Department of Computer Science (PG)
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Item NEUTRALITY HANDLING AND SENTIMENT CLASSIFICATION USING ENSEMBLESTACKING(In association with IBM,AICTE,CSIR with Sri Ramakrishna Engineering College, Coimbatore, 2019-07-11) Rajeswari P; Radha NSentiment analysis is concerned with the automatic extraction of sentiment information from naturallanguage text. With the sharp increase in the number of users in social media sites, sentiment analysis has become an emerging research area. With the assistance of sentiment analysis systems, unstructured data can be automaticallyremodeled into structured information. Opinions are collected from users regarding the product, services, brands, politics, or any topic. This feedback is incredibly helpful for business applications like selling analysis, publicrelations, product reviews, web promoter grading, product feedback, and client reviews. People are generallyinterested to search for positive and negative opinions including likes, dislikes of product, upvotes and downvotes ofreviews shared by users for features of particular product or service or movie. For that reason, product features oraspects have got a major role in sentiment analysis. In this work, various features are extracted and categorized likemorphological features, sentiment scores, semantic features, syntactic features, word embedding, and wordweighting. The main aim of this work is to handle neutrality sentences by comparing two different lexicons based approaches and reviews are classified using stacking ensemble techniques based on features. In the traditionalensemble technique, there are multiple classifiers to suit to a training set to approximate the target function. Sinceevery classifier has its own output, therefore we need a hybrid mechanism to boost accuracy. This can be possible viaMax voting, weighted, and averaging. This is the standard approach of ensemble learning. In Stacking the combinedmechanism is used, which contains two layers. In the first layer, several classifiers were conducted and generatedifferent classification results. These results become the input features for a classifier in the next layer, which canautomatically learn and predict the most appropriate results.Naive Bayes and K-nearest neighbor, Logistic Regressionand Random Forest classifiers are used for the ensemble model. The experiments show that the stacking methodperforming well in sentiment classification by using both Meta learners as logistic regression. The proposed methodyields 65%-85% of accuracy for seven different experiments based on features.Item LEAF IDENTIFICATION USING MACHINE LEARNING ALGORITHMS(In association with IBM,AICTE,CSIR with Sri Ramakrishna Engineering College, Coimbatore, 2019-07-11) Radha N; Rehana Banu HPlants play an important role to equalize the carbon-oxygen cycle in the earth. Without knowing the importance of valuable plants, the plants are at the extinction. To help the naïve user to know about plants and it is important there is demand to develop a system to classify the plants based on the leaves. Due to the boon of ICT and machine learning algorithms, the leaves can be easily classified. Plant Leaf images are collected in Coimbatore. The main aim of this paper is to classify the leaves using Support Vector Machine (SVM) using KBF kernel, K-Nearest Neighbor, AdaBoost classifiers and also the accuracy obtained in these classifiers are compared. The performance of the models is evaluated using 10-fold cross validation method and the results are discussed. The classifier using SVM and KNN outperforms well than Adaboost classifiersItem BODY JOINT AND TRAJECTORY GUIDED 3-D DEEP CONVOLUTIONAL DESCRIPTORS FOR HUMAN ACTIVITY RECOGNITION(In association with IBM,AICTE,CSIR with Sri Ramakrishna Engineering College, Coimbatore, 2019-07-11) Srilakshmi N; Radha NHuman Activity Identification (HAI) in videos is one of the trendiest research fields in the computer visualization. Among various HAI techniques, Joints-pooled 3D-Deep convolutional Descriptors (JDD) have achieved effective performance by learning the body joint and capturing the spatiotemporal characteristics concurrently. However, the time consumption for estimating the locale of body joints by using large-scale dataset and computational cost of skeleton estimation algorithm were high. The recognition accuracy using traditional approaches need to be improved by considering both body joints and trajectory points together. Therefore, the key goal of this work is to improve the recognition accuracy using an optical flow integrated with a two-stream bilinear model, namely Joints and Trajectory-pooled 3D-Deep convolutional Descriptors (JTDD). In this model, an optical flow/trajectory point between video frames is also extracted at the body joint positions as input to the proposed JTDD. For this reason, two-streams of Convolutional 3D network (C3D) multiplied with the bilinear product is used for extracting the features, generating the joint descriptors for video sequences and capturing the spatiotemporal features. Then, the whole network is trained end-to-end based on the two-stream bilinear C3D model to obtain the video descriptors. Further, these video descriptors are classified by linear Support Vector Machine (SVM) to recognize human activities. Based on both body joints and trajectory points, action recognition is achieved efficiently. Finally, the recognition accuracy of the JTDD model and JDD model are comparedItem SECURING PATIENTS CONFIDENTIAL INFORMATION USING ECG STEGANOGRAPHY(by CONFYY in association with PPG institute of technology Coimbatore, 2017-10-20) Sivaranjani B; Radha NThe main goal of this research work is to enhance the security of Patient's medical data. During the transmission, the data is concealed with ECG signal. The ECG signal of the human being vary from one person to person. Like other Biometric traits, ECG cannot be imitated and duplicated. Encryption is one of the best techniques that guarantees the security of sensitive information. This technique not only grants the information's security but also authentication. secret sub keeping system security. digital signature, and etc. Therefore, the purpose of adopting encryption techniques is to ensure the information's integrity and confidentiality, that prevents information from tampering, forgery and counterfeiting. The encryption method used in this work will encrypt the secret data into unreadable form by creating the information inaccessible to any hacker having a random method. In this paper, Haar Wavelet Transformation is used to decompose an ECG signal to different frequency sub-bands. RSA algorithm is used to encrypt the patient information with the help of key pairs. Arnold cat map technique is used to scramble the encrypted data for more security of information. And for embedding, Singular Value Decomposition (SVD) is used for effective transformation with high security. The proposed algorithm is evaluated based on MSE, PSNR, AD, NCC, MD, NAE, BER and accuracy. The experimental results prove that the performance obtained using proposed techniques give better results than the existing techniques.Item SECURING DATA TRANSMISSION IN MANET USING AN IMPROVED COOPERATIVE BAIT DETECTION APPROACH(Bharathiar University, Coimbatore, 2016-10-24) Nachammai M; Radha NMobile Ad hoc Network often called as MANET, which does not have any particular infrastructure, in which each mobile deviceis connected wirelessly and can move freely in any direction without having any restrictions in the network. Malicious nodes present in this network can easily launch highly vulnerable attacks like collaborative Black hole attack and Gray hole attack due to its dynamically changing topology. These attacks affect the routing process within MANET. Hence, Security is the primary concern for finding these nodes. But, to prevent or detect malicious nodes that causes Gray hole or a collaborative black hole attack is a challenge. In this scheme, the malicious nodes and its behaviors are detected using reverse tracing technique by sending RREQ and RREP. However, security for transmitting data is not considered by CBDS. In order to have secure transmission after the malicious node detection, our proposed system uses an improved Cooperative Bait Detection approach which incorporates CBDS with message security schemes. Finally, this approach is compared with the existing system by using performance metrics like End-to-End Delay, Packet Delivery Ratio (PDR), Throughput and Routing OverheadItem MULTIMODAL BIOMETRIC TEMPLATE AUTHENTICATION OF FINGER VEIN AND SIGNATURE USING VISUAL CRYPTOGRAPHY(Srisakthi Institute of Engineering, 2016-01-07) Nandhinipreetha A; Radha NIn this paper personal verification method using finger-vein and signature is presented. Among many authentication systems finger-vein is promising as the foolproof method of automatic personal identification. Finger-vein and signature image is pre-processed and features are extracted using cross number concept and principle compound analysis. Fusion technique is used to fuse the finger vein and signature images. Then the visual cryptographic scheme is applied for the biometric template to generate the shares. The shares are stored in a separate database, and then the biometric image is revealed only when both the shares are simultaneously available. At the same time, the individual image does not reveal the identity of the biometric image. The proposed work is evaluated with evaluation metrics FAR, FRR and accuracy.Item CANCELABLE MULTIMODAL BIOMETRIC USER AUTHENTICATION SYSTEM WITH FUZZY VAULT(Srisakthi Institute of Engineering, 2016-01-09) Soruba sree S.R; Radha NBiometrics refers to authentication techniques that rely on humans physical and behavioral characteristics that can be automatically checked. Biometric based authentication system provides robust security and ease of use than conventional methods of verification system. Multimodal biometric system is one of the major areas of study identified with large applications in recognition system. Unimodal biometric systems challenge with a wide variety of problems such as noisy data, Intra-class variations, non-universality, and spoof attacks. Some of these limitations can be solved in multimodal biometric system. In proposed work, face and fingerprint biometric traits are used for multimodal biometric authentication system. Biometric traits are transformed using distortion algorithm. After the transformation processes pre-processing of images are done to improve the clear visibility of images. The extractions of minutiae feature from fingerprint are achieved using Crossing Number concept and the face features are extracted using the Local Binary Pattern algorithm. To combine both the face and fingerprint features feature level fusion is used. In order to provide additional security to the proposed work the fuzzy vault is introduced by adding duplicate values and having a secret key to lock and unlock the system. Fuzzy vault and distortion acts as an additional layer of security in multimodal biometric user authentication systemItem A NEW FRAMEWORK FOR IRIS AND FINGERPRINT RECOGNITION USING SVM CLASSIFICATION AND EXTREME LEARNING MACHINE BASED ON SCORE LEVEL FUSION(Karpagam College of Engineering, Coimbatore jointly with CSI and IEEE, 2013-01-04) Sangeetha S; Radha NIn a Multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. Two biometric characteristics are considered in this study: iris and fingerprint. Multimodal biometric system needs an effective fusion scheme to combine biometric characteristics derived from one or more modalities. The score level fusion is used to combine the characteristics from different biometric modalities. Fusion at the score level is a new technique, which has a high potential for efficient consolidation of multiple unimodal biometric matcher outputs. Support vector machine and extreme learning techniques are used in this system for recognition of biometric traits. In this, the Fingerprint-Iris system provides better performance and comparison of support vector machine and extreme learning machine based on score-level fusion methods is obtained. In score-level fusion, ELM provides better performance as compare to the SVM. It reduces the classification time of current system. This work is valuable and makes an efficient accuracy in such applications. This system can be utilized for person identification in several applicationsItem SECURING IRIS AND FINGERPRINT TEMPLATES USING FUZZY VAULT AND SYMMETRIC ALGORITHM(Karpagam College of Engineering, Coimbatore jointly with CSI and IEEE, 2013-01-05) Sowkarthika S; Radha NThe important aspect of all verification system is authentication and security. This aspect necessitates the development of a method that ensures user security and privacy. The traditional methods such as tokens and passwords provide security to the users. Uncertainly, the attackers can easily compromise these techniques. In recent years, the combination of biometrics and cryptography techniques has been proved as a efficient way to achieve security. The important feature of using biometric template is that it cannot be exploit by an unauthorized user. Most commonly used biometric features are iris, retina, fingerprint, face, palmprint, hand geometry, voice and so on. Fuzzy vault is the concept which uses the combination of biometrics and cryptographic key generation technique. This fuzzy vault act as a additional layer of security. This paper proposes a biometric verification system investigating the combined usage of multimodal biometric features and fuzzy vault scheme. This approach uses of fingerprint and iris in order to provide higher accuracy rate. Experiments were conducted to investigate the performance of the proposed system in ensuring the user security and privacy.Item SURVEY ON FINGER RECOGNITION USING NEURAL NETWORKS(Bannari Amman Institute OfTechnology,Sathyamangalam, 2013-04-02) Radha N; Sangeetha SThe biometric identification plays a vital role in used authentication system. It is widely used Human Recognition system also. The fingerprint is most popular trait to recognize the Humanbeing. The survey of Unimodal biometric traits and their recognition methods using Neural networks are collected and analysed.