Cloud-based applications have gained significant traction in recent years due to their scalability and flexibility. However, ensuring optimal performance for such applications remains a challenge. This research paper proposes a novel algorithm aimed at enhancing the performance of cloud-based virtualized microservice applications using deep Q learning. The algorithm focuses on optimizing various aspects of the application, including resource allocation on virtual machines, load balancing, and credit. It leverages deep learning techniques to dynamically adjust resource allocation based on workload patterns and performance metrics. By intelligently distributing the workload across virtualized microservices, the algorithm aims to minimize response times and maximize resource utilization by applying concepts of deep learning. To validate the effectiveness of the proposed algorithm, extensive experiments are conducted on a realistic cloud-based virtual machine based microservice environment. Performance metrics such as response time, throughput, and resource utilization are measured and compared with a deep learning approach. The results demonstrate that the proposed algorithm significantly improves the performance of application in cloud environment. It achieves reduced response times, increased throughput, and better resource utilization compared to traditional load balancing techniques. Furthermore, the algorithm adapts to changing workloads and effectively manages resources, ensuring optimal performance even under varying conditions.

The aim of this study is to conduct an exploration of recent advancements in deep learning-based drusen detection, across a range of imaging modalities like fundus images, Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), and Ultra-Widefield fundus (UWF) images. Drusen are yellow deposits of lipids and proteins beneath the retina and serve as a distinctive feature of age-related macular degeneration (AMD) [1]. Deep learning has revolutionized retinal image processing, particularly in drusen diagnosis. The techniques and methodologies reviewed in this paper range from refinement of established models such as VGG19 with enhanced transfer learning to innovative approaches like the seamless fusion of patch and image-level models. This survey goes beyond mere enumeration of techniques, instead focusing on a critical evaluation of their effectiveness and applicability on diverse datasets, including DRIVE, STARE, and AREDS. Furthermore, this study offers a comparative analysis of the papers reviewed, unveiling the unique contributions and intricacies of each approach while offering a comprehensive overview of the current state of the field, enhancing understanding of the complexities and potentials within drusen detection.

The paper deals with an overview of RAMS strategies for complex systems in various process industries. This paper has reviewed a wide range of research articles and conference proceedings. These research publications dealt with the effect of failure and repair rates of subsystems on overall system performance. A comprehensive literature review regarding RAMS tactics has been conducted for twenty-five years. RAMS strategies address reliability, availability, maintenance and safety issues concerning industrial systems. These strategies aim to identify the failure-prone subsystems of industrial systems, which finally decide about the criticality of the subsystems and eventually improve the overall availability of the system by adopting the proper RAMS procedures.

Voltage instability and power losses is a key problem in the power system that increases the cost of operation of electric utilities and later increases the cost of electricity. One approach to solving the issue is to reconfigure the distribution network. By using a FACTS device, such as SVC (Static VAR Compensator) in a reconfigured network the loss can be reduced, and the voltage stability margin can be preserved. The primary challenge is locating the ideal system position for SVC connections. In this study, the machine learning technique is utilized to forecast the FVSI (Fast Voltage Stability Index) values of the lines for the preferred network placement of SVC. The ML algorithms are trained and predicated using the estimated FVSI values for various network reconfigurations. The proposed work has been tested using the IEEE 14 bus system.

The human cornea is composed of five layers, with the stroma layer being the thickest and the endothelium layer being the densest. The endothelium layer contains hexagonally structured cells, and disturbances in these cells occur in various dystrophies, including Fuch's dystrophy (FD), advanced Fuch's dystrophy (AFD), posterior polymorphous corneal dystrophy, Irido corneal dystrophy (ICD), mild polymegathism, and corneal guttata (CG). Thirteen (13) images were obtained from a specular microscope, capturing different dystrophies in various patients. These images were processed using the Artificial Intelligent Convolution Filter (AICF) algorithm. The algorithm extracted several parameters from the endothelium layer, including mean cell area, elongation of endothelial cells, Heywood circularity, compactness, hexagonality, standard deviation, and coefficient of variation. The mean cell area of images I1 to I13 ranged from 79.5 µm² to 3485 µm². The elongation of endothelial cells varied between 3.11 µm² and 3.98 µm². The compactness factor of endothelial cells ranged from 0.62 µm² to 0.93 µm², while Heywood circularity factor ranged from 0.8 µm² to 1.98 µm². The coefficient of variation spanned from 10 µm² to 99 µm², and the hexagonality of endothelial cells ranged from 46.6 µm² to 65.2 µm². These statistical parameters provide a meticulous representation of the healthy condition of the endothelial layer.

This review paper delves into the emerging realm of YouTube video summarization utilizing Natural Language Processing (NLP) techniques, a critical area of research with increasing prominence in our multimedia-rich digital age. The paper commences with a broad overview of the field, elaborating on the need for automated video summarization tools to navigate and condense the massive, ever-growing sea of YouTube content. Further, we systematically scrutinize the role and implementation of NLP methods in extracting meaningful textual data from videos, focusing on video transcripts, closed captions, user comments, and associated metadata. Subsequent sections dissect seminal and recent works, studying various NLP techniques such as text summarization, sentiment analysis, topic modeling, and deep learning architectures employed in this context. The paper also focuses on the various metrics used for evaluation and shows datasets generally used to assess the performance of these summarization systems. Finally, we identify current challenges and potential future directions for research in the area, acknowledging the evolving landscape of online video platforms and AI technologies. This review aims to provide researchers and practitioners with an encompassing perspective on the pivotal role of NLP in enabling more efficient, accurate, and intuitive navigation of YouTube content ultimately shaping our digital consumption experiences.

Billions of people all over the globe suffer from osteoporosis, the second most prevalent bone condition after arthritis. Osteoporosis is brought on by a decline in bone mineral density, which can cause discomfort, deformity, injuries, and, in extreme instances, fatalities. Although DXA is used to diagnose it, its high price, limited availability, and erratic BMD values make it unreliable. The diagnoses have significantly improved owing to computer-aided diagnosis. A precise diagnosis of the condition may be made using deep learning-based networks, which have demonstrated cutting-edge outcomes in the diagnostic sector. Under the transfer learning methodology, this research investigates the performance of known neural network designs with the objective of osteoporosis diagnosis. The ability to distinguish between x-rays of healthy individuals and those obtained from individuals with osteoporosis has been tested using eight well-known ImageNet pre-trained models to provide a thorough comparison. 372 X-rays, divided into training and test sets, in the investigations. Standard evaluation parameters (such as accuracy, precision, recall and f1-score) have been calculated for all architectures, and the majority of designs performed significantly well, with the highest achieving an average accuracy of up to 86.36% when distinguishing the specified classes.