http://202.88.229.59:8080/xmlui/handle/123456789/5456 2026-04-14T20:01:43Z 2026-04-14T20:01:43Z Sreejit Rama Krishnan http://202.88.229.59:8080/xmlui/handle/123456789/5818 2025-08-22T04:49:03Z 2024-07-01T00:00:00Z

AI and Iot Integrated Techniques for Detection and Prediction of Brain Diseases Sreejit Rama Krishnan Brain is the controlling centre of our body. Detection of brain diseases at an early stage can make a huge difference in attempting to cure them. The application of artificial intelligence (AI) in the assessment of medical images has led to accurate evaluations being performed automatically, which in turn has reduced the workload of physicians, decreased errors and times in diagnosis, and improved performance in the prediction and detection of brain diseases. The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way.

2024-07-01T00:00:00Z Asst.Prof.Sreejith Ramakrishnan http://202.88.229.59:8080/xmlui/handle/123456789/5458 2024-10-09T06:18:53Z 2023-11-01T00:00:00Z

Heart Disease Prediction Using Machine Learning Asst.Prof.Sreejith Ramakrishnan Machine Learning (ML), which is one of the most prominent applications of Artificial Intelligence, is doing wonders in the research field of study. In this paper machine learning is used in detecting if a person has a heart disease or not. A lot of people suffer from cardiovascular diseases (CVDs), which even cost people their lives all around the world. Machine learning can be used to detect whether a person is suffering from a cardiovascular disease by considering certain attributes like chest pain, cholesterol level, age of the person and some other attributes. Classification algorithms based on supervised learning which is a type of machine learning can make diagnoses of cardiovascular diseases easy. The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. Machine learning applications in the medical niche have increased as they can recognize patterns from data. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce misdiagnosis. This research develops a model that can correctly predict cardiovascular diseases to reduce the fatality caused by cardiovascular diseases. This paper proposes a method of k-modes clustering with Huang starting that can improve classification accuracy. Models such as random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), and XGBoost (XGB) are used. Machine Learning (ML), which is one of the most prominent applications of Artificial Intelligence, is doing wonders in the research field of study. In this paper machine learning is used in detecting if a person has a heart disease or not. A lot of people suffer from cardiovascular diseases (CVDs), which even cost people their lives all around the world. Machine learning can be used to detect whether a person is suffering from a cardiovascular disease by considering certain attributes like chest pain, cholesterol level, age of the person and some other attributes. Classification algorithms based on supervised learning which is a type of machine learning can make diagnoses of cardiovascular diseases easy. The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. Machine learning applications in the medical niche have increased as they can recognize patterns from data. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce misdiagnosis. This research develops a model that can correctly predict cardiovascular diseases to reduce the fatality caused by cardiovascular diseases. This paper proposes a method of k-modes clustering with Huang starting that can improve classification accuracy. Models such as random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), and XGBoost (XGB) are used.

2023-11-01T00:00:00Z Asst.Prof.Sreejith Ramakrishnan http://202.88.229.59:8080/xmlui/handle/123456789/5457 2024-10-09T06:16:33Z 2023-07-01T00:00:00Z

The Importance of Data Mining & Predictive Analysis Asst.Prof.Sreejith Ramakrishnan Data mining is the process of analyzing enormous amounts of information and datasets, extracting (or “mining”) useful intelligence to help organizations solve problems, predict trends, mitigate risks, and find new opportunities. Data mining is like actual mining because, in both cases, the miners are sifting through mountains of material to find valuable resources and elements. Data mining also includes establishing relationships and finding patterns, anomalies, and correlations to tackle issues, creating actionable information in the process. Data mining is a wide-ranging and varied process that includes many different components, some of which are even confused for data mining itself. Keywords— Knowledge Discovery in Data, or KDD, knowledge extraction, data pattern analysis, data archaeology, data dredging, information harvesting, business intelligence. Data mining is the process of analyzing enormous amounts of information and datasets, extracting (or “mining”) useful intelligence to help organizations solve problems, predict trends, mitigate risks, and find new opportunities. Data mining is like actual mining because, in both cases, the miners are sifting through mountains of material to find valuable resources and elements. Data mining also includes establishing relationships and finding patterns, anomalies, and correlations to tackle issues, creating actionable information in the process. Data mining is a wide-ranging and varied process that includes many different components, some of which are even confused for data mining itself. Keywords— Knowledge Discovery in Data, or KDD, knowledge extraction, data pattern analysis, data archaeology, data dredging, information harvesting, business intelligence.

2023-07-01T00:00:00Z