Journal of Applied Science and Education (JASE)

Thyroid Disease Detection Using Machine Learning

2023-05-19T05:50:25+00:00

The thyroid gland, which is in charge of controlling metabolism and other biological functions, is affected by thyroid illness, a frequent medical disorder. Successful thyroid disease management and therapy depends on early diagnosis and treatment. Recent years have seen the development of numerous machine learning methods and artificial intelligence (AI) algorithms to help with the early detection and diagnosis of thyroid disease. These methods entail evaluating a range of patient data, such as laboratory results, imaging studies, and clinical complaints. These algorithms can find patterns and correlations in vast volumes of patient data that might not be obvious to human ex-perts. This may result in earlier identification and more precise diagnosis of thyroid ill-ness, enhancing patient outcomes and lowering medical expenses. Additional study and development are necessary to improve these methods and incorporate them into clinical practice.

Heart Disease Prediction by using Random Forest Classifier

2023-05-17T16:07:30+00:00

This research presents data on a Machine Learning-based Artificial Intelligence system used in predicting cardiac illness. In this research, we learn how advances in machine learning have improved our ability to foresee who will and will not get heart disease. In both developed and less developed, non-industrialized countries, cardiovascular dis- eases are majorly the main reason of immortality for decades. Reducing mortality from cardiovascular infections requires both early detection and constant clinical supervision. However, it is unrealistic to expect accurate, consistent patient screening, and having a specialist confer with a patient for 24 hours isn't feasible due to the additional knowledge, time, and training it would require. Here, we have used ML algorithms and methods which are likely as linear regression, Random Forest, Decision tree, SVM, KNN, and others to construct and explore models for coronary sickness expectancy via the various cardiac attributes of patient and to spot impending coronary ill-ness. For more accurate diagnosis of heart infections, a Random Forest is developed. Due to its near-perfect accuracy in data preparation, this application necessitates thorough data analysis.

Understanding Covid-19 Situation Using Mathematical Modeling

2023-08-24T16:43:03+00:00

In light of the absence of an effective vaccine or specific antiviral treatments, mathematical modeling assumes a crucial role in enhancing our comprehension of disease dynamics and in devising strategies to manage the rapid spread of infectious diseases. Particularly during this period, forecasting holds paramount significance for healthcare planning and for effectively addressing the COVID-19 pandemic. To eluci-date the dynamics of the COVID-19 outbreak, this study introduces an extended SEIR compartment model, refined with the inclusion of contact tracing and hospitalization strategies. The model is calibrated employing daily COVID-19 data encompassing various Indian regions, including Kerala, Karnataka, Andhra Pradesh, Maharashtra, West Bengal, and the entirety of India. Employing the least squares method, we es-timate sensitive parameters after conducting a sensitivity analysis, which we ap-proach using partial rank correlation coefficient techniques. Our exploration focuses on the relative significance of system parameters, with a dedicated sensitivity analy-sis centered on the reproductive number R₀. To assess the model's resilience across parameter variations, we compute R₀ sensitivity indices. Our findings underscore the effectiveness of strategies that involve reducing disease transmission coefficients (s) and clinical outbreak rates (qa) in controlling COVID-19 outbreaks. Our study gener-ates short-term predictions for daily and cumulative confirmed COVID-19 cases across the five Indian provinces. These projections reveal distinct trends, with certain states demonstrating steady exponential growth, while others exhibit a decline in daily new cases. Examining the long-term perspective, our model predicts oscillatory dynamics for COVID-19 cases in India, suggesting the potential for the disease to follow a seasonal pattern. Consequently, our simulation points towards a power law trend in coronavirus cases in India by the close of September 2020, further contrib-uting to our understanding of the disease's progression.

Military Dominance in Post-Colonial States: A Case Study of Pakistan

2023-05-01T10:59:08+00:00

This article investigates the concept of military dominance in post-colonial states, with a particular focus on Pakistan. The article evaluates how the military has exerted signifi-cant influence over the country's government and governance structures, drawing on past and present analyses of Pakistan's political atmosphere. The article investigates the underlying foundations of military dominance in Pakistan using case studies and historical accounts, including the legacy of colonialism, political & economic instability, and external pressures. According to the article, the military's dominance of the political sphere has had a significant impact on the country's development and stability, as well as its democratic systems. The study also looks at potential approaches to decreasing military influence and increasing civilian oversight of government, such as constitution-al reforms, strengthening civil society, and improving electoral processes. Finally, this research aims to contribute to a broader understanding of military dominance in post-colonial states, as well as the challenges that countries trying to seek to transform to strong democratic government face.

Digital Storytelling and Peacebuilding Among the Residents of Ilaje Local Government Area, Ondo State

2023-11-22T07:48:27+00:00

This study investigated the impact of digital storytelling as a peacebuilding tool among the residents of Ilaje Local Government Area, Ondo State. Peacebuilding in conflictprone regions is of paramount importance to foster sustainable development and community cohesion. Digital storytelling has emerged as a promising communication approach, allowing individuals to share their experiences, perspectives, and narratives through multimedia platforms, transcending traditional barriers of space and time. The research adopted a mixedmethods approach, combining qualitative and quantitative data collection techniques to provide a comprehensive understanding of the effectiveness of digital storytelling in promoting peace and reconciliation within the local community. The qualitative phase comprises indepth interview and participant observation to explore the lived experiences and perceptions of individuals who have engaged in digital storytelling initiatives. In the quantitative phase, a structured survey was conducted among a representative sample of residents to measure the impact of digital storytelling on attitudes towards conflict resolution, intergroup relations, and overall peacebuilding efforts. By employing triangulation methods, the research aimed to validate findings and gain a nuanced understanding of the complex dynamics between digital storytelling and peacebuilding. Findings showed that digital storytelling provides a platform for residents of Ilaje Local Government Area, Ondo State to share their personal experiences and perspectives on peacebuilding initiatives. DST encourages empathy and understanding among the community members in Ilaje, fostering a sense of unity and cooperation towards peacebuilding efforts. The study recommended that the government should create a comprehensive digital storytelling training program aimed at empowering residents of Ilaje Local Government Area with the necessary skills to craft and share their stories effectively.

Fake News Detection Using LSTM in TensorFlow and Deep Learning

2023-05-31T17:43:09+00:00

Today's culture has seen a significant rise in fake news, especially with the emergence of social media platforms where misinformation may spread swiftly. False information may have detrimental effects, such as influencing elections, encouraging hate speech, and eroding trust in reliable news sources. Identification of false news has become a hot topic in recent years, with several solutions being proposed for the problem. The paper discusses LSTM (Long Short-Term Memory), Bidirectional LSTM (BiLSTM), and Convolu-tion Neural Network (CNN)-based deep learning-based algorithms for identifying fake news. The "ISOT Fake News dataset," "News Dataset from TI-CNN," and "Getting Real About Fake News dataset" are among the datasets that were used. Following prepro-cessing methods such as stop keyword removal, stemming, and tokenization are used, these datasets are subjected to word embedding. This processed data is used to train the LSTM model, which determines whether or not news reports are fake. Performance metrics including precision, recall, accuracy, and F1-score provide as proof of the rec-ommended model's efficacy in identifying fake news. Comparisons with other state-of-the-art models show its improved efficacy. In terms of both accuracy and F1-score, the CNN beat the standard LSTM and BiLSTM models. CNN-BiLSTM is most effective model, having superior findings as well as efficiency across the three datasets.

Role of Mathematics in Neuroscience

2022-12-25T17:43:38+00:00

With the growing role and application of mathematics in almost every field it is no doubt that it plays an experimental and crucial role in an advancing field of neurosci-ence which is still under constant research. The aim of the report is two fundamental points: to show how mathematical models that enlighten a few pieces of neuroscience can be built, principally by depicting both "exemplary" and recent models; what's more, to make sense of mathematical techniques by which these models can be investigated, in this way yielding pre-expressions and clarifications that can be applied as a powerful influence for experimental information. To show how somewhat straightforward mathematical models and their investigations can help comprehension of certain areas and activity of brain and central nervous systems. In computational neuroscience a model is recorded which is an estimate of the connection between a bunch of infor-mation; however, there is no formal intelligent way to "demonstrate" the model right or wrong. So mathematical recreations are done. The role of mathematics in neuroscience has been elaborated and discussed.

Robotics, AI and IoT Applications in Medical Treatment during the Pandemic

2023-05-21T05:44:42+00:00

Using current technologies, many nations have swiftly responded to the unanticipated Corona-virus disease 2019 (COVID-19) pandemic. Robotics, AI, and digital technology, for instance, have been used in hospitals and public spaces to preserve social distance, minimize person-to-person contact, enable quick diagnosis, monitor viral spread, and provide sanitation. In this article usage of technology in the pandemic situation is dis-cussed and several examples are given to better understand its implementation specific to COVID-19 scenario.

Harmful Effects of Chemical Pesticides

2023-05-19T05:16:50+00:00

After the rise of Green Revolution in Indian agriculture, pesticides have played an im-portant role in increasing crop production and protecting crops from pests, diseases and weeds. India is the third largest consumer (BWD) of pesticides in the world and the largest consumer among countries in South Asia. India is the second largest producer of common insecticides (thempb chmejpbpakme) in Asia after China and ranks 12th in the world. India is the 13th largest exporter of pesticides in the world after Brazil, US. A. Exports pesticides to countries like France and Netherlands. Over the decades, the con-sumption of pesticides in India has increased manifold from 154 MT in 1953-54 to 85000 MT in 2009-2010. Pesticide consumption per hectare in India is among the low-est in the world, at 0.6 kg per hectare recently. Other countries of the world like- U.K. Of. (5.7 kg. per hectare), France (5-6 kg. per hectare), Korea (7 kg. per hectare), U.S.A. (7 kg. per hectare), Japan (12 kg. per hectare), China (13 kg. per hectare) and Taiwan (17 kg. per hectare) are consumed. The highest consumption of pesticides in our country is An-dhra Pradesh, Maharashtra and Punjab, in which 45 percent of the total consumption is used. Most of the pesticides are used in crops like cotton, paddy, fruits and vegeta-bles. According to the Insecticides Act 1968, there are about 155 insecticides registered in India, including 57 insecticides, 44 fungicides, 33 weedicides, 7 rodenticides, 4 plant growth regulators, 4 fumigants, 3 octapadicides, 1 molluscicide, 1 nematode, 1 soil ster-ilizer. Of the total pesticides in our country, 60 percent insecticides, 18 percent fungi-cides, 16 percent weedicides, 3 percent biocides and 3 percent other chemicals are used in agriculture.