Reviewerly/exHarmony huggingface dataset:
Cross-platform spam messages classification based on the multiple machine learning algorithms

The proliferation of spam messages has had a detrimental impact on users' experience of emails and social media. Consequently, it is imperative to implement effective spam filtering mechanisms to enhance online experiences. Internet companies have leveraged machine learning algorithms to detect and thwart spam messages. Given the multitude of popular social media platforms, it is critical to evaluate the efficacy of prevalent machine learning algorithms across diverse online platforms. This study seeks to assess the performance of Support Vector Machine, Linear Regression, and Random Forest on social media. To this end, datasets containing spam and non-spam messages sourced from YouTube comment sections and Twitter will be procured. The text data will be transformed using a vectorizer to enable interpretation by machine learning models. Three models employing SVM, Linear Regression, and Random Forest will be trained and deployed to test their effectiveness. The models will be applied to detect spam messages in the test dataset, YouTube comment set, and Tweet set. The performance of the models will be evaluated based on accuracy, F1 score, and precision score. The findings indicate that the models' performance on various social media datasets is not satisfactory, as there is a significant reduction in accuracy.
An empirical comparison of machine learning methods for predicting breast cancer probability

Nowadays, there are many kinds of cancer that people cannot prevent early, so those diseases cannot be controlled effectively when people detect them. To solve the problem, more and more scientists started to study the features and possible reasons for those diseases. This study concentrates on the relationship between some basic physical features, breast cancer diagnosis, and patients' possibilities of getting breast cancer. The main methods in this research are machine learning. It includes logistic regression, decision tree, random forest, k-nearest neighbor, and gradient boosting model. These methods use their main ideas to construct and fit the training data, then they predict the testing data and compare them with the testing set. Each model has its prediction results and performance values. After that, they will also herald the patients' probability of getting breast cancer with their physical conditions. According to the evaluation values, the random forest model performed best. Regarding probability prediction, the models have almost similar results. This study is a simple attempt to study the possible reasons for breast cancer. People could concentrate on increasing the number of independent variables in future studies. For example, they can add potential triggers of breast cancer. This suggestion will improve the efficiency of disease prevention and explore some helpful treatments.
Optimized federated learning based on Adagrad algorithm and algorithm optimization

Federated learning allows you to train machine learning models without sharing your local data. Due to the No-iid problem, this paper is based on the Moon algorithm, which can have excellent performance in datasets of images with models that use deep learning and outperforms FedAvg, FedProx, and other algorithms, with the goal to decrease communication costs while enhancing efficiency more effectively. This study optimizes its gradient descent technique based on Moon's algorithm by utilizing Adaptive Gradient (AdaGrad) optimizer and combining with knowledge distillation to improve Moon's algorithm in order to better reduce communication costs and improve efficiency. That is, it reduces the loss and improves the accuracy faster and better in local training. In this paper, we experimentally show that the optimized moon can better solve the communication cost and improve the accuracy rate.
Comparison and analysis of DQN performance with different hyperparameters

Deep Q-learning Network (DQN) is an algorithm that combines Q-learning and deep neural network, its model can adopt high-dimensional input and low-dimensional output. As a deep reinforcement learning algorithm proposed ten years ago, its performance on some Atari games has surpassed all previous algorithms, even some human experts, which fully reflects DQNs high research value. The tuning of hyperparameters is crucial for any algorithm, especially for those with strong performance. The same algorithm can produce completely different results when using different sets of hyperparameters, and suitable values can considerably improve the algorithm. Based on the DQN we implement, we test on number of episodes, size of replay buffer, gamma, learning rate and batch size with different values. In each round of experiments, except for the target hyperparameter, all others use default values, and we recorded the impact of these changes on training performance. The result indicates that as the number of episodes continues to increase, the performance improves steadily and degressively. The same conclusion is also applicable to the size of replay buffer, while other hyperparameters need to be given values to have optimal performance.
Comparative study of feature extraction algorithms for panorama stitching

Panorama stitching is a fascinating and rapidly advancing research field. By integrating many photographs that were taken from various angles and viewpoints, with various exposure and color settings, a seamless image is primarily the aim of panorama stitching. This paper investigates the performance of three widely used feature extraction algorithms Speeded-Up Robust Features (SURF), Scale-Invariant Feature Transform (SIFT), and Oriented FAST and Rotated BRIEF (ORB) for panorama stitching. The study compares these algorithms in terms of accuracy, robustness, and speed. Results indicate that while SURF and SIFT produce more accurate and robust results than ORB, they require longer processing time. The study evaluates the approach on a real-world dataset and demonstrates its effectiveness in creating seamless and visually appealing panoramas. This study provides valuable insights into the trade-offs between different feature extraction algorithms and presents a practical solution for panorama stitching applications.
Research on the optimum path Taking the hospital delivery robot as an example

Medical delivery robot refers to the delivery robot used in the medical field. Compared with ordinary delivery robots, the medical delivery robot needs to work in an environment with many people, which means that it needs to deal with many random obstacles at any time. This article will discuss automatically avoiding obstacles, and formulate and compare algorithms to analyze the advantages and disadvantages of automatic road exploration algorithms of different algorithms. In order to accomplish this goal, this paper will use Matlab as the main development tool and use the A-star algorithm and the Euclid algorithm as the main heuristics in the main mathematical model of the program. This program needs to be able to complete the obstacle avoidance task in a map with random size, random position, random shape, and random number of obstacles, and be able to reach the end point from the starting point. In addition, this paper will also discuss the efficiency of the A-star algorithm and the Dijkstra algorithm in obstacle avoidance and route planning, and demonstrate why the A-star algorithm is more efficient by taking time and congestion indicators.
Applying multi-layer perceptron and ResNet for handwritten digits recognition

The task of handwritten digit recognition is to recognizing the handwritten digits from pictures. Applying machine learning based models to automatically perform handwritten digit recognition task can significantly improve efficiency. This paper applies two machine learning based models, including multi-layer perceptron and residual neural network, for such a task. Firstly, this paper introduces the basic concept of the simple multi-layer perceptron model and then presents the structure of the residual neural network model. Subsequently, such two models are trained on the MNIST corpus, one of the classical dataset for the handwritten digit recognition task. The data pre-processing, like the splitting of training and test set, is described. Also, the processes of testing and training of the two models are presented. According to the experiments on the test set of MNIST, it is observed that the residual neural network can achieve better performance where the accuracy score is 99.240%, while the accuracy score of the multi-layer perceptron model is 97.260%.
Deep learning based on the application of voice emotion

A language is a valuable tool for human development and progress, and it is also an important medium for human beings to transmit information and express emotions. Language signals are ubiquitous, and it is an indispensable part of human life. This article will take the analysis of language as the starting point, combined with the relevant content of computer deep learning, and summarize various methods of language emotion recognition based on a convolutional neural network. In recent years, with the gradual intelligentization of computers, more in-depth discoveries and research have been made on language emotion research. In the deep neural network sector, most of the models used are CNN, LSTM, MO-LSTM models, and this paper aims to propose a new CLDNN (CONVOLUTIONAL, LONG SHORT-TERM) that integrates CNN, DNN, and LSTM into the same network. MEMORY, FULL CONNECTED DEEP NEURAL NETWORKS) model, compare with it, and summarize the advantages and disadvantages of CLDNN.
Research on image inpainting methods based on machine learning

The technique of restoring sections of a picture that have been lost or damaged is known as "image inpainting." In light of recent developments in machine learning, academics have begun investigating the possibility of using deep learning methods to the process of picture inpainting. However, the current body of research does not include a comprehensive review of the many different inpainting methods that are based on machine learning, nor does it compare and contrast these methods. This article provides an overview of some of the most advanced and common machine learning based image restoration techniques that are currently available. These techniques include Multivariate inpainting technology and Unit inpainting technology, such as Context-Encoder Network, Generative Adversarial Network (GAN), and U-Net Network. We examine not just the benefits and drawbacks of each method, but also the ways in which it might be used in a variety of settings. At the conclusion of the piece, we predict that machine learning-based inpainting will continue to gain popularity and application in the years to come.
The development and advance of machine translation

Since the advent of global online information exchanging and communicating, there are growing number of cases showing how effective and useful machine translation can be. This paper offers a brief overview of Machine Translation, including Statistical Machine Translation and Neural Machine Translation. In the Statistical Machine Translation part, different translations using corresponding different bases are introduced. Then some key parts in Neural Machine Translation are discussed: the seq2seq model, LSTM (Long Short-Term Memory) and the encoder and decoder. And finally, the evaluation metrics of machine translation are concluded which contains not only human but automatic evaluation metrics as well.
Streaming process based on Spark and Kafka and its case application

E-commerce platforms gradually integrate into our lives. Online shopping has become a daily habit for people. People who shop online will generate online shopping records in real time. These online shopping records are often massive. E-commerce platforms use relevant big data technology to conduct statistics on real-time user data. In response to the current demand for e-commerce platforms for big data real-time computing technology, this article provides an idea of big data streaming computing, a framework for combining two big data technologies, Spark and Kafka, to process streaming data. This framework uses Kafka to send real-time data to SparkStreaming for calculation. Then the obtained data is visualized. This article applies these two big data technologies to simulate and implement an application case of an e-commerce platform. This case design and implementation enables real-time statistics of the click volume of a mobile phone brand on an e-commerce platform and dynamic display of streaming data on the Web.
Research on robot path planning methods

Mobile robots are used extensively across a variety of industries and fields. How to discover a start-to-end path without colliding has become a hot topic in recent years due to the complexity and uncertainty of the workplace. In various environments, a path planning technique should demonstrate high efficiency and speed. And this can reduce the energy consumption of the robots and greatly increase their working efficiency. This paper will conclude the presently popular path planning algorithm. Based on the different features of these algorithms, they are divided into three types: traditional path planning algorithm, neural-work-based algorithm, and sampling-based algorithm. Based on the new papers in these years, detailed introduction of the algorithms and their variants will be given. At the end of the paper, the thesis is summarized and the future research trend is prospected.
Research on pruning optimization techniques for neural networks

Large deep neural networks have been deploying in more and more application scenarios due to their success in multiple application scenarios. However, deep neural networks are difficult to apply to devices with fewer resources, as the large models and huge demand for computing resources make this difficult. Pruning optimization, as a critical model compression method, has become an essential part of the deployment process of deep neural networks and has extreme significance. This article summarizes the methods of deep neural network pruning optimization technology, sorts out the current research status of pruning optimization technology, analyzes different fine-grained pruning optimization technologies based on the different fine-grained levels of pruning optimization technology, and comparing the characteristics of different fine-grained pruning optimization techniques. This article also introduces the development process and current development direction of different fine-grained pruning optimization technologies, compares the effectiveness differences between different fine-grained pruning optimization technologies and looks forward to the combination of pruning optimization technology and model quantification technology. The end of the paper summarizes pruning optimization techniques and provides prospects.
Virtual reality technology in the field of education

Virtual Reality (VR), the technology has been around for years, are now developing rapidly since 2010s, VR game facilities can often be seen in shopping malls and the conception. Metaverse are now mentioned and are put into practise. Moreover, from word-of-mouth through books, blackboards, and ultimately multimedia, the educational approach has consistently developed. But in the process of practice, it has been proved that conventional education is predicated on teacher-cantered instruction, and students frequently wind up being passive knowledge carriers who are unable to fully acquire, interpret, and apply knowledge. In this paper, the new form of education which combined with VR technology will be discussed, and the analysis of positive effect of applying VR on education will be find out. The education of VR is divided into three parts to discuss, school safety education, subject education and vocational education. This paper will conduct a targeted analysis based on the respective attributes of these three types of education. With the knowledge of VR on education, people can carry out more in-depth cooperation and development on future VR and education based on these advantages.
Research on the sharing platform of college instructional resources based on blockchain technology

The integration of instructional resources in colleges is gradually entering the innovation era from electronic and information technology. A new issue that has emerged is the storage and reading of electronic instructional resources in colleges. Additionally, with the rapid development of blockchain technology, its use in numerous fields has burgeoned. In the information management field, confidential and reliable technologies are still needed to help manage resource storage. Blockchain, which is immutable and untrusted, can be well applied in the establishment of an educational resource sharing platform in universities. The main focus of this paper is as follows: first, to analyze and compare the three educational resource sharing systems proposed by Chinese scholars, enumerate the main contents and functions of each system, compare and analyze the differences of the three systems, and secondly, to put forward the construction framework of a new educational resource sharing platform according to the disadvantages still existing in the three systems. The paper also explores the research on college instructional resource sharing platforms using blockchain. It uses blockchain techniques to build an educational resource sharing system, provides a reliable platform with high security and traceability through intelligent contract technology, and connects user groups through "alliance chain + private chain". Its aim is to facilitate efficient use by users of different levels. The advantages of using blockchain technology to construct a sharing platform for university educational resources have practical significance in further promoting educational informatization.
The comparison of top-down and bottom-up methods in multi-person pose estimation

2D multi-person pose estimation is a process to detect all bodies in a two-dimensional picture. The main purpose of this report is to discuss the difference between top-down and bottom- up method in multi-person human pose estimation. This estimation will focus on many people in one picture. There are two popular methods in this area. The first is top-down method, which is to find people firstly and then to detect the body of each people. On the other hand, the bottom-up method is to detect body parts and then find each person. Their comparison and analyse in algorithm, speed and accuracy may help researchers to find more suitable methods when they research in human pose estimation. Generally, top-down methods have higher accuracy than bottom-up methods but have higher speed.
Breast cancer risk prediction leveraging K-nearest neighbor and logistic regression algorithms

Based on the latest global cancer data collected by IARC, it is estimated that 19.293 million novel cancer cases will be diagnosed in 2020 worldwide. Among them, there will be 2.261 million novel patients suffering from breast cancer, which occupies 11.7% of the entire number of novel cancer cases worldwide and 24.5% of the total number of novel cancers in women. Moreover, there will be 685,000 deaths of breast cancer in women worldwide in the same period, ranking first in the incidence and death of cancer in women. In this paper, the authors compare two classical algorithms, KNN and logistic regression, predicting the risk of breast cancer. The dataset includes medical records with various attributes. The study found that both algorithms achieved high accuracy in predicting breast cancer, with KNN achieving an accuracy rate of 97.5% and logistic regression achieving an accuracy rate of 95%. The study also found that age, tumor size, and lymph node status were the most important predictors of breast cancer for both algorithms. Overall, the study demonstrates the effectiveness of KNN and logistic regression algorithms in predicting breast cancer and provides valuable insights into the most important predictors of the disease. The findings can potentially contribute to the development of more accurate and efficient diagnostic tools for breast cancer diagnosis.
Research on accuracy analysis and improvement of recommender system based on amazon review

Recommender system is a system that uses artificial intelligence and data mining technology to recommend items or services that meet users' preferences based on their historical behaviors and interests. In modern society, people are faced with more and more choices, and the emergence of recommender system can help users filter out valuable content from complex information and improve user satisfaction and experience. In this paper, collaborative filtering is used to implement a recommender system based on Amazon review data set. Meanwhile, Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) are used to conduct dimensionality reduction and other operations on the data. Root Mean Squared Error (RMSE) A value expressed as a recommendation for accuracy. After the establishment of the recommender system and three precision analysis experiments, it achieves this by applying a selected filtering algorithm to the input supplied, which is frequently in the form of user reviews of products.
CMF-SMR: Convolutional matrix factorization for sequential movie recommendations

Recommender system (RS) has become an essential component of e-commerce, social media, and other online platforms. Collaborative filtering (CF) is one of the most commonly used techniques in RS that relies on user-item interactions to generate recommendations. However, CF suffers from the cold-start problem, sparsity, and scalability issues. To address these challenges, this work propose a hybrid system called Convolutional Matrix Factorization for Sequential Movie Recommendations (CMF-SMR), which combines matrix factorization (MF) with convolutional neural networks (CNNs). CMF-SMR leverages the non-linear feature extraction capabilities of CNNs and the representation learning abilities of deep learning to enhance the accuracy and robustness of traditional MF-based RS. Specifically, CNNs and MF were used to respectively extract features from user-item interaction data and use them as input for learning user and item representations. The learned representations are then used to predict user-item ratings. This work evaluates the performance of our proposed method on two publicly available datasets, and the experimental results demonstrate that our method outperforms several state-of-the-art techniques in terms of accuracy, scalability, and robustness. Moreover, this work conduct evaluation metrics to demonstrate the accuracy of our proposed method. Overall, our proposed CMF-SMR provides a promising solution for addressing the limitations of traditional CF-based RS and can be applied in various domains, including e-commerce, social media, and personalized content recommendation systems.