A Cost Efficient Resource Provisioning Approach Using Virtual Machine Placement

Abstract-Cloud computing is evolving technology in which pool of resources are provided as services. Resource provisioning in cloud computing achieves systematic services on client registration using services present in cloud computing. In resources provisioning there is tremendous query formation for each client for utilizing their resources i.e. memory utilization, CPU utilization, and other resources are utilizing capabilities in cloud computing. For resource provisioning in cloud two popular mechanisms are reservation and on-demand plan services. According to the cost estimation process of the cloud services there is challenging task in optimization of capacity utilization in deploying virtual machine placement. In this paper, we suggest an optimal virtual machine placement algorithm to implement optimized resource provisioning operations. The proposed OVMP algorithm makes a decision process on cloud service provider with consistent stochastic integer programming to dedicate resources from cloud service providers. These service professional accepts the cloud computing services with resource provisioning with suitable services. Our experimental results show the minimized budgets with provisioning resources in emerging cloud computing environments

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Abstract Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning. In the current study, the FastAI technology is used with the ResNet-32 model to precisely identify ductal carcinoma. ResNet-32 is having few layers comparted to majority of its counterparts with almost identical performance. FastAI offers a rapid approximation toward the outcome for deep learning models via GPU acceleration and a faster callback mechanism, which would result in faster execution of the model with lesser code and yield better precision in classifying the tissue slides. Residual Network (ResNet) is proven to handle the vanishing gradient and effective feature learning better. Integration of two computationally efficient technologies has yielded a precision accuracy with reasonable computational efforts. The proposed model has shown considerable efficiency in the evaluating parameters like sensitivity, specificity, accuracy, and F1 Score against the other dominantly used deep learning models. These insights have shown that the proposed approach might assist practitioners in analyzing Breast Cancer (BC) cases appropriately, perhaps saving future complications and death. Clinical and pathological analysis and predictive accuracy have been improved with digital image processing

Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection

Abstract Object detection is a critical and complex problem in computer vision, and deep neural networks have significantly enhanced their performance in the last decade. There are two primary types of object detectors: two stage and one stage. Two-stage detectors use a complex architecture to select regions for detection, while one-stage detectors can detect all potential regions in a single shot. When evaluating the effectiveness of an object detector, both detection accuracy and inference speed are essential considerations. Two-stage detectors usually outperform one-stage detectors in terms of detection accuracy. However, YOLO and its predecessor architectures have substantially improved detection accuracy. In some scenarios, the speed at which YOLO detectors produce inferences is more critical than detection accuracy. This study explores the performance metrics, regression formulations, and single-stage object detectors for YOLO detectors. Additionally, it briefly discusses various YOLO variations, including their design, performance, and use cases

Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data

The development of genomic technology for smart diagnosis and therapies for various diseases has lately been the most demanding area for computer-aided diagnostic and treatment research. Exponential breakthroughs in artificial intelligence and machine intelligence technologies could pave the way for identifying challenges afflicting the healthcare industry. Genomics is paving the way for predicting future illnesses, including cancer, Alzheimer’s disease, and diabetes. Machine learning advancements have expedited the pace of biomedical informatics research and inspired new branches of computational biology. Furthermore, knowing gene relationships has resulted in developing more accurate models that can effectively detect patterns in vast volumes of data, making classification models important in various domains. Recurrent Neural Network models have a memory that allows them to quickly remember knowledge from previous cycles and process genetic data. The present work focuses on type 2 diabetes prediction using gene sequences derived from genomic DNA fragments through automated feature selection and feature extraction procedures for matching gene patterns with training data. The suggested model was tested using tabular data to predict type 2 diabetes based on several parameters. The performance of neural networks incorporating Recurrent Neural Network (RNN) components, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) was tested in this research. The model’s efficiency is assessed using the evaluation metrics such as Sensitivity, Specificity, Accuracy, F1-Score, and Mathews Correlation Coefficient (MCC). The suggested technique predicted future illnesses with fair Accuracy. Furthermore, our research showed that the suggested model could be used in real-world scenarios and that input risk variables from an end-user Android application could be kept and evaluated on a secure remote server

6G assisted federated learning for continuous monitoring in wireless sensor network using game theory

In-Game theory Applications, the 6G-assisted federated learning in continuous monitoring applications with wireless sensor networks (WSN) is a significant concern. With increased applications comes the increased demand for advanced resource allocation and energy management systems. WSN can be determined as a self-configured, infrastructure-less wireless network monitoring physical or other surrounding conditions. In this study, the proposed system is concentrated on applying game theory to 6G-assisted federated learning for continuous monitoring in wireless sensor networks. The techniques imposed by the dual sink, such as Static and dynamic moving nodes approaches, are applied to the tentative node selection based on aggregated data transmission techniques. Based on the Static nodes and trusted nodes, the Aggregated data transmission is achieved high-level data transmission by combining individual-level data, i.e., the aggregate of the output data. This technique is performed with the wireless sensor network (WSN) platform with a future 6G network coordinating with the tool of NS4-Programmable Data Plane simulation. The proposed system simplifies the development of a behavioral model and bridges the gap between simulation and deployment. Finally, the combination of game theory with 6G-assisted federated learning for continuous monitoring applications in WSN solves problems and identifies several future directions. The outcome analysis of the proposed system is to design the wireless sensor network to yield a high network lifetime of more than 20 h and low power (less than 0.2 kJ energy) consumption for efficient communication in the future 6G cellular network

Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.

Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk