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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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) -
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. -
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" -
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. -
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. -
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. -
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. -
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. -
Investigations on the Design, Performance and Effect of Feed Mechanisms, Defected Ground Structures and Materials for Optimized Microstrip Antenna Array
Microstrip antenna exhibiting low-profle features such as and#64258;exible, lightweight and newlinelow production cost attracts majority of communication industries working the lower newlinepart of the microwave spectrum ranging from 1 GHz to 6 GHz. Also, the microwave integrated circuit technology enables the integration of feed systems and other microwave integrated circuits on the same substrate where the antenna is printed. However, single antenna topologies feature a number of drawbacks, including low gain, poor directivity, narrow bandwidth and limited coverage being low in profle. In the perspective of miniaturization, developments in wireless communication have had a signifcant impact on antenna or array design based on the gain, bandwidth and directivity requirements for specifc wireless applications. As a result, usage of single antenna is not considered appropriate for diversity reception, long-distance communication, signal-to-interference as well as signal-to-noise ratio maximization, and direction of arrival determination, interference rejection, and high power applications. A high-gain broadband antenna or array may be the good choice for outdoor line-of-sight access points to increase signal strength and coverage range. To meet these requirements the antenna designers either can use conventional antennas or rely on miniaturized antennas. When antenna arrays are built using such small antennas to enhance the above said parameters, suitable and compact feed networks are required to ft within the given space of the overall transmitter-receiver geometry. This research work addresses the challenges faced by antenna researchers in newlineminiaturization, maintenance of gain-bandwidth and high-directivity narrow-beam newlineradiation of microstrip antenna arrays, through an investigation made on the design, new mathematical modelling of feed mechanisms for arrays, their inand#64258;uence on 1D and 2D uniform and non-uniform arrays, and the performance enhancement by amalgamating proposed arrays with defected ground structures and metasurfaces. -
Three-dimensional eveluation model for assessing the effectiveness of knowledge management systems and portals /
Patent Number: 202241049097, Applicant: D Venkata Subramanian.
This invention is related to the field of web and semantic technology. For effective assessment of any Knowledge Management System (KMS), it is important to consider three primary dimensions/factors; being usability, availability and relevance. The proposed invention is a three-dimensional model provides an easy-to-use and comprehensive effectiveness assessment for any KMS or knowledge portals. -
Enhancing the efficiency of parallel genetic algorithms for medical image processing with Hadoop /
International Journal of Computer Applications, Vol.108, Issue 17, pp.92-97, ISSN No: 0975-8887. -
A study on emotional intelligence and work life balance of employees in the information technology industry in Bangalore, India /
Emotional Intelligence is a set of qualities and competencies that captures a broad collection of individual skills and dispositions, usually referred to as soft skills or inter and intra-personal skills, that are outside the traditional areas of specific knowledge, general intelligence, and technical or professional skills. Emotions are an intrinsic part of our biological makeup, and every morning they march into the office with us and influence our behavior. -
Financial management analysis of dividend policy pursued by selected Indian manufacturing companies /
Journal of Financial Management and Analysis, Vol.27, Issue 1, pp.223-229