J. S. Vimali

Bio: J. S. Vimali is an academic researcher from Sathyabama University. The author has contributed to research in topics: Communications system & Wireless network. The author has an hindex of 1, co-authored 7 publications receiving 5 citations.

Identification of Cyber Threats and Parsing of Data

Abstract: One of the significant difficulties in network safety is the arrangement of a mechanized and viable digital danger's location strategy. This paper presents an AI procedure for digital dangers recognition, in light of fake neural organizations. The proposed procedure changes large number of gathered security occasions over to singular occasion profiles and utilize a profound learning-based discovery strategy for upgraded digital danger identification. This research work develops an AI-SIEM framework dependent on a blend of occasion profiling for information preprocessing and distinctive counterfeit neural organization techniques by including FCNN, CNN, and LSTM. The framework centers around separating between obvious positive and bogus positive cautions, consequently causing security examiners to quickly react to digital dangers. All trials in this investigation are performed by creators utilizing two benchmark datasets (NSLKDD and CICIDS2017) and two datasets gathered in reality. To assess the presentation correlation with existing techniques, tests are carried out by utilizing the five ordinary AI strategies (SVM, k-NN, RF, NB, and DT). Therefore, the exploratory aftereffects of this examination guarantee that our proposed techniques are fit for being utilized as learning-based models for network interruption discovery and show that despite the fact that it is utilized in reality, the exhibition beats the traditional AI strategies.

Voice Pathology Detection Based on Deep Neural Network Approach

Abstract: The advancement of technology offers solution to the complex problems faced by the society and brings the wellbeing of the individuals. Smart healthcare is prominent nowadays for diagnosis, treatment and constant monitoring which reduces visitation of hospital, transport cost and waiting time. Voice pathology is a decease which affects the person vocal cord so that one who facing difficult in speech. If the decease not identified in time, it leads to permanent loss of voice for an individual. Traditionally, the decease is identified through oral examination or manual procedures. Due to the advent of smart phone, one can record the voice and send it to the cloud server for processing. Our system classifies the voice data and provides the decision to the user. This greatly reduces the transport cost, and waiting time for oral examination at medical center. The mobile phone recorded the patient voice data and it will be stored into the cloud. The voice data is synthesized to signals and with the help of deep Neural network the voice pathology can be identified. The system has been tested with the data set and the test result shows promising.

Sentiment Analysis of Twitter Data Using Techniques in Deep Learning

Abstract: Twitter is a trending platform in social media. Twitter has a capacity of storing huge amount of data about current news. So, every citizen in this society is indirectly connected to the news, to know about the current scenario a platform is needed, that platform is our Twitter. In Twitter, there are a lot of trending topics which may be either good or bad. There is ‘n’ number of comments and suggestions regarding each topic. So, to know whether a particular topic is good or bad, every comment mentioned cannot be studied by the user. There comes the sentiment analysis which makes our work easier. The data can be mined utilising the Twitter exploration function, which helps for data cleaning. To analyse the data, a deep learning algorithm is used. To advance the correctness of analysing data, long short-term memory (LSTM) and recurrent neural networks (RNN) are used. Hence, this paper will how Twitter data will get extracted and analysing tweets on a particular topic.

Breast Cancer Detection Using Machine Learning

Abstract: The most commonly diagnosed and second leading cause of cancer fatalities is breast cancer in women. AI and IoT integrated system that can help diagnose earlier breast cancer. The key tool for detecting breast cancer is mammograms. Yet cancer cells in breast tissue are difficult to identify. Since it has less fat and more muscle. To examine the irregular areas of density, mass and calcification that signify the presence of cancer, digitized mammography images are used. Several imaging techniques have been developed to detect and treat breast cancer early and to decrease the number of deaths, and many methods of diagnosis of breast cancer have been used to increase diagnostic accuracy. The current technique does not detect breast cancer reliably in the early stages, and most women have suffered from this.

Hand Gesture Recognition Control for Computers Using Arduino

Abstract: With the change in time, new technologies are coming into existence. One of them is hand gesture control used by robots. Hand gestures are used to give commands as an input to these robots. These gesture controls are used as input devices to computer systems. These existing systems are costly and unaffordable by general uses. The idea proposed in the article is to make a cheaper version of a hand gesture controller using the Arduino board. The input is given with the help of a pair of ultrasonic sensors. The sensors are used to calculate the changes in the distance of our hand movements while giving the gesture. The Arduino will calculate the changes in distance and recognize the action done by the user. The Arduino will request the system processor to perform the requested action by the user. The time taken to perform basic operations like open, minimize, maximize, play, save, etc., operations are reduced because of this proposed model.

Lightweight Deep Learning Model for Assessment of Substitution Voicing and Speech after Laryngeal Carcinoma Surgery

Abstract: Simple Summary A total laryngectomy involves the full and permanent separation of the upper and lower airways, resulting in the loss of voice and inability to interact vocally. To identify, extract, and evaluate replacement voicing following laryngeal oncosurgery, we propose employing convolutional neural networks for categorization of speech representations (spectrograms). With an overall accuracy of 89.47 percent, our technique has the greatest true-positive rate of any of the tested state-of-the-art methodologies. Abstract Laryngeal carcinoma is the most common malignant tumor of the upper respiratory tract. Total laryngectomy provides complete and permanent detachment of the upper and lower airways that causes the loss of voice, leading to a patient’s inability to verbally communicate in the postoperative period. This paper aims to exploit modern areas of deep learning research to objectively classify, extract and measure the substitution voicing after laryngeal oncosurgery from the audio signal. We propose using well-known convolutional neural networks (CNNs) applied for image classification for the analysis of voice audio signal. Our approach takes an input of Mel-frequency spectrogram (MFCC) as an input of deep neural network architecture. A database of digital speech recordings of 367 male subjects (279 normal speech samples and 88 pathological speech samples) was used. Our approach has shown the best true-positive rate of any of the compared state-of-the-art approaches, achieving an overall accuracy of 89.47%.

An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing

Abstract: The purpose of this research was to develop an artificial intelligence-based method for evaluating substitution voicing (SV) and speech following laryngeal oncosurgery. Convolutional neural networks were used to analyze spoken audio sources. A Mel-frequency spectrogram was employed as input to the deep neural network architecture. The program was trained using a collection of 309 digitized speech recordings. The acoustic substitution voicing index (ASVI) model was elaborated using regression analysis. This model was then tested with speech samples that were unknown to the algorithm, and the results were compared to the auditory-perceptual SV evaluation provided by the medical professionals. A statistically significant, strong correlation with rs = 0.863 (p = 0.001) was observed between the ASVI and the SV evaluation performed by the trained laryngologists. The one-way ANOVA showed statistically significant ASVI differences in control, cordectomy, partial laryngectomy, and total laryngectomy patient groups (p < 0.001). The elaborated lightweight ASVI algorithm reached rapid response rates of 3.56 ms. The ASVI provides a fast and efficient option for SV and speech in patients after laryngeal oncosurgery. The ASVI results are comparable to the auditory-perceptual SV evaluation performed by medical professionals.

Analysis and Prediction of Myocardial Infarction using Machine Learning

Abstract: A myocardial localized necrosis (also known as a stroke) refers to a tissue damage in the brain due to the lack of oxygen in the specified area. An ischemic stroke is caused by a lack of oxygen that arises due to a constricted blood supply; this condition might result in developing dead tissue if the blood supply is not restored in a relatively short period of time. Around 33% of the cases end up being lethal. To overcome this challenge, this research work has proposed a novel framework called Logistic relapse, an AI relapse calculation that is utilized to distinguish the myocardial localized necrosis in a patient. The calculation is prepared by utilizing an organized clinical dataset. By precisely distinguishing the myocardial localized necrosis in a patient, this effort will be remarkable in the clinical field.

Artificial Intelligence based Vision and Voice Assistant

Abstract: The major domain of the proposed work is Artificial Intelligence, Natural Language processing and Computer Vision. Every Computer needs input devices to access the input for the operating system of the computer. In the age of artificial Intelligence and machine learning, where computer can do everything as the man can do and also it can do tasks that humans cannot, at incredible speed. One major constrain in Artificial Intelligence is that machine cannot have consciousness like humans who can synchronize their senses to feel the environment. That is, they need various input devices with lot complicated circuits which should be maintained at frequent intervals. This proposed work is all about interacting with machines without usual input devices such as keyboard and mouse. Humans can directly access the operations of the system by Natural Language processing. Generally, it can be done by programming based on the system requirements. Normally, the user needs machine level programming codes to operate the operating system of the computer and it can be done by Python programming.

Artificial Intelligence based Vision and Voice Assistant

Abstract: The major domain of the proposed work is Artificial Intelligence, Natural Language processing and Computer Vision. Every Computer needs input devices to access the input for the operating system of the computer. In the age of artificial Intelligence and machine learning, where computer can do everything as the man can do and also it can do tasks that humans cannot, at incredible speed. One major constrain in Artificial Intelligence is that machine cannot have consciousness like humans who can synchronize their senses to feel the environment. That is, they need various input devices with lot complicated circuits which should be maintained at frequent intervals. This proposed work is all about interacting with machines without usual input devices such as keyboard and mouse. Humans can directly access the operations of the system by Natural Language processing. Generally, it can be done by programming based on the system requirements. Normally, the user needs machine level programming codes to operate the operating system of the computer and it can be done by Python programming.