DISEASE OF LUNG INFECTION DETECTION USING CNN MODEL -BAYESIAN OPTIMIZATION

Auscultation plays a role, in diagnosing and identifying diseases during examinations. However, it requires training and expertise, for application. This study aims to tackle this challenge by introducing a model that categorizes respiratory sounds into eight groups: URTI, Healthy, Asthma, COPD, LRTI, Bronchiectasis, Pneumonia, and Bronchiolitis. To achieve this categorization the study utilizes a Convolutional Neural Network (CNN) model that has been optimized using techniques. The dataset used in the study consists of 920 audio samples obtained from 126 patients with durations ranging from 10 to 90 seconds. Impressively, the model demonstrates a noteworthy 83% validation accuracy and an impressive 86% training accuracy, highlighting its robust and effective performance. To enhance user interaction and facilitate result visualization, the research team has developed a user-friendly interface using Flask, HTML, and CSS. This interface provides healthcare professionals and other stakeholders with the means to access and interpret the results of the experimental analysis. Overall, this research marks a significant stride in making respiratory sound analysis more accessible and accurate, thus contributing to improved disease diagnosis and patient care

Serial Parallel Reliability Redundancy Allocation Optimization for Energy Efficient and Fault Tolerant Cloud Computing

Serial-parallel redundancy is a reliable way to ensure service and systems will be available in cloud computing. That method involves making copies of the same system or program, with only one remaining active. When an error occurs, the inactive copy can step in as a backup right away, this provides continuous performance and uninterrupted operation. This approach is called parallel redundancy, otherwise known as active-active redundancy, and its exceptional when it comes to strategy. It creates duplicates of a system or service that are all running at once. By doing this fault tolerance increases since if one copy fails, the workload can be distributed across any replica thats functioning properly. Reliability allocation depends on features in a system and the availability and fault tolerance you want from it. Serial redundancy or parallel redundancies can be applied to increase the dependability of systems and services. To demonstrate how well this concept works, we looked into fixed serial parallel reliability redundancy allocation issues followed by using an innovative hybrid optimization technique to find the best possible allocation for peak dependability. We then measured our findings against other research.Comment: 5 Pages, 1 Figure, 2 Table

A deep learning approach to real-time short-term traffic speed prediction with spatial-temporal features

In the realm of Intelligent Transportation Systems (ITS), accurate traffic speed prediction plays an important role in traffic control and management. The study on the prediction of traffic speed has attracted considerable attention from many researchers in this field in the past three decades. In recent years, deep learning-based methods have demonstrated their competitiveness to the time series analysis which is an essential part of traffic prediction. These methods can efficiently capture the complex spatial dependency on road networks and non-linear traffic conditions. We have adopted the convolutional neural network-based deep learning approach to traffic speed prediction in our setting, based on its capability of handling multi-dimensional data efficiently. In practice,the traffic data may not be recorded with a regular interval, due to many factors, like power failure, transmission errors,etc.,that could have an impact on the data collection. Given that some part of our dataset contains a large amount of missing values, we study the effectiveness of a multi-view approach to imputing the missing values so that various prediction models can apply. Experimental results showed that the performance of the traffic speed prediction model improved significantly after imputing the missing values with a multi-view approach, where the missing ratio is up to 50%

Comparative Study of Properties of Different Types of Binder Mixes Modified with Silica Fume

The benefits of using Portland Pozzolanic Cement are fairly established. They offer benefits with respect to the cost of manufacturing of cement because pozzolona are by-products or waste materials replacing a part of Portland clinker, hence fewer primary energy and row materials are required in production of cement. This leads to more efforts towards the use of waste materials with lower environmental impact The PSC Portland slag cement contains ground granulated blast furnace slag as constituent of cement . The Fly ash cement contains fly ash as cement constituent. In India slag cement has main share (about 60%) of the cement market. It is used for almost all types of concrete structures, while it is exclusive material for structures in marine environments. Whereas the Fly ash cement is not that appreciated in the market. The quality of fly ash cement depends on nature and amount of fly ash added to the clicker during manufacturing of the cement and the its fineness. Deficiency associated with the use of Fly ash cement is its low strength specially in early age. Similarly research papers show that slag cement gain strength at early stage but rate of gain of strength is low leading to comparatively less ultimate strength. Research studies indicate that inclusion of Silica Fume in binder mix positively improves the strength of the matrix and its chemical resistance but can create increase in water demand, placing difficulties, plastic shrinkage etc. However, all these materials have certain shortfalls but a proper combination of them can compensate each other’s drawbacks which may result in a good matrix product with enhance overall quality. The aim of the present work is to make a comparative study of properties of these two types of cements mixed in different proportions. In addition study is made to determine the effect of addition of different proportions of silica fume to these binder mixes. All these studies are made on mortar mix of proportion 1:3 with one part of binder mix and three parts of sand. Performance of the mortar mixes will be studied and compared in terms of compressive strength, capillary absorption and porosity tests

“AN EMPIRICAL EVIDENCE OF INTERNATIONAL PORTFOLIO DIVERSIFICATION IN EMERGING MARKETS NAMELY 2 ASIAN POWER HOUSES INDIA AND CHINA TO THE U.K. INVESTOR’S’’

This paper studies, the relationship of long-term and short-term between the U.K. stock market and two top Asian emerging markets, which is India and China. From the correlation coefficient, it is found there are low short-term correlations between these Asian emerging markets and the U.K. The unit root test is used to determine the existence of non-stationarity in the time series data. The results of Augmented Dickey Fuller test and Phillips Perron test shows there is existence of non-stationarity in the time series index data. The Johansen cointegration method of bilateral and multilateral indicates that there is no long-term relationship among these stock markets. Then the Granger-causality test is applied in order to determine the short-term relationship. This test is used because we find no evidence of long-term relationship. The Granger’s Causality test does reveal causality running from the China to U.K. market and India to U.K. market, but none from the U.K. market. Therefore, it shows there is no short-term relationship between U.K. and the Asian emerging market. Overall, the results from all the tests from our data suggest that U.K. investors can have gains from international portfolio diversification when they invest into these Asian emerging markets

Exploring the Potential of Water-Soluble Cu(II) Complexes with MPA–CdTe Quantum Dots for Photoinduced Electron Transfer

Three water-soluble copper complexes based on the amine/pyridine functionalities were investigated, along with quantum dots, as a catalyst–photosensitizer assembly, respectively, for fundamental understanding of photoinduced electron transfer. Luminescence quenching and lifetime measurements were performed to try and establish the actual process that leads to the quenching, such as electron transfer, energy transfer, or complex formation (static quenching). Cyclic voltammetry and dynamic light scattering experiments were also performed. Irrespective of the similar reduction potentials of the three complexes, very different photoluminescence properties were observed

Review of biosensing with whispering-gallery mode lasers

This is the final version. Available from Springer Nature via the DOI in this record. Lasers are the pillars of modern optics and sensing. Microlasers based on whispering-gallery modes (WGMs) are miniature in size and have excellent lasing characteristics suitable for biosensing. WGM lasers have been used for label-free detection of single virus particles, detection of molecular electrostatic changes at biointerfaces, and barcode-type live-cell tagging and tracking. The most recent advances in biosensing with WGM microlasers are described in this review. We cover the basic concepts of WGM resonators, the integration of gain media into various active WGM sensors and devices, and the cutting-edge advances in photonic devices for micro- and nanoprobing of biological samples that can be integrated with WGM lasers.Engineering and Physical Sciences Research Council (EPSRC)Engineering and Physical Sciences Research Council (EPSRC)Biotechnology and Biological Sciences Research Council (BBSRC