Rajesh Kumar M

Afnan Al-Ali, Somaya Al-Maadeed, Moutaz Saleh et al.

Dysarthria is a neurological speech disorder that can significantly impact affected individuals' communication abilities and overall quality of life. The accurate and objective classification of dysarthria and the determination of its severity are crucial for effective therapeutic intervention. While traditional assessments by speech-language pathologists (SLPs) are common, they are often subjective, time-consuming, and can vary between practitioners. Emerging machine learning-based models have shown the potential to provide a more objective dysarthria assessment, enhancing diagnostic accuracy and reliability. This systematic review aims to comprehensively analyze current methodologies for classifying dysarthria based on severity levels. Specifically, this review will focus on determining the most effective set and type of features that can be used for automatic patient classification and evaluating the best AI techniques for this purpose. We will systematically review the literature on the automatic classification of dysarthria severity levels. Sources of information will include electronic databases and grey literature. Selection criteria will be established based on relevance to the research questions. Data extraction will include methodologies used, the type of features extracted for classification, and AI techniques employed. The findings of this systematic review will contribute to the current understanding of dysarthria classification, inform future research, and support the development of improved diagnostic tools. The implications of these findings could be significant in advancing patient care and improving therapeutic outcomes for individuals affected by dysarthria.

Ritin Raveendran, Aviral Singh, Rajesh Kumar M

One of the biggest problems in neural learning networks is the lack of training data available to train the network. Data augmentation techniques over the past few years, have therefore been developed, aiming to increase the amount of artificial training data with the limited number of real world samples. In this paper, we look particularly at the MNIST handwritten dataset an image dataset used for digit recognition, and the methods of data augmentation done on this data set. We then take a detailed look into one of the most popular augmentation techniques used for this data set elastic deformation; and highlight its demerit of degradation in the quality of data, which introduces irrelevant data to the training set. To decrease this irrelevancy, we propose to use a similarity measure called Complex Wavelet Structural Similarity Index Measure (CWSSIM) to selectively filter out the irrelevant data before we augment the data set. We compare our observations with the existing augmentation technique and find our proposed method works yields better results than the existing technique.

Karthik R, Anvita Dwivedi, Haripriya M et al.

Image inpainting algorithms are used to restore some damaged or missing information region of an image based on the surrounding information. The method proposed in this paper applies the radial based analysis of image inpainting on GRNN. The damaged areas are first isolated from rest of the areas and then arranged by their size and then inpainted using GRNN. The training of the neural network is done using different radii to achieve a better outcome. A comparative analysis is done for different regression-based algorithms. The overall results are compared with the results achieved by the other algorithms as LS-SVM with reference to the PSNR value.

Bharath K P, Sylash K, Pravina K et al.

Low cost and low power consumption processor play a vital role in the field of Digital Signal Processing (DSP). The OMAP-L138 development kit which is low cost, low power consumption, ease and speed, with a wide variety of applications includes Digital signal processing, Image processing and video processing. This paper represents the basic introduction to OMAP-L138 processor and quick procedural steps for real time and non-real time implementations with a set of programs. The real time experiments are based on audio in the applications of audio loopback, delay and echo. Whereas the non-real time experiments are generation of a sine wave, low pass and high pass filter.

Sai Abhishikth Ayyadevara, P N V Sai Ram Teja, Bharath K P et al.

One of the most arduous and captivating domains under image processing is handwritten character recognition. In this paper we have proposed a feature extraction technique which is a combination of unique features of geometric, zone-based hybrid, gradient features extraction approaches and three different neural networks namely the Multilayer Perceptron network using Backpropagation algorithm (MLP BP), the Multilayer Perceptron network using Levenberg-Marquardt algorithm (MLP LM) and the Convolutional neural network (CNN) which have been implemented along with the Minimum Distance Classifier (MDC). The procedures lead to the conclusion that the proposed feature extraction algorithm is more accurate than its individual counterparts and also that Convolutional Neural Network is the most efficient neural network of the three in consideration.

Hanisha Chowdary N, Karan K, Bharath K P et al.

Data privacy and data security are always on highest priority in the world. We need a reliable method to encrypt the data so that it reaches the destination safely. Encryption is a simple yet effective way to protect our data while transmitting it to a destination. The proposed method has state of art technology of steganography and encryption. This paper puts forward a different approach for data hiding in speech signals. A ten-digit number within speech signal using audio steganography and encrypting it with a unique key for better security. At the receiver end the same unique key is used to decrypt the received signal and then hidden numbers are extracted. The proposed approach performance can be evaluated by PSNR, MSE, SSIM and bit-error rate. The simulation results give better performance compared to existing approach.

Tejas K, Swathi C, Rajesh Kumar M

In this contemporary world digital media such as videos and images behave as an active medium to carry valuable information across the globe on all fronts. However there are several techniques evolved to tamper the image which has made their authenticity untrustworthy. CopyMove Forgery CMF is one of the most common forgeries present in an image where a cluster of pixels are duplicated in the same image with potential postprocessing techniques. Various state-of-art techniques are developed in the recent years which are effective in detecting passive image forgery. However most methods do fail when the copied image is rescaled or added with certain intensity before being pasted due to de-synchronization of pixels in the searching process. To tackle this problem the paper proposes distinct novel algorithms which recognize a unique approach of using Hus invariant moments and Discreet Cosine Transformations DCT to attain the desired rescale invariant and intensity invariant CMF detection techniques respectively. The experiments conducted quantitatively and qualitatively demonstrate the effectiveness of the algorithm.

Tejas K, Swathi C, Rajesh Kumar M

With the increase in interchange of data, there is a growing necessity of security. Considering the volumes of digital data that is transmitted, they are in need to be secure. Among the many forms of tampering possible, one widespread technique is Copy Move Forgery CMF. This forgery occurs when parts of the image are copied and duplicated elsewhere in the same image. There exist a number of algorithms to detect such a forgery in which the primary step involved is feature extraction. The feature extraction techniques employed must have lesser time and space complexity involved for an efficient and faster processing of media. Also, majority of the existing state of art techniques often tend to falsely match similar genuine objects as copy move forged during the detection process. To tackle these problems, the paper proposes a novel algorithm that recognizes a unique approach of using Hus Invariant Moments and Log polar Transformations to reduce feature vector dimension to one feature per block simultaneously detecting CMF among genuine similar objects in an image. The qualitative and quantitative results obtained demonstrate the effectiveness of this algorithm.

R Karthik, Dhruv Tyagi, Amogh Raut et al.

This paper presents a suitable and efficient implementation of a feature extraction algorithm (Pan Tompkins algorithm) on electrocardiography (ECG) signals, for detection and classification of four cardiac diseases: Sleep Apnea, Arrhythmia, Supraventricular Arrhythmia and Long Term Atrial Fibrillation (AF) and differentiating them from the normal heart beat by using pan Tompkins RR detection followed by feature extraction for classification purpose .The paper also presents a new approach towards signal classification using the existing neural networks classifiers.

Maghilnan S, Rajesh Kumar M

Sentiment analysis has evolved over past few decades, most of the work in it revolved around textual sentiment analysis with text mining techniques. But audio sentiment analysis is still in a nascent stage in the research community. In this proposed research, we perform sentiment analysis on speaker discriminated speech transcripts to detect the emotions of the individual speakers involved in the conversation. We analyzed different techniques to perform speaker discrimination and sentiment analysis to find efficient algorithms to perform this task.

Rishi Charan, Manisha. A, Karthik. R et al.

The most pressing challenge in the field of voice biometrics is selecting the most efficient technique of speaker recognition. Every individual's voice is peculiar, factors like physical differences in vocal organs, accent and pronunciation contributes to the problem's complexity. In this paper, we explore the various methods available in each block in the process of speaker recognition with the objective to identify best of techniques that could be used to get precise results. We study the results on text independent corpora. We use MFCC (Melfrequency cepstral coefficient), LPCC (linear predictive cepstral coefficient) and PLP (perceptual linear prediction) algorithms for feature extraction, PCA (Principal Component Analysis) and tSNE for dimensionality reduction and SVM (Support Vector Machine), feed forward, nearest neighbor and decision tree algorithms for classification block in speaker recognition system and comparatively analyze each block to determine the best technique

Tejas K, Ashok Reddy K, Pradeep Reddy D et al.

Vehicles play a vital role in modern day transportation systems. Number plate provides a standard means of identification for any vehicle. To serve this purpose, automatic licence plate recognition system was developed. This consisted of four major steps: Pre-processing of the obtained image, extraction of licence plate region, segmentation and character recognition. In earlier research, direct application of Sobel edge detection algorithm or applying threshold were used as key steps to extract the licence plate region, which does not produce effective results when the captured image is subjected to the high intensity of light. The use of morphological operations causes deformity in the characters during segmentation. We propose a novel algorithm to tackle the mentioned issues through a unique edge detection algorithm. It is also a tedious task to create and update the database of required vehicles frequently. This problem is solved by the use of Internet of things(IOT) where an online database can be created and updated from any module instantly. Also, through IoT, we connect all the cameras in a geographical area to one server to create a universal eye which drastically increases the probability of tracing a vehicle over having manual database attached to each camera for identification purpose.

Karthik Reddy, Tejas K, Swathi C et al.

The rising need of secret image sharing with high security has led to much advancement in lucrative exchange of important images which contain vital and confidential information. Multi secret image sharing system (MSIS) is an efficient and robust method for transmitting one or more secret images securely. In recent research, n secret images are encrypted into n or n+ 1 shared images and stored in different database servers. The decoder has to receive all n or n+1 encrypted images to reproduce the secret image. One can recover partial secret information from n-1 or fewer shared images, which poses risk for the confidential information encrypted. In this proposed paper we developed a novel algorithm to increase the sharing capacity by using (n, n/8) multi-secret sharing scheme with increased security by generating a unique security key. A unrevealed comparison image is used to produce shares which makes the secret image invulnerable to the hackers