Reviewerly/exHarmony huggingface dataset:
Data Steganography Using LSB, X-OR &amp; AES Algorithm

The way of hiding or storing your data to make it masked for private information without getting it stolen from outsiders. It is an effective way of hiding important information for various uses and is termed steganography. It is generally a technique that is used for masking or hiding your required data inside any of the ordinary, non-secret, data without getting it detected by threats. The binding of data with secret keys helps the information getting it tough to be in contact with unknown entities. To make it gettable to a known entity the masked information including files, data, images, audio, video, etc the data must be stored with some keys. To make the data unnoticeable the secret message must be stored in something that is not been imaginable or which is not secret. Hence the data getting hacked is at least risk. This is a technique which is been used for thousands of years. The secret force uses the steganography technique ages for keeping important data safe and secured from getting used by enemies. For example, in the Ancient Greek system, the method of hiding the message used by the Greek people was just like writing the message on wood and after that, the message on the written wood would be waxed to obscure it. In the same way in today’s world, there are various algorithms and techniques to hide secret information for various uses along with proper ways to manage to encrypt and decrypt the data. But on the other hand, various intruders kept on attacking the steganographic data for their benefit whether it can be for educational purposes or blackmailing purposes. Therefore, we have created a module that is made for handling the secret message encryption and decryption of message from various modes that usually seems to be unnoticeable, which is simply the day-to-day media use as the general purpose of uses by users. In this model, simple masking of data is been managed to hide the data. The model uses the AES algorithm to make it more secure along with the LSB technique. It can also be using RSA or other cryptographic algorithms but we are more concerned with the basics of the model hence we are using the simple algorithm for basic educational purposes.
Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a “long-lie.” Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD.
$$\mathrm T^2$$Net: an improved image-based text transfer framework using background inpainting and text conversion

Abstract Text, which is regarded as one of the important clues for visual recognition, can provide rich and accurate high-level semantic information. Therefore, the detection and recognition of textual data have become a research hotspot in computer vision and artificial intelligence. However, the difficulty of data collection and the non-uniform distribution of characters still poses challenges for accurate text recognition, especially for recognizing complicated character sets, such as Chinese. To address small-sample text recognition, we propose an improved image-based text transfer framework, named $$\mathrm T^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>T</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> Net. This work can replace or modify the text content in an image so as to arbitrarily expand a recognition data set. Considering that the main challenge of text transfer lies in decoupling the complex interrelationship between text and background, a text content mask branch is first added into a background inpainting module so as to more realistically restore background textures. Second, a text recognition model is developed to guide the readability of the text transfer results in the text conversion module. Finally, a text fusion module is used to fuse the independent migrations of background and text. We examined the performance of our proposed framework in a real-word scene text recognition data set. Qualitative and quantitative results have proved the efficiency of our method in comparison with previous works.
Vita Health – A Complete Healthcare System

Good health has been a major concern since the inception of mankind although for some people attaining good health requires taking prescribed medicines or pills routinely. However, many patients find it very difficult to keep track of taking their medication at the right time and proportion. This happens due to several reasons such as heavy workload, forgetfulness, old age, busy schedule, and many more. Some people undergo multiple medications in a day and it can be quite confusing for them to take appropriate medication at the correct and appropriate time as directed.The solution to the problem is to create a Speech based medicine intake tracker and Healthcare assistant named Vita Health. The proposed ideology serves the patients about their medicine dosage quantity and time in the form of voice notes that read aloud the name of the medicine. This app scans the medicine package and prescription then checks the expiry and reminds on the date on which it is expired. Predicting accurate medicine through printed prescriptions can be a real challenge in this solution. Our main aim is to deeply research about approaches to overcome this challenge. Some of them are OCR (Optical Character Recognition), NLP(Natural Language Processing), and CNN(Convolutional Neural Networks). There is a field to include the starting and ending dates and time slots within which the medicine is to be taken. There is a list of contacts of doctors near you where you can chat and contact them. In case the patient forgets or skips to take the medicines, a reminder is sent to the doctor and to nearby ones to keep a track and remind them to take the medicine.
Classifying Sign Language Gestures using Decision Trees: A Comparison of sEMG and IMU Sensor Data

The use of machine learning technologies for the identification of sign language has gained popularity recently. This arises from the recognition of sign language as a valuable means of communication specifically designed for individuals who are mute or hearing-impaired. In order to build an optimised model based on sEMG, accelerometer, gyro, and magnetometer data, this research article compares decision tree classifiers, notably J48, Random tree, REPTree, and Random forest. This data is collected through the Myo arm band worn on both forearms of the user. The experiment is designed using the open-source Waikato Environment for Knowledge Analysis (WEKA) framework. To evaluate the effectiveness of the four algorithms, three attribute selection techniques information gain-based, correlation-based, and learner-based feature selection were used. The trial results showed that, among the investigated algorithms, the Random Forest method had the highest accuracy, measuring 97.9472%.
A Novel Multi-Sensor Tactile Detachable Bumper Design for Obstacle Avoidance, Collision Detection, and Recovery in Autonomous Mobile Robots

A tactile bumper is the most dependable sensor for an autonomous mobile robot to perceive its surroundings when all the high-end sensors like infrared (IR), ultrasonics, cameras or lidars fail to operate. Typically, these bump sensors mounted on the lower half of the robot barely manage the plight of sensor failures however do not fully protect the robot. This paper presents a novel bumper design that can be easily attached onto any mobile robot. The bumper plate covers the entire front of the robot without exposing anything to the environment. It does not just sense the direction of the collision but also gives the point of the impact. It also protects the bumper plate from getting stuck in places. The bumper plate has a vent through which the range sensors mounted at the front of any mobile robot can glance without any modifications. An IR cliff sensor mounted at the bottom of the bumper protects it from falling from high surfaces. It also allows the robot to navigate the environment with the help of a line drawn on the floor. An in-house developed mobile robot running on subsumption architecture successfully demonstrates the effectiveness of the bumper in an unknown environment with static and dynamic obstacles.
Exploratory study on Evolutionary Random Forests for Classification in Medical Datasets

This paper presents an exploratory study on the efficacy of different machine learning algorithms for classification in medical datasets, with a particular focus on a recently published evolutionary random forest algorithm. The study is motivated by the increasing availability of medical measurements obtained from various new sources such as wearables, and continued improvements in existing measurement techniques, which have resulted in an increase in the number of variables that can be measured per patient. Meanwhile, recruiting patients and collecting data often remain a costly and time-consuming endeavor, resulting in datasets with high dimensionality and low instance to feature ratios. The study aims to evaluate the performance of these machine learning algorithms and to investigate their sensitivity to varying sample sizes. Additionally, the study examines whether the use of an evolutionary random forest algorithm can improve performance and robustness in these datasets. The study was conducted on nine different datasets to assess the extent to which the findings can be generalized. The results indicate that the evolutionary random forest generally outperforms other classification algorithms. Furthermore, the performance gap often widens at lower instance to feature ratios. Future work may build on these findings to develop more sophisticated machine learning algorithms that are tailored to specific medical classification applications.
Performance Analysis of ELA-CNN model for Image Forgery Detection

With the growth of technology, the amount of content being shared on day-to-day basis is a figure beyond our comprehension. This data can be shared in the form of photos and videos and can be spread using social media platforms like WhatsApp and Instagram. With this also comes the threat of forged images being used to share false information. Advanced tools and software have made it easy and quick to generate such images. Due to this, researchers have been focusing on devising methods to tackle the same in a hope to assist people in finding the authenticity of questionable images. Copy-move and splicing are two forms of commonly used forgery techniques. When a region of the image is copied and pasted on the same image it is referred to as copy-image forgery whereas a region of a different image is copied and pasted on another image it is said to be spliced forgery. Our technique makes use of error level analysis (ELA) that points out the difference between the compression ratios of authentic and manipulated image and hence highlights the latter. The extracted information is then fed into a variant of CNN which classifies the image as original or forged. We also provide a comparative analysis of the proposed method with statistical and deep learning models and it will be tested on the CASIA V2, MICC-F220 and MICC-F2000 forged image datasets.
Synopsis Creation for Research Paper using Text Summarization Models

This paper proposes the comparison between three text summarization models - BERT, BART and T5. All the three models focus on summarizing a single research paper for generating a summary which is automatic and relevant. After the analysis and implementation of the three pretrained models, it is noticed that T5 is the best suited for our problem statement. Many researchers, professionals as well as students need to be up-to-date about the new scientific documents for the project they are working on or to gain something new out of it. They frequently feel that the abstract is not informative enough in order to establish significance. The final system aims at resolving the mentioned problem.
Progress in Combining Image Captioning and Visual Question Answering Tasks

The combination of image captioning and visual question answering (VQA) tasks has emerged as a captivating and innovative research domain. The goal of the image captioning task is generating a textual description that succinctly describes the contents of an image, while VQA focuses on providing natural language responses to questions posed about the image. Both tasks necessitate a profound comprehension of the image’s content, as well as the semantic relationships within it, while also requiring the ability to generate responses in a natural language format. The rapid progress in deep learning has propelled significant advancements in both these tasks. In this paper, we present a comprehensive overview of recent advancements in integrating image captioning and VQA tasks. Initially, we delve into the intricacies of image captioning and VQA tasks individually, followed by an exploration of various methodologies for their integration. Additionally, we examine the associated challenges and propose strategies to overcome them. Moreover, we analyze the datasets and evaluation metrics commonly employed in these tasks. The paper concludes by highlighting the importance of generating context-based captions that can effectively answer the most probable questions related to the image, thereby facilitating the VQA task. Overall, this review highlights the notable progress achieved in combining image captioning and VQA, while acknowledging the persisting challenges and outlining opportunities for future research in this dynamic and evolving field. Importantly, the integration of these tasks holds immense potential for enhancing the performance of real-world applications such as robotics, autonomous vehicles and image search.
Handwritten Analysis for Gender Identification using CNN

In this research, we provide a new method for determining a writer's gender based only on their handwriting samples, using deep learning methods. Our model utilizes a Convolutional Neural Network (CNN) to perform binary classification of the writer's gender and compares the performance of three different CNN architectures: ResNet50, VGG-19, and InceptionV3. The ICDAR 2013 dataset, which contains images of both English and Arabic handwriting samples, was used for training and testing the model. The unique features of the handwriting are discovered through CNN without the need for any prior feature extraction. The significance of this research lies in its application for forensic investigations, where demographic information such as gender, age, handedness, and ethnicity of the writer can be determined based on the handwriting left at a crime scene. This study demonstrates the potential of deep learning techniques in handwriting analysis and highlights the importance of developing models for gender prediction using only handwriting samples.
Deep CNN: Classification of Known Classes and Unknown Classes Based on Softmax Prediction Probabilities

Detecting unknown classes in an image classification is one of the key challenges deep learning developers confront. The idea of image recognition is built on deep learning, which can process enormous amounts of unstructured data by initializing weights and biases in neural networks with random values. By using Convolutional Neural Network (CNN) algorithm it is possible to identify between known classes (labeled data) and unknown class with open-set classification. The datasets Flavia Leaf Dataset and Swedish Leaf Dataset are considered for training and datasets that are not a part of training set are also considered for testing. The number of accurate and inaccurate predictions for each class is calculated using the confusion matrix for each class along with performance indicators such as accuracy.
Video Summarization using Speech Recognition and Text Summarization

Videos on the internet have been increasingly becoming the chief source of knowledge and information in today’s digital age. However, with increasing length of videos and diminishing time to spare in everyone’s lives, a need has emerged for Video Summarization tools that can provide a good summary about the content of videos without the need to watch videos in their entirety. In this paper, we introduce a two-fold approach to fetch the subject matter of videos through effective summarization. The employed approach comprises of two phases: the first phase involves performing speech-to-text conversion using an Automatic Speech Recognition(ASR) system based on a Convolutional Neural Network(CNN) for generating respective transcripts for input videos while the second phase involves performing Extractive Text Summarization to summarize the text generated by extracting the important information.
Sentiment Analysis using Multi-objective Optimization-based Feature Selection Approach

Polarity categorization affects sentiment analysis. Categorization is a fundamental knowledge discovery challenge. Categorization accuracy depends on data quality. Hence, data must be preprocessed to obtain meaningful information. In real-world applications, data is enormous, and many properties are duplicates or useless. The classification depends on feature selection. It finds the best data representation qualities. Feature selection reduces model training time. When unnecessary attributes are eliminated, models learn better. Combinatorial optimization makes feature selection harder. Feature selection balances decreasing features and enhancing classification performance. We propose two multi-objective optimization techniques for the feature selection. The particle swarm optimization (PSO) and Krill Herd Algorithm (KHA) are applied for optimal feature selection. The proposed model consists of key steps such as review pre-processing, multi-objective optimization-based feature selection, and supervised classification. The performance of both PSO-based and KHA-based models is evaluated using the two sentiment analysis datasets. The results show the efficiency of both models in terms of precision, recall, accuracy, and F1-score parameters.
Smart Intelligent System that can Code Like a Human Being

According to recent studies, a large number of data scientists spend most of their time on tasks like data cleaning and organizing data. They need to memorize big complex syntaxes for all the major tasks in the data science life cycle. Often these tasks are redundant. Therefore, we propose to build an intelligent system that enables data scientists to perform all the tedious and time-consuming tasks such as EDA, data cleansing, data preprocessing, data visualization, modeling, and data science lifecycle evaluation. Just state the logic of your query in natural language the system will automatically output all relevant Python code snippets. Existing applications involving the text-to-code generation and code search are limited and a lot of them do not work in non-ideal conditions. The reason behind it is the data set on which the existing models have been built. These datasets do not consider real-world factors such as slang, acronyms, and paraphrases. Therefore, a new dataset was created consisting of real-world user queries, representing the scenarios a user is most likely to face daily. We plan to build a logic-oriented system that only needs to convey the logic correctly in text in natural language. It saves a lot of time, allowing data scientists to spend most of their time building logic instead of focusing on code.
Emotion Detection from Textual Data Using Supervised Machine Learning Models

Emotion detection and recognition from text is a recent field of research that is closely related to Sentiment analysis. Many people express themselves using text, photographs, music, and video. Text communication using web-based networking platforms, however, could be a little overwhelming. Every second, a substantial amount of unstructured data is produced on the Internet as a result of social media sites. This is where sentiment analysis, which recognises polarity in texts, can be useful. It assesses the author’s attitude towards a specific object, administration, person, or location and concludes if it is positive, negative, or neutral. In some cases, sentiment analysis is inadequate, necessitating emotion detection, which precisely ascertains a person’s mental/emotional state. The development of a text-based emotion detection and prediction model is the primary goal of this work. The development of emotion analysis is confronting several market hurdles, with accuracy being the key one. As a result, the Decision Trees, Naive Bayes, Support Vector Machine, Logistic Regression, k-Nearest Neighbors and Random Forest, supervised machine learning classification algorithms were examined. The six main emotions recognized by Ekman are joy, fear, anger, love, surprise, and sadness, these were the foundation through which the model was constructed. strategies for preprocessing data containing stemming, stop-words, numerals, and punctuation marks removal, tokenization, and spelling correction were implemented. This review paper delves into the degrees of several emotion models as well as the technique of emotion detection from text.