AI, Volume 1, Issue 3 (September 2020) – 6 articles

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances. Full article

In multi-agent systems, agents often need to cooperate and form coalitions to fulfil their goals, for example by carrying out certain actions together or by sharing their resources. In such situations, some questions that may arise are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents’ abilities to carry out certain tasks? In this article, we address the question of how to identify and evaluate the potential agent coalitions, while taking into consideration the uncertainty around the agents’ actions. Our methodology is the following: We model multi-agent systems as Multi-Context Systems, by representing agents as contexts and the dependencies among agents as bridge rules. Using methods and tools for contextual reasoning, we compute all possible coalitions with which the agents can fulfil their goals. Finally, we evaluate the coalitions using appropriate metrics, each corresponding to a different requirement. To demonstrate our approach, we use an example from robotics. Full article

Product recommendation can be considered as a problem in data fusion—estimation of the joint distribution between individuals, their behaviors, and goods or services of interest. This work proposes a conditional, coupled generative adversarial network (RecommenderGAN) that learns to produce samples from a joint distribution between (view, buy) behaviors found in extremely sparse implicit feedback training data. User interaction is represented by two matrices having binary-valued elements. In each matrix, nonzero values indicate whether a user viewed or bought a specific item in a given product category, respectively. By encoding actions in this manner, the model is able to represent entire, large scale product catalogs. Conversion rate statistics computed on trained GAN output samples ranged from 1.323% to 1.763%. These statistics are found to be significant in comparison to null hypothesis testing results. The results are shown comparable to published conversion rates aggregated across many industries and product types. Our results are preliminary, however they suggest that the recommendations produced by the model may provide utility for consumers and digital retailers. Full article

Maize kernel traits such as kernel length, kernel width, and kernel number determine the total kernel weight and, consequently, maize yield. Therefore, the measurement of kernel traits is important for maize breeding and the evaluation of maize yield. There are a few methods that allow the extraction of ear and kernel features through image processing. We evaluated the potential of deep convolutional neural networks and binary machine learning (ML) algorithms (logistic regression (LR), support vector machine (SVM), AdaBoost (ADB), Classification tree (CART), and the K-Neighbor (kNN)) for accurate maize kernel abortion detection and classification. The algorithms were trained using 75% of 66 total images, and the remaining 25% was used for testing their performance. Confusion matrix, classification accuracy, and precision were the major metrics in evaluating the performance of the algorithms. The SVM and LR algorithms were highly accurate and precise (100%) under all the abortion statuses, while the remaining algorithms had a performance greater than 95%. Deep convolutional neural networks were further evaluated using different activation and optimization techniques. The best performance (100% accuracy) was reached using the rectifier linear unit (ReLu) activation procedure and the Adam optimization technique. Maize ear with abortion were accurately detected by all tested algorithms with minimum training and testing time compared to ear without abortion. The findings suggest that deep convolutional neural networks can be used to detect the maize ear abortion status supplemented with the binary machine learning algorithms in maize breading programs. By using a convolution neural network (CNN) method, more data (big data) can be collected and processed for hundreds of maize ears, accelerating the phenotyping process. Full article

Objective: We have recently identified using multilayer perceptron analysis (artificial intelligence) a set of 25 genes with prognostic relevance in diffuse large B-cell lymphoma (DLBCL), but the importance of this set in other hematological neoplasia remains unknown. Methods and Results: We tested this set of genes (i.e., ALDOB, ARHGAP19, ARMH3, ATF6B, CACNA1B, DIP2A, EMC9, ENO3, GGA3, KIF23, LPXN, MESD, METTL21A, POLR3H, RAB7A, RPS23, SERPINB8, SFTPC, SNN, SPACA9, SWSAP1, SZRD1, TNFAIP8, WDCP and ZSCAN12) in a large series of gene expression comprised of 2029 cases, selected from available databases, that included chronic lymphocytic leukemia (CLL, n = 308), mantle cell lymphoma (MCL, n = 92), follicular lymphoma (FL, n = 180), DLBCL (n = 741), multiple myeloma (MM, n = 559) and acute myeloid leukemia (AML, n = 149). Using a risk-score formula we could predict the overall survival of the patients: the hazard-ratio of high-risk versus low-risk groups for all the cases was 3.2 and per disease subtype were as follows: CLL (4.3), MCL (5.2), FL (3.0), DLBCL not otherwise specified (NOS) (4.5), multiple myeloma (MM) (5.3) and AML (3.7) (all p values < 0.000001). All 25 genes contributed to the risk-score, but their weight and direction of the correlation was variable. Among them, the most relevant were ENO3, TNFAIP8, ATF6B, METTL21A, KIF23 and ARHGAP19. Next, we validated TNFAIP8 (a negative mediator of apoptosis) in an independent series of 97 cases of DLBCL NOS from Tokai University Hospital. The protein expression by immunohistochemistry of TNFAIP8 was quantified using an artificial intelligence-based segmentation method and confirmed with a conventional RGB-based digital quantification. We confirmed that high protein expression of TNFAIP8 by the neoplastic B-lymphocytes associated with a poor overall survival of the patients (hazard-risk 3.5; p = 0.018) as well as with other relevant clinicopathological variables including age >60 years, high serum levels of soluble IL2RA, a non-GCB phenotype (cell-of-origin Hans classifier), moderately higher MYC and Ki67 (proliferation index), and high infiltration of the immune microenvironment by CD163-positive tumor associated macrophages (CD163+TAMs). Conclusion: It is possible to predict the prognosis of several hematological neoplasia using a single gene-set derived from neural network analysis. High expression of TNFAIP8 is associated with poor prognosis of the patients in DLBCL. Full article

Both noise and structure matter in single image super-resolution (SISR). Recent researches have benefited from a generative adversarial network (GAN) that promotes the development of SISR by recovering photo-realistic images. However, noise and structural distortion are detrimental to SISR. In this paper, we focus on eliminating noise and geometric distortion during super-resolving noisy images. It includes a denoising preprocessing module and a structure-keeping branch. At the same time, the advantages of GAN are still used to generate satisfying details. Especially, on the basis of the original SISR, the gradient branch is developed, and the denoising preprocessing module is designed before the SR branch. Denoising preprocessing eliminates noise by learning the noise distribution and utilizing residual-skip. By restoring the high-resolution(HR) gradient maps and combining gradient loss with space loss to guide the parameter optimization, the gradient branch brings additional structural constraints. Experimental results show that we have obtained better Perceptual Index (PI) and Learned Perceptual Image Patch Similarity (LPIPS) performance on the noisy images, and Peak Signal to Noise Ratio(PSNR) and Structure Similarity (SSIM) are equivalent compared with the most reported SR method combined with DNCNN. Taking the Urban100 dataset with noise intensity in 25 as an example, four indexes of the proposed method are respectively 3.6976(PI), 0.1124(LPIPS), 24.652(PSNR) and 0.9481(SSIM). Combined with the performance under different noise intensity and different datasets reflected in box-and-whiskers plots, the values of PI and LPIPS are the best among all comparison methods, and PSNR and SSIM also achieve equivalent effects. Also, the visual results show that the proposed method of enhancing the super-resolving effect of noisy images through structural information and denoising preprocessing(SNS) is not affected by the noise while preserving the geometric structure in SR processing. Full article