Analysis of operations research applications in agricultural research: A literature review
Agriculture is a productive process requiring the transformation of a set of productive inputs into output with the aim of satisfying wants. These inputs are limited and as such place a constraint in the transformational process. It, therefore, implies that decision-making on the allocation of these limited agricultural resources is a major area of concern in attaining the objectives of agricultural production. Operations research, an analytical method used in problem-solving and decision-making in organizations, have been applied for over 70 decades in decision-making in agriculture. A review of applications of Operations research by some researchers in agriculture problems at farm level, regional sector level, environment protection, risks, and uncertainty analysis, formulating livestock rations and feedstuffs, forestry management, etc., shows that its application in agriculture is extensive and its potential for development is limitless. The application is constrained by complex interacting drivers existing in productivity, markets, the environment, and the people. These drivers include accuracy of data, quantifiability of data. natural disasters, instability of prices, demand for products, changes in government subsidies and policies, and dependence on an electronic computer. In conclusion, the decision to implement the results from Operations research lies with human beings and so this human element is still the most significant part of the decision-making process. The changes and adjustments in the natural and economic environment, and new improved information in the subject area, must be incorporated in the mathematical models and their parameters to account for the change.
Published by: Abasilim Chinwe Frances
Author: Abasilim Chinwe Frances
Paper ID: V7I2-1219
Paper Status: published
Published: March 25, 2021
Plant leaf disease detection by Features Selection and Learning Approaches Review
Agricultural productivity is something on which the economy highly depends. This is one of the reasons that disease detection in plants plays a vital role in the agriculture field, as having disease in plants is quite natural. If proper care is not taken in this area, then it causes severe effects on plants and due to which respective product quality, quantity, or productivity is affected. In this proposed approach optimized segmentation to find active area for features and reduce noise, then extract texture base features and learning by ensemble classifier approach. In Proposed framework main emphasis on getting sufficient features from disease and learning combination of classifier use linear and nonlinear classification function.
Published by: Nisha Sharma, Dr. Sukhvinder Kaur, Dr. Rahul Malhotra
Author: Nisha Sharma
Paper ID: V7I2-1255
Paper Status: published
Published: March 25, 2021
Detecting autism from facial image
Autism is a serious developmental spectrum disorder that puts constraints on the ability to communicate linguistic, cognitive, and social interaction skills. Autism spectrum disorder screening is the process of detecting potential autistic traits in an individual where the early diagnose shortens the process and has more accurate results. The methods used to predict the presence of autism by doctors involve physical identification of facial features and questioners, this conventional method of diagnosis needs more time, cost and in the case of pervasive developmental disorders, the parents feel inferior to come out in open. Therefore, a time-efficient and accessible ASD screening are imminent to help health professionals and inform individuals whether they should pursue formal clinical diagnosis or not. A screening tool that could identify ASD risk during infancy offers the opportunity for intervention before the full set of symptoms is present. The proposed model by using a convolution neural network classifier helps in predicting the early autistic traits in children through facial features in images, with the least cost, less time, and a greater amount of accuracy when compared to the traditional type of diagnosis.
Published by: Shaik Jahanara, Shobana Padmanabhan
Author: Shaik Jahanara
Paper ID: V7I2-1181
Paper Status: published
Published: March 25, 2021
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
