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Comparative study of various metals in the sewage samples of three major drains of the city-Patna, Bihar, India /
Mapana Journal Of Science, Vol.16, Issue 4, pp.23-35, ISSN: 0975-3303. -
Vappav /
Patent Number: 202241028305, Applicant: Sashi Kumar D.
The primary objective and the motto behind this invention of the device Vappav is for the safety of food items and food parcels which needs to have a minimum contact from people and to make sure that the delivery of these parcels are safe without many people touching it, which further ensures the safety and hygienic of the food items. -
A system and method for integrated monitoring, control and management of various parameters of agriculture and crop growth and a device for implementing the same /
Patent Number: 202041010535, Applicant: S Rakesh Kumar.
A system and method for integrated monitoring and management of vital agricultural parameters that are key for sustained plant growth and enhanced crop output, qualitatively and quantitatively is provided. The key aspects of sustained and healthy growth of agricultural crops, viz., temperature, moisture, humidity are monitored and controlled through means, comprising of temperature sensor, soil moisture sensor, motor pump progressively controlled by Arduino and the collated data are displayed through screen. -
Retail merchandising aided by shop display systems /
Patent Number: 202211009293, Applicant: Rakesh Kumar Yadav.
The present invention is a retail merchandising aided by shop display systems: comprises of, three or more display modules but at least 3 should be available therein, once side of the display structure should be open and provided space to access it, but the other side is closed thereby pivoted vertically to provide the provision for fastening it firmly, therein displaying the items have a better space along with facility is given to the system that it state the items placed in the display modules while pressing the corresponding button provided to the individual display unit. -
Device for analysis of market communication in tourism /
Patent Number: 202221009290, Applicant: Dr.Shilpa Bhakar.
The present invention is a device for analysis of market communication in tourism comprises of, a display unit is split into at the 4 parts to convert into a segmented display, thereby, an electronic kit is fitted into the display unit which is being getting the data therein, it is received by an analyser, this analysed data is send to the display unit to show the graphical representation of the tourism information under four different displaying ways. -
Design of an real time smart health care system through data mining and IOT /
"Patent Number: 201941041826, Applicant: Dr. Dinesh Kumar.
Due to the advancements in the medical field , the life expectancy has been i increased significantly .This project is mainly useful forthe elderly people who live without the support from others . They need the assistance for the various parameters. IoT in health careplays a major role in identifying the medical facilities for the patients and the doctors . Our Proposed system with various sensors todiagnose the patients health condition from the remote place" -
System and method for detecting presence of an intruder near a neonatal crib /
Patent Number: 201941028405, Applicant: Karthick Raghunath K M.
Present disclosure provides systems and methods for monitoring presence of an intruder near a neonatal crib in real time. A set of input signals are captured from the one or more sensors that are operatively coupled to the neonatal crib. Attributes pertaining to distance of the intruder from the neonatal crib are determined based on the received input signals. Based on the determined distance, an executable set of instructions generate a rule. -
What fuels the employees in startups?: Data on hybrid/colocated/virtual working environment towards efficiency
Purpose: This article examines the concepts of workplace satisfaction and productivity using data. The data will be used to investigate the variables contributing to employee satisfaction to achieve optimum efficiency through various startup working environments. Design/ Methodology/ Approach: Descriptive causal investigation. A structured instrument scale questionnaire via the internet to 256 employees working for highly organized organizations in Bangalore, India, using Qualtrics. The researcher adopted a simple random sampling method. Findings: The respondents in the data believed that the pre-covid workplace was advantageous. The hybrid model's prevalence of autonomy and flexibility increases work productivity. When employees are given more responsibility, their job satisfaction and productivity increase. Research Limitations/ Implications: Collecting data in a startup was extremely difficult due to the difficulty of obtaining permission, and through the analysis, it was determined that businesses have a responsibility to provide supplemental benefits to remote employees, which may increase the level of job satisfaction and enjoyment experienced by these individuals. 2023 The Author(s) -
Heart Disease Prediction Using Ensemble Voting Methods in Machine Learning
Heart disease is the leading cause of mortality globally according to the World Health Organization. Every year, it results in millions of mortalities and thus billions of dollars in economic damage throughout the world. Many lives can be saved if the disease is detected early and accurately. The typical methods to predict or diagnosis heart diseases require medical expertise. Such facilities and experts are relatively expensive and not very commonly available in under developed and developing countries. Recent times, much research is done on leveraging technology for the prediction as well as diagnosis of heart diseases. Machine Learning techniques have been extensively deployed as quick, inexpensive, and noninvasive ways for heart disease identification. In this work, we present a machine learning approach in detecting heart disease using a dataset that contains vital body parameters. We used seven different models and combined them with Soft-Voting and Hard-Voting ensemble approaches to improve accuracy in 7-model and various 5-model combinations. The ensemble combinations of 5 models achieved the highest test accuracy score of 94.2%. 2022 IEEE. -
Cardiovascular Disease Prediction through Ensembled Transfer Learning on Cardiac Magnetic Resonance Imaging
Cardiovascular Diseases (CVD) cause more deaths worldwide than most of the other diseases. The diagnosis of cardiovascular disease from Magnetic Resonance Imaging plays a major role in the medical field. The technological revolution contributed a lot to increase the effectiveness of CVD diagnosis. Many Artificial Intelligence methods using Deep Learning models are available to assist the cardiologist in the diagnosis of CVD from Magnetic Resonance Imaging (MRI). In this study, we leverage on the merits of deep learning, transfer learning, and ensemble voting to improve the accuracy of Artificial Intelligence-based CVD detection. VGG16, MobileNetV2, and InceptionV3, trained on ImageNet, are the models used and the dataset is the Automatic Cardiac Diagnosis Challenge dataset. We customized the classification layers of all three models to suit the CVD detection problem. The results from these models are ensembled using the soft-voting and hard-voting approaches. Test accuracies obtained are 97.94% and 98.08% from hard-voting and soft-voting respectively. The experimental results demonstrated that the ensemble of outputs from transfer learning-based Deep Learning models produces much improved results for CVD diagnosis from MRI images. 2022 Sibu Cyriac, Sivakumar R. and Nidhin Raju. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Comparison of Full Training and Transfer Learning in Deep Learning for Image Classification
The deep learning algorithms on a small dataset are often not efficient for image classification problems. Make use of the features learned by a model trained on large similar dataset and saved for future reference is a method to solve this problem. In this work, we present a comparison of full training and transfer learning for image classification using Deep Learning. Three different deep learning architectures namely MobileNetV2, InceptionV3 and VGG16 were used for this experiment. Transfer learning showed higher accuracy and less loss than full-training. According to transfer learning results, MobileNetV2 model achieved 98.96%, InceptionV3 model achieved 98.44% and VGG16 model achieved 97.405 as highest test accuracies. The full-trained models did not achieve as much accuracy as that of transfer learning models on the same dataset. The accuracies achieved by full-training for MobileNetV2, InceptionV3 and VGG16 are 79.08%, 73.44% and 75.62% respectively. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Pneumonia Detection using Ensemble Transfer Learning
Pneumonia is among the most common illnesses and causes to death among the young children worldwide. It is more serious in under-developed countries as it is hard to diagnose due to the absence of specialists. Chest X-ray images have essentially been utilized in the diagnosis of this disease. Examining chest X-rays is a difficult task, even for an experienced radiologist. Information Technology, especially Artificial Intelligence, have started contributing to accurate diagnosis of pneumonia from chest X-ray images. In this work, we used deep learning, transfer learning, and ensemble voting to increase the accuracy of pneumonia detection. The models utilized are VGG16, MobileNetV2, and InceptionV3, all pre-trained on ImageNet, and used the Kaggle RSNA CXR image dataset. The results from these models are ensembled using the weighted average ensemble approach to achieve better accuracy and obtained 98.63% test accuracy. The results are promising, and the proposed model can assist doctors in detecting pneumonia quickly and accurately from Chest X-Ray. 2022 IEEE. -
Seismic Activity-based Human Intrusion Detection using Deep Neural Networks
Human intrusion detection systems have found their applications in many sectors including the surveillance of critical infrastructures. Generally, these systems make use of cameras mounted on strategic locations for surveillance purposes. Cameras based detection systems are limited by line-of-sight, need regular maintenance and dependence of electricity for operations. These are all detrimental to the efficiency of these detection systems, especially in remote locations. To overcome these challenges, intrusion detection systems based on seismic activities have been in use. The seismic activities collected through geophones from the human footfalls can act as the input for these detection systems. This also poses a challenge as the data generated by the geophones for the seismic activities produced from footsteps are not always identical and hence not accurate. In this proposed work, a Deep Neural Network based approach has been used on the dataset collected from the geophones to effectively predict the presence of humans. The results gave a success rate with 94.86% accuracy with testing data and 92.00% accuracy with real-time data with the geophones deployed on an area covered with grass. 2022 IEEE. -
Application of LSTM Model for Western Music Composition
Music is one of the innate creative expressions of human beings. Music composition approaches have always been a focal point of music-based research and there has been an increasing interest in Artificial Intelligence (AI) based music composition methods in recent times. Developing an accurate algorithm and neural network architecture is imperative to the success of an AI-based approach to music composition. The present work explores the composition of western music through neural network using a Long Short-Term Memory (LSTM) algorithm. Compositions from seminal western composers such as J.S. Bach, W.A. Mozart, L.V. Beethoven, and F. Chopin were used as the dataset to train the neural network. Seven compositions were generated by the LSTM model and these outputs were presented to a group of thirty volunteers between 18-24 years of age. They were surveyed to identify the music piece as composed by a human or AI and how interesting they found the melodies of each piece. It was found that the LSTM model generated compositions that were thought to be made by a human and create melodies of interest from the perception of the volunteers. It is expected that through this study, more AI-based composition approaches can be developed which encompass more and more of the musical phenomenon. 2022 IEEE. -
Interaction of Generational Differences with Gender and Residential Nature in Attitudes Toward Interfaith Marriages
The present study examined the interaction effects of generations, gender, and residential nature on attitudes toward interfaith marriage in a sample of 1190 Indian participants from iGen, Xennials and Millennials, and Baby Boomers generations. Data were collected using a socio-demographic response sheet and the Attitude Scale, with lower ratings indicating positive attitudes and higher ratings indicating negative attitudes. The results of this study demonstrated that generational differences are significantly associated with gender and residential nature. There was a significant interaction between generation and gender and generation and residential nature on attitudes toward interfaith marriages. 2024 Taylor & Francis Group, LLC. -
Filmography of social issues in Punjabi cinema /
Through this dissertation, the researcher aims to find out the various social issues reflected in the cinema of the Punjab. The main focus of the research is on three films. The first film is Maarhi Da Deeva which is based on a book by Punjabi writer Gurdial Singh and it talks about the trials and tribulations faced by the lower classes at the hands of the upper classes in a rural setup in the Punjab of 1980s. -
Analysing the impact of the taxation law amendment of 2019 on corporate taxation in India
The Taxation Law (Amendment) Act, 2019 in India has brought major changes in the taxation revenue as well as in legal provisions. The actual ground reality of the Amendment on a microeconomic level is unknown, but a correlation analysis on macroeconomic indicators show that there is a high positive correlation between the corporate tax revenue and the GDP growth. The author also interlinks the effects of tax cuts on the economy with privatization and how it can mitigate the risks of tax evasion. There is a generalized misconception with privatization that it leads to a significant loss in taxation revenue. The study shows that in fact, privatization helps to expand the earnings of the Government by widening the taxation structure and slab, which the author has found through statistics. It is high time to have strong regulatory measures to prevent tax evasion by encouraging more corporate entities to become a part of the tax base. Indian Institute of Finance. -
Translation and Validation of the Malayalam Version of the Subjective Happiness Scale
The subjective happiness scale (SHS) is a brief instrument used to measure global subjective happiness that has been translated from its original English to many other languages. To date, there is no reported translation of this scale into Malayalam, a language spoken by over 32 million people especially in the southern state of Kerala, India. In the present study, 656 community-dwelling older adults participating in the Kerala Einstein study (KES) completed the Malayalam version of the SHS. The Malayalam version demonstrated high internal consistency and good convergent validity, as assessed by comparison to measures of depression and anxiety. We also used factor analysis to determine that the Malayalam version of the SHS has a unidimensional structure, akin to the original English as well as other language adaptations. Our study adds to the repertoire of tools to measure happiness in non-English-speaking populations, enabling future research to explore the foundations of well-being across diverse cultures. The Author(s) 2024. -
Psychosocial correlates of resilience among older adults in Mexico
There is a tremendousglobal increase in the older adultspopulation. Mental health in older age is as important in as it is for other age categories. Majority of older adults show healthy states, vitality, good humor and enthusiasm in performing various activities, interest in continuing to contribute to their family and society despite the difficulties of this stage of life due to large part to resilience they have. The aim of the study was to establish social and psychosocial factors associated with resilience.A cross-sectional and correlation study was conducted on older adults who were hospitalized in a public General Hospital of Mexico in 2013. Resilience, gender, occupation, family environment, self-esteem, presence of critical life events, and the presence of significant persons were assessed. 186 older adults participated. Higher levels of resilience were found in males and employed people. Participants with a functional family and high self-esteem had the highest levels of resilience. Besides, 15% of the variance of the total resilience score was explained by family environment, and 27% was explained by self-esteem (p<0.05).Although all participants were older adults, individual characteristics such as gender, occupation and self-esteem; besides family environment, were found to be associated to the levels of resilience in this population. Specific programs- -enhancing these factorsare needed to improve resilience. 2019 Oriental Scientific Publishing Company. All rights reserved. -
Diabe Maigre and Diabe Gras Revisited
[No abstract available]