IJSRET Volume 9 Issue 1, Jan-Feb-2023

Uncategorized

Birth and Death Process under the Influence of Catastrophes
Authors:- M. Reni Sagayaraj, R. Roja, S. Bhuvaneswari

Abstract- Birth and death process have been studied very extensively in the past (see kendall (1948), bartlett (1955), feller (1957), harris (1963) and bailey (1964)). recently such processes have been studied allowing disasters to occur randomly over time decrementing the population size (see brockwell et al (1982), pakes (1987), bartoszynski et al(1989), buhelr and puri (1989) and peng et al (1993)). the motivation to study these processes stem from the fact several biological populations (for example, ungulate populations on sub-artic islands and populations of grizzy bears in yellowstone park) exhibit this type of behaviour (for detailed account of such examples, see hanson and trckwell (1987)). catastrophes are instantaneous events, each killing some of the members of the population who are present at the time of occurrence of the disaster.

A Comparative Analysis of Weather Forecasting Techniques
Authors:- Prashant Shivhare, Shivank Soni

Abstract- The annual rainfall of India has three seasons per year accounting for about 11% each in the pre-monsoon (January-May) and the northeast monsoon (October-December)and 78% in the southwest monsoon season also known as summer monsoon (June-September). The maximum amount of the rainfall occurs during southwest monsoon (SWM), which governs the agricultural economy of India and hence for administrative purposes. While the season recurs annually, the variation about the long term expected value can be as high as 40-50% in some parts of the country. Variability during SWM season is an uncertain quantity which India faces every year. This uncertainty cans be year to year, season to season (within year), month to month (within season and with inyear) and so on depending on the requirement in the practical purposes. The hugevariation in the rainfall causes droughts and floods. The distress caused by droughts and floods due to extreme variations of the monsoon can be mitigated to some extent if the rainfall time series can be modeled efficiently for simulation and forecasting of SWM data. Hence this becomes the primary reason to develop new models for Indian monsoon rainfall. Rainfall data is a strongly non-Gaussian time series exhibiting non-stationarity.The main objective of the present paper is to compare new statistical approaches to model and forecast Indian monsoon rainfall data. The prediction of earthquakes, floods, rainfalls are predicted by linear data using least square methods. However, in reality this data is non-linear and varies over a period of time, therefore these models failed to give exact results. To overcome this disadvantage the researcher has considered the models based on time series together with data mining techniques for effective prediction. Most of the weather data contains hidden patterns, therefore data mining techniques help to identify these hidden patterns more accurately. Therefore it is necessary to predict weather changes more significantly. The proposed work is highlighted in this direction. In this paper, an attempt is made compare weather prediction models based on the spatial and temporal dependencies among the climatic variables together with forecasting analysis.

Breast cancer Prediction using Deep Learning Technique
Authors:- M. Tech. Scholar Adarsh Gupta, Prof. Sachin Mahajan

Abstract- Breast cancer is the second most frequent form of the disease, behind lung cancer. The most prevalent kind of cancer is that of the lung. Women of reproductive age are more likely to be diagnosed with breast cancer than men. Early detection of breast cancer is essential for reducing the death rate; this is due to the fact that the actual cause of breast cancer is unclear. Early detection of cancer may increase the likelihood of survival by up to 8%. This includes X-rays, mammograms, and even MRIs in certain cases. What’s the matter even the most skilled radiologists have difficulty recognizing minute lumps, bumps, and masses, which results in a large number of false positives and false negatives? This is a really bad sign. A great number of people have the goal of creating more effective apps to diagnose breast cancer at an earlier stage. Photos may now be analyzed by new technology, which can then learn from the results. We used a Deep Convolutional Neural Network (CNN) in this investigation to differentiate between calcifications, masses, asymmetry, and carcinomas. Earlier studies made use of fundamental algorithms to accomplish this goal. The cancer was categorized as either benign or malignant, which made it possible to provide more effective treatment. An earlier training session had been completed for the model. To begin, we put this approach to use in order to successfully complete transfer learning. ResNet50. In a similar vein, we enhanced our model for deep learning. During the process of neural network training, the importance of its learning rate cannot be overstated. The learning rate may be adapted to changes using the method that we provide. When one is first being educated, they will make several mistakes.

A Review of Breast Cancer using Machine Learning
Authors:- M.Tech. Scholar Adarsh Gupta, Prof. Sachin Mahajan

Abstract- Breast cancer is, after lung cancer, the most prevalent form of the disease in the globe. Women are the demographic most likely to be affected by this condition. Breast cancer is the most common kind of cancer to result in a woman’s death if she is of childbearing age. Because there is always more to learn and there is room for improvement in every line of work, medical imaging is not an exception to this rule. It is expected that the death rate associated with cancer would decrease if it is discovered early and effectively treated. The diagnosis accuracy of persons working in the health care profession may be improved via the use of machine learning techniques. The technique known as deep learning has the potential to differentiate between breasts that are healthy and those that have cancer (also known as neural networking). This method might be used to differentiate between healthy breast tissue and breast tissue affected by illness. Long-term research on the topic aimed, among other things, to examine breast cancer and screening practices among Indian women. This was one of the primary goals of the inquiry. A literature study was carried out with the assistance of several databases along with additional sources. Participants in the study were instructed to use phrases linked to breast cancer such as “breast carcinoma” and “breast cancer awareness,” in addition to terms such as “knowledge” and “attitude,” as well as the gender neutral term “women.” In addition, India had a role in the study that was done. This search does not look for articles that have been published in the English language in the last 12 years.

A Comparison of Social Security Agency’s Efficiency in Indonesia: Pre and During Covid-19
Authors:- Krisna Winda Putri , Muhammad Firdaus, Syamsul Hidayat Pasaribu

Abstract- The Covid-19 outbreak have brought detrimental effect for social and economic sectors. Many workers get laid-off, and firms get bankruptcy. As the impact, the rate of unemployment becomes higher globally, including Indonesia. This issue has some impact to the operational of social security agency for employment. To be compared with 2019, some performance indicators like number of participants experienced declining 2020, and it was resulted to contribution revenue. Efficiency measurement should be performed in order to analyse whether social security agencies had operated efficiently. This research used 30 branch offices to be the samples. To calculate the efficiency value, Data Envelopment Analysis (DEA) method had be functioned. Based on the findings, branch offices become more efficient in pandemic situation than previous year. In 2020, there were 17 efficient branch offices, meanwhile its last year, only 12 branch offices which operated perfectly significant. Suggestion for the institution were optimizing the usage of inputs, strengthening the role of external agent, collaborating with the government and law enforcement, and doing some publication to get people’ awareness.

Regional Sustainability Of Pension System In Indonesia
Authors:- Lahvem Alginda, Yeti Lis Purnamadewi, Sahara

Abstract- As of 2015, BPJS Employment manages pension social insurance for Indonesian citizens. The age of this pension system is still relatively new and continuous improvements still need to be made. The financial management technique used is Pay As You Go (PAYG). There are many factors that affect the sustainability of PAYG pension system, starting from demographic aging factors to macroeconomic factors. This study will use the life expectancy variable as a demographic aging parameter; GDP Per Capita and Unemployment rate as macroeconomic parameters and emigration as one of the labor market related factors. Because Indonesia is a very large country, this sustainability assessment is carried out at the regional level. This study aims to conduct an assessment of the sustainability of pension seystem in 11 BPJS Employment regional offices which cover 34 provinces. The analysis method used is Importance – Performance Analysis (IPA). It was found that there are several regions that are in quadrants I and II, namely Quadrant I: GDP Per Capita (Regions 10 and 11); Life Expectancy (Regions 10 and 11); Unemployment Rate (Regions 7 and 11); Emigration: Region 7, 10 and 11. Meanwhile for Quadrant II: GDP per Capita (Region 3 and 7); Life Expectancy (Regions 3 and 7); Unemployment Rate (Region 3 and 10) and Emigration (Region 3). Pension administrators together with the Indonesian government can focus on variables and regions that are in quadrants I and II to maintain the sustainability of pension system.

“Analysis & Prediction of Heart Attack using Machine Learning”

Authors:- Kumar Saurav, Hritwiz Yash, Affan

Abstract- Heart-related sicknesses or Cardiovascular Diseases (CVDs) are the fundamental justification behind countless demise on the planet throughout recent many years and have arisen as the most perilous infection, in India as well as in the entire world. In this way, there is a need for a solid, precise, and practical framework to analyze such infections in time for legitimate treatment. AI calculations and strategies have been applied to different clinical datasets to computerize the examination of enormous and complex information. Numerous scientists, lately, have been utilizing a few AI strategies to assist the well-being with the caring industry and the experts in the determination of heat-related sicknesses. The heart is the following significant organ contrasting with the mind which has a greater need in the Human body. It siphons the blood and supplies it to all organs of the entire body. The expectation of events of heart illnesses in the clinical field is huge work. Information examination is valuable for forecasting from more data and it assists the clinical focus with anticipating different illnesses. An enormous measure of patient- related information is kept up with on a month-to-month premise. Put-away information can be helpful for the wellspring of foreseeing the event of future infections. A portion of the information mining and AI procedures are utilized to anticipate heart infections, like Artificial Neural Network (ANN), Random Forest, and Support Vector Machine (SVM). Prediction and diagnosing of coronary illness become a difficult variable looked by specialists and clinics both in India and abroad. To decrease the enormous size of passing from heart illnesses, a speedy and proficient recognition strategy is to be found. Information mining strategies and AI calculations assume a vital part around here. The scientists speeding up their examination attempts to foster programming with the help of AI calculations which can assist specialists with choosing both expectations and diagnosing coronary illness. The fundamental goal of this examination project is to foresee the coronary illness of a patient utilizing AI calculations.

A Review on Design Optimisation and Structural Analysis Of Piston

Authors:- M.Tech. Scholar Ajay Shrivas, Prof. Prakash Kumar Pandey

Abstract- Heart-related sicknesses or Cardiovascular Diseases (CVDs) are the fundamental justification behind countless demise on the planet throughout recent many years and have arisen as the most perilous infection, in India as well as in the entire world. In this way, there is a need for a solid, precise, and practical framework to analyze such infections in time for legitimate treatment. AI calculations and strategies have been applied to different clinical datasets to computerize the examination of enormous and complex information. Numerous scientists, lately, have been utilizing a few AI strategies to assist the well-being with the caring industry and the experts in the determination of heat-related sicknesses. The heart is the following significant organ contrasting with the mind which has a greater need in the Human body. It siphons the blood and supplies it to all organs of the entire body. The expectation of events of heart illnesses in the clinical field is huge work. Information examination is valuable for forecasting from more data and it assists the clinical focus with anticipating different illnesses. An enormous measure of patient- related information is kept up with on a month-to-month premise. Put-away information can be helpful for the wellspring of foreseeing the event of future infections. A portion of the information mining and AI procedures are utilized to anticipate heart infections, like Artificial Neural Network (ANN), Random Forest, and Support Vector Machine (SVM). Prediction and diagnosing of coronary illness become a difficult variable looked by specialists and clinics both in India and abroad. To decrease the enormous size of passing from heart illnesses, a speedy and proficient recognition strategy is to be found. Information mining strategies and AI calculations assume a vital part around here. The scientists speeding up their examination attempts to foster programming with the help of AI calculations which can assist specialists with choosing both expectations and diagnosing coronary illness. The fundamental goal of this examination project is to foresee the coronary illness of a patient utilizing AI calculations.

A Review on Design Optimisation of Connecting Rod

Authors:- M.Tech.Scholar Arvind Kumar Lodhi, Prof. Prakash Kumar Pandey

Abstract- Connecting rod is a component inside of an internal combustion engine. The piston is connected to the crank by connecting rod and it is the principal part to transmit power from the piston to the crankshaft. In terms of structural stability and performance, it is considered a critical factor. The main effort in reducing weight has been to optimize the form and remove materials, which is not often possible. In order to manufacture lightweight connecting rod. Furthermore, the connecting rod is a vital component of high volume production output. The reciprocal piston is connected to the rotating shaft and the piston thrust is sent to the shaft. Each motor that uses an inner combustion engine contains, based on the engine number of cylinders, at least one connecting rod. It is only rational to optimize the connecting rod design. The goal may also be met to lower the engine part weight and thereby reduce inertia loads, reduce motor weight, and improve motor efficiency and save power.

How Do The Employee Competencies, Product Innovation, Benefits, And Pricing Affect Service Quality: A Case Study Of BPJS Ketenagakerjaan
Authors:- Mochamad Azkha Rinaldhy, Ma’mun Sarma , Heti Mulyati

Abstract- BPJS Ketenagakerjaan has challenges in maintaining active participation in the self-employed sector, although this is mandatory according to the Regulation of the Minister of Manpower of the Republic of Indonesia Number 1 of 2016, due to the nature of registration based on the awareness of each individual and there is no obligation to pay fines if they do not pay contributions, making many self-employed participants not committed to paying contributions. This research aims to determine whether employee competencies, product innovation, benefits, and price affect service quality. The study used a questionnaire to collect the data from 200 participants of BPJS Ketenagakerjaan in the West Nusa Tenggara area. The analytical method used Logistic Regression and SEM analysis. The results showed that only product innovation had no significant effect on service quality.

Structural Analysis Of Rcc T-Girder Bridge With Different Loading Condition Using Staad Pro
Authors:- PG Student Pooja Sharma, Asst.Prof. Aslam Hussain

Abstract- In order to facilitate access across physical impediments like a water ways, valley, or highways, bridges are those constructions that are created to span them without blocking the way underneath. It is possible to create a prediction model that is capable of predicting structural behaviour of RCC T-girder bridges in terms of effectiveness using various span conditions, T girder shows better outcomes when compared to other beam deck which is economical for shorter spans, and with increasing the length of span dead load also increase . This is due to researchers’ growing interest in bridge modelling by using different span condition to check effectiveness of girder. On increasing the length of span, the requirements of cross girders (diaphragms) will also increases as to get desired effectiveness between main girders. For this a database from previous literature is collected and model has been developed by using staad Pro. This model can be used for determining the bending moment, shear, torsion and displacement of RCC-T girder by considering various loads, span condition simultaneously. The main objective of this paper is to check whether the nature of girder on different span is significant or notand best suited configuration and location of displacement on RCC T girder is analysed. The present analyses are carried out in stadd pro software. There are four of them: IRC 21-2000, IRC 5-2015, IRC 6-2016, and IRC 112-2011.

An Improvisation of Strength Parameters of Rigid Pavements by Using Industrial Wastes: A Review
Authors:- Assistant Professor Pusa Sai Sudha, Associate Professor Dr. Srikanth Ramvath

Abstract- Pervious cement is an extraordinary high porosity concrete utilized for flatwork applications that permits water from precipitation and different sources to go through, in this way lessening the overflow from a site and re-energizing ground water levels. Its void substance goes from 18 to 35% with compressive qualities of 2.74 to 27.56 MPa . Regularly, pervious cement has practically zero fine total and has barely sufficient cementitious glue to cover the coarse total particles while protecting the interconnectivity of the voids. Pervious cement is generally utilized in stopping regions, regions with light traffic, person on foot walkways, and nurseries and adds to supportable construction.In this venture we are utilizing scrap marble to make pervious cement and furthermore checking different boundaries like porousness and compressive strength concerning various kinds of total like precise, adjusted, and flaky sort. 3D squares produced using a wide range of total where projected and compressive strength test (at 7 and 28 days) alongside invasion test (at 28 days) where done.

Machine Learning Based Approach for Brain Tumor Detection
Authors:- Dr.E.Shanmugapriya , O.Rajasekar

Abstract- Automated defect detection in medical imaging has become the emergent field in several medical diagnostic applications. Automated detection of tumor in Magnetic Resonance Imaging (MRI) is very crucial as it provides information about abnormal tissues which is necessary for planning treatment. The objective of this project is to analysis the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. The availability of an automated computer analysis tool that is more objective than human readers can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. A computer-assisted classification method combining conventional MRI and perfusion MRI is developed and used for differential diagnosis. The proposed scheme consists of several steps including ROI definition, feature extraction, feature selection and classification. The extracted features include tumor shape and intensity characteristics as well as rotation invariant texture features. Feature subset selection is performed using Support Vector Machines (SVMs) with recursive feature elimination. The Convolution neural network method for defect detection in magnetic resonance brain images is human inspection. This method is impractical for large amount of data. So, automated tumor detection methods are developed as it would save radiologist time. The MRI brain tumor detection is complicated task due to complexity and variance of tumors. In this paper, tumor is detected in brain MRI using convolution neural network algorithm. The proposed work is divided into three parts: preprocessing Segmentation and classification steps are applied on brain MRI images, texture features are extracted using Gray Level Co-occurrence Matrix (GLCM),DWT and then classification is done using svm algorithm.

The Effect of Investment on Youth Unemployment Rate in Indonesia
Authors:- Fatkhu Rokhim, Tanti Novianti, Lukytawati Anggraeni

Abstract- This study aims to analyze the effect of investment (domestic and foreign investment) as well as other factors on youth unemployment in Indonesia. This study uses secondary data obtained from the Central Statistics Agency (BPS) and the Coordination and Investment Agency (BKPM). The data used is panel data from time series data for 2015 – 2021 and cross sections covering 34 provinces in Indonesia. The results of the descriptive analysis show that there are provinces that have high investment but also have high youth unemployment, such as the provinces of South Sumatra, West Java, Banten, Central Sulawesi and North Maluku. The results of the panel data regression analysis show that the domestic investment has a positive and significant influence on youth unemployment in Indonesia. The government through the Coordination and Investment Agency (BKPM) is expected to encourage large companies entering Indonesia to collaborate with local companies and Micro Small Medium Enterprises (MSMEs) to focus more on labor-intensive industries.

Inter laminar Fracture of Aerospace Composites Materials
Authors:- Research Scholar Imran Abdul Munaf Saundatti, Dr. G R Selokar

Abstract- The interlaminar fracture toughness is a measure of the capacity of material to oppose delamination. The experimental assurance of the protection from delamination is significant in aviation applications. Distinctive sort of examples and experimental methods are utilized to measure the interlaminar fracture toughness of composite materials. The point of the present research is to pick up a superior comprehension of interlaminar facture of polymer framework composites in various modes, and to create scientific model to anticipate the critical strain energy discharge rates. Accentuation has been set on the root revolution at the crack tip which was accepted to be a critical factor which influences the delamination fracture toughness, and critical burden. A joined experimental and hypothetical investigation has been directed to decide the job of root revolution on critical burden.

Enterprises Social Security Employment Contributions During Covid-19 Pandemic
Authors:- Setyo Ardy Gunawan, Sahara, Yeti Lis Purnamadewi

Abstract- The implementation of social restrictions during the COVID-19 pandemic caused an economic slowdown and made it difficult for many enterprises to keep running, including the obligation to pay social security contributions for employment. To overcome the issue, the government provides policy to ease the burden on enterprises and avoid the occurrence of labor layoffs. However, there are still many companies that are laying off their workers during the pandemic and cause the unemployment rate increased resulting in a decrease in the number of contributions paid by enterprises for employment social security participation. If this problem persists, the sustainability of social security funds will be threatened and payment of benefits to participants will be disrupted. This study aims to analyse the changes on the contributions, registered labor, and reported wages of enterprises toward social security participation before and during the pandemic. The objective will be addressed by analysing contribution paid, number of registered workers, and total wages reported by enterprises before and during the COVID-19 pandemic with a tabular descriptive analysis using a paired t-test. The result indicates that there is a significant decrease in contributions, registered labor, and reported wages for enterprises during the pandemic compared to before the pandemic.