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Aloysius, A.
- An Experimental Study for the Comparison of LCOM Values and IQ Based on Time Analysis
Authors
1 St Joseph's College, Tiruchirappalli, IN
Source
Software Engineering, Vol 4, No 3 (2012), Pagination: 81-85Abstract
An experiment was conducted to perceive the comparison between lcom values and IQ based on time analysis. One of themost important and significant software attributes for assessing objectoriented software quality. In this experiment many strategies andmethods were used to perform lcom. To implement the strategy a test was conducted for 60 students undergoing their post graduation in computer applications and a program comprehension was performed with individual groups. The correlation value of the comprehension time for each of the program was calculated. From the study it was found that when in the case of intersection and when values are high it will lead to less complexity and in other cases if no intersection is found between one to n methods it will certainly lead to high complexity.
Keywords
Cognitive Science, IQ, Debugging, Statistical Test.- A Review on Lack of Cohesion in Method
Authors
1 St Joseph's College, Tiruchirappalli, IN
Source
Software Engineering, Vol 4, No 2 (2012), Pagination: 76-79Abstract
Cohesion is an important software attribute; it is one of the significant criterions for assessing object oriented software quality. Modules with high cohesion have a propensity to be preferable because high cohesion is associated with several desirable traits of software including robustness, reliability, reusability, and understandability while in the other case low cohesion is associated with undesirable traits such as being difficult to maintain, difficult to test, difficult to reuse, and even difficult to understand. This paper puts together the various techniques of lcom which has been proposed by various authors and this will give the overview about Lcom. This paper incorporates an assortment of aspects of lcom, which allows the reader to get a clear perspective on lcom. A selected choice of research articles were fused into this paper to facilitate the ease of a researcher searching for articles related to cohesion which in event makes the study of that researcher more competent.Keywords
Cognitive Science, Cohesion, LCOM, LCOM1, LCOM2, LCOM3, TCC.- A Study on Emotional Quotient (EQ) and Debugging Ability of Component Based System (CBS)
Authors
1 St. Joseph’s College (Autonomous), Tiruchirappalli, Tamilnadu, IN
2 Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli, Tamilnadu, IN
Source
Software Engineering, Vol 3, No 10 (2011), Pagination: 430-435Abstract
There is a lack of measuring techniques in the field of software engineering when compared to the other engineering disciplines. The core focus of the software developers is to reduce the complexity of the software developed. The interest in cognitive methods and techniques has grown to be a great aid in software development. The aim of this paper is to perform a study on a set of students to test/measure their debugging capabilities based on their emotional quotient. These innovative techniques are on the verge of becoming main stream to adapt to measure newer technologies, while newer technologies emerge researchers invent newer methods to measure the complexity of software development. The Pearson correlation statistical method is used to analyze the outcome pertaining to the EQ of the students and their debugging capabilities to derive the result. Debugging capabilities of programmers tend to differ according to their emotional levels, debugging of programs using Component Based Systems and the Emotional Quotient’s (EQ) relation is studied and experiments are conducted to evaluate various emotional and stress levels in correspondence to specific debugging capabilities of the programmer in subject to different technologies. From this study it is found that the student’s ability to debug a piece of code drastically differs based on his/her emotional quotient.Keywords
Cognitive Science, Eq, Debugging, Statistical Test, Component Based Programming.- An Analysis of Dependency between Personality Traits and Debugging Ability in Component Based System (CBS)
Authors
1 Computer Science, St. Joseph’s College, Tiruchirappalli, IN
Source
Software Engineering, Vol 3, No 7 (2011), Pagination: 288-292Abstract
In recent years, the winds of cognitive science is gently ushering changes in the latest software technologies. Also the traditional software complexity measures focuses only on addressing the complexity of the procedure oriented and object oriented software development. Thus envisioning new areas like component based system would lead to better utilization of resources and make the end user’s task easier. Thus an experiment has been conducted to study the link between personality traits and program debugging in component based systems. In our experimental setting, the debugging test was conducted in java bean programs and Personality test in Eysenck's personality inventory and the results were correlated. From the results, it is observed that there is a positive correlation between the personality traits and debugging. Psychoticism personality people performed better in debugging compared to the other two personality traits students.Keywords
Cognitive Science, Personality Traits, Debugging, Statistical Test, Component Based Programming.- An Investigation into the Relationship between Intel-Ligence Quotient (IQ) and Debugging Ability of Component Based Systems (CBS)
Authors
1 Computer Science, St. Joseph's College, Tiruchirappalli, IN
2 Computer Science, St Joseph‟s College-Tiruchirappalli, IN
3 Computer Science, St Joseph‟s College, Tiruchirappalli, IN
Source
Software Engineering, Vol 3, No 7 (2011), Pagination: 293-298Abstract
In recent years there has been a great upsurge in the field of cognitive science too. Also the traditional software complexi-ty measures focuses only on addressing the complexity of the proce-dural oriented and object oriented software development. Thus envi-sioning new areas like Component Based System would lead to better utilization of resources and make the end user‟s task. An experiment has been conducted to study the relation between Intelligent Quotient (IQ) and program debugging in Component Based systems on the assumption that programmers are not consistent in their debugging capabilities. In our experimental setting, the debugging test in Com-ponent based programming language features and the IQ test based on Binet-Simon scale were conducted and the results were correlated. From the results, we observe that there is a positive correlation be-tween the IQ value and debugging ability of the students. Thus there is evidence that similar cognitive skills are used for debugging and IQ Test.Keywords
Cognitive Science, IQ, Debugging, Statistical Test, Component Based Programming.- An Investigation into the Impact of OO Design on Program Debugging with Individual and Collaborative Approaches
Authors
1 Department of Computer Science, St. Joseph’s College, Tiruchirappalli – 620002, IN
Source
Software Engineering, Vol 2, No 9 (2010), Pagination: 186-190Abstract
Object Oriented (OO) programming paradigm is widely used in software development. The primary objective of this paper is to study the impact of OO design features on the primary cognitive process namely debugging. It is an accepted fact that software engineering processes require a significant amount of time in debugging. The debugging in turn contains program comprehension and testing as the central tasks. In pair programming, two programmers work collaboratively on the same algorithm, design or programming task, sitting side by side at one computer. This practice has been used several times in the last decades as an improved way of developing software. Various researchers emphasize the need for resources to understand the relation between the cognitive activities on Program debugging and the structure of programming language. The design features were extracted using the OO metrics. From the study conducted, it is clear that depth of inheritance, coupling, number of children, and number of public methods should be reduced to enhance the debugging of any program when performed individually or in pair. It is also observed that in order to enhance the debugging ability the coupling should be minimized.Keywords
Pair (Collaborative) Programming, Program Debugging, Complexity Metric.- On Validating Class Level Cognitive Metrics
Authors
1 Department of Computer Science, St. Joseph’s College, Tiruchirappalli – 620002, IN
2 Department of Computer Science and Engineering, Bharathidasan University, Tiruchirappalli – 620023, IN
Source
Software Engineering, Vol 2, No 3 (2010), Pagination: 55-58Abstract
The interest in the application of cognitive science in computing has grown recently to a greater extend in the software industry. The programmers and project managers are focusing on better techniques for reduction of software complexity in software development. Object oriented technology becomes an increasingly popular software development environment. The traditional software complexity measures focus only on addressing the complexity of the procedure oriented software development. They cannot fulfill the requirements of object-oriented software. Hence, newer techniques and methods are to be developed by researchers. Earlier, Arockiam et. al have proposed a new complexity measure namely Extended Weighted Class Complexity (EWCC) which is an extension of Weighted Class Complexity (WCC). EWCC is the sum of cognitive weights of attributes and methods of the class and that of the classes derived. The aim of this paper is to validate EWCC and other complexity metrics with respect to program comprehension. From the experiments conducted, it is proved that EWCC is a better indicator of complexity of classes with inheritance.Keywords
CK Metrics, Extended Weighted Class Complexity (EWCC).- Sentiment Analsis Using Voting based Unsupervised Ensemble Machine Learning in Cancer Detection
Authors
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
Authors
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.