Hybrid Single Image Super-Resolution Algorithm for Medical Images

High-quality medical microscopic images used for diseases detection are expensive and difficult to store. Therefore, low-resolution images are favorable due to their low storage space and ease of sharing, where the images can be enlarged when needed using Super-Resolution (SR) techniques. However, it is important to maintain the shape and size of the medical images while enlarging them. One of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image resolution. Consequently, this paper suggests a multi-SR and classification framework based on Generative Adversarial Network (GAN) to generate high-resolution images with higher quality and finer details to reduce blurring. The proposed framework comprises five GAN models: Enhanced SR Generative Adversarial Networks (ESRGAN), Enhanced deep SR GAN (EDSRGAN), Sub-Pixel-GAN, SRGAN, and Efficient Wider Activation-B GAN (WDSR-b-GAN). To train the proposed models, we have employed images from the famous BreakHis dataset and enlarged them by 4× and 16× upscale factors with the ground truth of the size of 256 × 256 × 3. Moreover, several evaluation metrics like Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), Multiscale Structural Similarity Index (MS-SSIM), and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency, training time, and storage space. The obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.     

Meta-heuristics for Feature Selection and Classification in Diagnostic BreastCancer

One of the most common kinds of cancer is breast cancer. The early detection of it may help lower its overall rates of mortality. In this paper, we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal images. The proposed approach starts with data preprocessing the input images and segmenting the significant regions of interest. In addition, to properly train the machine learning models, data augmentation is applied to increase the number of segmented regions using various scaling ratios. On the other hand, to extract the relevant features from the breast cancer cases, a set of deep neural networks (VGGNet, ResNet-50, AlexNet, and GoogLeNet) are employed. The resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification accuracy. The selected features are used to train a neural network to finally classify the thermal images of breast cancer. To achieve accurate classification, the parameters of the employed neural network are optimized using the continuous dipper throated optimization algorithm. Experimental results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the literature. Moreover, several experiments were conducted to compare the performance of the proposed approach with the other approaches. The results of these experiments emphasized the superiority of the proposed approach.     

Simulation of Water-Soil-Structure Interactions Using Incompressible Smoothed Particle Hydrodynamics

In the present work, an incompressible smoothed particle hydrodynamic (SPH) method is introduced to simulate water-soil-structure interactions. In the current calculation, the water is modelled as a Newtonian fluid. The soil is modelled in two different cases. In the first case, the granular material is considered as a fluid where a Bingham type constitutive model is proposed based on Mohr-Coulomb yield-stress criterion, and the viscosity is derived from the cohesion and friction angle. In addition, the fictitious suspension layers between water and soil depending on the concentration of soil are introduced. In the second case, Hooke’s law introduces elastic soil. In ISPH, the pressure is evaluated by solving the pressure Poisson equation using a semi-implicit algorithm based on the projection method and an eddy viscosity for water is modelled by a large eddy simulation with the Smagorinsky model. In the proposed ISPH method, the pressure is stabilized to simulate the multiphase flow between soil and water. Numerical experiments for water-soil suspension flow of Louvain erosional dam break with flat soil foundation, is simulated and validated using 3D-ISPH method. Coupling between water-soil interactions with different solid structures are simulated. The results revealed that, the suspension layers with the Bingham model of soil gives more accurate results in the experiment as compared to the case of the Bingham model without suspension layers. In addition, the elastic soil model by the Hooke’s law can simulate soil hump accurately as compared to the Bingham model. From the simulations, avoiding erosion behind the structure for preventing the structure break during flood are investigated by using an extended structure or a wedge structure.     

Evaluation of Therapeutic Targets in Histological Subtypes of Bladder Cancer

Histologically, bladder cancer is a heterogeneous group comprising urothelial carcinoma (UC), squamous cell carcinoma, adenocarcinomas (ACs), urachal carcinomas (UrCs), and small cell neuroendocrine carcinomas (SCCs). However, all bladder cancers have been treated so far uniformly, and targeted therapy options are still limited. Thus, we aimed to determine the protein expression/molecular status of commonly used cancer targets (programmed cell death 1 ligand 1 (PD-L1), mismatch repair (MMR), androgen and estrogen receptors (AR/ER), Nectin-4, tumor-associated calcium signal transducer 2 (Tacstd2, Trop-2), epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (HER2), and fibroblast growth factor receptor 3 (FGFR3)) to give first insights into whether patients with SCC, AC/UrCs, and squamous-differentiated carcinomas (Sq-BLCA) of the bladder could be eligible for targeted therapies. In addition, for MMR-deficient tumors, microsatellite instability was analyzed. We completed our own data with molecular data from The Cancer Genome Atlas (TCGA). We present ratios for each drug and cumulative ratios for multiple therapeutic options for each nonurothelial subtype. For example, 58.9% of SCC patients, 33.5% of AC/UrCs patients, and 79.3% of Sq-BLCA patients would be eligible for at least one of the analyzed targets. In conclusion, our findings hold promise for targeted therapeutic approaches in selected patients in the future, as various drugs could be applied according to the biomarker status.     

A Heuristics-Based Cost Model for Scientific Workflow Scheduling in Cloud

Scientific Workflow Applications (SWFAs) can deliver collaborative tools useful to researchers in executing large and complex scientific processes. Particularly, Scientific Workflow Scheduling (SWFS) accelerates the computational procedures between the available computational resources and the dependent workflow jobs based on the researchers’ requirements. However, cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate (near-optimal) solution within polynomial computational time. Motivated by this, current work proposes a novel SWFS cost optimization model effective in solving this challenge. The proposed model contains three main stages: (i) scientific workflow application, (ii) targeted computational environment, and (iii) cost optimization criteria. The model has been used to optimize completion time (makespan) and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context. This will ultimately reduce the cost for service consumers. At the same time, reducing the cost has a positive impact on the profitability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers. To evaluate the effectiveness of this proposed model, an empirical comparison was conducted by employing three core types of heuristic approaches, including Single-based (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Invasive Weed Optimization (IWO)), Hybrid-based (i.e., Hybrid-based Heuristics Algorithms (HIWO)), and Hyper-based (i.e., Dynamic Hyper-Heuristic Algorithm (DHHA)). Additionally, a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets. The proposed model provides an efficient platform to optimally schedule workflow tasks by handling data-intensiveness and computational-intensiveness of SWFAs. The results reveal that the proposed cost optimization model attained an optimal Job completion time (makespan) and total computational cost for small and large sizes of the considered dataset. In contrast, hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.     

Performance Enhancement of Dynamic Spectrum Access via Channel Reservation for Cognitive Radio Networks

Wireless Personal Communications - In cognitive radio networks models, quality of service (QoS) of primary users (PUs) must be assured. Dynamic spectrum access is a paradigm by which a radio system...     

Millimeter-Wave Concurrent Beamforming: A Multi-Player Multi-Armed Bandit Approach

The communication in the Millimeter-wave (mmWave) band, i.e., 30~300 GHz, is characterized by short-range transmissions and the use of antenna beamforming (BF). Thus, multiple mmWave access points (APs) should be installed to fully cover a target environment with gigabits per second (Gbps) connectivity. However, inter-beam interference prevents maximizing the sum rates of the established concurrent links. In this paper, a reinforcement learning (RL) approach is proposed for enabling mmWave concurrent transmissions by finding out beam directions that maximize the long-term average sum rates of the concurrent links. Specifically, the problem is formulated as a multiplayer multiarmed bandit (MAB), where mmWave APs act as the players aiming to maximize their achievable rewards, i.e., data rates, and the arms to play are the available beam directions. In this setup, a selfish concurrent multiplayer MAB strategy is advocated. Four different MAB algorithms, namely, ϵ-greedy, upper confidence bound (UCB), Thompson sampling (TS), and exponential weight algorithm for exploration and exploitation (EXP3) are examined by employing them in each AP to selfishly enhance its beam selection based only on its previous observations. After a few rounds of interactions, mmWave APs learn how to select concurrent beams that enhance the overall system performance. The proposed MAB based mmWave concurrent BF shows comparable performance to the optimal solution.     

Beamforming with 2D-AOA estimation for pilot contamination reduction in massive MIMO

Telecommunication Systems - Connected vehicles network is designed to provide a secure and private method for drivers to use the most efficiently the roads in certain area. When dealing with the...     

Electrocoagulation/electroflotation of real printing wastewater using copper electrodes: A comparative study with aluminum electrodes

Most studies investigated electrocoagulation/electroflotation process (EC/EF) using either aluminum or iron electrodes. The main aim of this study is to investigate the performance of EC/EF to treat printing wastewater under various experimental conditions using copper electrodes. The effects of several variables, including different electrode materials (copper and aluminum), different current densities, electrolysis time, and spacing between electrodes on the removal efficiency of various parameters were investigated. The results showed that the maximum removal efficiencies for COD,TDS, and oil and grease were obtained when using a copper electrode. The maximum removal efficiencies were obtained at a gap distance of 4 cm.     

Design and synthesis of novel bichalcophene derivatives with double anchoring groups for dye-sensitized solar cell applications: sensitization and co-sensitization with N-719

Journal of Materials Science: Materials in Electronics - Novel dyes F1-3 based on bichalcophene-pyrimidine-2,4,6-trione derivatives with dual anchoring were developed, synthesized, and evaluated as...