Federated learning inspired Antlion based orchestration for Edge computing environment.

Edge computing is a scalable, modern, and distributed computing architecture that brings computational workloads closer to smart gateways or Edge devices. This computing model delivers IoT (Internet of Things) computations and processes the IoT requests from the Edge of the network. In a diverse and independent environment like Fog-Edge, resource management is a critical issue. Hence, scheduling is a vital process to enhance efficiency and allocation of resources properly to the tasks. The manuscript proposes an Artificial Neural Network (ANN) inspired Antlion algorithm for task orchestration Edge environments. Its aim is to enhance resource utilization and reduce energy consumption. Comparative analysis with different algorithms shows that the proposed algorithm balances the load on the Edge layer, which results in lower load on the cloud, improves power consumption, CPU utilization, network utilization, and reduces average waiting time for requests. The proposed model is tested for healthcare application in Edge computing environment. The evaluation shows that the proposed algorithm outperforms existing fuzzy logic algorithms. The performance of the ANN inspired Antlion based orchestration approach is evaluated using performance metrics, power consumption, CPU utilization, network utilization, and average waiting time for requests respectively. It outperforms the existing fuzzy logic, round robin algorithm. The proposed technique achieves an average cloud energy consumption improvement of 95.94%, and average Edge energy consumption improvement of 16.79%, 19.85% in average CPU utilization in Edge computing environment, 10.64% in average CPU utilization in cloud environment, and 23.33% in average network utilization, and the average waiting time decreases by 96% compared to fuzzy logic and 1.4% compared to round-robin respectively.

UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans.

Gastrointestinal (GI) cancer is leading general tumour in the Gastrointestinal tract, which is fourth significant reason of tumour death in men and women. The common cure for GI cancer is radiation treatment, which contains directing a high-energy X-ray beam onto the tumor while avoiding healthy organs. To provide high dosages of X-rays, a system needs for accurately segmenting the GI tract organs. The study presents a UMobileNetV2 model for semantic segmentation of small and large intestine and stomach in MRI images of the GI tract. The model uses MobileNetV2 as an encoder in the contraction path and UNet layers as a decoder in the expansion path. The UW-Madison database, which contains MRI scans from 85 patients and 38,496 images, is used for evaluation. This automated technology has the capability to enhance the pace of cancer therapy by aiding the radio oncologist in the process of segmenting the organs of the GI tract. The UMobileNetV2 model is compared to three transfer learning models: Xception, ResNet 101, and NASNet mobile, which are used as encoders in UNet architecture. The model is analyzed using three distinct optimizers, i.e., Adam, RMS, and SGD. The UMobileNetV2 model with the combination of Adam optimizer outperforms all other transfer learning models. It obtains a dice coefficient of 0.8984, an IoU of 0.8697, and a validation loss of 0.1310, proving its ability to reliably segment the stomach and intestines in MRI images of gastrointestinal cancer patients.

Stochastic modeling and parameter estimation of turbogenerator unit of a thermal power plant under classical and Bayesian inferential framework.

The work reported in present study deals with the development of a novel stochastic model and estimation of parameters to assess reliability characteristics for a turbogenerator unit of thermal power plant under classical and Bayesian frameworks. Turbogenerator unit consists of five components namely turbine lubrication, turbine governing, generator oil system, generator gas system and generator excitation system. The concepts of cold standby redundancy and Weibull distributed random variables are used in development of stochastic model. The shape parameter for all the random variables is same while scale parameter is different. Regenerative point technique and semi-Markov approach are used for evaluation of reliability characteristics. Sufficient repair facility always remains available in plant as well as repair done by the repairman is considered perfect. As the life testing experiments are time consuming, so to highlight the importance of proposed model Monte Carlo simulation study is carried out. A comparative analysis is done between true, classical and Bayesian results of MTSF, availability and profit function.

A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers.

Virtualisation is a major technology in cloud computing for optimising the cloud data centre's power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center's size expands resulting in increased energy usage. To address this problem, a resource allocation optimisation method that is both efficient and effective is necessary. The optimal utilisation of cloud infrastructure and optimisation algorithms plays a vital role. The cloud resources rely on the allocation policy of the virtual machine on cloud resources. A virtual machine placement technique, based on the Harris Hawk Optimisation (HHO) model for the cloud data centre is presented in this paper. The proposed HHO model aims to find the best place for virtual machines on suitable hosts with the least load and power consumption. PlanetLab's real-time workload traces are used for performance evaluation with existing PSO (Particle Swarm Optimisation) and PABFD (Best Fit Decreasing). The performance evaluation of the proposed method is done using power consumption, SLA, CPU utilisation, RAM utilisation, Execution time (ms) and the number of VM migrations. The performance evaluation is done using two simulation scenarios with scaling workload in scenario 1 and increasing resources for the virtual machine to study the performance in underloaded and overloaded conditions. Experimental results show that the proposed HHO algorithm improved execution time(ms) by 4%, had a 27% reduction in power consumption, a 16% reduction in SLA violation and an increase in resource utilisation by 17%. The HHO algorithm is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures.

Availability optimization of power generating units used in sewage treatment plants using metaheuristic techniques.

Metaheuristic techniques have been utilized extensively to predict industrial systems' optimum availability. This prediction phenomenon is known as the NP-hard problem. Though, most of the existing methods fail to attain the optimal solution due to several limitations like slow rate of convergence, weak computational speed, stuck in local optima, etc. Consequently, in the present study, an effort has been made to develop a novel mathematical model for power generating units assembled in sewage treatment plants. Markov birth-death process is adopted for model development and generation of Chapman-Kolmogorov differential-difference equations. The global solution is discovered using metaheuristic techniques, namely genetic algorithm and particle swarm optimization. All time-dependent random variables associated with failure rates are considered exponentially distributed, while repair rates follow the arbitrary distribution. The repair and switch devices are perfect and random variables are independent. The numerical results of system availability have been derived for different values of crossover, mutation, several generations, damping ratio, and population size to attain optimum value. The results were also shared with plant personnel. Statistical investigation of availability results justifies that particle swarm optimization outdoes genetic algorithm in predicting the availability of power-generating systems. In present study a Markov model is proposed and optimized for performance evaluation of sewage treatment plant. The developed model is one that can be useful for sewage treatment plant designers in establishing new plants and purposing maintenance policies. The same procedure of performance optimization can be adopted in other process industries too.

Deep Learning Model for the Automatic Classification of White Blood Cells.

Blood cell count is highly useful in identifying the occurrence of a particular disease or ailment. To successfully measure the blood cell count, sophisticated equipment that makes use of invasive methods to acquire the blood cell slides or images is utilized. These blood cell images are subjected to various data analyzing techniques that count and classify the different types of blood cells. Nowadays, deep learning-based methods are in practice to analyze the data. These methods are less time-consuming and require less sophisticated equipment. This paper implements a deep learning (D.L) model that uses the DenseNet121 model to classify the different types of white blood cells (WBC). The DenseNet121 model is optimized with the preprocessing techniques of normalization and data augmentation. This model yielded an accuracy of 98.84%, a precision of 99.33%, a sensitivity of 98.85%, and a specificity of 99.61%. The proposed model is simulated with four batch sizes (BS) along with the Adam optimizer and 10 epochs. It is concluded from the results that the DenseNet121 model has outperformed with batch size 8 as compared to other batch sizes. The dataset has been taken from the Kaggle having 12,444 images with the images of 3120 eosinophils, 3103 lymphocytes, 3098 monocytes, and 3123 neutrophils. With such results, these models could be utilized for developing clinically useful solutions that are able to detect WBC in blood cell images.

A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network.

Brain tumors are the most common and aggressive illness, with a relatively short life expectancy in their most severe form. Thus, treatment planning is an important step in improving patients' quality of life. In general, image methods such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images are used to assess tumors in the brain, lung, liver, breast, prostate, and so on. X-ray images, in particular, are utilized in this study to diagnose brain tumors. This paper describes the investigation of the convolutional neural network (CNN) to identify brain tumors from X-ray images. It expedites and increases the reliability of the treatment. Because there has been a significant amount of study in this field, the presented model focuses on boosting accuracy while using a transfer learning strategy. Python and Google Colab were utilized to perform this investigation. Deep feature extraction was accomplished with the help of pretrained deep CNN models, VGG19, InceptionV3, and MobileNetV2. The classification accuracy is used to assess the performance of this paper. MobileNetV2 had the accuracy of 92%, InceptionV3 had the accuracy of 91%, and VGG19 had the accuracy of 88%. MobileNetV2 has offered the highest level of accuracy among these networks. These precisions aid in the early identification of tumors before they produce physical adverse effects such as paralysis and other impairments.

Multiple myeloma and hypercoagulability leading to internal jugular vein thrombosis in a dialysis patient.

A 68-year-old male patient with end-stage renal failure on maintenance hemodialysis using a right internal jugular dual-lumen catheter developed thrombosis of the internal jugular vein with extension into the superior vena cava after removal of the catheter. As he developed a lower backache with MRI finding of disease of the D11 and D12 vertebrae, a bone biopsy performed showed multiple myeloma. Anticoagulant therapy led to recanalization of the thrombosed veins.