International Journals

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/164

Browse

End date: 2023Subject: search.filters.subject.Classification

Search Results

Now showing 1 - 6 of 6
  • No Thumbnail Available
    Item
    EXPERT AUTOMATED SYSTEM FOR PREDICTION OF MULTI-TYPE DERMATOLOGY SICKNESSES USING DEEP NEURAL NETWORK FEATURE EXTRACTION APPROACH
    (IJISAE, 2023) Kalaivani A; Karpagavalli S; Kamal, Gulati
    One of the most prevalent illnesses on the planet is skin issues. Due to the complexity of types of skin, and hair types, it is difficult to evaluate it despite its popularity.Consequently, skin conditions pose a serious public health danger. When they reach the invasive stage of evolution, they become harmful. Medical professionals are very concerned about dermatological disorders. The number of people who suffer from skin illnesses is growing substantially as a result of rising pollution and bad food. People frequently ignore the early indications of skin conditions. A hybrid approach can minimize human judgment, producing positive results quickly. A thorough examination suggests that frameworks for recognizingvarious skin disorders may be built using deep learning techniques. To find skin illnesses, it is necessary to distinguish between theskin and non-skin tissue. Through the use of feature extraction-baseddeep neural network approaches, a classification system for skin diseases was established in this study. The main goal of this system is to anticipate skin diseases accurately while also storing all relevant state data efficiently and effectively for precise forecasts. The significant issues have been addressed, and a unique, feature extraction-based deep learning modelis introduced to assist medical professionals in properly detecting the type of skin condition.The pre-processing stage is when the inputdataset is first supplied, helping to clear the image of any undesired elements. Then, for the training phase, the proposed Feature Extraction Based Deep Neural Network (FEB-DNN) is fed the features collected from each of the pre-processed frames. With the use of measured parameters, the classification system categorizesincoming treatment data as various skin conditions. Finding the ideal weight values to minimizetraining error is crucial while learning the proposed framework. In this study, an optimization strategy is used to optimizethe weight in the structure. Based on the feature extraction approach, the suggested multi-type framework for diagnosing skin diseases has a 91.88% of accuracyrate for the HAM image dataset and identifies several skin disorder subtypes than the earlier models thatcan aid in treatment response and decision-makingwhich alsohelp doctors make an informed decision.
  • No Thumbnail Available
    Item
    DISCOVERING TAMIL WRITER IDENTITY USING GLOBAL AND LOCAL FEATURES OF OFFLINE HANDWRITTEN TEXT
    (International Review on Computers and Software (IRECOS), 2013) Thendral T; Vijaya M S; Karpagavalli S
    Writer identification is the process of identifying the individual based on their handwriting. Handwriting exhibits behavioral characteristics of an individual and has been considered as unique. The style and shape of the letters written vary slightly for same writer and entirely different for different writers. Also alphabets in the handwritten text may have loops, crossings, junctions, different directions etc. Hence accurate prediction of individual based on his/her handwriting is highly complex and challenging task. This paper proposes a new model for discovering the writer’s identity based on Tamil handwriting. Writer identification problem is formulated as classification task and a pattern classification technique namely Support Vector Machine has been employed to construct the model. It has been reported about 93.8% of prediction accuracy by RBF kernel based classification model.
  • No Thumbnail Available
    Item
    A NOVEL APPROACH FOR PASSWORD STRENGTH ANALYSIS THROUGH SUPPORT VECTOR MACHINE
    (Academy Publishers, Finland, 2009-11) Karpagavalli S; Jamuna K S; Vijaya M S
    Passwords are ubiquitous authentication methods and they represent the identity of an individual for a system. Users are consistently told that a strong password is essential these days to protect private data. Despite the existence of more secure methods of authenticating users, including smart cards and biometrics, password authentication continues to be the most common means in use. Thus it is important for organizations to recognize the vulnerabilities to which passwords are subjected, and develop strong policies governing the creation and use of passwords to ensure that those vulnerabilities are not exploited. This work employs machine Learning technique to analyze the strength of the password to facilitate organizations launch a multi-faceted defense against password breach and provide a highly secure environment. A supervised learning algorithm namely Support Vector Machine is used for classification of password. The linear and nonlinear SVM classification models are trained using the features extracted from the password dataset. The trained model shows the prediction accuracy of about 98% for 10-fold cross validation
  • No Thumbnail Available
    Item
    MACHINE LEARNING APPROACH FOR PREOPERATIVE ANAESTHETIC RISK PREDICTION
    (Academy Publishers, Finland, 2009-05) Karpagavalli S; Jamuna K S; Vijaya M S
    Risk is ubiquitous in medicine but anaesthesia is an unusual speciality as it routinely involves deliberately placing the patient in a situation that is intrinsically full of risk. Patient safety depends on management of those risks; consequently, anaesthetist has been at the forefront of clinical risk management. Anaesthetic risk classification is of prime importance not only in carrying out the day-to-day anaesthetic practice but coincides with surgical risks and morbidity condition. The preoperative assessment is made to identify the patients risk level based on American Society of Anesthesiologists (ASA) score that is widely used in anaesthetic practice. This helps the anaesthetist to make timely clinical decision. Machine Learning techniques can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care. This paper presents the implementation of three supervised learning algorithms, C4.5 Decision tree classifier, Naive Bayes and Multilayer Perceptron in WEKA environment, on the preoperative assessment dataset. The classification models were trained using the data collected from 362 patients. The trained models were then used for predicting the anaesthetic risk of the patients. The prediction accuracy of the classifiers was evaluated using 10-fold cross validation and the results were compared.
  • No Thumbnail Available
    Item
    BIOLOGICAL SOFTWARE FOR RECOGNITION OF SPECIFIC REGIONS IN ORGANISMS
    (Bioscience Biotechnology Research Communication, 2020-03) Kavitha A.S; Viji Gripsy J; Menaka V
    The identification of specific regions in an organism of a plant, animal or a single-celled life form, or something that has interdependent parts and that is being compared to a living creature. User can choose and select any type of organism’s image such as plant or animal. After successful selection, the proposed tool automatically analyses and finds the name of the organisms and identifies the specific region with some other related information in an effective manner. In comparative and evolutionary genomics, a detailed comparison of common features between organisms is essential to evaluate genetic distance. However, identifying differences in matched and mismatched genes among multiple genomes is difficult using current comparative genomic approaches due to complicated methodologies or the generation of meager information from obtained results. This research reduces the manual activity to analyze the details. Using this software tool, it easily helps us to know all the related organisms information of specific region and also can make report effectively. This makes the system user-friendly consequently reducing the manual work. The system has been developed with advanced features. The objective of this work is to establish an identification of specific region organisms and related information. The system developed with the main intension to progress an effective and user friendly tool for identification of the specific region in organisms. From the experimental results, the proposed system grasps and more and more identification accuracy.
  • No Thumbnail Available
    Item
    A NEW FRAMEWORK FOR TEA PLANT RECOGNITION USING EXTREME LEARNING MACHINE WITH VERY FEW FEATURES
    (International Journal of Applied Engineering Research, 2015) Arunpriya C; Antony Selvadoss Thanamani
    Due to more and more tea varieties in the current tea market, rapid and accurate identification of tea varieties is crucial for tea quality control. Tea quality mainly depends on the variety of leaf, growing environment, manufacturing conditions, size of ground tea leaves and infusion preparation. In the past few years, tea cultivar has been assessed by morphological assessment coupled with pattern recognition. This paper uses an efficient machine learning approach called Extreme Learning Machine (ELM) for the classification purpose. The proposed approach consists of four phases which are as preprocessing, feature extraction, feature clustering and classification. Additionally, this work proposes an iterative algorithm for feature clustering and applies it to leaf recognition. Feature clustering is a powerful tool to reduce the dimensionality of the selected feature. For improving the accuracy and performance of tea leaf recognition, ELM is implemented. The classifier is tested with 20 leaves from each variety and compared with k-NN and RBF approach. The proposed ELM classification produces effective results.