Yang. Turner Syndrome Prognosis with Facial Features Extraction and Selection Schemes. Lecture Notes in Electrical Engineering 2020, 72 -78.
AMA StyleXiang Gao, Jianqiang Li, Yan Pei, Faheem Akhtar, Qing Wang, Ting Yang, Ke Huang, Jun Li, Ji-Jiang Yang. Turner Syndrome Prognosis with Facial Features Extraction and Selection Schemes. Lecture Notes in Electrical Engineering. 2020; ():72-78.
Chicago/Turabian StyleXiang Gao; Jianqiang Li; Yan Pei; Faheem Akhtar; Qing Wang; Ting Yang; Ke Huang; Jun Li; Ji-Jiang Yang. 2020. "Turner Syndrome Prognosis with Facial Features Extraction and Selection Schemes." Lecture Notes in Electrical Engineering , no. : 72-78.