A Novel System for AYUSH Healthcare Services using Classification and Regression

There are roughly 4000 AYUSH hospitals spread out across India under various councils and hospitals run by the Indian government. Today’s atmosphere makes it more challenging than ever to locate a suitable AYUSH facility for the treatment. The AYUSH Ministry provides India’s top option for healthcare delivery. The government is examining strategies to lower expenditures while enhancing patient care. We are proposing the ground-breaking idea of e-healthcare which involves various novel features like suggesting various tools to the patients those need to communicate with the healthcare professionals as per their convenience remotely. This research suggests an interactive system using Android in line with this trend. By integrating different bio-medical data sources that contain information pertinent to the hospital demographics, their inpatient procedure rates, Outpatient department, etc., we proposed a system that surveys on the various AYUSH hospitals to find. This system uses the Google Map API for tracking and highlighting the location to the nearby AYUSH hospitals with opening and closing timings. Additionally, the proposed system plays a crucial role in emergency scenarios by supporting the user in performing the necessary first aid techniques. Using this strategy, the entire system demonstrates that this research provides a superior method for making decisions than past studies

REMOVAL OF GAUSSIAN AND IMPULSE NOISE IN THE COLOUR IMAGE PROGRESSION WITH FUZZY FILTERS

This paper is concerned with algebraic features based filtering technique, named as the adaptive statistical quality based filtering technique (ASQFT), is presented for removal of Impulse and Gaussian noise in corrupted colour images. A combination of these two filters also helps in eliminating a mixture of these two noises. One strong filtering step that should remove all noise at once would inevitably also remove a considerable amount of detail. Therefore, the noise is filtered step by step. In each step, noisy pixels are detected by the help of fuzzy rules, which are very useful for the processing of human knowledge where linguistic variables are used. The proposed filter is able to efficiently suppress both Gaussian noise and impulse noise, as well as mixed Gaussian impulse noise. The experiments shows that proposed method outperforms novel modern filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peaksignal- to-noise ratio (PSNR) and the normalized color difference (NCD). The expectations filter achieves a promising performance

Quality Enhancement for Underwater Images using Various Image Processing Techniques: A Survey

Underwater images are essential to identify the activity of underwater objects. It played a vital role to explore and utilizing aquatic resources. The underwater images have features such as low contrast, different noises, and object imbalance due to lack of light intensity. CNN-based in-deep learning approaches have improved underwater low-resolution photos during the last decade. Nevertheless, still, those techniques have some problems, such as high MSE, PSNT and high SSIM error rate. They solve the problem using different experimental analyses; various methods are studied that effectively treat different underwater image distorted scenes and improve contrast and color deviation compared to other algorithms. In terms of the color richness of the resulting images and the execution time, there are still deficiencies with the latest algorithm. In future work, the structure of our algorithm will be further adjusted to shorten the execution time, and optimization of the color compensation method under different color deviations will also be the focus of future research. With the wide application of underwater vision in different scientific research fields, underwater image enhancement can play an increasingly significant role in the process of image processing in underwater research and underwater archaeology. Most of the target images of the current algorithms are shallow water images. When the artificial light source is added to deep water images, the raw images will face more diverse noises, and image enhancement will face more challenges. As a result, this study investigates the numerous existing systems used for quality enhancement of underwater mages using various image processing techniques. We find various gaps and challenges of current systems and build the enhancement of this research for future improvement. Aa a result of this overview is to define the future problem statement to enhance this research and overcome the challenges faced by previous researchers. On other hand also improve the accuracy in terms of reducing MSE and enhancing PSNR etc

Understanding error log event sequence for failure analysis

Due to the evolvement of large-scale parallel systems, they are mostly employed for mission critical applications. The anticipation and accommodation of failure occurrences is crucial to the design. A commonplace feature of these large-scale systems is failure, and they cannot be treated as exception. The system state is mostly captured through the logs. The need for proper understanding of these error logs for failure analysis is extremely important. This is because the logs contain the “health” information of the system. In this paper we design an approach that seeks to find similarities in patterns of these logs events that leads to failures. Our experiment shows that several root causes of soft lockup failures could be traced through the logs. We capture the behavior of failure inducing patterns and realized that the logs pattern of failure and non-failure patterns are dissimilar.Keywords: Failure Sequences; Cluster; Error Logs; HPC; Similarit

Manufacturing Systems Line Balancing using Max-Plus Algebra

In today\u27s dynamic environment, particularly the manufacturing sector, the necessity of being agile, and flexible is far greater than before. Decision makers should be equipped with effective tools, methods, and information to respond to the market\u27s rapid changes. Modelling a manufacturing system provides unique insight into its behavior and allows simulating all crucial elements that have a role in the system performance. Max-Plus Algebra is a mathematical tool that can model a Discrete Event Dynamic System in the form of linear equations. Whereas Max-Plus Algebra was introduced after the 1980s, the number of studies regarding this tool and its applications is fewer than regarding Petri Nets, Automata, Markov process, Discrete Even Simulation and Queuing models. Consequently, Max-Plus Algebra needs to be applied and tested in many systems in order to explore hidden aspects of its function and capabilities. To work effectively; the production/assembly line should be balanced. Line balancing is one of the manufacturing functions that tries to divide work equally across the production flow. Car Headlight Manufacturing Line as a Discrete Manufacturing System is considered which is a combination of manufacturing and assembly lines composed of different stations. Seven system scenarios were modeled and analyzed using Max-Plus to balance the car headlights production line. Key Performance Indicators (KPIs) are used to compare the various scenarios including Cycle Time, Average Deliver Rate, Total Processing Lead Time, Stations\u27 Utilization Rate, Idle Time, Efficiency, and Financial Analysis. FlexSim simulation software is used to validate the Max-Plus models results and its advantages and drawbacks compared with Max-Plus Algebra. This study is a unique application of Max-Plus Algebra in line balancing of a manufacturing system. Moreover, the problem size of the considered model is at least twice (12 stations) that of previous studies. In the matter of complexity, seven different scenarios are developed through the combination of parallel stations and buffers. Due to that the last scenario is included four parallel stations plus two buffers Based on the findings, the superiority of scenario 7 compared to other scenarios is proved due to its lowest system delivering first output time (14 seconds), best average delivery rate (24.5 seconds), shortest cycle time (736 seconds), shortest total processing lead time (11,534 seconds), least percentage of idle time (12%), lowest unit cost ($6.9), and highest efficiency (88%). However, Scenario 4 has the best utilization rate at 75%

Recent Trends and Applications of Soft Computing: A Survey

Abstract: This paper is survey on the development of soft computing applications in various domains. Specifically, it briefly reviews main approaches of soft computing (in the wide sense) , the more recent development of soft computing, and finalise by presenting a panoramic view of applications: from the most abstract to the most practical ones. Within this context, fuzzy logic (FL), genetic algorithms (GA) and artificial neural networks (ANN), as well as their fusion are reviewed in order to examine the capability of soft computing methods and techniques to effectively address various hard-to-solve design tasks and issues. This paper presents applications of using different Soft Computation methods in both industrial, biological processes, in engineering design, in investment and financial Trading. It analyses the literature according to the style of soft computing used, the investment discipline used, the successes demonstrated, and the applicability of the research to real world trading

Modeling and Performance Analysis of Manufacturing Systems Using Max-Plus Algebra

In response to increased competition, manufacturing systems are becoming more complex in order to provide the flexibility and responsiveness required by the market. The increased complexity requires decision support tools that can provide insight into the effect of system changes on performance in an efficient and timely manner. Max-Plus algebra is a mathematical tool that can model manufacturing systems in linear equations similar to state-space equations used to model physical systems. These equations can be used in providing insight into the performance of systems that would otherwise require numerous time consuming simulations. This research tackles two challenges that currently hinder the applicability of the use of max-plus algebra in industry. The first problem is the difficulty of deriving the max-plus equations that model complex manufacturing systems. That challenge was overcome through developing a method for automatically generating the max-plus equations for manufacturing systems and presenting them in a form that allows analyzing and comparing any number of possible line configurations in an efficient manner; as well as giving insights into the effects of changing system parameters such as the effects of adding buffers to the system or changing buffers sizes on various system performance measures. The developed equations can also be used in the operation phase to analyze possible line improvements and line reconfigurations due to product changes. The second challenge is the absence of max-plus models for special types of manufacturing systems. For this, max-plus models were developed for the first time for modeling mixed model assembly lines (MMALs) and re-entrant manufacturing systems. The developed methods and tools are applied to case studies of actual manufacturing systems to demonstrate the effectiveness of the developed tools in providing important insight and analysis of manufacturing systems performance. While not covering all types of manufacturing systems, the models presented in this thesis represent a wide variety of systems that are structurally different and thus prove that max-plus algebra is a practical tool that can be used by engineers and managers in modeling and decision support both in the design and operation phases of manufacturing systems