A Study about Unemployment in India – 2004 - 2018

Unemployment is one of the growing economic concerns for a developing country like India, which has the world’s largest youth population. Young Indians face a lot of barriers due to poverty and lack of technical skills required to get into the right job. Though there are a lot of reforms in the education sector, gaining a stable position in the labor market is difficult. Most men in rural areas are now shifting to casual jobs rather than farming activities and women tend to be self-employed. This paper explores the trend in the unemployment rate from 2004 -2018. It provides an overall analysis of the unemployment rate among males and females, rural and urban areas, states and the union territories as well as the relationship between GSDP and unemployment. The paper provides a comparative analysis on the unemployment rate during the Covid-19 pandemic

LPG Gas Leakage Detection and Prevention System

Gas leakage has been a major concern in recent times. Though its usage has been extended in various industries, a small unattended leakage can lead to catastrophic damage. The objective of this project is to present the design of a cost effective automatic alarming system, which can detect and prevent liquefied petroleum gas leakage in various premises. This system alerts the user by sending him an SMS and alerting the neighbors by buzzer alarm after the gas leaks above setpoint1.The stepper motor is used to close the gas pipe valves. To reduce risks to human life, main power supply is cut off, if the gas leakage goes above setpoint2. This device ensures safety and prevents suffocation and explosion due to gas leakage. This project is implemented using ARM 7 processor and simulated using keil software

Health-Seeking Behaviour and the use of Artificial Intelligence-based Healthcare Chatbots among Indian Patients

Artificial Intelligence (AI) based healthcare chatbots can scale up healthcare services in terms of diagnosis and treatment. However, the use of such chatbots may differ among the Indian population. This study investigates the influence of health-seeking behaviour and the availability of traditional, complementary and alternative medicine systems on healthcare chatbots. A quantitative study using a survey technique collects data from the Indian population. Items measuring the awareness of chatbot’s attributes and services, trust in the chatbots, health-seeking behaviour, traditional, complementary and alternative medicine, and use of chatbots are adapted from previous scales. A convenience sample is used to collect the data from the urban population. 397 samples were fetched, and statistical analysis was done. Awareness of the chatbot’s attributes and services impacted the trust in the chatbots. Health-seeking behaviour positively impacted the use of chatbots and enhanced the impact of trust of a chatbot on the use of a chatbot. Traditional, complementary and alternative medicine was not included in the chatbot, which negatively impacted the use of chatbots. At the same time, it dampened the impact of trust in chatbots on the use of chatbots. The study was limited to the urban population and a convenience sampling because of the need to use the Internet and a smart device for accessing the chatbots. The results of the study need to be used cautiously. The results can be inferred from the relationships’ existence rather than the impact’s magnitude. The study’s outcome encourages the availability of chatbots due to the health-seeking behaviour of the Indian urban population. The study also highlights the need for creating intelligent agents with knowledge of Traditional, complementary and alternative medicine. The study contributes to the knowledge of using chatbots in the Indian context. When earlier studies focus mainly on the chatbot features or user characteristics in the intention studies, this study looks at the healthcare system and the services unique to India

FORMULATION AND COMPARATIVE EVALUATION OF ONDANSETRON HYDROCHLORIDE MOUTH DISSOLVING TABLETS IN INDIA

Objective: The aim of the present study was to prepare the ondansetron hydrochloride Mouth Dissolving Tablets (MDTs) followed by its comparison with ethical and non-ethical (generic) marketed tablets. Methods: Prior to the formulation, drug excipient compatibility study was carried out by FTIR spectroscopy. The λmax was determined by UV spectroscopy. The ondansetron hydrochloride MDTs were prepared by direct compression method using Sodium Starch Glycolate (SSG) as super disintegrant and camphor as a sublimating agent. Then the prepared MDTs were subjected to evaluation of post compression parameters such as thickness and diameter, weight variation, wetting time, hardness, friability, disintegration and dissolution. The results obtained were compared with that of ethical and non-ethical marketed ondansetron hydrochloride 4 mg tablets. Results: The λmax was found at 310 nm. FTIR study revealed that excipients used in the prepared formulations are compatible with the drug. The thickness and diameter was in the range of 2.646 to 3.27 mm and 6.0 to 8.12 mm, respectively. Friability was in the range of 0.43 to 0.88 % and had a slightly higher friability (1.27%) for sublimated tablets. Wetting time and disintegration time were in the range of 15 to 40 sec and 23 to 50 sec, respectively. The 100 % drug release was found within 180 sec for all the codes. These results were then compared with non-ethical film coated ondansetron marketed tablets. Conclusion: Ondansetron hydrochloride MDT 4 mg tablets prepared in the laboratory were under specified IP limits. The experimental findings demonstrated that any of these ethical and non-ethical tablets of ondansetron hydrochloride can be selected, advised by the physician or pharmacist, as per the patient’s need and economical status

Molecular classification of breast cancer using IHC markers: experience from a tertiary cancer center in south India

Background: Breast cancer is a very heterogeneous disease. Molecular or intrinsic subtypes of breast cancer are based on the gene expression profiling. Doing gene expression profiling in each case is practically difficult. So most of the labs depend on immunohistochemistry to classify breast tumors into various molecular-like subtypes. In this study, we have used immune histochemistry to classify tumors into various subtypes. Methods: We have retrospectively collected the data of breast cancer patients treated at Apollo Cancer Center, Chennai, in whom ER, PR, HER 2 Neu and Ki 67 were done, and the data was analyzed. Results: The commonest molecular subtype observed in the present study was Luminal B HER2 positive, constituting 40% of the cases, followed by a HER2 positive (non-luminal) subtype in 20% of cases. The triple negative subtype was the third most frequent, comprising 18% of the cases. The least frequent subtype was Luminal A, seen in only 8% of cases. Conclusions: There is a higher proportion of luminal B HER2 positive and triple negative subtypes in our study population compared to the other studies in published literature. The proportion of luminal A was lesser in our study compared to the literature

Deep Reinforcement Learning for Action Based Object Tracking in Video Sequences

In this paper, we propose a valuable route for visual object tracker which catches a bounding box to zone of premium physically in the video frames by recognizing the activity got the hang of utilizing the convolution neural systems. The proposed convolution neural network used to control tracking actions is done with various training video sequences and fine-tuned during the actual tracking of the object. Pretrain of the video is done using deep reinforcement learning (RL) along with the supervised learning. Mostly named information from the RL can be utilized for semi supervised learning and assessing through object tracking benchmark dataset, the proposed tracker is confirmed to accomplish a good performance. The proposed method, which operates in real time on without graphics processing unit, outperforms the state of real time trackers with proper accuracy with performance 10%

Improving the classification of Land use Objects using Dense Connectitvity of Convolutional Neural Networks

Land use is an important variable in remote sensing which describes the functions carried out on a piece of land in order to obtain benefits and is especially useful to the personnel working in the fields of urban management and planning. The land use information is maintained by national mapping agencies in geo-spatial databases. Commonly, land use data is stored in the form of polygon objects; the label of the object indicates land use. The main goal of classification of land use objects is to update an existing database in an automatic process. Recently, Convolutional Neural Networks (CNN) have been widely used to tackle this task utilizing high resolution aerial images (and derived data such as digital surface model). One big challenge classifying polygons is to deal with the large variation in their geometrical extent. For this challenge, we adopt the method of Yang et al. (2019) to decompose polygons into regular patches of fixed size. The decomposition leads to two sets of polygons: small and large, where the former suffers from a lower identification rate. In this paper, we propose CNN methods which incorporate dense connectivity and integrate it with intermediate information via global average pooling to improve land use classification, mainly focusing on small polygons. We present different network variants by incorporating intermediate information via global average pooling from different stages of the network. We test our methods on two sites; our experiments show that the dense connectivity and integration of intermediate information has a positive effect not only on the classification accuracy on the whole but also on the identification of small polygons. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Pattern self-medication use of analgesics in Pune, Maharashtra, India

Background: Objective of current study was to find out self-medication pattern and to study awareness of ADRs to analgesics self-medication.Methods: II MBBS students collected the information of names of analgesics self-medication, dose, frequency of administration, health related problem for use of self-medication, source of information for the use of self-medication and information about ADRs. Students also educated the population about ADRs to analgesics with the help of ADR checklist.Results: Paracetamol was most commonly taken as self-medication and 39% persons reported ADRs with paracetamol.  Ibuprofen, diclofenac, paracetamol and aspirin were taken less than WHO DDD for joint pain. 79% study population was ignorant about ADRs to analgesics. Headache (37%) was common health related problem for self-medication of analgesics.   Conclusion: Information about problems with repeated use of analgesics like liver damage, analgesic nephropathy, gastric ulceration/bleeding should be provided by pharmacists either orally or with the help of leaflets or display board. Headache is common health related problem for the use of analgesics as self-medication. Pharmacists should take help of assistance tool to diagnosis headache like screener for migraine and guidelines for chronic headache for timely visit of self-medicating person to physician.