Department of Computer Science (PG)

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    BAYESIAN CLASSIFICATION FOR IMAGE RETRIEVAL USING VISUAL DICTIONARY
    (Springer International Publishing, 2013) Nazirabegum, M K; Radha, N
    Image Retrieval is one of the most promising technologies for retrieving images through the query image. It enables the user to search for the images based upon the relevance of the query image. The main objective of this paper is to develop a faster and more accurate image retrieval system for a dynamic environment such as World Wide Web (WWW). The image retrieval is done by considering color, texture, and edge features. The bag-of-words model can be applied to image classification, by treating image features as words. The goal is to improve the retrieval speed and accuracy of the image retrieval systems which can be achieved through extracting visual features. The global color space model and dense SIFT feature extraction technique have been used to generate a visual dictionary using Bayesian algorithm. The images are transformed into set of features. These features are used as an input in Bayesian algorithm for generating the code word to form a visual dictionary. These code words are used to represent images semantically to form visual labels using Bag-of-Features (BoF). Then it can be extended by combining more features and their combinations. The color and bitmap method involves extracting only the local and global features such as mean and standard deviation. But in this classification technique, color, texture, and edge features are extracted and then Bayesian Algorithm is applied on these image features which gives acceptable classification in order to increases the accuracy of image retrieval.
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    A SURVEY ON NETWORK INTRUSION SYSTEM ATTACKS CLASSIFICATION USING MACHINE LEARNING TECHNIQUES
    (IOP Publishing Ltd, 2021) Deepa, V; Radha, N
    Wireless Local Area Network (WLAN) security management is now being confronted by rapid expansion in wireless network errors, flaws and assaults. In recent times, as computers are used extensively through network and application creation on numerous platforms, attention is provided to network security. This definition includes security vulnerabilities in both complicated and costly operating programs. Intrusion is also seen as a method of breaching security, completeness and availability. Intrusion Detection System (IDS) is an essential method for the identification of network security vulnerabilities and abnormalities. A variety of significant work has been carried out on intrusion detection technologies often seen as premature not as a complete method for countering intrusion. It has also become a most challenging and priority tasks for security experts and network administrators. Hence, it cannot be replaced by more secure systems. Data mining used for IDS can effectively identify intrusion and the identified intrusion values are used to predict further intrusion in future. This paper presents a detailed review of literature about how data mining techniques were utilized for intrusion detection. First, intrusion detection on various benchmark and real-time datasets by data mining techniques are studied in detail. Then, comparative study is conducted with their merits and demerits for identifying the challenges in those techniques and then this paper is concluded with suggestions of solutions for enhancing the efficiency of intrusion detection in the network.
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    PERFORMANCE ANALYSIS OF ABSTRACT-BASED CLASSIFICATION OF MEDICAL JOURNALS USING MACHINE LEARNING TECHNIQUES
    (Springer Link, 2021-09-14) Deepika, A; Radha, N
    Researchers face many challenges in finding the opt web-based resources by giving the queries based on keyword search. Due to advent of Internet, there are huge biological literatures that are deposited in the medical database repository in recent years. Nowadays, as many web-based medical researchers evolved in the field of medicine, there is need for an intelligent and efficient extraction technique required to filter appropriate and opt literature from the growing body of biomedical literature repository. In this research work, new combination of model is proposed in order to find the new insights in applying the combination of algorithm on biological data set. The information in the biomedical field is the basic information for healthy living. National Center for Biotechnology Information (NCBI)’s PubMed is the major source of peer-reviewed biomedical documents for researchers and health practitioners in the field of health-related management. In this paper, abstracts available in PubMed database is used for experimentation. In recent years, deep learning-based neural approach models provide an efficient way to create an end-to-end model that can accurately measure classification labels. This research work is a systematic analysis of performance of the supervised learning models such as Naïve Bayes (NB), support vector machine (SVM) and long short-term memory (LSTM) by implementing on textual medical data. The novelty in this work is the process of incorporating certain topic modelling techniques after the pre-processing phase to automatically label the documents. Topic modelling is a useful technique in increasing the efficiency and improves the ability of researchers to interpret biological information. So, the classification algorithms thus proposed are implemented in combination with popular topic modelling algorithms such as latent Dirichlet algorithm (LDA) and non-negative matrix factorization (NMF). The final performance of the combination of algorithms is also analysed and is found that SVM with NMF outperforms the other models.
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    ANDROID APPLICATIONS FOR LUNG NODULES CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK
    (IGI Global, 2023) Karthikeyan, M P; Banupriya, C V; Kowsalya, R; Jayalakshmi, A
    Digital image processing is currently used in various fields of research. One of them is in the field of medicine. In fact, experienced radiologists have difficulty distinguishing the cancerous portions of the blood vessels in the lung or detecting fine nodules that suggest lung cancer on X-ray images. Previous studies have shown that doctors and radiologists fail to detect cancerous patches in 30% of positive cases. Implementation of CAD system to classify and detect parts of cancer has been developed, but the results obtained from this implementation are that there are still many errors in the classification results. Therefore, this study will develop android app image technique to perform the classification process of lung cancer. With this research, it is hoped that the developed algorithm can help doctors and radiologists to detect cancer in a short time with more accuracy. Finally, after 20 iterations, a percentage of 90.65% was attained for the test results' performance in classifying 10 X-ray pictures.
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    AN OPTIMIZED WEIGHTED CONSENSUS CLUSTERING WITH REMOVAL OF LESS INFORMATIVE COMPOSITE CLUSTERS
    (IEEE, 2022-03) Sangeetha M; Kousalya R
    The most demanding processes in clinical diagnostics are the proper classification of cancer from a large amount of Gene Expression Data (GED). This article proposes a Weighted Consensus of Lion Optimized K-means Ensemble with Peak Density Clustering (WECLO K-means-PDC) algorithm, which disregards the less informative composite clusters to increase the accuracy of classifying the GED. This algorithm refines the clusters at all iterations by considering the Symmetric Neighbourhood (SN) correlation among data elements. For this clustering, the lion optimization algorithm is applied instead of random subspace and sampling which assumes the cluster validation metrics as fitness functions for effective clustering. Also, the SN Graph (SNG) is constructed over each data element using the adaptive PDC combined with K-means clustering. This SNG helps to choose the number of cluster centroids and refines the clusters at each iteration of K-means clustering without computing the cut off distance between two data elements. By using the SNG, the outliers are signified as the data elements having fewer than two neighbours. Moreover, all data elements are allocated to a suitable cluster by the breadth-first search on SNG and the less informative composite clusters are removed. Finally, the experimental outcomes show that the WECLO K-means-PDC on Leukemia, Lymphoma, Prostate cancer, SRBCT and breast cancer databases achieve 85%, 85.4%, 84.8%, 84.3% and 85% of accuracy, respectively compared to the classical algorithms.
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    CLUSTER BASED DATA-AGGREGATION USING LIGHTWEIGHT CRYPTOGRAPHIC ALGORTIHM FOR WIRELESS SENSOR NETWORKS
    (ScienceDirect, 2022) Kowsalya R; Roseline Jeetha B
    This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
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    RECURRRENT NEURAL NETWORK BASED MODEL FOR AUTISM SPECTRUM DISORDER PREDICTION USING CODON ENCODING
    (Journal of The Institution of Engineers (India): Series B, 2022-09) Sudha V, Pream; Vijaya, M S
    Deep learning methods are noteworthy tools that go together with traditional machine learning techniques to enable computers learn from data and create smarter applications. Deleterious gene classification is an important task in a standard computational framework for biomedical data analysis. As gene sequences are high dimensional and do not represent explicit attributes for computational modelling, extracting features from them becomes a complex task. Recently neural deep learning architectures automatically extract valuable features from input patterns. The principal idea of this work is to exploit the power of Recurrent Neural Networks (RNN) to learn sequential patterns through high-level information associated with observed signals which in turn can be used for classification. Classification of affected genes that cause disease like Autism-spectrum disorder (ASD) is a noteworthy challenge in biomedical research. Long Short Term Memory (LSTM) units go well with sequence-based tasks with long-term dependencies and hence this work examines a stacked LSTM architecture for classifying genes causing ASD. The model is trained and tested with two hand crafted datasets and a codon encoded dataset. Experiments revealed the superiority of these advanced recurrent units compared to the traditional Deep Neural Networks and Bi-directional RNNs distinctively with codon encoded dataset
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    STUDYING THE EFFECTIVENESS OF COMMUNITY DETECTION ALGORITHMS USING SOCIAL NETWORKS
    (SpringerLink, 2022-10-01) Kiruthika R; Vijaya M.S
    Social network analysis is a significant area of research for analyzing the interconnection between the people within network. Community detection is one of the most important applications in SNA. The main motive of CD is to discover the collection of node that are tightly correlated within the network and weakly correlated to another network for partitioning the network to form the group of communities. The aim of this work is to detect communities from undirected disjoint social networks in which it is implemented on lesmis and email-Eu-core-department-labels networks. Effective partitioning and detection of the network are the primary factors for implementing this work by using Girvan–Newman, greedy modularity maximization, and Kernighan–Lin bipartition CD algorithms. The effectiveness of these CD algorithms is analyzed with respect to ground-truth communities based on measures such as recall, normalized mutual information score, precision, and F1-score. Experimental results show that the greedy modularity maximization algorithm provides best results for CD on email-Eu-core-department-labels network with respect to corresponding ground-truth communities.
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    A STUDY ON MACHINE LEARNING-BASED APPROACHES FOR PM2.5 PREDICTION
    (SpringerLink, 2022-01-17) Santhana Lakshmi V; Vijaya M.S
    Clean air and water is the fundamental need of humans. But people are exposed to polluted air produced due to several reasons such as combustion of fossil fuels, industrial discharge, dust and smoke which generates aerosols. Aerosols are tiny droplets or solid particles such as dust and smoke that floats in the atmosphere. The size of the aerosol also called as particulate matter ranges from 0.001–10 μm which when inhaled by human affects the respiratory organs. Air pollution affects the health of 9% of the people every year. It is observed as the most important risk factor that affects human health. There is a need for an efficient mechanism to forecast the quality of air to save the life of the people. Statistical methods and numerical model methods are largely employed for the predicting the value of PM2.5. Machine learning is an application of artificial intelligence that gives a system capability to learn automatically from the data, and hence, it can be applied for the successful prediction of air quality. In this paper, various machine learning methods available to predict the particulate matter 2.5 from time series data are discussed.
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    RIVER WATER QUALITY PREDICTION AND INDEX CLASSIFICATION USING MACHINE LEARNING
    (IOP Science, 2022) Jitha, P Nair; M S, Vijaya
    Various pollutants have had a substantial impact on the quality of water in recent years. The quality of water directly impacts human health and the environment. The water quality index (WQI) is an indicator of effective water management. Water quality modelling and prediction have become essential in the fight against water pollution. The research aims to build an efficient prediction model for river water quality and to categorize the index value according to the water quality standards. The data has been collected from eleven sampling stations located in various locations across the Bhavani River, which flows through Kerala and Tamilnadu. The water quality index is determined by 27different parameters affecting water quality like dissolved oxygen, temperature, pH, alkalinity, hardness, chloride, coliform, etc. Data normalization and feature selection are done to construct the dataset to develop machine learning models. Machine learning algorithms such as linear regression, MLP regressor, support vector regressor and random forest has been employed to build a water quality prediction model. Support vector machines (SVM), naïve bayes, decision trees, MLP classifiers, have been used to develop a classification model for classifying water quality index. The experimental results revealed that the MLP regressor efficiently predicts the water Quality index with root mean squared error as 2.432, MLP classifier classifies the water quality index with 81% accuracy. The developed models show promising output concerning water quality index prediction and classification.