Grammar Detection, also referred to as Parts of Speech Tagging of raw text, is considered an underlying building block of the various Natural Language Processing pipelines like named entity recognition, question answering, and sentiment analysis. In short, forgiven a sentence, Parts of Speech tagging is the task of specifying and tagging each word of a sentence with nouns, verbs, adjectives, adverbs, and more. Sentiment Analysis may well be a procedure accustomed to determining if a given sentence's emotional tone is neutral, positive or negative. To assign polarity scores to the thesis or entities within phrase, in-text analysis and analytics, machine learning (ML) and natural language processing (NLP), approaches are incorporated. This Sentiment Analysis using POS tagger helps us urge a summary of the broader public over a specific topic. For this, we are using the Viterbi algorithm, Hidden Markov Model (HMM), Constraint-based Viterbi algorithm for POS tagging. By comparing the accuracies, we select the foremost accurate model's result for Sentiment Analysis for determining the character of the sentence.

Grammar Detection for Sentiment Analysis through Improved Viterbi Algorithm

The digital watermarking technique is a quite promising technique for both image copyright protection and secure transmission. However, many existing techniques are not as one might have expected for robustness and capacity simultaneously. In this paper, we propose a robust semi-blind image watermarking scheme with a high capacity. Firstly, we perform a discrete wavelet transformation (DWT) transformation on the carrier image. Then, the watermark images are compressed via a compressive sampling technique for saving storage space. Thirdly, a Combination of One and Two-Dimensional Chaotic Map based on the Tent and Logistic map (TL-COTDCM) is used to scramble the compressed watermark image with high security and dramatically reduce the false positive problem (FPP). Finally, a singular value decomposition (SVD) component is used to embed into the decomposed carrier image to finish the embedding process. With this scheme, eight 256×256 grayscale watermark images are perfectly embedded into a 512×512 carrier image, the capacity of which is eight times over that of the existing watermark techniques on average. The scheme has been tested through several common attacks on high strength, and the experiment results show the superiority of our method via the two most used evaluation indicators, normalized correlation coefficient (NCC) values and the peak signal-to-noise ratio (PSNR). Our method outperforms the state-of-the-art in the aspects of robustness, security, and capacity of digital watermarking, which exhibits great potential in multimedia application in the immediate future.

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A Hyper-Chaotically Encrypted Robust Digital Image Watermarking Method with Large Capacity Using Compress Sensing on a Hybrid Domain

Gesture recognition is widely used in communication, virtual reality, smart cockpit, smart home, etc. This paper proposes an application system based on MediaPipe for intelligent recognition of 26 letter gestures in English. The system uses images captured by a monocular camera, reads images using OpenCV, creates a hand detection model, uses MediaPipe to identify 21 key points of the hand, constructs a gesture recognition module for English letters, and recognizes letters through rough gesture recognition algorithm and fine gesture recognition algorithm. The experimental results show that the recognition accuracy of all English letters in this system is more than 81%, and the recall rate is more than 83%, which can be applied to gesture recognition related applications of English letters.

MediaPipe based Gesture Recognition System for English Letters

Images are one of the essential data being generated daily. Many images are taken in our day-to-day life. It is vital to keep those images secure. This paper proposes a new image encryption algorithm utilizing a 3D Logistic Map and an improved Chirikov Map. There have been several suggested image encryption techniques using chaotic maps. Some of these are time expensive and sophisticated, while others have limited key space. A nonlinear 3D Logistic Map performs better in terms of randomization attributes and security measures. With the modification in the Chirikov Map from the standard map adds more robustness to the algorithm by producing a cipher image which is a more secure and random image compared with the plain-text image. This paper performed well-known cryptographic tests to assess the security and performance analysis of the proposed scheme. The proposed scheme is resistant to brute force attacks and differential cryptanalysis. The algorithm is susceptible, even to a slight change in the secret key.

A Novel Image Encryption using 3D Logistic Map and Improved Chirikov Map

Copyright protection of digital images has become vital in the modern era where multimedia content is the dominant form of traffic in networked multimedia communication systems. Hence, there is a need for building technologies around robustness and computational efficiency for watermark embedding and extraction through state-of-the-art cryptographic algorithms. In this paper, we have proposed a blind image watermarking system that applies Henon chaotic map for watermark embedding and Elliptic Curve Cryptography (ECC) for securely sending the algorithmic parameters to the receiver. Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) are utilised for hybrid decomposition of the host image. The metrices used for the evaluation of the robustness and imperceptibility of this watermarking system are the Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NC) and the Central Processing Unit (CPU) runtime was recorded in order to assess the system’s execution speed.

Blind Digital Image Watermarking using Henon Chaotic Map and Elliptic Curve Cryptography in Discrete Wavelets with Singular Value Decomposition

Specially-abled people (speech and hearing impaired) rely on hand-gestures for communication on a daily basis. Majority of the people aren’t aware of the universally accepted hand-gestures alphabet, making communication difficult between the two groups of people. In an attempt to fill this void, this research work proposes a real time hand-gesture based recognition system based on the American Sign Language (ASL) dataset and capturing data through a BGR webcam and processing it using Computer Vision (OpenCV). The 29 static gestures (the alphabet) from the official, standard ASL dataset were trained with the help of Vision Transformer Model (ViT). The model showed an accuracy rate of 99.99% after being trained with 87,000 RGB samples for 1 epoch (2719 batches of 32 images each).

Jai Surya Kode

Papers

Blind Digital Image Watermarking using Henon Chaotic Map and Elliptic Curve Cryptography in Discrete Wavelets with Singular Value Decomposition

American Sign Language Static Gesture Recognition using Deep Learning and Computer Vision