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Uma Maheswari, M.
- Hierarchy Query Result Based Navigation
Abstract Views :227 |
PDF Views:2
Authors
Affiliations
1 Department of Information Technology, Periyar Maniammai University, Thanjavur, Tamil Nadu, IN
2 Periyar Maniammai University, Thanjavur, Tamil Nadu, IN
1 Department of Information Technology, Periyar Maniammai University, Thanjavur, Tamil Nadu, IN
2 Periyar Maniammai University, Thanjavur, Tamil Nadu, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 5 (2012), Pagination: 260-264Abstract
Search queries on biomedical databases, such as PubMed, often return a large number of results, only a small subset of which is relevant to the user. Ranking and categorization, which can also be combined, have been proposed to alleviate this information overload problem. Results categorization for biomedical databases is the focus of this work. A natural way to organize biomedical citations is according to their MeSH annotations. MeSH is a comprehensive concept hierarchy used by PubMed. In this paper, we present the BioNav system, a novel search interface that enables the user to navigate large number of query results by organizing them using the MeSH concept hierarchy. First, the query results are organized into a navigation tree. At each node expansion step, BioNav reveals only a small subset of the concept nodes, selected such that the expected user navigation cost is minimized.Keywords
Interactive Data Exploration and Discovery, Search Process, Graphical User Interfaces, Interaction Styles.
- Sentiment Analsis Using Voting based Unsupervised Ensemble Machine Learning in Cancer Detection
Abstract Views :145 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, St. Josephs College, Tiruchirappalli, IN
1 Department of Computer Science, St. Josephs College, Tiruchirappalli, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2791-2796Abstract
Within the field of natural language processing, sentiment analysis is one form of data mining used to make inferences about the emotional tenor of a speakers words. Computational linguistics is employed to examine the text in order to deduce and assess ones mental knowledge of the Web, social media, and associated references. One of the numerous advantages of sentiment analysis is that it can help improve the quality of healthcare by making use of medical data to produce the most positive outcome possible. Natural language processing challenges can change how sentiment analysis looks and works in a variety of contexts. Some of the challenges are specific to the data type, while others are universal to any method of text analysis. The primary objective of this study was to evaluate how challenging it is to analyse sentiment in the healthcare sector. Given the aforementioned complexities, the objective was to look into whether or not the currently available SA tools are adequate for handling any healthcare-related issue. With such motivation, in this paper, we develop an unsupervised ensemble machine learning (ML) algorithm that includes K-means clustering; Principle Component Analysis; Independent Component Analysis and k-nearest neighbors. The unsupervised ensemble ML model is assessed via voting meta-classifier over various cancer datasets. The simulation is conducted to test the efficacy of the model in terms of accuracy, precision, recall and f-measure over various datasets. The results of simulation against the cancer datasets show that the proposed method achieves higher rate of ensemble accuracy than the other existing ensemble models.Keywords
Natural Language Processing, Sentiment Analysis, Unsupervised ML.Natural Language Processing, Sentiment Analysis, Unsupervised ML.References
- W.M. Yafooz and A. Alsaeedi, “Sentimental Analysis on Health-Related Information with Improving Model Performance using Machine Learning”, Journal of Computer Science, Vol. 17, No. 2, pp. 112-122, 2021.
- S.M. Srinivasan, P. Shah and S.S. Surendra, “An Approach to Enhance Business Intelligence and Operations by Sentimental Analysis”, Journal of System and Management Sciences, Vol. 11, No. 3, pp. 27-40, 2021.
- M.H. Song, “A Study on Explainable Artificial Intelligence-based Sentimental Analysis System Model”, International Journal of Internet, Broadcasting and Communication, Vol. 14, No. 1, pp. 142-151, 2022.
- H.S. Saraswathi and C.K. Raju, “Computer-Aided Diagnosis of Pancreatic Ductal Adenocarcinoma Using Machine Learning Techniques”, Proceedings of International Conference on Sentimental Analysis and Deep Learning, pp. 959-972, 2022.
- N. Banerjee and S. Das, “Lung Cancer Prediction in Deep Learning Perspective”, Proceedings of International Conference on Computational Analysis and Deep Learning for Medical Care: Principles, Methods, and Applications, pp. 237-255, 2021.
- S.A. Alanazi, A. Khaliq, F. Ahmad and S. Afsar, “Publics Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques”, International Journal of Environmental Research and Public Health, Vol. 19, No. 15, pp. 9695-9703, 2022.
- M. Kentour and J. Lu, “An Investigation into the Deep Learning Approach in Sentimental Analysis using Graph-Based Theories”, Plos One, Vol. 16, No. 12, pp. 1-8, 2021.
- S.K. Pathuri and J. You, “Feature-Based Sentimental Analysis on Public Attention towards COVID-19 Using CUDA-SADBM Classification Model”, Sensors, Vol. 22, No. 1, pp. 80-95, 2021.
- P. Taranath, S. Das and S. Gowrishankar, “Analysis of Healthcare Industry Using Machine Learning Approach: A Case Study in Bengaluru Region”, Sentimental Analysis and Deep Learning, pp. 1-13, 2022.
- L. Lyu, “Lung Cancer Diagnosis Based on Convolutional Neural Networks Ensemble Model”, Proceedings of International Conference on Artificial Intelligence, Networking and Information Technology, pp. 360-367, 2021.
- A.K. Saha and M. Rahman, “An Efficient Deep Learning Approach for Detecting Pneumonia Using the Convolutional Neural Network”, Proceedings of International Conference on Sentimental Analysis and Deep Learning, pp. 59-68, 2022.
- Leilei Sun, Chuanren Liu, Chonghui Guo, Hui Xiong and Yanming Xie, “Data-driven Automatic Treatment Regimen Development and Recommendation”, Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 1865-1874, 2016.
- C. Doulaverakis, “GalenOWL: Ontology-Based Drug Recommendations Discovery”, Journal of Biomedical Semantics, Vol. 3, pp. 1-14, 2012.
- Y. Bao and X. Jiang, “An Intelligent Medicine Recommender System Framework”, Proceedings of IEEE International Conference on Industrial Electronics and Applications, pp. 1-8, 2016.
- K. Shimada, “Drug-Recommendation System for Patients with Infectious Diseases”, Proceedings of IEEE International Conference on Machine Learning, pp. 1-7, 2005.
- J. Li, H. Xu, X. He, J. Deng and X. Sun, “Tweet Modeling with LSTM Recurrent Neural Networks for Hashtag Recommendation”, Proceedings of IEEE International Conference on Neural Networks, pp. 1570-1577, 2016.
- Zhang, Yin and Limei Peng, “CADRE: Cloud-Assisted Drug Recommendation Service for Online Pharmacies”, Mobile Networks and Applications, Vol. 20, pp. 348-355, 2014.
- Sentiment Analysis in Melanoma Cancer Detection Using Ensemble Learning Model
Abstract Views :125 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, St. Josephs College, Tiruchirappalli, IN
1 Department of Computer Science, St. Josephs College, Tiruchirappalli, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 2 (2023), Pagination: 2859-2862Abstract
Machine learning has the potential to improve healthcare by allowing clinicians to spend more time caring for patients and less time diagnosing them. This would allow clinicians to spend more time improving patient quality of life. Consequently, it is able to compute the risk of melanoma on a patient level and advise users to schedule a medical checkup rather than evaluating whether or not a specific lesion image that is provided by a patient is malignant. This is because the result of this is that it is able to compute the risk of melanoma at the patient level. By doing so, both the credibility and legislation issues are resolved, and the application is transformed into one that is adaptable. In this paper, we develop a machine learning ensemble to classify the melanoma cancer. The simulation is conducted in terms of training, testing accuracy, precision and recall. The results show that the proposed method achieves higher classification rate than other methods.Keywords
Machine Learning, Ensemble, Prediction, MelanomaReferences
- V.R. Allugunti, “A Machine Learning Model for Skin Disease Classification using Convolution Neural Network”, International Journal of Computing, Programming and Database Management, Vol. 3, No. 1, pp. 141-147, 2022.
- Leilei Sun, Chuanren Liu, Chonghui Guo, Hui Xiong and Yanming Xie, “Data-driven Automatic Treatment Regimen Development and Recommendation”, Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 1865-1874, 2016.
- R. Manikandan and M. Ramkumar, “Sequential Pattern Mining on Chemical Bonding Database in the Bioinformatics Field”, Proceedings of International Conference on AIP, Vol. 2393, No. 1, pp. 1-13, 2022.
- C. Alvino Rock and R.J.S. Jeba Kumar, “Computer Aided Skin Disease (CASD) Classification using Machine Learning Techniques for iOS Platform”, Tracking and Preventing Diseases with Artificial Intelligence, pp. 201-216, 2022.
- B.R. Nanditha, “Oral Cancer Detection using Machine Learning and Deep Learning Techniques”, International Journal of Current Research and Review, Vol. 14, No. 1, pp. 64-78, 2022.
- N. Sultana, “Predicting Sun Protection measures against Skin Diseases using Machine Learning Approaches”, Journal of Cosmetic Dermatology, Vol. 21, No. 2, pp. 758-769, 2022.
- Y.N. Chen and W.B. Wei, “Machine Learning Models for Outcome Prediction of Chinese Uveal Melanoma Patients: A 15‐Year Follow‐Up Study”, Cancer Communications, Vol. 42, No. 3, pp. 273-276, 2022.
- A.R. Khan, “Facial Emotion Recognition using Conventional Machine Learning and Deep Learning Methods: Current Achievements, Analysis and Remaining Challenges”, Information, Vol. 13, No. 6, pp. 268-278, 2022.
- M. Pinto, A. Ammendolia and A. De Sire, “Quality of Life Predictors in Patients with Melanoma: A Machine Learning Approach”, Frontiers in Oncology, Vol. 12, pp. 843611-843618, 2022.
- N. Sengupta and U. Ghone, “Scarcity of Publicly Available Oral Cancer Image Datasets for Machine Learning Research”, Oral Oncology, Vol. 126, pp. 105737-105743, 2022.