International Conference

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/177

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End date: 2020

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Now showing 1 - 10 of 12
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    OPTIMIZED BOUNDARY DETECTION ALGORITHM FOR POSTAL SIGNS RECOGNITION SYSTEM USING VARIANT BASED PARTICLE SWARM INTELLIGENCE
    (IEEE, 2016-10-06) Subashini P; Krishnaveni M; Manjutha M
    Sign Language is the only mode of communication for deaf and dumb people to convey their messages. Many difficulties are faced by the hearing impaired people when they come across certain areas like Banking, Hospital and Post Office. Especially, there is no proper communication aid available in post offices to support disabled people. From available literature, it is understood that computational methods have been existing in the area of sign language recognition for hearing impaired people. These recognition system acts as an interpreter to accomplish the conversion of sign language into text or voice. This paper proposes an efficient object tracking method, that improves the performance of the video recognition system, by introducing Variant based Particle Swarm Optimization (VPSO) technique in Kalman Filter (KF) through postal video signs. The experimental results prove that VPSO based Efficient Kalman Filter (EKF) provides results better than a traditional KF.
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    AN OPTIMIZED CEPSTRAL FEATURE SELECTION METHOD FOR DYSFLUENCIES CLASSIFICATION USING TAMIL SPEECH DATASET
    (IEEE, 2019-10-14) Manjutha M; Subashini P; Krishnaveni M; Narmadha V
    Speech is the most important and indispensable mode of communication between humans. In communication, the continuous flow of speech gets affected due to the interruption of emotional, panic and psychological factors that cause syllable or word repetition, prolongation and interjection. Speech dysfluency is a primary challenge for speech pathologist to isolate the normal speech from the stuttered speech. The primary objective of this paper is to propose a novel approach through optimized cepstral features selection that improves the classifiers accuracy. In this paper, Particle Swarm Optimization (PSO) and Synergistic Fibroblast Optimization (SFO) were introduced to select optimal features from conventional MFCC (Mel-Frequency Cepstrum Coefficients). The optimized cepstral features from PSO and SFO of pre-processed Tamil speech data is used to discriminate among different categories of speech signals like Normal, Moderate and Sever stutter through machine learning classification methods such as Support Vector Machine (SVM) and Naive Bayes (NB). From the experimental results, the optimal selection of cepstral features using SFO algorithm has achieved high accuracy of 96.08% employed with NB which outperforms well to the feature selection of PSO and classical MFCC. The evaluation of the proposed methodology is done by using performance metrics like sensitivity, specificity, precision, f-score and accuracy.
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    ANALYSIS ON REGULARITY OF SPEECH ENERGY BASED ON OPTIMAL THRESHOLDING FOR TAMIL STUTTERING DATASET
    (IEEE, 2019-10-17) M, Manjutha; P, Subashini; M, Krishnaveni
    All over the world millions of people were affected by speech disorders in which one of the significant speech disorders is stuttering. Over the past two decade immense number of research is going on in the field of fluency disorder, and still it is necessary to enhance the analysis of stuttering disorder regional-wise. The speech signal tempo will vary with each individual where the specific fluctuation in the velocity of stutter speech is typical and it is due to the intervals in the speech rate which has a significant difference in normal stuttered speech. In this paper, Regularity of Speech Energy (RSE) was analyzed as normal, moderate and severe through Tamil speaking stuttered dataset. The analysis was done based on the energy threshold obtained during the irregular release of energy which is henceforth analyzed using optimal thresholding based on Particle Swam optimization (PSO) and Synergistic Fibroblast optimization (SFO) techniques. In order to evaluate the experimental analysis on RSE, statistical measures such as mean, standard deviation, Mean Square Error (MSE) and Root Mean Square Error (RMSE) were calculated. The experimental results of analysis on RSE have proved that stuttered speaker's signal releases low energy when compared to the normal speaker where the optimal threshold energy enhances the detection of hidden speech energy.
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    Diagnosis of Diabetes Mellitus using Bayesian Network
    (Sri Venkateswara Eductional Trust, 2015-01) J, Maria Shyla
    Diabetes mellitus, simply referred to as diabetes, is a group of metabolic diseases.There exists more than one type of diabetes, with each type having its own risks.Diabetes is ascribed to the acute conditions under which the production and consumption of insulin is disturbed in the body which consequently leads to the increase of glucose level in the blood. Bayesian networks are considered as helpful methods for the diagnosis of many diseases. They, in fact, are probable models which have been proved useful in displaying complex systems and showing the relationships between variables in a graphic way. The advantage of this model is that it can take into account the uncertainty and can get the scenarios of the system change for the evaluation of diagnosis procedures. In this study, decision tree and Bayesian models have been compared. The results indicated that the Bayesian model is much more accurate in diabetes diagnosis.
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    PPI RECONSTRUCTION TO PRIORITIZE CANDIDATE GENE WITH MULTI OBJECTIVE BAT ALGORITHM
    (Coimbatore Institute of Information Technology / Nehru Arts and Science College, 2018-11-26) J, Maria Shyla; M, Renuka Devi
    Candidate gene prioritization is the process of identifying new genes as potential candidates of being associated with a disease. A random walk-based algorithm called Random Walker on the Reliable Heterogeneous Network (RWRHN) was used to prioritize potential candidate genes for inherited disease. In this paper, the prioritization of candidate gene is improved by considering the gene similarity along with the topological similarity and phenotype similarity. Then, the similarity of genes of proteins is calculated based on gene expression data. A sub space clustering is obtained by utilizing multi objective BAT algorithm. Based on topological similarity matrix and gene similarity the PPI network is reconstructed. Finally RWRHN is applied to prioritize potential candidate genes. Thus the prioritization of candidate gene is improved with the consideration of gene similarity.
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    IDENTIFICATION OF SUBGROUPS IN A DIRECTED SOCIAL NETWORK USING EDGE BETWEENNESS AND RANDOM WALKS
    (PSGR Krishnammal College for Women, 2017-12) Sathiyakumari K; Vijaya M S
    Social networks have obtained masses hobby recently, largely because of the success of online social networking Web sites and media sharing sites. In such networks, rigorous and complex interactions occur among several unique entities, leading to huge information networks with first rate commercial enterprise ability. Network detection is an unmanaged getting to know challenge that determines the community groups based on common place hobbies, career, modules, and their hierarchical agency, the usage of the records encoded in the graph topology. Locating groups from social network is a tough mission because of its topology and overlapping of various communities. In this research, edge betweenness modularity and random walks is used for detecting groups in networks with node attributes. The twitter data of the famous cricket player is used here and network of friends and followers is analyzed using two algorithms based on edge betweenness and random walks. Also the strength of extracted communities is evaluated using on modularity score and the experiment results confirmed that the cricket player’s network is dense.
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    COMMUNITY DETECTION BASED ON GIRVAN NEWMAN ALGORITHM AND LINK ANALYSIS OF SOCIAL MEDIA
    (P S G R Krishnammal College for Women, 2016-11) Sathiyakumari K; Vijaya M S
    Social networks have acquired much attention recently, largely due to the success of online social networking sites and media sharing sites. In such networks, rigorous and complex interactions occur among numerous one-of-a-kind entities, main to massive statistics networks with notable enterprise capacity. Community detection is an unsupervised learning task that determines the community groups based on common interests, occupation, modules and their hierarchical organization, using the information encoded in the graph topology. Finding communities from the social network is a difficult task because of its topology and overlapping of different communities. In this research, the Girvan-Newman algorithm based on Edge-Betweenness Modularity and Link Analysis (EBMLA) is used for detecting communities in networks with node attributes. The twitter data of the well-known cricket player is used right here and community of friends and fans is analyzed based on three exclusive centrality measures together with a degree, betweenness, and closeness centrality. Also, the strength of extracted communities is evaluated based on modularity score using proposed method and the experiment results confirmed that the cricket player’s network is dense.
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    SPIDERNET: AN INTERACTION TOOL FOR PREDICTING MALICIOUS WEB PAGES
    (P S G R Krishnammal College for Women, 2015-02) Krishnaveni S; Sathiyakumari K
    Malicious code injection poses a serious security issue over the Internet or over the Web application. In malicious code injection attacks, hackers can take advantage of defectively coded Web application software to initiate malicious code into the organization's systems and network. The vulnerability persevere when a Web application do not properly sanitize the data entered by the user on a Web page. Attacker can steal confidential data of the user like password, pin number, and etc., these attacks resulting defeat of market value of the organization. This research work is model the malicious Web page prediction as a classification task and provides a convenient solution by using a powerful machine learning technique such as Support Vector Machine (SVM), Extreme Learning Machine (ELM). The main aim of this research work is to predict the type of the malicious attack like Redirect, Script injection and XSS using the machine learning approaches; in this case, the prediction time is taken into consideration. The supervised learning algorithms such as SVM and ELM are employed for implementing the prediction model.
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    CORONARY ARTERY DISEAE (CAD) PREDICTION AND CLASSIFICATION
    (Vivekanandha College of Engineering For Women, 2015-03-11) S, Nithya
    Data mining is the extraction of concealed prescient data from expansive databases furthermore a capable of new innovation with incredible potential to examine critical data in their data warehouses. Data mining algorithms anticipate future patterns and behaviors, permitting organizations to make proactive and knowledge driven choices. The computerized, forthcoming analyses offered by data mining move beyond investigations of past occasions gave by review tools common place of choice emotionally supportive networks. Data mining algorithms can answer business addresses that customarily were excessively prolonged to determine. They scour databases for concealed examples, discovering the prediction of disease that specialists may miss in light of the fact that it lies outside their desires. Manual checking is highly impossible to diagnose for this disease. To predict heart disease several approaches have been carried out. This comparative study paper provides a thorough analysis of various algorithms made towards heart disease prediction. Several data mining and soft computing approaches arestudied. This study concludes that the performance of various algorithms comparison of accuracy, sensitivity and specificity of several algorithms and approaches.
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    PANCREATIC TUMOR SEGMENTATION IN RECENT MEDICAL IMAGING – AN OVERVIEW
    (“Advances in Intelligent Systems and computing” 2194-5357 @ Springer Nature Switzerland AG 2020 S. pp.514 -522, 2020-01-07) Sindhu A; Radha V
    Pancreatic tumor is one of the deadliest diseases, which is the fourth leading cause of cancer death worldwide. Detecting pancreatic cancer at an early stage may increase the life of the patients. Pancreatic tumor segmentation is one of the difficult challenges in medical field. Accurate and Efficient segmentation in medical images are emerging as a challenging task during radiotherapy planning. Various medical modalities like MRI, CT and PET are widely used for diagnosing the abnormalities present in the medical images. Image segmentation plays an important part for the exact detection of the tumor in diagnosing, detecting, treatment and planning. In this review paper, various algorithms are used for segmenting the pancreatic tumor in medical images were discussed.