International Journal of Computational and Innovative Sciences

A Systematic Review: The Role of Fuzzy Logic and Blockchain in Healthcare Data Analytics

2023-12-28T15:38:23+00:00

Healthcare industry has always been under pressure to deliver high-quality services while minimizing errors and ensuring patient safety. The healthcare industry is facing a number of challenges including decision-making accuracy, diagnostic errors, and data security. Blockchain and fuzzy logic design are emerging technologies that may provide potential solutions in order to address these challenges. Blockchain technology offers a secure and decentralized system for storing and sharing patient healthcare data, while fuzzy logic design deals with uncertainty and imprecision, improving patient healthcare decision-making and reducing errors. In this systematic review, the combination of fuzzy logic design and blockchain technology are highlighting the potential benefits in patient healthcare applications, while addressing major concerns in healthcare like decision-making accuracy, diagnostic errors, and data security. The successful implementation of these technologies in patient healthcare may have a significant contributions in handling uncertainty and imprecision in complex healthcare systems. In future, it may be incorporated with machine learning techniques like federated learning and explainable artificial intelligence that could improve decision-making and accuracy in more better way.

Transforming Business Through Sensors, Big Data, and Machine Learning in Modern Animal Farming

2023-12-27T04:42:07+00:00

When it comes to animal production, humans have always depended on gut feelings, common knowledge, and sensory inputs, even when domesticating animals started thousands of years ago. Our achievements in farming and animal husbandry have been substantial thus far thanks to this. More centralised, large-scale, and efficient animal farming may be possible as a result of both the increasing demand for food and the development of sensing technologies. As we know it, it could revolutionise animal husbandry. This study takes a high-level look at the possibilities and threats that sensor technology pose to animal producers’ ability to increase their output of meat and other animal products. The purpose of this study is to investigate how sensors, big data, artificial intelligence, and machine learning may assist animal producers in improving animal comfort, increasing productivity per hectare, decreasing production costs, and increasing efficiency. It delves into the difficulties and restrictions of technology as well. This study explores the many uses of animal farming technology in order to comprehend its worth in assisting farmers in bettering the health of their animals, increasing their profitability, and decreasing their impact on the environment.

Optimizing Software Defect Prediction: A Genetic Algorithm Based Comparative Analysis

2023-12-25T12:58:00+00:00

Software quality assurance is a crucial activity during the initial stages of the software
development life cycle. Over the past two decades, various frameworks have been developed to
ensure software quality. By predicting defective modules at the initial stages, the resources
available for software development can be efficiently used to ensure timely delivery of good-
quality software. Numerous software defect prediction models have been proposed and developed
using supervised and unsupervised machine learning methodologies, along with the integration of
statistical methodologies. Software metrics contain hidden patterns that can be extracted and
utilized to identify defective modules using a machine learning approach. This study applies a
genetic algorithm (GA) to select relevant features that play a vital role in predicting defective
modules, and explores supervised classification techniques by incorporating seven widely used
NASA datasets. The three most used classification techniques, namely decision tree, support
vector machine, and naïve Bayes, were selected for the analysis. Precision, accuracy, recall,
Matthew’s correlation coefficient, F-measure, and receiver operating characteristic were selected
as the performance parameters. The results of this study can serve as a baseline for comparing and
verifying the results of new models that implement GA for optimal feature selection.

Intelligent Heart Disease Prediction Using CatBoost Empowered with XAI

2023-12-27T04:43:40+00:00

Heart disease refers to a class of diseases involving the heart and blood vessels, including coronary artery disease, heart failure, and arrhythmias. Concerns associated with heart disease include increased risk factors like hypertension, smoking, high cholesterol, and a sedentary lifestyle. Early prediction and effective management are crucial to mitigate these risks and prevent complications. In the context of heart disease prediction, the CatBoost machine learning (ML) classifier was applied to a dataset divided into training and testing, resulting in a notable testing accuracy of 84.4%. The SHAP (SHapley Additive exPlanations) explainability technique was employed to illuminate the decision-making process of CatBoost predictions. Through computation and visualization of SHAP values, this approach enhances transparency, fairness, and interpretability, revealing insights into the pivotal features influencing heart disease prediction. The integration of the XAI (Explainable Artificial Intelligence) technique in this research seeks to deepen the knowledge of the complex dynamics involved in heart disease prediction.

Predictive Analytics in Cardiovascular Health: Leveraging Deep Learning Algorithms for Early Cardiac Disease Identification

2023-12-25T12:55:47+00:00

Cardiac diseases have remained a major issue in today's era, if diagnosed at some early stages, not only human lives be saved at the initial level of disease, a proactive approach can be employed worldwide accordingly. Nowadays, cardiac diseases are frequent, increasing so rapidly in humans due to improper diet, smoking, lack of exercise, diabetes, people having stress, blood pressure, and more specifically deficient knowledge about the disease occurrence. Most healthcare units lack classification and decision-making techniques to anticipate the disease, consequently unable to perform necessary precautionary measures to decrease the disaster impacts of disease, therefore, it is required to work on such effective approaches having the projection of prior identification of disease and present more reliable decision-making results. The proposed model will provide a reliable Recurrent Neural Network (RNN) approach toward cardiac disease prediction along with present the improvement in the success ratio of the previous research and decrease the possible loss and execution time. This proposed model achieves more than accuracy of 97% in the prediction of cardiac disease at an early stage.