Reviewerly/exHarmony huggingface dataset:
Inshort Text Summarization of News Article

Text summarization is a process of reducing a large amount of text into shorter version while retaining the most important information. When applied to news articles, text summarization can be used to provide a brief overview of the article’s main points, saving readers time and allowing them to quickly determine if the article is of interest to them. The massive increase in the volume of news articles being published every day, it is becoming increasingly difficult for readers to keep up with the latest developments. Text summarization, is technique of producing a short overview of a longer text, can assist readers in quickly grasping the major ideas of an article to present a method for abstractive text summarization of news articles using recurrent neural networks and sentence scoring approaches. The results of this experiment shows that proposed approach outcomes the existing methods in terms of summarization quality and efficacy. This approach provides a promising direction for improving the efficiency and accuracy of text summarization for news articles.
Comparison of Faster RCNN and YOLO V3 for Video Anomaly Localization

Video Anomaly Localization techniques are interesting and emerging tasks in computer vision that is used to locate the position of an anomalous object with bounding boxes. Convolutional neural network-based object localization methods have been developed which detect and classify objects with high precision and accuracy. Many algorithms have been developed for object detection and localization with excellent outcomes. These algorithms are fast RCNN, faster RCNN, and YOLO. In this paper, a comprehensive analysis of faster RCNN and YOLO V3 algorithms for anomaly localization is performed based on the parameters FPS, mAP, Precision, Recall and F1 Score.
Comparative Study of Regression Models for PV Power Prediction

Electric power production is a necessity and cannot be avoided. Thermal power plants are the primary sources of power generation, and they result in substantial emissions of carbon dioxide. Moreover, the depletion of fuels is also an issue of concern. As a result, solar energy is being promoted globally. The solar system’s behavior is stochastic and dynamic. A comparative study of the Poisson regression, Linear regression, and Lasso regression models has been done to predict the nature of such behavior and forecast the generated electric power by the solar plant, based on the weather parameters such as temperature, humidity, and luminous intensity. The Linear regression model predicts the output power of PV systems more accurately over the Poisson regression and Lasso regression models.
Intra-frame Copy-move Video Forgery Detection

With the increase in sharing of videos worldwide over social networks, presence of high-quality fakes is on increase. Forged videos affect the authenticity and integrity of the video as a whole. This can lead to serious implications. For example, in case of video to be used in courts as an evidence, presence of forgery can implicate innocents or help criminal to escape justice. This calls for the detection mechanisms to counter. This leads to the discovery of several different approaches to detect copy-move forgery by analysing the side effects due to tempering. One of the most common approaches is copy-move video forgery which consists of duplicating area of frame. Traditional approach detects for patterns related to duplication manually which is not so successful. In contrast, methods related to deep learning gives better results. Therefore, this research follows deep learning model using pertained architecture to detect copy-move video forgery.
Gaussian Process based Path Following Control of Snake Robots

In this paper, the path following control problem of snake robot is addressed by utilizing data-driven modelling and control techniques. The presence of unmodelled dynamics in snake robot deteriorates the performance and stability of existing feedback linearization based control laws. Control is further made more difficult by the snake robot's highly variable operational environment. In this paper Gaussian processes are used to model the unknown dynamics present in the system. The torque control law is modified to compensate for the presence of unknown dynamics. Furthermore to achieve path following control with reduced oscillations in head position and to eliminate the overshoot the Line of Sight(LOS) guidance law is modified. The performance of the proposed control law is validated through simulation results.
Healthcare Data Protection using Federated Learning Technology

Federated learning makes it possible to train models collectively without distributing raw data. Recent assaults, however, show that merely preserving data location during training procedures does not give substantial user privacy. Instead, there is a need for a federated learning system that can avoid generalization over both the training-related messages and the final trained model, while also preserving the model’s appropriate forecasting ability. Current federated learning systems either use differential privacy, which can result in low accuracy provided there are a large number of participants, or functional encryption which is susceptible to generalization. In this research, an alternate method, Polymorphic Cipher Functional Encryption for balancing the limitations of these methods is proposed. The increase of noise infusion is minimized as the number of people involved grows without compromising privacy and while maintaining a reasonable rate of trust by integrating differential privacy with functional encryption. Further, the proposed system is evaluated using Deep Convolutional Neural Networks model by applying it to diabetes dataset. The suggested technique proves to be a modular strategy that guards against generalization hazards and generates models with a high degree of accuracy.
Sign language recognition of Malayalam alphabets using transfer learning

Sign languages, like any other natural language, rely on a set of symbols or gestures to convey meaning. The recognition of these symbols is crucial for the development of technologies that aim to improve communication between deaf and hearing individuals. This paper aims to use transfer learning to interpret the Indian sign language alphabets in Malayalam. This work also focuses on creating a dataset for the unified finger spelling alphabets in Malayalam language. The proposed method uses the ResNet50 model to classify images of static sign language alphabets into the corresponding letters of the alphabet. The method was evaluated and achieved an accuracy of 97.62% for training and 92.35% for validation.
A Study of Collocations in Sentiment Analysis

Collocations are not merely frequently appearing word combinations (n-grams). Words in collocations have some kind of strong association among them. Collocations play an important role in various natural language processing (NLP) applications. Sentiment analysis is one of the growing areas of research in NLP because of its utilization in various business strategies. The present paper investigates collocations in positive and negative sentiments and their usefulness in sentiment analysis. We considered Amazon Products Review dataset for the purpose and analyzed positive and negative reviews separately. Different statistical techniques; Pointwise Mutual information (PMI), Chi Square test (Chi2), t-test, and likelihood ratio (LH) have been used to extract collocations from these texts and the common collocations have been extracted and analyzed. We found that collocation may be a potential feature for sentiment analysis.
Equation Detection in the Camera Captured Handwritten Document

One of the most important tasks in the realm of document analysis and recognition is the detection of equations in documents that were acquired using a camera. The procedure includes several steps, including pre-processing of the images, segmentation, feature extraction, and classification. The suggested method comprises taking a user-provided input expression image and classifying it into one of three types of equations: simple, complex, and highly complex. By choosing a decision boundary set off from the initial hyperplane, the SVR algorithm encodes the image, producing a model that fits the data better. The result is then obtained by character-wise segmenting the image and comparing it with trained models. Two recurrent neural networks make up the RNN encoder-decoder that is used. One RNN creates a fixed-length vector representation from a sequence of symbols, and a different RNN decodes that representation into a different sequence of symbols. 1900 images containing various equations made up the dataset utilized for training, validating, and testing the SVR and RNN. The accuracy of the system was about 93.64%.
Augmented Reality based Furniture Application

In the modern digital transformation era, providing customers with confidence in the suitability of the furniture they choose for various situations in which it will be used is the major problem in online furniture sales. While buying furniture, purchasers must ensure the furniture's model, size, and color are appropriate for and complement the space in which they will be put. The flexibility and appropriateness of the furniture product when placed in a particular area may be seen by prospective buyers with suitable instruments. For furniture to be sold online correctly, these issues must be resolved. By using Augmented Reality, this is feasible, to do. When it comes to online product customization, certain application models in the furniture sector employ Augmented Reality and mobile technology. A 3D product visualization tool, product pricing, and an enhanced renderer are all included. For the purpose of creating AR-based furniture technology, Google AR is used in the development process. It provides a framework for people or teams to creatively and flexibly address challenging tasks and issues in order to generate and deliver the best performance. The study is finished using Android Studio and Google AR services. Based on the results of the studies, it is advised that this application be enhanced to have a greater degree of performance. This might be achieved by creating a variety of various creator patterns using barcodes or QR codes, classifying surfaces like ceilings and walls so that the program can accurately show 3D things on them, and improving 3D models to fit them in the projecting space.
Automatic Vehicle Classification and Speed Tracking

The current traffic in large cities and urban areas has grown significantly, so a surveillance system is required to monitor traffic and avoid unnecessary delays and accidents. In this research study, computer vision-based speed estimation and object detection have been implemented for various automatic vehicles. Various image processing and deep learning-based methods and models have been used to test the proposed system. An open-source image dataset of five automobiles, car, bus, bike, truck, and local four-wheeler (CNG), has been utilized in this work. These 3,293 total images have been annotated with Roboflow framework and trained with YOLOv4, YOLOv5, YOLOv7, deep learning models, and the Haar cascade method. Average mAP scores of 0.956, 0.857 and 0.821 have been obtained for YOLOv5, YOLOv4 and YOLOv7 models, respectively, for different categories of vehicles. YOLOv4 and the Haar cascade methods have been employed to estimate the speed of the detected vehicles. The YOLOv4 technique performed best in speed assessment of various automobiles.
A Comparative Study of Detecting Violation of Face Mask and Social Distancing using Different Deep Learning Models

The growing pandemic caused by COVID-19 has been making headlines recently, causing a global disaster with an unavoidable spread. The WHO recommends wearing a face mask and maintaining physical distance. This research can assist in keeping track of social distance and it can also spot face masks. The entire system is set up in public settings. Images and videos are used by this system to identify persons. Frame by frame processing is done on the video. People are recognized by image processing with TensorFlow and OpenCV. MobileNetV2 and ResNet50 are used for detecting the face mask violation. It first recognizes the crowd’s presence before calculating the centroid distances between individuals using an object tracking algorithm and a distance algorithm. In order to assess the social distance between people, the Euclidean distance is typically employed to calculate how far off each pair of centroids of the bounding box is identified from one another. At a distance of six feet, the program can determine the number of pixels in an image. The detection models MobileNetV2 and ResNet50, respectively, obtained accuracy of 97% and 98% with the large dataset, which contains 12000 samples with and without masked faces.
Notes Sharing and Student performance Analysis Web Application

Students frequently experience issues while taking notes, which interferes with their ability to study. There are many reasons students miss to take notes in college. Without notes, they might miss on important information and it would be really hard for the students during their exam times. Students will have a place to congregate and engage in collaborative learning through our application. It is possible for students to their materials (both written notes and documents in PPT, PDF, DOC, etc. format) for free access by other students in any branch or semester. Each user will log in to the application using their individual account. This application will also help the students to earn money. Students who wish to earn money while studying can lock their notes and the other students who want to download the notes have to pay the uploader. This way the application will benefit students to earn part time and also encourage students to take notes in class. There are few existing models for the said application but users face many difficulties like access to notes only after payment, does not accept hand written notes, must upload notes regularly, allows upload only for a particular stream. The proposed method provide solutions like students from different streams can upload their notes, notes can be downloaded for free or paid version based on how the uploader has set the download option. The other important feature is that Student Performance Analysis is done using Random Forest Regressor. The future work of this application is to enable student credit other students for their work by including credit system which will motivate the students to do a better job. This application is intended to be an Educate and Share application.
Machine Learning for Translating Pseudocode to Python: A Comprehensive Review

Translating pseudocode to a programming language can be difficult and time-consuming, especially for individuals unfamiliar with the languages involved. However, recent advances in Natural language processing (NLP) have made it possible to create a model that can automatically convert pseudocode written in natural language to code in a specific programming language. This research study proposes a solution to this problem by using an NLP transformer model to translate pseudocode into Python code automatically. The model will be trained on pseudocode examples and corresponding Python code translations. This study leverages a large dataset of pseudocode examples and their corresponding Python code translations to achieve this. By training the model on this dataset, it will be able to learn the patterns and structures present in the pseudocode and match them with their corresponding Python syntax. This will allow the model to translate pseudocode accurately into Python code, even for complex algorithms and structures.
Stealth Encoding - Concealing data with the Least Significant Bit Method

Data security is a major issue for computer users these days. So, we conceal the textual data using steganography, the art and science of concealing information. In this case, we conceal both the message’s content and the fact that it is being sent. In order to make it challenging to Figure out that a hidden message is present in a specific film, this technique involves concealing secret data within a conventional, non-secret file. At its destination, the secret data is subsequently removed. Several uses of steganographic technology have distinct requirements. Here, we suggest a method for concealing data, including reverse data hiding for data extraction. The textual data will be secured in different frames of the video and audio using the Least Significant Bit (LSB) and related algorithms. We can conclude that the original data will never change the characteristics of video or audio even after applying steganography techniques.
Multimedia IoT Data Compression using Deep Learning Technique

Due to the considerable complexity of the multimedia data acquired, IoT (Internet of Things) and multimedia will encounter a number of energy and communication overload limits. One well-known solution to the problem of large datasets is to use the lossy compression algorithm. However, the lower transmission rate required by current lossy compression techniques to ensure endurable apparent image/picture quality only results in a minor reduction in the amount of communicated data and, as a result, a minor reduction in power and bandwidth use. An effective firmness technique is required to strike a fair equilibrium flanked by information volume and visual degradation. A deep-learning super-resolution technique is utilized in this study to restore high-resolution photographs from damaged photos with a considerable compression ratio as input. The results of the study clearly show how the suggested approach improves the graphical fidelity of compressed and down scaled images. The suggested method reduces largely the communication transparency and energy consumption of constrained multimedia IoT framework components as a result.
Fake Currency Note Recognition using Extreme Learning Machine

Color printing technology has made it simpler for forgers to make and copy large numbers of fake currency. The production of legible banknotes was once the sole purview of print shops, but today even a novice printer with a simple laser printer can do the job. Because of this, the prevalence of counterfeit money has increased dramatically. A number of problems, including corruption and the existence of “black money,” have hounded India for a long time. There is also a serious problem with the circulation of counterfeit currency. The outcome is a system that can detect fake currency with greater speed and precision. It is a serious crime to create counterfeit currency, which is money that has not been authorized by the government. The vast majority of them do this professionally. The circulation of counterfeit currency retards economic development and devalues legitimate currency. In this study, it uses image processing to the problem of identifying fake Indian notes. This technique finishes the initial acquisition before the image is processed further. Pre-processing includes actions such as cropping, smoothing, and modifying the image; converting the image to greyscale; applying image segmentation; extracting features; and resizing the image. The model is then trained using an ELM. Proposed model exceeds both the CNN and SVM models in terms of accuracy, at around 97.8 percent.
Application of Horizontal Federated Learning for Critical Resource Allocation - Lessons from the COVID-19 Pandemic

The COVID-19 pandemic has caused disruption all over the industry. Significantly healthcare systems have been most affected due to a scarcity and acute demand for critical hospital resources. In this paper, we have proposed a framework for resource allocation using the characteristics of horizontal federated learning. We have used data of COVID cases in India and its states. We sourced the available critical hospital resources data and conducted the experimental analysis of the ten most vulnerable states of the country. The resource allocation method relies on the severity, spectrum of disease, and the patient’s length-of-stay in the hospital. The overall proposed methodology manages the limited resources and optimizes its use for the upcoming COVID cases. The collaborative feature of federated learning in the paper helped to update the information on patients, resources, and infection rates of different states, which in turn helped declare the overall severity of the pandemic in the country. Going further, our study will be helpful in the healthcare system’s authority to plan on hospital resources and manage them efficiently.
Qualitative Sentiment Analysis of YouTube Contents based on User Reviews

Sentiment analysis (a branch of Natural Language Processing) is one of the most crucial components of assessing online contents in current digital age. One of the leading competitors in the digital content market is YouTube, where thousands of new videos are uploaded every day. To assess the opinions of the users and customize advertisements and future contents accordingly, an appropriate sentiment analysis model is very crucial. Therefore, this study proposes a novel combination of deep learning technique called Encoder-decoder based Attention model to conduct sentiment analysis on YouTube reviews. In the proposed approach, the squeeze-and-excitation attention layer is utilized. Nearly 7,00,000 user comments from 8000 YouTube channels are utilized to train and test the proposed model in this study. Three different sentiments-positive, negative, and neutral—are assigned to the comments in the dataset for training purposes. The results show that the maximum accuracy of the model is 92.8% whereas maximum F1-score is 91.9%, surpassing several ML based state-of-the-art approaches. The evaluated metrics verify the balanced performance of the proposed approach. Moreover, the impact of the attention mechanism on the proposed approach is also assessed. Model accuracy for sentiment analysis is significantly improved by combining attention mechanism with encoder-decoder. As a generalized framework, this research can serve as a valuable guideline for future sentiment analysis on different languages.
Implementation of LabVIEW based Smart Assistive Gloves for Deaf and Dumb People

Disabled people face many problems in their daily life especially in communication with others. Since for many learning sign languages is not feasible or easy so this study looks into a method that will help dumb, deaf and partially paralyzed people to communicate with others without learning sign language. This study designs a device that help disabled people to communicate. The main questions are that how can device should always be within the reach of the patient. This research focuses on improving the lifestyle of disabled people. This topic is important to address because to bring equality for people from all walks of life. they must feel it. The most important task in this situation is to express themselves. This study looks for ways to make the existing devices more comfortable for the patients to use and to modify the technologies that are unsuitable. This study uses flex sensors that are stitched to a pair of gloves. The flex sensor signals are sent to LabVIEW via Arduino UNO, and the appropriate message for the finger bend is either displayed or heard. The use of Wi-Fi for transition is a huge boost in the area for transmission of the signals when compared to Bluetooth. This makes the gloves easier to use. The uses of Wi-Fi instead of increases the area of reception when compared to Bluetooth. Wi-Fi is faster as well as it has more coverage when it comes to range of transmission.