Emotional intelligence, personality and mental health among sportsperson
The present study was undertaken to investigate differences between emotional intelligence, personality, and mental health among sportspersons and non-sportspersons by Shivani Nishad under the supervision of Dr. Monika Gwalani. Sample of the study 100 sportspersons(50) and non-sportspersons(50). The hypothesis of the study is that there is a statistically significant difference in the measure of emotional intelligence, personality, and mental health among sportspersons and non-sportspersons. Pethe and Hyde’s emotional intelligence test, Neo five-factor inventory, and Jagdish and Srivastava’s mental health inventory used for the study. In order to assess the statistically significant difference between sportspersons and non-sportspersons on the measure of emotional intelligence, personality, and mental health by ‘t’ test. There is a statistically significant difference in the sub-dimensions of measure of emotional intelligence which are- self-awareness, self-motivation, emotional stability, managing relations, and altruistic behavior. There is a statistically significant difference in the dimensions of the measure of personality which are- Extraversion, Openness, and Conscientiousness. There is also a statistically significant difference in the sub-dimension of the measure of mental health and they are- Positive Self-Evaluation, perception of reality, integration of personality, autonomy, group-oriented attitudes, and environmental-mastery. The implications of the outcome are that indulgence in physical activity makes a person physiologically fit and also psychologically and mentally fit. Physical activities are an easy, inexpensive, and appropriate strategy and approach that should be emphasized to increase mental health in adolescence. The results indicated that there is a higher level of emotional intelligence and mental health among the sportsperson than non-sportsperson also a statistically significant difference in dimensions of personality i.e. extraversion, openness, and conscientiousness.
Published by: Shivani Nishad, Monika Gwalani
Author: Shivani Nishad
Paper ID: V7I2-1246
Paper Status: published
Published: March 24, 2021
Military-jihadist nexus in Pakistan
The advent of the religious right-wing as a formidable political force in Pakistan seems to be an outcome of direct and indirect patronage of the dominant military over the years. Ever since the creation of the Islamic Republic of Pakistan, the military establishment has formed a quasi-alliance with the conservative religious elements who define a strongly Islamic identity for the country. The alliance has provided Islamism with regional perspectives and encouraged it to take advantage of the concept of jihad. This trend found its most blatant manifestation through the Afghan War. Thanks to the centrality of Islam in Pakistan’s national identity, secular leaders and groups find it extremely difficult to make a national consensus against groups that describe themselves as soldiers of Islam. Using two case studies, the article argues that the political survival of both the military and therefore the radical Islamist parties is predicated on their tacit understanding. It contends that without the de-radicalization of jihadis, the efforts to ‘mainstream’ them through the electoral process have huge implications for Pakistan’s political system also for prospects of regional peace.
Published by: Dr. Hrishikesh M. Bevanur
Author: Dr. Hrishikesh M. Bevanur
Paper ID: V7I2-1232
Paper Status: published
Published: March 24, 2021
Predictive classification of breast cancer using machine learning
Breast cancer is a disease in which cells in the breast grow out of control. There are different kinds of breast cancer. The kind of breast cancer depends on which cells in the breast turn into cancer. Breast cancer can begin in different parts of the breast. A breast is made up of three main parts: lobules, ducts, and connective tissue. The lobules are the glands that produce milk. The ducts are tubes that carry milk to the nipple. The connective tissue (which consists of fibrous and fatty tissue) surrounds and holds everything together. Most breast cancers begin in the ducts or lobules. Breast cancer can spread outside the breast through blood vessels and lymph vessels. When breast cancer spreads to other parts of the body, it is said to have metastasized. Advances in screening and treatment for breast cancer have improved survival rates dramatically since 1989. According to the American Cancer Society (ACS), there are more than 3.1 million breast cancer survivors in the United States. The chance of any woman dying from breast cancer is around 1 in 38 (2.6%). The ACS estimate that 268,600 women will receive a diagnosis of invasive breast cancer and 62,930 people will receive a diagnosis of noninvasive cancer in 2019. In the same year, the ACS report that 41,760 women will die as a result of breast cancer. However, due to advances in treatment, death rates from breast cancer have been decreasing since 1989. However, The required facility for diagnosing cancer accurately and at the earliest stage using the results of the biopsy is not available to all general hospitals. Identifying and diagnosing cancer at the earliest stage is crucial as the possibility of cancer spreading increases. Therefore, A computerized system that identifies cancer at the earliest stage with minimal time with the greatest accuracy and which reduces cancer recurrence and mortality has to be developed. This paper concentrates and summarises the different machine learning algorithms which may be implied in cancer diagnosis to improve the accuracy of the diagnosis and identification.
Published by: E. S. Dharani, S. Ishwarya, Dr. R. Kanimozhi
Author: E. S. Dharani
Paper ID: V7I2-1247
Paper Status: published
Published: March 23, 2021
Face detection and emotion recognition system
Recognizing Human facial expressions and emotions by computer is an interesting and challenging problem. Recently there has been an increasing interest in improving the interaction between humans and computers. The face is a feature that can differentiate from person to person. Emotion is expressed through face, body gestures, and speech. Emotions through faces vary from situation to situation and person to person. The Face Detection and Emotion Recognition System automatically recognizes the faces and emotions of the persons accurately in an image. The convolutional neural network concept applied with machine learning and image processing is used in classifying universal emotions such as Happiness, Sadness, Anger, Disgust, Surprise, Fear, and Neutral. Color images that are showing the human faces are given as input to the detection system. This face emotion recognition system mainly consists of four steps. They are Image Pre-Processing, Face Detection, Facial Feature Extraction, and Emotion Recognition. Image Pre-Processing is a step to change the image in Binary or Grayscale format and resizing the image in 48x48 pixels. Face detection is a method and that is capable of verifying or identifying and capturing the frames of the faces from an image. Feature extraction is a method to identify the characteristics of the person’s face captured in the image and comparing the faces in the image whether the faces of the persons are the same or not. In the final step, Emotion recognition is trying to acquire the various expressions of emotions that a person can make through their faces to communicate with each other. In emotion recognition step it can predict the emotion of the person in any kind of images or videos. This system can detect the faces and recognize the emotion of the person accurately by considering the live feed camera images and pre-existing image and video clues.
Published by: Tamatam Sowjanya
Author: Tamatam Sowjanya
Paper ID: V7I2-1245
Paper Status: published
Published: March 23, 2021
Coronavirus: A challenge that influenced nearly all fields of science
While the world was busy in fighting over toilet papers, scrolling the memes down the social media, binge watching TV all day long and getting beaten up by police for getting outdoors, there were some people in white coats out there who were working for day and night in the laboratories all around the globe. It was first used in print in the year 1968 by an informal group of virologists in the journal Nature to designate a new family of viruses. The name refers to the characteristic appearance of virions (which is the infective form of the virus) by electron microscopy, which have a fringe of large, bulbous surface projections which make the structure of virus resemble the structure of solar corona or halo. This morphology in coronavirus is created by the viral spike peplomers, which are proteins on the surface of the virus. Out of the wide range of coronaviruses the one causing COVID-19 in humans and which also is the biggest challenge for science is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 or better said SARS-CoV-2. We are unaware of the reason, but the spikes on the surface of coronavirus are just the right shape to lock onto these ACE2 enzymes and bind tightly. Scientists of many fields have jumped in the race to defeat the coronavirus. But what is the use of this in the fight against coronavirus? The whole world is in the midst of a COVID-19 pandemic. When a safe and effective vaccine is found, COVAX have decided to facilitate the equitable access and distribution of these vaccines to protect people in all countries.
Published by: Aditya Anil Kadam
Author: Aditya Anil Kadam
Paper ID: V7I2-1230
Paper Status: published
Published: March 23, 2021
Reversible data embedding using difference expansion scheme for watermarking
Reversible Data Embedding is a lossless reversible data embedding technique that embeds invisible data known as payload into the input image and uses the Difference Expansion (DE) technique. This paper uses a DE-based reversible scheme to reduce the amount of auxiliary information embedded and to increase the watermark length. The key concepts discussed in this paper are: Reversible Embedding, Extraction, and Restoration of the original dataset with minimal distortion. By observing redundancy, reversibility can be explored. Payload, PSNR, and Watermark Length have been used to assess the effectiveness of DE. Four data sets have been used - Lena, Boat, Airplane, and APC. For Lena, the payload obtained is 31.14 and the payload was found to be 0.76 implying that more information can be embedded in an image than in the standard methods.
Published by: Shriya Tatachar, Rakesh K. R., G. R. Namita, Rohit Kulkarni
Author: Shriya Tatachar
Paper ID: V7I2-1222
Paper Status: published
Published: March 23, 2021
