Department of Computer Science (UG)
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Item MALWARE FAMILY CLASSIFICATION MODEL USING USER DEFINED FEATURES AND REPRESENTATION LEARNING(Springer Link, 2020-11-20) Gayathri, T; Vijaya, M.SMalware is very dangerous for system and network user. Malware identification is essential tasks in effective detecting and preventing the computer system from being infected, protecting it from potential information loss and system compromise. Commonly, there are 25 malware families exists. Traditional malware detection and anti-virus systems fail to classify the new variants of unknown malware into their corresponding families. With development of malicious code engineering, it is possible to understand the malware variants and their features for new malware samples which carry variability and polymorphism. The detection methods can hardly detect such variants but it is significant in the cyber security field to analyze and detect large-scale malware samples more efficiently. Hence it is proposed to develop an accurate malware family classification model contemporary deep learning technique. In this paper, malware family recognition is formulated as multi classification task and appropriate solution is obtained using representation learning based on binary array of malware executable files. Six families of malware have been considered here for building the models. The feature dataset with 690 instances is applied to deep neural network to build the classifier. The experimental results, based on a dataset of 6 classes of malware families and 690 malware files trained model provides an accuracy of over 86.8% in discriminating from malware families. The techniques provide better results for classifying malware into families.Item ECO-FRIENDLY BLOCKCHAIN FOR SMART CITIES(Elsevier, 2023-01-01) Hepziba Gnanamalar, R; Ebenesar Anna Bagyam, JThe term “smart city” denotes a complete idea to relieve pending issues of cutting-edge city regions that have advanced into a crucial painting subject for practitioners and pupils alike. However, the query stays as to how towns can become “clever.” The software of the records era is normally taken into consideration as a key motive force withinside the “smartization” of towns. Detailed frameworks and techniques are consequently having to manual, operationalize, and degree the implementation manner in addition to the effect of the respective technology. This chapter discusses about blockchain era, a unique motive force of technological transformation that contains a mess of underlying technology and protocols, and its capability effect on clever towns. The chapter deals with the query of ways the blockchain era may also advantage the improvement of city regions. Based on a complete literature review, we gift a framework and study propositions. Nine software fields of a blockchain era withinside the smartization of towns: (1) healthcare, (2) logistics and deliver chains, (3) mobility, (4) energy, (5) management and services, (6) e-voting, (7) factory, (8) domestic, and (9) education are discussed. Contemporary-day tendencies in those fields illustrate how they're laid low with the blockchain era and derive propositions to manual destiny studies’ endeavors. Further sections of the chapter are well organized as follows. Section 1 is the introductory part of the eco-friendly blockchain technology for smart cities. Section 2 reflects types of blockchain executions and some common consent algorithms. Section 3 illustrates the benefits of blockchain combination with smart cities and reconnoiters ways it plays an essential role in creating and benefitting smart cities. Fifteen benefits of smart cities with blockchain are discussed in Section 4. Section 5 defends how blockchain is eco-friendly, and finally, Section 6 summarizes and concludes all sections. © 2023 Elsevier Inc. All rights reserved.Item MULTI-CLASS CLASSIFICATION OF INSECTS USING DEEP NEURAL NETWORKS(IEEE Xplore, 2023-01-23) Santhiya M; Priyadharshini M; Agshalal Sheeba J; Karpagavalli SInsects are crucial to the functioning of nature. There are more than a million described species of living beings in the modern world. Since the majority of today’s farmers and agriculturalists are newer generations of people, identifying and classifying insects is essential. The classification of insects is a difficult undertaking in the agricultural industry. In the proposed work, multi-class classification of insects using a Convolutional Neural Network architecture, VGG19 had been carried out. In the taxonomic classification of insects, 5 insects fall within insecta class which include butterfly, dragonfly, grasshopper, ladybird, and mosquito data had been collected to train, test, and validate the convolutional neural network, The performance of the model had been analyzed using different parameters and presented.Item OPTIMIZING PRE-PROCESSING FOR FOETAL CARDIAC ULTRA SOUND IMAGE CLASSIFICATION(Springer Link, 2023-03-28) Divya M.O; Vijaya M.SRecent research shows that Foetal cardiac anomalies which gets diagnosed postnatally makes a grave negative impact on the delivery outcome. The situation becomes lethal when severe anomalies get diagnosed after the baby is born. Many medical researches shows that delivery outcome could be better when the anomaly is diagnosed prenatally. There are hardly any research and development happening in this area where automation and prediction are on prime focus for finding the cardiac anomaly using Ultra Sound Imaging Technique (USIT). The USIT during the second trimester is universal for every pregnant woman also the second trimester is the best time to take appropriate medical assistance for the foetus in case of anomaly. This research is experimental study to setup a standard dataset for foetal cardiac anomaly USITs and to identify the appropriate pre-processing technique for binary classification of USIT. The 1200 images in the dataset are organised in two classes half of the images are with anomaly and other half without anomaly. The class with anomaly includes images representations from 17 anomalies which is theoretically established as structural anomalies of heart. All anomalies are present in the dataset approximately equal in ratio. The dataset has undergone the following pre-processing techniques, blur removal, noise removal and contrast normalisation. The Alex-net model is trained to create a binary classifier for the FetalEcho dataset after applying the different pre-processing techniques. Eight rounds of classification have been performed with eight versions of the FetalEcho dataset. The worst results were shown by the row dataset (FetalEcho_V01) when the classification experiment have been performed with the AlexNet classifier. The dataset FetalEcho_V05, created after removing blur and noise, is identified as the best performance for classification, amongst the eight datasets.Item LUNG CANCER DISEASE PREDICTION AND CLASSIFICATION BASED ON FEATURE SELECTION METHOD USING BAYESIAN NETWORK, LOGISTIC REGRESSION, J48, RANDOM FOREST, AND NAÏVE BAYES ALGORITHMS(2023-03-31) Viji Cripsy J; Divya TPeople who have never smoked can get lung cancer, but smokers have a higher risk than non-smokers. Any aspect of the respiratory system can be affected by lung cancer, which can start anywhere in the lungs, Different classification methods are used for lung cancer prediction. This article uses five different classification algorithms to predict lung cancer in patients using Kaggle dataset. Bayesian Network, Logistic Regression, J48, Random Forest and Naive Bayes methods are used, Based on the carefully identified correct and incorrect cases, the quality of the result was measured using the evaluation technique and the WEKA tool. The experimental results showed that Logistic Regression performed best (91.90 % ), followed by Naive Bayes (90.29 % ), Bayesian Network (88.34 % ), j48 (86.08 % ) and Random Forest (90.93 % ).Item RELAXED HYBRID ROUTING TO PREVENT CONSECUTIVE ATTACKS IN MOBILE AD-HOC NETWORKS(ACM Journals, 2023-06-05) Viji Gripsy J; Kanchana K RIn the current trends, Wi-Fi networks and cellular ad-hoc community (MANET) have yielded incredible opportunity and recognition. This opportunity and popularity insisted on many forms of studies to recognition on it. This enormously bendy nature of the MANET additionally creates many community performance associated and protection associated problems. Numerous security vulnerabilities threaten the technique in MANET in diverse ways. The new and changed protocol is called Secure Route Discovery-Ad-hoc On-demand Distance Vector (SRD-AODV) protocol. This protocol includes one-of-a-kind additives and techniques to offer each proactive and reactive answers through deploying powerful authentication the use of the Modified Elliptic Curve Diffie-Hellman Algorithm (MECDHA) techniques. This additionally aims to comfort the records packets and routing desk records and subsequently the incursion detection and prevention from sequential attacks in MANET.Item AN EXPLORATORY DATA ANALYSIS ON AIR QUALITY DATA OF TRIVANDRUM(Springer Link, 2023-05-31) Santhana Lakshmi V; Vijaya M.SData analysis is the most integral part of any research. It is the process of examining the data using statistical methods to identify the hidden patterns and trends which aid in making decisions. This helps in understanding the distribution, correlation, outliers, and missing values found in the data. In this paper, data analysis is performed over the air pollutant data and the meteorological data that influences air pollution. The meteorological data for the period of 4 years of Trivandrum city was taken for the purpose of analysis. The dataset includes 26,544 instances and 23 features. Pollutant parameters such as PM2.5, PM10, CO, SO2, ozone, NOX, and NH3 are considered for analysis. Meteorological features taken for analysis include temperature, dew, humidity, wind speed, wind direction, etc. Meteorological features play a substantial role in identifying air pollution. Boxplots, heat maps, pair plots, and histograms were used to reveal the distribution and correlation between the attributes. From the analysis, it has been identified that the features like sea level pressure, PM2.5, PM10, CO, NOX, NH3, SO2, and ozone are positively correlated with air quality index whereas features like, dew, humidity, wind speed, cloud cover are negatively correlated with air quality index. The results of the data analysis assist in preparing the data for further research.Item ARTIFICIAL INTELLIGENT MODELS FOR AUTOMATIC DIAGNOSIS OF FOETAL CARDIAC ANOMALIES: A META-ANALYSIS(Springer Link, 2023-01-01) Divya M.O; Vijaya M.SThe foetal anomaly scanning is one of the most challenging areas where accuracy of diagnosis much fluctuating with respect to the expertise of the radiologist and the mental equilibrium of the radiologist at the time of scanning. Amongst the various anomalies, foetal heart anomaly diagnosis expects precise and sensitive intellectual presence since perilous congenital heart diseases are one of the common causes resulting in the major population of infant mortality or into permanent natal faults. The accuracy of manual diagnosis of foetal cardiac abnormalities from the ultrasound scan images vary based on the human expertise and the presence of mind. Therefore, the scope of computer-assisted judgement can produce accurate diagnosis irrespective of the operator’s profile. Numerous researches are going on to explore the scope of computer-assisted judgement of abnormalities using ultrasound imaging technique (USIT), specifically using machine learning and deep learning models. This work exploits the opportunities of computer-assisted diagnosis in foetal cardiac anomaly diagnosis as this is one of the most sensitive areas where appropriate diagnosis can save a life and a wrong diagnosis may lose a life unnecessarily.Item BOOTSTRAPPING OF FINE-TUNED SEGMENTATION AND CLASSIFICATION NETWORK FOR EPIDERMIS DISORDER CATEGORIZATION(Springer Link, 2023-07-25) Kalaivani A; Karpagavalli SAs all people have been affected by various skin related illnesses, categorization of skin disorders has become prominent in recent healthcare system. To identify and categorize skin related syndromes, many transfer learning frameworks were used. Amongst, a Fine-tuned Segmentation and Classification Network (F-SegClassNet) achieved better efficacy by using the novel unified loss function. Nonetheless, it was not apt for the datasets that lack in training images. Hence in this article, Bootstrapping of F-SegClassNet (BF-SegClassNet) model is proposed which solves the imbalanced images in the training set via generating the group of pseudo balanced training batches relying on the properties of the considered skin image dataset. This model fits the distinct abilities of Deep Convolutional Neural Network (DCNN) classifier so that it is highly useful for classifying the skin disorder image dataset with a highly imbalanced image data distribution. According to the Bootstrpping, better tradeoff between simple and complex image samples is realized to make a network model that is suitable for automatic skin disorders classification. In this model, statistics across the complete training set is calculated and a new subset is produced that retains the most essential image samples. So, the skin images are segmented and categorized by this new model to identify the varieties of epidermis infections. At last, the testing outcomes exhibits BF-SegClassNet-model accomplishes the mean accuracy with 96.14% for HAM dataset which is compared to state-of-the-art models.Item A SURVEY AND ANALYSIS OF DEEP LEARNING TECHNIQUES FOR BIRD SPECIES CLASSIFICATION(IEEE Xplore, 2023-06-16) Sivaranjani B; Karpagavalli SThe ability to accurately identify the species of a bird in an image is crucial. A bird’s species identification can be accomplished using images and audios. In earlier periods, the audio of birds are utilized to possibly recognize the different species of birds. But, background noise from things like birds, insects, and the wind makes it difficult for this method to produce a reliable result. Comparatively, observer’s finds images are better than audios. Using images, people are better able to discriminate between birds. However, because of the inexperience of most bird watchers and the similarity of bird forms and backgrounds, identifying birds can be difficult. To address this, Deep Learning (DL) models have been implemented to efficiently extract features from photos collected for recognition. DL models for bird species identification provides more accuracy. The recently proposed transfer learning and spatial pyramid pooling efficiently classify bird spicies. Another recently proposed Mask-CNN based method solved few shot classifcation problem effectively. But, both of these method are suffered to distinguish the subcategory of spicies form main categories. In this article, the of bird species identification techniques are studied in brief to encourage further research in this field. First, the review is planned to investigate the DL algorithms for identifying the different bird species types. Next, the merits and demerits of every algorithms are analyzed based on its performance. Finally, potential improvements are emphasized to achieve greater efficiency in identifying the bird species.