Keywords : analysis

Deep Learning for Covid-19 Identification: A Comparative Analysis

Suresh P; Justin Jayaraj K; Aravintha Prasad VC; Abishek Velavan; Mr Gokulnath

International Research Journal on Advanced Science Hub, 2022, Volume 4, Issue 11, Pages 272-280
DOI: 10.47392/irjash.2022.068

Covid 19 was an epidemic in 2022. Detection of Covid in X-Ray samples is crucial for diagnosis and treatment. This was also challenging for the identification of covid by radiologists. This study proposes Transfer Learning for detecting Covid-19 from X-Ray images. The proposed Transfer Learning detects the normal x-ray and covid 19 x-ray samples. In addition to this proposed model, different architectures including trained Desnet121, Efficient B4, Resnet 34, and mobilenetv2 were evaluated for the covid dataset. Our suggested model has compared the existing covid-19 detection algorithm in terms of accuracy. The Experimental model detects covid 19 patients with an accuracy of 98 percent. Our proposed work is to analyse the covid19 by the automation with helps of deep learning algorithms which results in high accuracy in detection Covid19 using x-ray samples. This model can assist radiologists and doctors in the diagnosis of covid-19 and make the test more accessible.

Performance enhancement of Waffles (Appalam) drying using mixed mode solar dryer

Subramanian C.; Chandru Deva Kannan P.; Karthikeyan L.; Prakashraj G.; Nadaraj P.

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICEST 1S, Pages 40-45
DOI: 10.47392/irjash.2021.018

In this present work, investigations are made to study the performance of mixed mode solar dryer for waffles drying. The design, development and performance analysis of mixed mode solar dryer for drying of waffles (appalam) is reported. The design was developed under metrological conditions at Puducherry. A novel numerical model was developed to enhance the productivity rate by reducing the drying time of the waffles (appalam).

Modeling and Analysis of Cylinder Block for V8 Engine

Rupa Athimakula; Lakshmi Kanth; Sai BargavChowdary B; Ismail Kakarwada

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue 12, Pages 30-40
DOI: 10.47392/irjash.2020.245

Loss of heat is a significant factor in the performance of internal combustion engines. In addition, a heat transfer phenomenon causes mechanical stresses that are thermally induced, compromising the efficiency of engine components.In engine design, the capability to determine heat transfer in engines plays a vital role. Today, the simulations are progressively being made at a much earlier stage of engine production with numerical simulations In the current research V type multi-cylinder assemblage is modeled. This design is introduced to ANSYS and completed the consistent state thermal and constructional investigation for anticipating heat stress, heat transference, heat flux in contrasting and two distinct materials (FU 4270, FU 2451) from presented material (Aluminium). Heat transfer is the significant part of power change in internal ignition engines. Finding problem areas in a strong wall is utilized as a driving force makes a plan a superior chilling system. Quick transitory heat fluxes with the ignition chamber and the strong divider have to be explored to comprehend the impacts of non-consistent temperatures.

K-Means Clustering on the Performance Evaluation of Faculty using Data Mining Techniques

Preeti Jain; Umesh kumar Pandey

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue Special Issue ICAET 11S, Pages 36-41
DOI: 10.47392/irjash.2020.230

The motivation behind this paper is to give an outline of broadly utilized measurements, to examine the qualities, advantages and disadvantages of different measurements, to portray current instructive information mining rehearses, and to give rules to assessing execution models of staff. has been discovered to be reliant on various boundaries extensively going from the person's capabilities, experience, level of commitment, research exercises attempted to institutional help, monetary achievability, top administration's help and so on The models that are basic for assessing the yield of workforce range across various verticals, however the paper locations and covers the introduction of staff dependent on contribution from understudies. The other personnel introduction assessor is the regulatory element that might be a private body or an administration unit, the affiliation or the college's self-and friend resources. The boundaries fill in as standard markers for an individual and a gathering and may influence the end later on. The standard proposed in this paper is to utilize Data Mining strategies to lead pulling out and investigation of workforce results. The fundamental idea driving the utilization of Data Drawing is to group the yield of workforce on various measures subject to novel requirements and furthermore to separate the conditions between the boundaries that will assist with finding important relations between them. Basically, these binds help to arrange new dynamic patterns. The paper limits contribution from the Department of Computer Applications through qualified foundations to understudies. The examination depends on numerous highlights, and as opposed to following the ordinary methodology, the paper legitimizes the utilization of mining approaches. K-implies is a sort of non-various leveled (gathered) information grouping that endeavors’, contingent upon the methods (mm) that have been pre-masterminded, to segment information into at least two classes. The k-implies technique is utilized in numerous investigations since it is quick and fit for consolidating a lot of information with an exceptionally short computation. The k-implies calculation is the easiest and most often utilized bunching strategy. This is on the grounds that K-implies can possibly aggregate huge volumes of information with sensibly brisk and powerful preparing time.

Analysis of Student Academic Performance through Expert systems

Kandula Neha; S Jahangeer Sidiq

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue Special Issue ICIES 9S, Pages 48-54
DOI: 10.47392/irjash.2020.158

Predicting the performance of students is one of the most important topics required for learning contexts such as colleges and universities, as it helps to design successful mechanisms that boost tutorial outcomes and prevent dropouts among various items. These are benefited by automating the many processes involved in the activities of usual students which handle huge volumes of information collected from package tools for technology-enhanced learning. Thus, the careful analysis and interpretation of these information would provide us with valuable data regarding the data of the students and therefore the relationship between them and hence the tutorial tasks. This data is the supply which feeds promising algorithms and methods able to estimate the success of the students. During this analysis, virtually many papers were analysed to show radically different trendy techniques widely applied to predict the success of students, along with the goals they need to achieve in this area. These computing-related techniques and approaches are mainly machine learning techniques, deep learning techniques, Artificial Neural Networks & Neural Networks Convolution, etc. This paper demonstrates the analysis and their comparisons of various methods used to forecast Student Academic success.

Machine learning amalgamation of Mathematics, Statistics and Electronics

Trupti S. Gaikwad; Snehal A. Jadhav; Ruta R. Vaidya; Snehal H. Kulkarni

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue 7, Pages 100-108
DOI: 10.47392/irjash.2020.72

Interdisciplinary research is a manner of research carried out by an individual or group of persons. The knowledge, data, techniques, concepts are incorporated from two or more disciplines. In this paper we tried to throw light on this concept. Machine learning is a branch of computer science which uses the information, tools for collection of data, methods for analysis from the subjects like Electronics, Mathematics and Statistics. Why we use machine learning? Because it plays an influential role in prediction of data. Machine learning is used to find hidden patterns and essential ideas from data as well as it solve complex problems. In today’s world, many applications have large volume of data like structured, unstructured and semi structured. This unexploited resource of knowledge can be used to improve business decisions. As data diversifies many are adapting to machine learning tool for analysis of data, so that, they can exploit intelligence and benefit from that data at most. Machine learning adopts different algorithms and each algorithm performs different functionality. In this paper, we tried to explain through example, how Electronics is used for collection of data while Mathematics and Statistics are used for analysis and finally using Machine learning results can be predicted.