International Journals

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    RELAXED HYBRID ROUTING TO PREVENT CONSECUTIVE ATTACKS IN MOBILE AD-HOC NETWORKS
    (ACM Journals, 2023-06-05) Viji Gripsy J; Kanchana K R
    In 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.
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    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.
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    A SURVEY: IOT BASED HOME SECURITY AND AUTOMATION SYSTEM
    (Kalahari Journals, 2022-01) Vijayalakshmi K; Rasika S; Ponmalar S; Deepa V
    Advance in knowledge from last few decades opens doors to various threats to human and his environments. Individuals with the progression in security had taken numerous measures to control the bullying for protecting their properties. From time to time numerous interruption finding systems conventional for earmark intruders from home environment and provide tangible benefits to users, but can also expose users to significant security risk. Smart home security system is gaining popularity for industry, government, and academia as well as for distinct that has the potential to bring significant private, specialized and economic benefits. This paper signifies smart home security system and response rapidly to alarm incidents and has a friendly user interface. Special emphasis is placed on the experimental security analysis of such developing smart home platform by separating into two case scenarios. The paper will conclude by discussing future perspective and challenges associated with the development of security system for home.
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    PENETRATION OF TECHNOLOGY TO VIRTUAL REALITY IN ARTIFICIAL INTELLIGENCE AND ITS CHALLENGES
    (2020-02) Reshmi S; Jawahar S; Ahamed Johnsha Ali S
    Virtual reality makes an imaginary biosphere as well as factual ecosphere which smears to mainframe imitation milieus. Virtual reality comprises the domains of applications such as training simulators, medical and physical condition centre. Second life is an art of technologies in virtual reality. It contains the influence of both positive and negative authenticity of average people in life is ventured. It is a gruelling situation by performing benign and with an erudition outlook. Artificial intelligence is a deputize turf of mainframe discipline. In AI, the advance technologies is to be pervasive with impacts and ramifications in health, security and governance. It combines with other emerging and converging technologies. In accounting field, the predictable trend brings tremendous changes and progress to the artificial intelligence technologies.
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    TAMIL SPEECH ENABLED INTERFACE USING ROBUST TAMIL CONSONANT-VOWEL MODEL
    (International Journal of Pure and Applied Mathematics, 2018) S, Karpagavalli; J, Viji Gripsy; E, Chandra
    There are a lot of assistive technology tools are available to help individuals with learning disabilities. Assistive technology tools improve the skills of the children such as listening, mathematical, organizing, memory, reading and writing. Speech recognition technologies are highly effective and beneficial for children with one or more challenges that include learning disabilities like dyslexia, discalculia and dysgraphia. In the proposed work, an application,”TamilEasyApp‟ has been developed for children with both reading and writing disability, with an objective to support the children to learn Tamil words effectively. It has been developed by using Robust Tamil Consonant-Vowel Model (RTCVM) which addresses both speech rate variability and environment variability. A list of 100 Tamil words is prepared according to their educational need as well as the educational theories involved in teaching disabled children. In the presence of trained teachers, nearly 25 children including male and female within the age group 7 to 15 are participated in recording and uttered each Tamil words 10 times during their regular learning time. The impact of the application is gathered from the teachers after the usage of the application for a period of two months. The performance of the children is assessed by conducting post-test after the introduction of the application and compared with their skill level in regular learning before the introduction of application. The pre-test and post-test details and observations are collected from the special trainers. The overall observation indicates that the motivation and independent learning among the children significantly progressed through the use of speech assistive tool.
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    A REVIEW ON SUB-WORD UNIT MODELING IN AUTOMATIC SPEECH RECOGNITION
    (IOSR Journal of VLSI and Signal Processing, 2016-12) Karpagavalli S; Chandra E
    The primary issue in designing a speech recognition system is the choice of suitable modeling unit. Speech recognition systems may be based on any one of the modeling unit like, word, phoneme and syllable. The selection of sub-word unit depends on many factors such as vocabulary size, complexity of the task, language. Phoneme is the most commonly used sub-word unit in state-of-the-art speech recognition systems, which is an indivisible unit of sound of a particular language. The choice of sub-word units, and the way in which the recognizer represents words in terms of combinations of those units, is the problem of sub-word modeling. This paper explores the various sub-word unit models used in speech recognition and presents the advantages and disadvantages of each sub-word unit.
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    A REVIEW ON AUTOMATIC SPEECH RECOGNITION ARCHITECTURE AND APPROACHES
    (International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016) Karpagavalli S; Chandra E
    Speech is the most natural communication mode for human beings. The task of speech recognition is to convert speech into a sequence of words by a computer program. Speech recognition applications enable people to use speech as another input mode to interact with applications with ease and effectively. Speech recognition interfaces in native language will enable the illiterate/semi-literate people to use the technology to greater extent without the knowledge of operating with computer keyboard or stylus. For more than three decades, a great amount of research was carried out on various aspects of speech recognition and its applications. Today many products have been developed that successfully utilize automatic speech recognition for communication between human and machines. Performance of speech recognition applications deteriorates in the presence of reverberation and even low levels of ambient noise. Robustness to noise, reverberation and characteristics of the transducer is still an unsolved problem that makes the research in the area of speech recognition still very active. A detailed study on automatic speech recognition is carried out and presented in this paper that covers the architecture, speech parameterization, methodologies, characteristics, issues, databases, tools and applications.
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    STOP CONSONANT-SHORT VOWEL (SCSV) CLASSIFICATION FOR TAMIL SPEECH UTTERANCES
    (2016-02) S, Karpagavalli; E, Chandra
    Tamil Language is one of the ancient Dravidian languages spoken in south India. Most of the Indian languages are syllabic in nature and syllables are in the form of Consonant-Vowel (CV) units. In Tamil language, CV pattern occurs in the beginning, middle and end of a word. In this work, CV units formed with Stop Consonant – Short Vowel (SCSV) were considered for classification task. The work carried out in three stages, Vowel Onset Point (VOP) detection, CV segmentation and classification. VOP is an event at which the consonant part ends and vowel part begins. VOPs are identified using linear Prediction residuals which provide significant characteristics of the excitation source. To segment the CV units, fixed length spectral frames before and after VOPs are considered. Both production based features - Linear Predictive Cepstral Coefficients (LPCC) and perception based features - Mel Frequency Cepstral Coefficients (MFCC) are extracted and given as input to the classifiers designed with multilayer perceptron and support vector machine. A speech corpus of 200 Tamil words uttered by 15 native speakers was used, which covers all SCSV units formed with Tamil stop consonants (/k/,/ch/,/d/,/t/,/p/) and short vowels (/a/,/i/, /u/, /e/, /o/). The classifiers are trained and tested for its performance using various measures. The results indicate that the model built with MFCC using support vector machine RBF kernel outperforms.
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    CLASSIFICATION OF LUNG DISEASE USING LOCAL AND GLOBAL DESCRIPTORS
    (International Journal of Computer Applications, 2016-02) Pradeebha R; Karpagavalli S
    Recent trends indicate that instances of chronic respiratory diseases are on the rise in India mainly due to vehicular pollution, air and dust pollution, habit of smoking and also increased population. A World Health Organization report indicates that India has a ranking number one in the world for lung disease deaths. Respiratory diseases like asthma, chronic obstructive pulmonary disease (COPD), Interstitial Lung Disease (ILD), pneumonia, tuberculosis (TB) are emerging as most important health problems in the country. The proposed work is aimed at establishing more advanced diagnostic strategy for lung diseases using CT scan images. Lung diseases such as Emphysema, Pneumonia, Bronchitis are classified using CT scan images which is collected from National Biomedical Imaging Archive (NBIA). A total of 366 images are used, out of which 300 images are used for training and 66 images are used for testing. The classification task carried out with classifier support vector machine (SVM) using Histogram of Oriented Gradient (HOG) –global descriptors and Local Binary Pattern (LBP) – local descriptors. The performance of the model built using Support Vector Machine indicates that it is effective in the prediction of lung disease with 98% predictive accuracy.
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    OFFLINE HANDWRITTEN MATHEMATICAL EXPRESSION RECOGNITION
    (International Journal of Innovative Research in Computer and Communication Engineering, 2016-01) Padmapriya R; Karpagavalli S
    Recognition of handwritten mathematical expressions is helpful in writing technical documents as well as useful in converting handwritten documents with mathematical equations into electronic format. Symbol recognition in mathematical expressions is a complex task due to large character set and writer variability in size and style of symbols. In this work, mathematical expression recognition task carried out in different phases which include data collection, preprocessing, segmentation, feature extraction, symbol classification as well as mathematical expression. A set of 50 simple algebraic expressions written by 10 writers, each equation with 10 to 15 symbols converting 23 unique symbols are collected. The expressions are scanned and converted into image files. The images are preprocessed to remove noises, normalize the size and enhance. The symbols in each equation is segmented and features like, zonal, structural, skeleton based, directional are extracted. Multilayer Perceptron (MLP) and Support Vector Machine (SVM) classifiers are used to classify the symbols. The accuracy of symbol classification and whole algebraic expression recognition is analyzed. An interface to automatic mathematical expression recognition is developed with effective classifier.