Reviewerly/exHarmony huggingface dataset:
Quantitative Assessment of Hand Signal Recognition Using Landmarks Detection: A Comparative Study of Machine Learning Techniques

Hand movement comprehension and interpretation are critical in various domains, including robotics, human-computer interface, and sign language identification. Computer vision techniques have lately been utilized to improve the accuracy and usefulness of hand signal identification. This study investigates alternative machine learning algorithms for identifying hand gestures that involve landmark detection. In computer vision, it is common to identify specific areas on an object in order to extract relevant properties from it, in this case, the hand. This procedure is known as “landmark detection.” In this paper, researchers use the Python OpenCV package to extract landmarks from images of hand gestures, which research then uses as input features for machine learning models. The performance of four machine learning algorithms is compared: Support Vector Machine (S-VM), Random-Forest (RF), k-Nearest-Neighbors (K-NN), and Multi-Layer Perceptron (MLP). The research evaluates each model's performance using a unique dataset of hand signal images obtained with a Kinect sensor. With an average accuracy of 95%, the data show that the S-VM technique surpasses the other three algorithms. The RF algorithm performs admirably, with an average accuracy of 92%. The MLP algorithm performs the worst, with an average accuracy of 83%, followed by the K-NN method, which performs moderately, with an average accuracy of 88%. Overall, the study emphasizes the need to select the appropriate machine learning algorithms and the importance of landmark detection for identifying hand gestures. While K-NN and MLP algorithms may require further feature engineering or optimization, S-VM and RF approaches are acceptable for hand signal recognition utilizing landmark detection.
A Hybrid Enhancing Aspect-Based Sentiment Analysis with BERT for Aspect Extraction and Diverse ML Classifiers

Social media involvement in human life is becoming huge where people can express their feelings and opinions. Sentiment analysis (SA) is a method of analysing, handling, concluding, and inferencing subjective texts with the sentiment. Aspect-Based Sentiment Analysis (ABSA) acts an important part in understanding people's opinions and sentiments towards various aspects of products or services. The main key challenges faced in ABSA are product dependent, target dependent, aspect dependent. In this work, a hybrid approach that combines the power of BERT for aspect extraction and diverse ML classifiers for SA. First, a set of diverse ML classifiers used to analyze the sentiment associated with each extracted aspect. By utilizing multiple classifiers, we aim to enhance the robustness and generalization of sentiment classification. Each classifier is trained on a different feature representation, such as PMI, bag-of-words and TF-IDF to capture various aspects of the textual data. Later, BERT leveraged to identify and extract relevant aspects from text. BERT's contextualized embeddings extracts the meaning of words in the context of the sentence, enabling accurate aspect extraction. To evaluate the proposed hybrid approach, trained model is experimented on three benchmark datasets which are based on Amazon reviews, Twitter and TripAdvisor datasets. Our results demonstrate that the combination of BERT for aspect extraction and SVM classifier significantly improves the accuracy of 96.91%, 95.63%, 95.66% on three datasets compared to traditional approaches.
Online English Education Ability Evaluation System Based on RBF Algorithm

With the continuous development of information technology, online learning has become an important, universal, efficient, and convenient way in people's daily lives, and is widely used in the field of education. However, studying online education systems as an emerging phenomenon has only emerged in recent years and has achieved certain results. This article was based on the radial basis function (RBF) algorithm to evaluate and design control strategies for English remote video websites, as well as related data statistical analysis and processing. The main implementation is the construction of platform models and the establishment of functional models, selection of testing methods, and the organization of user feedback information, as well as the construction of backend database management functional modules. When calculating the value of the output layer, the same function as the radial basis function was used for calculation. Adopting the above methods can greatly improve the accuracy of testing. Afterwards, this article conducted performance testing on the system. The test results showed that the accuracy of the system could reach over 85%. The recall rate was over 83%, the response time was between 2s and 3s, and the resource utilization rate is over 94 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> . Research on online English education ability evaluation system based on RBF algorithm has found that RBF algorithm can be used for predicting student performance in online English education environment. By analyzing and modeling students' historical learning data, the system can use RBF algorithm to predict students' performance on different tasks and question types, including grades, mastery of knowledge points, and learning progress.
Exploring Multiple Classification Systems for Online Time Series Anomaly Detection

Online time series play a crucial role in the analysis and management of massive amounts of data. These series capture data points chronologically according to their acquisition time. Detecting anomalies (outliers) in these time series is crucial for understanding patterns and making informed decisions. This work exposes various techniques from the literature for online time series anomaly detection, and categorises them into statistical techniques. The paper shows several applications for statistical methods, machine learning, and hybrid methods that leverage the advantages of both statistical and deep learning techniques. Furthermore, ensembles are exposed as an efficient technique used with the mentioned models for anomaly detection in online time series. The paper discusses the challenges associated with temporal correlation, including the need for effective visualisation tools such as DeepVats. By providing an overview of the existing techniques and their applications, this work aims to contribute to the advancement of online time series anomaly detection and provide insight for future research in the field.
Design of a Constructive English Translation Intelligent Recognition Model Based on Deep Learning and RBF Algorithm

In today's globalized society, English has become one of the most widely used communication languages, and the accuracy of English translation is crucial for cross language communication. Therefore, this experiment aims to explore the application of deep learning and RBF (Radial Basis Function) algorithm in the design of English Translation Intelligent (ETI) recognition models. Through experiments, it was found that the combination of deep learning and RBF algorithm could improve the translation accuracy and sentence similarity of English translation models. Deep learning and RBF algorithms could improve the model's translation accuracy and sentence similarity, respectively. Combining them could significantly improve the model's translation performance. Specifically, the experimental results showed that the translation model combining deep learning and RBF algorithm achieved a translation accuracy of 90% and sentence similarity of 80%. The translation accuracy of using deep learning models alone was 75%, and sentence similarity was 70%; the translation accuracy using RBF algorithm alone was 80%, and the sentence similarity was 75%. This indicated that deep learning and RBF algorithms could be applied to the design and implementation of intelligent recognition models for English translation. The combination of the two could improve translation accuracy and sentence similarity.
Youtube Spam Detection Scheme Using Stacked Ensemble Machine Learning Model

This work introduced a method to identify English spam comments on YouTube, a platform that has experienced a significant increase in spam activity. Despite YouTube's efforts to implement its own spam filtering system, it still struggles to effectively block spam comments. To address such an issue, we analyzed existing research on YouTube spam comment detection and performed classification experiments using an ensemble model that combines various techniques. A stacked ensemble model combines the predictions of multiple individual models to improve the accuracy and robustness of spam detection in social media applications. It leverages the strengths of different models and captures a broader range of features, leading to a more accurate classification of spam and non-spam content. It has then implemented a web extension to hide the spam comments from the page by modifying the CSS (Cascading Style Sheets). The comment data utilized in experiments was sourced from popular music videos by artists such as Psy, Katy Perry, LMFAO, Eminem, and Shakira.
Sentiment Analysis for Hateful Content on Social Media

Hate speech in social media has become a significant concern in today's age, making detecting and analyzing hate speech accurately an area of high importance. Sentiment analysis can aid in automatically detecting hate speech and quantifying its severity, providing valuable insights to social media platforms and law enforcement agencies in taking appropriate action against hate speech. This study aimed to develop and evaluate a deep learning model for hate speech detection and scoring on social media platforms. LSTM and CNN deep learning classifiers were employed and evaluated using standard metrics such as Test Accuracy, Precision, Recall, and F1 Score. The LSTM model performed the best, with 82.67% test accuracy. For regression, seven machine learning models and three deep learning models were tested, with a 3-layer deep learning regressor achieving the best score of 91.28%. The results suggest that deep learning models can effectively detect and categorize hate speech on social media platforms.
Efficient CNN Based Sign Language Recognition System Using Optimization Technique

Convolutional Neural Network (CNN) - based sign language recognition has significant importance in bridging communication gaps between individuals who are deaf or hard of hearing and the general population. Sign language users can communicate effectively with each other through CNN-based sign language recognition systems that instantly translate sign language into written or spoken language. Integrating these technologies into public services, workplaces, educational institutions, and Internet platforms will improve everyone's participation and engagement without restrictions. People with hearing loss can live more freely using sign language recognition software. This article proposes developing a CNN architecture along with stochastic gradient descent optimizer in the framework which effectively identifies sign language. The experiment is conducted on various convolutional and pooling layer counts, filter sizes, and strides to find an architecture that balances accuracy and computational efficiency. For a dataset of hand gestures, it detected sign language with an accuracy of 98.5% using CNN. Such high accuracy indicates that the model is effective in learning and recognizing the patterns and features present in the dataset.
Preventing Monkey Menace Using YOLO Based Object Detection Model

Monkey menace is a serious problem in many urban and rural areas, where monkeys cause damage to property, crops, and attack humans. YOLOv5 (You Only Look Once version 5) is a computer vision technique that can be used to detect monkeys in images and video frames, which can help in mitigating the monkey menace. YOLOv5 uses deep learning to detect objects in real-time, and it has achieved state-of-the-art results in various object detection tasks. In monkey detection, YOLOv5 can accurately detect and classify different types of monkeys such as Rhesus macaques, Bonnet macaques, and Langurs. The algorithm uses a pre-trained model that has been trained on a large dataset of images of different monkeys, and it can also be fine-tuned on a smaller dataset of specific monkeys to improve its performance. YOLOv5 performs monkey detection by dividing an image into grids and applying a convolutional neural network to each grid to detect the presence of a monkey. The algorithm then generates bounding boxes around the detected monkeys, and it can also classify the monkeys based on their species. After Detecting the Monkeys, it produces an alert message and alarm instantly.
Decentralized Record Encryption Authentication and Management System (DREAMS) for Healthcare

Personal health records (PHRs) are being widely maintained by the patients to provide a clear summary of their health history. PHRs include a wide range of data collected from different sources such as pharmacies, healthcare centers and even the patients' personal devices. As there is a wide scope for PHR systems, a lot of companies have come forward providing PHR management services. However, PHR services that are automated have a drawback that the data ownership might be taken away from the patients, as a result of thirdparty involvement. This leads to a compromise of security and privacy of patients' data. In order to address this issue a blockchain technique called decentralized medical record encryption, storage, and management system (DREAMS) is proposed which allows patients to make a final decision on record access rights by using Interplanetary File System (IPFS), MetaMask and decentralized application on the blockchain. DREAMS ensures that the patient data is private and secured and can only be accessed by the authorized parties. It also prevents data breaches in healthcare by providing a secure and tamper-proof system for storing and managing medical records.
Alzheimer Disease Prediction Using Recursive Feature Elimination and Artificial Neural Network

Alzheimer's Disease (AD) is a devastating neurodegenerative disorder that affects a significant portion of the aging population worldwide. Early prediction and detection of Alzheimer's disease are crucial for providing timely interventions, personalized treatment plans, and improved patient care. In recent years, machine learning techniques have shown promise in aiding the prediction of Alzheimer's disease by leveraging the analysis of diverse data sources, including demographic information, genetic markers, biomarkers, neuroimaging data, and cognitive assessments. In this paper, a combined technique called Recursive feature elimination with Artificial Neural Network (R-ANN) is introduced for fast processing and better prediction of AD. In the combined technique, the Recursive Feature Elimination (RFE) selects the most important features in the dataset for reducing the computational complexity and improves the disease prediction accuracy. RFE can handle non-linear relationships between features and the target variable by utilizing machine learning models capable of capturing complex interactions. This allows RFE to potentially capture non-linear dependencies between features and the disease being predicted. The selected features are used in ANN for prediction of AD. The empirical results prove that the accuracy, precision, recall and F-measure of proposed R-ANN is 4.71%, 4.6%, 3.41%and 3.37% better than Regularized Extreme Learning Machine (RELM)technique respectively for AD prediction.
Human Age Estimation from Images in Real-Time Application Using Machine Learning and Deep Learning Models

The objective of the project is to build an accurate age estimation system applying machine learning (ML) and deep learning (DL) techniques. Due to its numerous applications in areas including biometrics, recognition of faces, age-based promotion, and forensic investigations, accurately calculating human age from photos of faces has attracted a lot of attention. To understand the complex correlations between face features and age, the model will be trained on a broad dataset of facial images with associated age labels. Data gathering and exploratory data analysis are the first steps in the project's phased strategy, which aims to comprehend the dataset's features and spot any biases or outliers. The utilization of feature engineering approaches, such as landmark detection, texture analysis, and geometric features, will be utilized to extract pertinent and discriminative characteristics from the facial photos. The machine learning (ML) and deep learning (DL) algorithms will use these constructed attributes as input. Training multiple machine learning (ML) and deep learning (DL) models are being carried out. Accuracy is being put to use for evaluating the models' performance. To evaluate the advancements in age estimation accuracy, the results had been compared to previous research. The Gradio platform been applied to incorporate the models into a user-friendly interface which permits to enter facial image data and get results for real-time age estimate.
TxtPrePro: Text Data Preprocessing Using Streamlit Technique for Text Analytics Process

In natural language processing, cleaning up a lot of scraped text data is an important step that involves getting rid of irrelevant and noisy data from the text corpus. Text data obtained from web scraping or other sources may contain various unwanted elements such as HTML tags, non-textual characters, and punctuation marks, which can negatively impact the accuracy and efficiency of NLP algorithms. The study simplifies obtaining text data from multiple websites by employing TxtPrePro, a simple pipeline for scraping and text preprocessing that can be used for topic modeling, text summarization, sentiment analysis and other purposes. The proposed method used web scraping to collect a large amount of text data then performed proper text preprocessing, obtained some information such as tables visualizations and obtained a cleaned corpus.
Image Instance Segmentation and Optimization Using Quantization for Embedded Devices

Deep Neural Networks (DNN) face numerous challenges in deploying highly effective models on edge devices due to their high computational overhead, power consumption, and memory usage with respect to various fields, such as manufacturing, medicine, autonomous driving, defense, and security, where accurate object identification and segmentation are crucial, to remit to these obstacles, This study proposes an approach for effective instance segmentation of the Common Objects in Context (COCO) database using the Mask R-CNN algorithm. The study also investigates the effects of quantizing the neural network from float32 (FP32) to integer8 (INT8) using the PyTorch framework, specifically for deployment on an edge device. The current research approach shows promising results in achieving high segmentation accuracy while maintaining low computational requirements when deployed on edge devices. The model size was reduced to x4 of the base model and the accuracy drop was at a minimum of 4%.
Spectacles Recommendation System Based on the Face Shape Using CNN Model

Everyone likes to be attractive. Many people gain self-confidence in the presence of other people. Spectacles are arguably one of the most significant instruments to display one's attitude, in addition to any stylish accessories. Choosing the best frames is the most difficult struggle for a person. Recommending glasses based on their face shape is the main objective of this project. This project works on the automatic extraction of face shapes and classification methods for spectacles frame shapes. Convolutional Neural Network technology was used in the development of this system to choose the best eyewear based on the user's facial shape. The person's face is taken as an input image with good accuracy and the best frame for their face shape is recommended. This research will apply the concepts and principles of an innovation that can provide the frame shape of the glasses.
Action Detection for Sign Language Using Machine Learning

This research intends to build an effective and quick algorithm for identifying the alphabets in American Sign Language (ASL) using natural hand movements, increasing communication accessibility for people with hearing impaired limitations. The system's ultimate goal is to act as a translator between spoken language and sign language, enabling more effective and efficient communication between those with hearing loss and others who don't have any hearing loss. The research uses image processing, machine learning, and CNN-based artificial intelligence to recognize ASL movements and generate outputs that are simple to interpret. The potential impact of this work on communication accessibility for people who have hearing loss is significant.
Evaluation on the Application of Data Mining in the Evaluation System of Teaching Ability of Double Teacher Teachers

This article mainly focuses on the evaluation data of students in a certain major in a university, exploring the underlying information and patterns through various analytical methods, in order to provide scientific basis for teachers' teaching improvement and management decision-making. This article also uses correlation analysis method to explore the correlation between different indicators and their impact on student evaluation, and uses factor analysis to reinterpret the original evaluation index system, identifying several main factors that reflect students' perspectives, such as teacher teaching skills, knowledge level, attitude, and emotions, and uses data mining technology to discover the causal relationship between evaluation indicators. In the evaluation of student satisfaction for dual teacher teachers, the proportion of highly satisfied students is 33%, and the proportion of satisfied students is 25%. Through the above research, important references and support can be provided for teachers, professional leaders, and educational reform leaders, helping them better formulate relevant policies and measures, and promoting the continuous improvement and innovation of education and teaching quality.