Discretized Linear Regression and Multiclass Support Vector Based Air Pollution Forecasting Technique

Air pollution is a vital issue emerging from the uncontrolled utilization of traditional energy sources as far as developing countries are concerned. Hence, ingenious air pollution forecasting methods are indispensable to minimize the risk. To that end, this paper proposes an Internet of Things (IoT) enabled system for monitoring and controlling air pollution in the cloud computing environment. A method called Linear Regression and Multiclass Support Vector (LR-MSV) IoT-based Air Pollution Forecast is proposed to monitor the air quality data and the air quality index measurement to pave the way for controlling effectively. Extensive experiments carried out on the air quality data in the India dataset have revealed the outstanding performance of the proposed LR-MSV method when benchmarked with well-established state-of-the-art methods. The results obtained by the LR-MSV method witness a significant increase in air pollution forecasting accuracy by reducing the air pollution forecasting time and error rate compared with the results produced by the other state-of-the-art methodsComment: 9 pages, 7 figures, Published with International Journal of Engineering Trends and Technology (IJETT

Novel Regression and Least Square Support Vector Machine Learning Technique for Air Pollution Forecasting

Air pollution is the origination of particulate matter, chemicals, or biological substances that brings pain to either humans or other living creatures or instigates discomfort to the natural habitat and the airspace. Hence, air pollution remains one of the paramount environmental issues as far as metropolitan cities are concerned. Several air pollution benchmarks are even said to have a negative influence on human health. Also, improper detection of air pollution benchmarks results in severe complications for humans and living creatures. To address this aspect, a novel technique called, Discretized Regression and Least Square Support Vector (DR-LSSV) based air pollution forecasting is proposed. The results indicate that the proposed DR-LSSV Technique can efficiently enhance air pollution forecasting performance and outperforms the conventional machine learning methods in terms of air pollution forecasting accuracy, air pollution forecasting time, and false positive rate.Comment: 11 pages, 7 figures, 3 tables, Article Published in April 2023, Volume 71, Issue 04, of SSRG-International Journal of Engineering Trends and Technology (IJETT)", ISSN: 2231-538

Reversible cerebral vasoconstriction syndrome in a young primigravida woman with pre-eclampsia

Reversible cerebral vasoconstriction syndrome (RCVS) is characterised by severe headache and is associated with reversible segmental vasoconstriction of cerebral arteries. Conditions associated with RCVS are commonly pregnancy with or without pre-eclampsia, neurological procedures, head trauma. Thunderclap headache is the chief clinical presentation. Visual disturbances and focal neurological deficits are also frequently encountered. Posterior reversible encephalopathy syndrome and RCVS are often overlapping and hence most cases of RCVS are diagnosed late. We reported a young primigravida who had no comorbidities presenting to the ER with elevated blood pressure and generalised tonic and clonic seizures. Post-delivery her headache persisted and clinically her neurological status started deteriorating. Later she was diagnosed as RCVS. Treatment is based on expert opinion. Nimodipine, nifedipine or verapamil have been used in most patients

Micro Sp-Open Sets in Micro Topological Spaces

In this paper, a new class of open sets called Micro Sp- Open sets in Micro topological spaces are introduced and its fundamental properties are analyzed. Also, some operations on Micro Sp-open sets are investigated

PREVALENCE OF OBESITY AMONG NURSING STUDENTS IN SRM COLLEGE OF NURSING, SRM UNIVERSITY,KATTANKULATHUR, KANCHEEPURAM DISTRICT.

Objective: The objective of the study was to determine the prevalence of obesity among the nursing students.Methods: The research approach was quantitative and the research design adopted was cross-sectional research design. The researcher used non- probability purposive sampling technique, and 80 students were selected for the study. World Health Organization body mass index scale was used to assess the prevalence of obesity.Results: Among 80 samples taken for the study 24 (30%) students are in the stage of underweight; 43 (53.8%) students are in normal weight; and 13 (16.2%) are in the stage of pre-obesity.Conclusion: The study findings revealed that 16.2% of the students are in pre-obese stage; hence, awareness regarding complications of obesity may prevent obesity among the nursing students.Keywords: Obesity, Body mass index, Complications, Underweight, Students

Influence of Processing Parameters in Sicp - Aluminium Alloy Composite Produced by Stir Casting Method

Metal matrix composites are gaining wider acceptance in aerospace and defence industries due to their starving need for lightweight — high strength materials. The control of processing parameters is very important in order to obtain good quality casing with minimal defects. In this study the influence of processing parameters on the properties of SiC particle reinforced aluminium alloy (Al- 10%Si- 0.6%Mg) composite was investigated. The composites are prepared by stir casting method. This method involves mixing of SiC in the molten aluminium alloy aided with mechanical agitation. The processing parameters that were investigated include melt temperature and stirring speed. Specimens produced were subjected to mechanical testing. Microstructure observed through scanning electron microscope and optical microscope was correlated to the observed mechanical behavior. The results show that the melt temperature appears to have little effect on the mechanical properties and particle settling. SEM photographs show that there is particle clustering at low melt temperature

A STUDY TO ASSESS THE LEVEL OF MENTAL HEALTH AMONG B.Sc NURSING FIRST YEAR STUDENTS IN SRM COLLEGE OF NURSING, KATTANKULATHUR.

Objectives: The present study aimed to determine the level of mental health adopted by 1st year B.Sc. nursing students.Methods: A total of 50 1st year B.Sc., nursing students were selected using non-probability convenient sampling technique. The tools used for data collection were William C. Menninger mental health assessment scale, and socio-demographic pro forma.Results: The studies revealed that 40 (80%) students have moderate mental health, 10 (20%) students have good mental health, and none of them have poor mental health. There is a significant association between the level of mental health among nursing students and with their demographic variable sex.Conclusion: The nurse administrator should plan and organize an educational program for nursing students, to prepare them to cope up with any stressful situations. Hence, the researcher emphasizes the need for more research to improve the level of mental health and by applying the research finding for future

New Characterization Of (1,2)S_P-Kernel In Bitopological Spaces

Let J(G)=(V,E) be a jump graph. Let D be a nominal prevailing (dominating) set in a jump graph J(G). If V-D contains a prevailing set D\primeof J(G), then D\prime is called an inverse prevailing set with respect to D. The nominal cardinality of an inverse prevailing set of a jump graph J(G) is called inverse domination number of J(G). In this paper, we computed some interconnections betwixt inverse domination number of jump graph for some graphs

Bone health after menopause: effect of surgical menopaus on bone mineral density and osteoporosis

Background: Natural menopause or surgical menopause is associated with endocrinological changes and alteration in bone and mineral metabolism. Hence this study was conducted to assess the bone mineral density changes in women with surgical menopause. Methods: This is a prospective observational study conducted in the department of obstetrics and gynaecology at Sri Ramachandra medical college, which is a tertiary care teaching hospital. 60 women with surgical menopause were included in the study. BMD was assessed by dual energy X-ray absorptiometry at the lumbar spine and hip joint. All the data was entered in Microsoft excel spread sheet and analysed by using SPSS software.Results: Among 60 study subjects, 41 individuals had a normal BMD, 16 had osteopenia, and 3 were diagnosed with osteoporosis. Osteopenia and osteoporosis is significantly higher in patients who had undergone hysterectomy with removal of ovaries. Observations of osteopenia and osteoporosis were significantly higher with increasing number of years post hysterectomy.Conclusions: Prevalence of osteoporosis is high in patients who undergo hysterectomy. Oophorectomy is associated with postoperative bone loss. Targeted management strategies should include routine BMD assessment and hormone therapy improves management of bone health in this population. Further more studies are needed in large populations to test alternative treatments for post oophorectomy osteoporosis