International Journals
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Item EFFICIENT AUTHENTICATION SCHEME TO DETECT THE SPOOFER LOCATION USING PASSIVE IP TRACEBACK TECHNIQUES(International Journal of Innovative Computer Science & Engineering, 2017-06) A, Sheela RiniInternet plays a vital role in the modern world. As the internet grows day by day the security problem also arises. Intruders spoof the packets by using their spoofed IP addresses. Nowadays installing Intrusion Detection Systems (IDS) coupled with firewalls, and monitoring networks enables us to quickly detect and react to unauthorized access. However, even if these tools can detect illegitimate activities, their sources cannot be identified. Denial of service and Distributed denial-of-service (DDoS) attacks present an Internet-wide threat. In Denial of service attacks huge amount of un-wanted packets are sent by the attacker to the IP address which they want to attack. The same attack is take place in DDos also but in a distributed manner. The reason is that denial of service (DoS) attacks, which have recently increased in number, can easily hide their sources and forge their IP addressesItem A SURVEY ON MANET ENERGY CONSUMPTION CHALLENGES(International Journal of Advances in Science Engineering and Technology, 2016-05) Sasikala S; Suganyadevi SIn mobile ad hoc network progressive approach and circulated methodology are more functional when contrasted with the level construction modeling. Energy preserving in mobile ad hoc network is critical. Comparative the circling free way is likewise vital. In spite of the fact that setting up right and proficient courses is an imperative configuration issue in mobile ad hoc networks (MANETs), an all the more difficult objective is to give energy efficiency, since mobile hubs' operation time is the most basic restricting factor. Keeping in mind the end goal to increase the lifetime of ad hoc networks movement ought to be sent by means of a course that can maintain a strategic distance from hubs with low consumption of energy while minimizing the aggregate transmission power. In a MANET, the energy exhaustion of a hub does not influence the hub itself just but rather the general network lifetimeItem EXPLORING THE NUANCES OF INTERNET OF THINGS IN HEALTH CARE ASSISTING SYSTEM(International Research Journal of Engineering and Technology (IRJET), 2019-02) S, LakshmipriyaInternet of Things (IoT) is one of the influential technology which is widely used. This technology associated in a wide variety of network products, systems and sensors, which yield benefits of advancements in computing power, electronics miniaturization, and network interconnection to deliver new capabilities not previously possible. The growth of these associated ‘smart’ technologies distribute different chances for renewing teaching and learning, as well as real-time, on-demand data, for evoking immediate changes. The fields of computer science and electronics have combined to consequence into one of the most notable technological advances in the procedure of realization of the Internet of Things (IoT).Item RECURRENT NEURAL NETWORK BASE MODEL TO PREDICT AUTISM SPECTRUM DISORDER CAUSATIVE GENES(Science and Engineering Research Support Society(SERSC) International journal of Advanced science and technology, 2019) Pream Sudha V; Vijaya M SRecognizing genes causing Autism Spectrum Disorder (ASD) is still a complex task. The role played by domain experts is crucial in identifying relevant contributive features and as recognizing hand-crafted attributes occupies a great deal of time, a varying successful solution is necessary. The swift advancements in the design of deep architecture models have shown substantial accomplishment in sequential data processing tasks. Deep learning models examine the data to discover associations among the features and enable faster learning without being explicitly programmed to do so. Hence the principal goal of this work is to categorize the ASD genes by applying deep learning based models without feature engineering. One hot encoding method is used to encode the gene sequences as vector of numerical values and to further simplify the input representation to aid the prediction of ASD gene sequences. Recurrent Neural Network (RNN) models like Bidirectional Recurrent Neural Network (BRNN), Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are employed to build the prediction models using user defined and self learned features. The performances of the models evaluated using cross validation with various metrics like precision, recall, accuracy and F-measure confirm that GRU model shows promising results using one hot encoding technique.Item DEEP LEARNING BASED PREDICTION OF AUTISM SPECTRUM DISORDER USING CODON ENCODING OF GENE SEQUENCES(International Journal of Engineering and Advanced Technology, 2019) Pream Sudha V; Vijaya M SThe development of computational tools to recognize Autism Spectrum Disorder (ASD) originated by genetic mutations is vital to the development of disease-specific targeted therapies. Identifying genes causing the genetically transmitted ASD is still a challenging task. As genomics data is dependent on domain specific experts for identifying efficient features and extracting hand-crafted attributes involves much time, an alternate effective solution is the need of the hour. The rapid developments in the design of deep architecture models have led to the broad application of these models in a variety of research areas and they have shown considerable success in sequential data processing tasks. The primary goal of this work is to classify the ASD gene sequences by employing a Deep Neural Network based model. This in turn will enable effective genetic diagnoses of this disease and facilitate the targeted genetic testing of individuals. This work utilizes codon encoding and one hot encoding technique to transform the mutated gene sequences which are exploited for self learning the features by deep network. Experiments showed that the performance of the proposed model was better than that of the conventional Multilayer Perceptron with promising accuracy of 77.8%, 80.1% and 81.2% for three different datasets.Item IDENTIFICATION OF AUTISM SPECTRUM DISORDER USING A MULTI-LABEL APPROACH(Journal of Advanced Research in Dynamical & Control Systems, 2019) Pream Sudha V; Vijaya M SThere has been an increase in the application of Multi-dimensional models in domains like bioinformatics, image processing, video and audio processing and text categorization. Multi-dimensional approach has its advantages with regard to predictive accuracy and time taken to build the model. The search for candidate genes of Autism spectrum disorder (ASD) is complicated as it involves significant interactions among mutations in several genes. This work investigates multi- dimensional learning which builds a model to predict ASD related multiple variables simultaneously using varied features. The study explored the different methods of multi- dimensional learning. ASD related gene sequences were analysed using different characteristics and each sequence was represented by a profile of 58 features from three different categories specific to gene, substitution matrix and amino acid. By combining three different problem transformation approaches with three base classifiers and using two algorithm adaptation methods a total of 15 different configurations were constructed. The different configurations were evaluated with multiple measurements including Hamming Loss, Hamming score, Zero One loss, Exact match, Accuracy, training and testing time. The results showed that the problem transformation algorithm Nearest Set replacement together with Naïve Bayes classifier outperformed the other configurations with 93.4 % accuracy and Hamming loss of 0.06, 0.12 Zero one loss.Item FEATURE SELECTION TECHNIQUES FOR THE CLASSIFICATION OF LEAF DISEASES IN TURMERIC(International Journal of Computer Trends and Technology (IJCTT), 2017) V, Pream SudhaCrop maintenance is one of the crucial factors that determine the quantity and quality of the agricultural products. Protecting crops from plant diseases is an important aspect that increases the profit of the farmer. This study aims at developing a computational model that will facilitate crop production by accurately identifying diseases that affect productivity of turmeric plants. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot, and leaf blotch. This system uses technologies such as feature selection and machine learning techniques for the identification and classification of diseases in turmeric leaf. Principal component analysis, Information gain and Relief-f attribute evaluator methods were investigated in combination with machine learning algorithms like Support Vector Machine, Decision Tree and Naïve Bayes. The performance of the models were evaluated using 10 fold cross validation and the results were reported. Comparatively, the model using SVM applied to features selected using Information gain performed well with an accuracy of 93.75.Item IDENTIFICATION AND CLASSIFICATION OF LEAF DISEASES IN TURMERIC PLANTS(International Journal of Engineering Research and Applications, 2016) Nandhini M; Pream Sudha V; Vijaya M SPlant disease identification is the most important sector in agriculture. Turmeric is one of the important rhizomatous crops grown in India. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot, and leaf blotch. The identification of plant diseases requires close monitoring and hence this paper adopts technologies to manage turmeric plant diseases caused by fungi to enable production of high quality crop yields. Various image processing and machine learning techniques are used to identify and classify the diseases in turmeric leaf. The dataset with 800 leaf images of different categories were pre-processed and segmented to promote efficient feature extraction. Machine learning algorithms like support vector machine, decision tree and naïve bayes were applied to train the model. The performance of the model was evaluated using 10 fold cross validation and the results are reportedItem A SURVEY ON DEEP LEARNING TECHNIQUES APPLICATIONS AND CHALLENGES(International Journal of Advance Research In Science And Engineering, 2015) V, Pream Sudha; R, KowsalyaDeep learning is an emerging research area in machine learning and pattern recognition field. Deep learning refers to machine learning techniques that use supervised or unsupervised strategies to automatically learn hierarchical representations in deep architectures for classification. The objective is to discover more abstract features in the higher levels of the representation, by using neural networks which easily separates the various explanatory factors in the data. In the recent years it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. This paper presents a brief overview of deep learning, techniques, current research efforts and the challenges involved in itItem A COMPARATIVE STUDY OF FUZZY MODELS IN DOCUMENT CLUSTERING(International Journal on Computer Science and Engineering, 2012) G, Manimekalai; K, Sathiyakumari; V, PreamsudhaThe availability of large quantity of text documents from the World Wide Web and business document management systems has made the dynamic separation of texts into new categories as a very important task for every business intelligence systems. Text document clustering is one of the emerging and most needed clustering techniques used to cluster documents with regard to similarity among documents. It is used widely in digital library management system in the modern context. Document clustering is widely applicable in areas such as search engines, web mining, information retrieval, and topological analysis. There are several clustering approaches available in the literature to cluster the document. But most of the existing clustering techniques suffer from a wide range of limitations. The existing clustering approaches face the issues like practical applicability, very less accuracy, more classification time etc. Thus a novel approach is needed for providing significant accuracy with less classification time. In recent times, inclusion of fuzzy logic in clustering provides better clustering results. One of the widely used fuzzy logic based clustering is Fuzzy C-Means (FCM) Clustering. In order to further improve the performance of clustering, this thesis uses Modified Fuzzy C-Means (MFCM) Clustering. The documents are ranked using Term Frequency–Inverse Document Frequency (TF–IDF) technique. From the experimental results, it can be observed that the proposed technique results in better clustering when compared to the FCM clustering technique