Reviewerly/exHarmony huggingface dataset:
Virtual Mouse and Gesture Based Keyboard

Nowadays, computer science has evolved to the point where a computer can authenticate its owner using a basic picture-processing algorithm. People are currently adopting this vision in many parts of daily life, such as facial recognition, colour detection, autonomous autos, and so on. This concept uses hand and face motions to make an optical mouse and a hand-gesture-based virtual keyboard using computer vision. The computer's camera will scan a view of the different movements made by a person's face and eye. Based on the movement of the motions, the computer's mouse or pointer will move, even completing right and left clicks using different gestures. Similarly, several gestures may be used to access keyboard operations. For example, selecting letters can be done with one finger, and left and right swiping can be done with a four-figure motion. It will function as a virtual mouse and keyboard without the use of any connectors or other hardware. The camera is the only piece of hardware utilized in the project, and Python is used for the coding on the Anaconda platform. The computer will recognize user movements and respond correctly after mapping a few of them with the mouse and keyboard.
Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes

Explainable Artificial Intelligence (XAI) has the potential to revolutionize healthcare by providing more transparent, trustworthy, and understandable predictions made by AI models. To this end, the present study aims to develop an explainable NLP model for predicting patient admissions to the emergency department based on triage notes. We utilize transformer models to leverage the extensive textual data captured in triage notes, while also delivering interpretable results by using the LIME approach. The results show that the proposed model provides satisfactory accuracy along with an interpretable understanding of the factors contributing to patient admission. In general, this work highlights the potential of NLP in improving patient care and decision-making in emergency medicine.
A Comparative Analysis of Data Augmentation Approaches for Improved Minority Behavior Detection in Digital Games

Previous research in behavioral- and game analytics showed that data augmentation plays a crucial role against the challenges of detecting minority entities (e.g. premium or retaining users) in behavioral datasets. By putting more emphasis on the minority entities, data augmentation allows us to utilize existing solutions without the need for extensive adjustments. In this study, we build upon previous work in this area by providing a comparison from both a methodology perspective and a data alteration perspective. The comparison focuses on three methods: Synthetic Minority Oversampling Technique (a nearest neighbor based approach), Variational Autoencoders, and Generative Adversarial Networks (both deep learning based approaches). We conduct an empirical evaluation using retention prediction in a freemium mobile game. Our findings indicate that each method offers advantages in terms of improved generalization results for different evaluation measures.
Mortgage Document Requirements Across Lenders in the United Kingdom

The United Kingdom (U.K.) has the most extensive banking system in Europe. The banks demand legal documents from customers as proof of authentication and verification which are displayed on their respective websites. But since the documents are all scattered in different locations throughout the websites, it is quite difficult to find them unless you read everything, which in itself is a time-consuming process. Different banks have different ways of displaying documents ranging from tabular formats, bullet points, paragraphs, external links, etc. Also, these web pages and their content are updated too frequently, so a one-time assessment of documents is unreliable. Also, different banks use different wordings and have different document classification categories. Hence, there is a need for a real-time system that fetches only the documents as per the customer type for the respective banks, ignoring unwanted data. This paper proposes a generalized algorithm that is applicable to all banks to fetch the demanded documents, ignoring the unwanted data, and thus saving time and energy.
Analyzing the Performance of Naive Bayes, Logistic Regression, SVM and Random Forest for Identifying Hate speech from Twitter Social Media

The spread of hate on social media and other platforms is of great concern because it has the implicit to be a serious detriment to society and the country and is disastrous. The development of mass media has led to lower exposure of hate speech and discrimination. detest speech generally refers to a person or group of people predicated on race, color, race, gender, race, religion, etc It's defined as humiliating communication grounded on certain characteristics. The donation of Structure through named images, type of caption and words used in the textbook of ) can explain the causes of virality and what is associated with it. Fake news and hate speech are not the result of the internet age. Fake news and hate have been around since the morning of mortal history, that is, for times- people have fabricated and fabricated all the time. still, the phenomenon of social media has changed how, where and what it's associated with fake news and hate speech. Where lies and culmination formerly appeared on the internet, now fake news and hate speech are taking their place on social media. The thing of the design is to propose results that stoners can use to identify and filter to count hate speech on Twitter. Using colorful bracket styles, we can determine whether tweets are hate speech. We use traditional shadowing algorithms Analogous as Naive Bayes, Logistic Regression, SVM and Random Forest. From data collection, preprocessing, point engineering and type to, we will estimate and give the delicacy and quality of all classifiers and their results in discrimination quests on Twitter..
Visual Insight Recommendation: From Ranking Insight Visualizations to Insight Types

Visualization recommendation systems make understanding data more accessible to users of all skill levels by automatically generating visualizations for users to explore. However, most existing visualization recommendation systems focus on ranking all possible visualizations based on the attributes or encodings, which makes it difficult to find the most interesting or relevant insights. We therefore introduce a novel class of visualization recommendation systems that automatically rank and recommend both groups of related insights and the most important insights within each group. Our approach combines results across different learning-based methods to discover insights automatically and generalizes to a variety of attribute types (e.g., categorical, numerical, and temporal), including non-trivial combinations of these attribute types. We then implemented a new insight-centric visualization recommendation system, SpotLight, which ranks annotated visualizations in visual insight groups. Finally, we conducted a user study which showed that users are able to quickly understand and find relevant insights in unfamiliar data.
A Real Time Obstacle Detection Using Camera and Laser Line Generator

Detecting obstacles has been the major focus nowadays in the technological era. It has the most research subject over the last few decades. There are many applications which are using computer vision techniques such as security, surveillance, medical applications etc. Researchers have focused on coloured images where lighting is an important factor for images. As per their result.capturing a scene and detecting objects is critical and camera and laser is used in this case. In this paper we will be able to take in live web cam image and highlights space where there are obstacles in the path. And the black area will demonstrate the free space available. By the help of laser line generator to assist in detecting the accuracy of obstacles through devices. The proposed model present in this paper present an effective model for obstacle detection using a camera and a laser line generator, on that system we can mount various devices such as robots, cars, helmets to test our model how effectively they are performing
Contrastive Learning Based Unsupervised Sentence Embeddings for Hinglish

Hinglish language is a hybrid mixture of Hindi and English text in Latin script. According to a survey by a company named milestone localization, 58 % of the population of India who are using technology prefer reading and sending social media messages in Hinglish. Sentence embeddings are basically the vector representation of that sentence in numerical form, which computers can understand. There are a lot of applications where embeddings are mandatory. The current state of the art sentence embedding model, SBERT (sentence Bert) is trained on specific English-supervised labeled datasets, STS (semantic textual similarity), and NLI (natural language inference). These datasets are in paired format and require time-consuming and expensive human annotations. So building these models for low-resourced languages like Hindi becomes hard because there aren't enough specialized datasets. The propsed work ia slight modification of unsupervised model simcse (Simple Contrastive Learning of Sentence Embeddings), which was introduced in 2021 by authors Tianyu Gao et al. The work revolves around finetuning an unsupervised embedding model on approximately 10,000 hinglish sentences. We evaluated a couple of transformers based on simcse with a Hiinglish transliterated version of standard STS data. Out of all the transformer distill-bert model performed best with a Sperman's similarity score of 0.60 A comparative analysis of the embedding quality of different transformer models is also discussed.
Machine Learning-based Traffic Classification and Channel Allocation in IoT: A Survey

The increasing number of Internet of Things devices has resulted in the development of dynamic and heterogeneous traffic patterns. Efficient communication is required in order to maintain the smooth functioning of these networks. Traditional methods of channel allocation and traffic classification are not able to handle the complexity of these traffic patterns. Machine learning techniques are being widely used to address the various challenges related to the development and maintenance of IoT networks. They can help improve the efficiency of the networks by identifying and prioritizing the traffic patterns. This paper reviews the current state of the art in machine learning-based channel allocation and traffic classification techniques. The goal of this paper is to provide a comprehensive analysis of the current state-of-art techniques in the field of machine learning-based traffic classification and channel allocation. It also aims to highlight the potential future directions of this technology. The paper reviews the current state of the art in machine learning-based channel allocation and traffic classification techniques for the Internet of Things. The complexity of the traffic patterns encountered by the Internet of Things network is a major challenge that can be solved through the use of machine learning techniques. This paper presents an overview of the various techniques that are being used to improve the efficiency of IoT networks. The paper also reviews the various open research questions and the potential directions in the domain of machine learning in the context of IoT channel allocation and traffic classification. The objective of this survey paper is to provide practitioners and researchers with a convenient reference.
Research on symmetric encryption and decryption of multi-user shared data for cloud storage environment

This paper mainly studies the symmetric encryption and decryption technology for multi-user shared data in cloud storage environment. With the popularity of cloud storage technology, more and more users store multi-user shared data on third-party cloud platforms. In order to protect the privacy and integrity of multi-user shared data, encryption technology has become an important means to deal with cloud security challenges [1]. The traditional multi-user shared data encryption technology has some problems in the scenario of multi-user shared data. For example, it is difficult to restrict the access to multi-user shared data after decryption. Therefore, this paper proposes a symmetric encryption technology for multi-user shared data based on access control, which uses attribute encryption to achieve controllable access to multi-user shared data, and uses group cryptography and obfuscation technology to ensure the confidentiality and integrity of multi-user shared data. The experimental results show that this technology not only ensures the security of multi-user data sharing, but also has high efficiency and user friendliness. As a result, it can meet the encryption requirements in large-scale multi-user data sharing scenarios. The research of this technology has certain reference significance for the security improvement of cloud storage field.
Blind Text Reader and Currency DetectorUsing Raspberry Pi

The blind text reader with currency detection project on the Raspberry Pi aims to assist individuals with visual impairments. The project utilizes a Raspberry Pi, a camera module, and various software components to capture images of text and banknotes, perform optical character recognition (OCR) to extract text and convert it into speech using a text-to-speech engine. Additionally, the project incorporates image processing techniques and machine learning algorithms to detect and recognize the value of banknotes, providing real-time currency information. This project showcases the potential of technology in enhancing accessibility and independence for visually impaired individuals.
Co-Tuning of Cloud Infrastructure and Distributed Data Processing Platforms

Distributed Data Processing Platforms (e.g., Hadoop, Spark, and Flink) are widely used to store and process data in a cloud environment. These platforms distribute the storage and processing of data among the computing nodes of a cloud. The efficient use of these platforms requires users to (i) configure the cloud i.e., determine the number and type of computing nodes, and (ii) tune the configuration parameters (e.g., data replication factor) of the platform. However, both these tasks require in-depth knowledge of the cloud infrastructure and distributed data processing platforms. Therefore, in this paper, we first study the relationship between the configuration of the cloud and the configuration of distributed data processing platforms to determine how cloud configuration impacts platform configuration. After understanding the impacts, we propose a co-tuning approach for recommending optimal co-configuration of cloud and distributed data processing platforms. The proposed approach utilizes machine learning and optimization techniques to maximize the performance of the distributed data processing system deployed on the cloud. We evaluated our approach for Hadoop, Spark, and Flink in a cluster deployed on the OpenStack cloud. We used various benchmarking workloads in our evaluation. Our results reveal that, in comparison to default settings, our co-tuning approach reduces execution time by 17.5% and ${\$}$ cost by 14.9% solely via configuration tuning.
Enhancing Credit Card Security: A Machine Learning Approach for Fraud Detection

Transactions increase in frequency as the world swiftly adopts digitisation and a cashless economy. The use of credit cards has dramatically increased in recent years. Financial institutions face a large loss as a result of the expanding related fraud activities. As a result, we must evaluate transactions to determine which are real and which are fraudulent. In this essay, we present a comprehensive study of the various methods for detecting credit card fraud. These methods make use of Bayesian Belief Networks, algorithms, Decision Trees, Logistic Regression, Genetic Random Forests, Markov Models, and Hidden Support Vector Machines (SVM). There is a thorough examination of several approaches offered. Following analysis, we put the SVM and LR models into practice, along with addressing data imbalances and feature extraction. We summarise the advantages and disadvantages of the same as stated in the respective publications as our conclusion to the paper. We want to use a credit card approach that is effective.
Breast Histopathology Image Embedding's in Invasive Ductal Carcinoma Detection using Self-Supervised Vision Transformers Techniques

Breast histopathology, which studies diseases in breast tissue such as breast cancer, is both time-consuming and challenging since malignant spots can often be small and difficult to detect amongst the large regions of benign tissue. We apply self-supervised Vision Transformers for generating image embeddings of breast histopathology data and evaluate their performance on the task of detecting invasive ductal carcinoma (IDC). The DINO algorithm was developed by Facebook AI for self-supervised training of the Vision Transformer, which is a form of self-distillation with no labels. After DINO's self-supervised pre training, we fine-tune our Vision Transformers on breast histopathology data, and then apply the Vision Transformers to generate image embeddings in the IDC detection architecture. A WSI dataset from 162 patients with IDC was used to assess the approach. 49 slides were saved for independent testing whereas 113 slides were chosen for instruction. An expert pathologist's skilled delineation of the cancerous zone on the digital slides served as the basis for the quantitative evaluation. For the automatic detection of IDC regions in WSI, our approach yielded the best quantitative results in terms of F-measure and balanced accuracy (71.80%, 84.23%). Our findings also imply that at least some of the tissue classification errors (false positives and false negatives) were brought on more by the inherent challenges of getting a highly detailed annotation of the diseased area of interest by a pathologist with extensive experience than by any fundamental problems with the approach.
American Sign Language Detection using Deep Learning

This paper presents a novel approach to hand gesture detection and recognition using machine learning techniques. The proposed approach employs Recurrent Convolutional Neural Networks (RCNNs) for hand gesture recognition, which outperforms traditional computer vision techniques. The system was trained and evaluated on a large dataset of diverse hand gestures, demonstrating high accuracy and processing efficiency. Additionally, the system was put to the test in real-world scenarios, showcasing its potential for practical applications in gaming, sign language interpretation, and human-computer interaction. In addition, this paper presents a novel approach to American Sign Language (ASL) recognition using deep learning techniques, specifically Long Short-Term Memory (LSTM) networks. The proposed method involves collecting a dataset of ASL letters using a webcam and custom-built software that enables users to perform ASL gestures in front of the camera. The dataset was carefully designed with various variations, such as different hand shapes, orientations, and lighting conditions, to improve the recognition system's accuracy. The pre-processed images were then used to train an LSTM model, which achieved a remarkable 92% accuracy. In order to pre-process the photographs and extract important features, the Mediapipe module was employed, and it was found to be a reliable and efficient solution for handling the volume of data. The team's approach could be used to help deaf people and others who don't speak sign language communicate. The experimental results and implementation details provide a comprehensive understanding of the proposed approach and its potential for future developments in hand gesture detection and recognition using machine learning and Python.
Classifying Architectural Images of Digital Heritage: A CNN-SVM Hybrid Approach

India is a nation blessed with a plethora of cultural landmarks, including 37 UNESCO World Heritage Sites, many of which are world-famous architectural structures. We must protect cultural heritages because they bind successive generations together throughout time. Researchers, travelers, architects, etc. They visit several historical locations, where it is frequently challenging for them to recognize and learn more about the historical significance of the building they are fascinated by. Due to the scope and accuracy of the information, it is difficult to archive, preserve, and share knowledge about these cultural treasures. It is easier to examine and comprehend heritage assets when images are accurately predicted to correspond to their right name (heritage site). It takes an enormous amount of time and work to classify data that includes images. In this study, the authors have designed a new hybrid model used to extract the features from architectural images and further classified into different shapes of the architectural sites and achieved 95% accuracy and the highest precision rate is achieved for the Altar class i.e. 88.05%, the highest recall rate is achieved for the Vault class i.e. 87.36 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , and the highest Fl score rate is achieved for Flying buttress i.e. 89.10%.
A Comprehensive Survey and Bibliometric Analysis of Convolution Neural Network

Deep Learning and its techniques have reformed the fields of NLP, Image Processing, computer vision, speech recognition, and many other fields. Day by day, the evolutionary changes have been made in deep learning architectures like number of parameters, resources, epochs, augmentation of images, and it makes deep learning more powerful in AI and ML field. It has overcome the issue of segmentation of images and proved to be a good method. Success of deep learning is primarily dependent on the enormous dataset. In this paper we have discussed about CNN deep learning, its architectures, and it's working. We have also provided the statistical analysis of the CNN research trends, growth, and scientific research in industries. This paper will be beneficial to new researchers who wish to work in deep learning-CNN approach.
Recaptured Image Forensics Using Transfer Learning

Recaptured Image detection is a field of security forensics that deals with detecting the originally captured image from its reimaged counterpart. The different algorithms that have been proposed to differentiate the original and recaptured images work with great accuracy on a large dataset and high-definition images. But the challenge remains for small-scale and small- size images as the existing algorithms required customized hand engineering whilst dealing with different datasets. Our proposed system is a dual learning model, which takes into account the enormous research for computer vision and applies concept of transfer learning for recaptured images detection. The ensemble of 6 pre-trained networks, namely AlexNet, Res Net, InceptionNet, GoogleNet, SqueezeNet, and MobileNet, has been compared on same dataset. It will allow institutional level detection of image spoofing and the recaptured image forgery for small-scale images with considerable accuracy without requiring specialized preprocessing on their end. We have achieved the highest accuracy of 90 percent through our model containing Transfer Learned MobileNet and SVM classifier. As per the latest resources, this is the first instance of applying transfer learning for recaptured image detection.