Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

Sediment flux from Lesser Zab River in Dokan Reservoir: Implications for the sustainability of long‐term water resources in Iraq

Prudent management of Iraqi water resources under climate change conditions requires plans to be based on actual figures of the storage capacity of existing reservoirs. With the absence of sediment flushing measures, the actual storage capacity of Dokan Reservoir (operated since 1959) has been affected by the amount of sediment delivered during its operational life leading to an undetermined reduction in its storage capacity. In consequence, there has not been an update on the dam's operational storage capacity curves. In this research, new operational curves were established for the reservoir based on a recent bathymetric survey undertaken in 2014. The reduction in reservoir capacity during the period between 1959 and 2014 was calculated by the mean of the difference between the designed storage capacity and the storage capacity which was concluded from the 2014 bathymetric survey. Moreover, the rate of sediment transported to the reservoir was calculated based on the overall quantities of accumulated sediment and the water discharge of the Lesser Zab River into the reservoir. The results indicate that the dam capacity is reduced by 25% due to sedimentation of an estimated volume of 367 million cubic metres at water level 480 m.a.s.l. The annual sedimentation rate was about 6.6 million cubic metres, and the sediment yield was estimated to be 701.2 t?km^?3?year.     

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models.     

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.     

Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

     

Characterization of methylumbilliferone (mendiaxon r)-induced choleresis in the isolated perfused rat liver

The influence of the choleretic drug methylumbilliferone on bile formation in the isolated perfused rat liver is characterized. The compound induces rapidly an elevation of bile flow, bile acid secretion and soium excretion. The increased production of bile is of canalicular origin. The choleretic effect was defined as "bile acid like" choleresis due to excretion of the drug into the bile. It is discussed that the excretion of methylumbilliferone can influence the transport of bile in form of a positive cooperation on transport mechanism.     

Compact Deep Color Features for Remote Sensing Scene Classification

Neural Processing Letters - Aerial scene classification is a challenging problem in understanding high-resolution remote sensing images. Most recent aerial scene classification approaches are based...     

A computer-aided diagnostic system for liver tumor detection using modified U-Net architecture

The Journal of Supercomputing - A multimedia-based medical decision-making system is an ultimate requirement in the medical imaging domain. In the healthcare sector, achieving quick and efficient...     

Characterization of effluent water qualities from satellite membrane bioreactor facilities

Membrane bioreactors (MBRs) are often a preferred treatment technology for satellite water recycling facilities since they produce consistent effluent water quality with a small footprint and require little or no supervision. While the water quality produced from centralized MBRs has been widely reported, there is no study in the literature addressing the effluent quality from a broad range of satellite facilities. Thus, a study was conducted to characterize effluent water qualities produced by satellite MBRs with respect to organic, inorganic, physical and microbial parameters. Results from sampling 38 satellite MBR facilities across the U.S. demonstrated that 90% of these facilities produced nitrified (NH₄-N <0.4 mg/L-N) effluents that have low organic carbon (TOC <8.1 mg/L), turbidities of <0.7 NTU, total coliform bacterial concentrations <100 CFU/100 mL and indigenous MS-2 bacteriophage concentrations <21 PFU/100 mL. Multiple sampling events from selected satellite facilities demonstrated process capability to consistently produce effluent with low concentrations of ammonia, TOC and turbidity. UV-254 transmittance values varied substantially during multiple sampling events indicating a need for attention in designing downstream UV disinfection systems. Although enteroviruses, rotaviruses and hepatitis A viruses (HAV) were absent in all samples, adenoviruses were detected in effluents of all nine MBR facilities sampled. The presence of Giardia cysts in filtrate samples of two of nine MBR facilities sampled demonstrated the need for an appropriate disinfection process at these facilities.