bluuebunny/crossref_metadata_2025 huggingface dataset:
A literature review on multimodal deep learning models for detecting mental disorders in conversational data: Pre-transformer and transformer-based approaches
Authors: Shao Zilei
Published: October 2023

This paper provides a comprehensive review of multimodal deep learning models that utilize conversational data to detect mental health disorders. In addition to discussing models based on the Transformer, such as BERT (Bidirectional Encoder Representations from Transformers), this paper addresses models that existed prior to the Transformer, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The paper covers the application of these models in the construction of multimodal deep learning systems to detect mental disorders. In addition, the difficulties encountered by multimodal deep learning systems are brought up. Furthermore, the paper proposes research directions for enhancing the performance and robustness of these models in mental health applications. By shedding light on the potential of multimodal deep learning in mental health care, this paper aims to foster further research and development in this critical domain.
The prediction and feature importance analysis of stroke based on the machine learning algorithm
Authors: Jia Guantong , Jin Guo
Published: October 2023

The prediction of the probability of the patients stroke is a challenging task in the past decades. This study aims to predict the probability of stroke in patients using machine learning algorithms. Logistic regression model was used in this study to build the prediction model. In addition, the data preprocessing technology e.g. missing value processing and feature encoding was also carried out. The dataset collected from the Kaggle Platform used for the analysis contains various clinical and demographic features of the patients. The model achieved an accuracy of 96.3% in predicting stroke probability. Furthermore, the feature importance analysis was conducted to identify the most significant features that contribute to the prediction. The results demonstrated that some features such as age, glucose level, work type and hypertension were the most important features for predicting stroke probability. The findings of this study could help healthcare professionals in identifying high-risk patients and providing timely interventions to prevent stroke occurrence.
The application of artificial intelligence in internet security
Authors: Cao Xiyu
Published: October 2023

The global integration of the internet has led to a significant increase in the importance of cybersecurity. Artificial Intelligence (AI) has emerged as a viable solution for enhancing cybersecurity measures. AI has the potential to improve the speed and effectiveness of detecting and responding to cyber threats. This study explores the intersection of network security and AI, with a focus on the various ways in which AI can be used to enhance network security, such as intrusion detection, malware detection, and behavioural analytics. The study also examines the potential risks associated with AI in cybersecurity, including the possibility of AI being utilized for cyber-attacks. Additionally, the study discusses the challenges associated with implementing AI for network security, such as the lack of available large datasets for AI training, network infrastructure complexity, and the requirement for skilled AI professionals in cybersecurity. Ethical considerations arising from the use of AI in network security are also addressed. The study emphasizes the need for a balanced approach towards integrating AI into cybersecurity measures, taking into account potential benefits and challenges.
A review on the application of reinforcement learning to the 2048 game
Authors: Mi Hongyu
Published: October 2023

In light of the rapid development of Artificial Intelligence (AI), this paper pries into the application of reinforcement learning on the 2048 game which has important implications on the algorithms ability to solve various real-life problems including but not limited to robotics, auto-driving, and financial trading. The paper summarizes the possible usage of the 2048 model in Q-learning, convolutional neural networks, genetic, and Monte Carlo tree search algorithms. Then the paper scopes into the current progress and methods used in solving the 2048 game with reinforcement learning. Starting off with Q-learning, its implementation and limitations, such as large state space, sparse rewards, limited exploration, computational complexity, and a lack of human-like intuition, are illustrated. Deep Q-learning handles the problem of large state space from Q-learning. However, most of the disadvantages that are found in Q-learning are shared by Deep Q-learning and hence should become the focus of future research on using reinforcement learning to solve 2048.
Comparison of machine learning models for MRI image-based brain tumor classification and segmentation
Authors: Liao Yiming
Published: October 2023

Brain tumors have a high-risk factor and are extremely harmful to the human body. With the development of science and technology in recent years, automatic segmentation has become popular in medical diagnosis because it provides higher accuracy than traditional hand segmentation. At present, more and more people start to study and improve it. Due to the non-invasive nature of MRI, MR images are often used to segment and classify brain tumors. However, limited by the inaccuracy and inoperability of manual segmentation, it is very necessary to have a complete and comprehensive automatic brain tumor segmentation and classification algorithm technology. This article discusses the benefits, drawbacks, and areas of application of several traditional algorithms as well as more modern, improved, and more advanced algorithms. Segmentation methods and classification methods can be used to classify these techniques. Convolutional neural networks (CNN), Support vector machines, and Transformers are examples of classification methods. Random forests, decision trees, and improved U-Net algorithms are examples of segmentation methods. To discuss the capability of classification and segmentation, there are three sections in the area used for segmenting brain tumors with three types, including Tumor Core, Enhance Tumor, and Whole Tumor, which could be abbreviated as TC, ET, and WT. Through the comparative analysis of these methods, useful insights for future research are provided.
Enhancing network security through machine learning: A study on intrusion detection system using supervised algorithms
Authors: Huang Zi , Li Zhenmin , Zhang Jiaming
Published: October 2023

The topic of Intrusion Detection System (IDS) has become a highly debated issue in cybersecurity, generating intense discussions among experts in the field. IDS can be broadly categorized into two types: signature-based and anomaly-based. Signature-based IDS employ a collection of known network attacks to identify the precise attack the network is experiencing, while anomaly-based IDS employ machine learning models to detect anomalies present in the network traffic that could indicate a potential attack. In this study, we concentrate on anomaly-based IDS, evaluating the effectiveness of three supervised learning algorithms - Decision Tree (DT), Naive Bayes (NB), and K-Nearest Neighbor (KNN) - to determine the most suitable algorithm for each dataset based on its source. We conducted tests to evaluate each algorithm's performance and choose the best one for each dataset. Our findings show that anomaly-based IDS is highly effective in enhancing network security, providing valuable insights for organizations looking to improve their security measures.
Research on image inpainting methods based on machine learning
Authors: Liu Zhengnan , Qin Mingyang
Published: October 2023

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.
Three-dimensional tilings of blocks and bracelets and related {2,1,2} sequences
Authors: Fang Hanzhang
Published: October 2023

This paper discusses the combinatorial interpretation of the H_n numbers, where each Hn denotes the number of ways to tile a 2 2 n block with 2 2 1 plates and 6-block L shapes. It then investigates a closely related tiling sequence, which is tiling a 22n bracelet with the same two building blocks, and discusses its relation with Hn. The recursive equation for both integer sequences are found using one to one correspondence, induction and Newtons Sum. Additionally, in the case of H_n numbers, its related Lucas Style sequence P_n is found. The relationship between the P_n numbers, the Bn numbers and the Hn numbers is similar to that of the Lucas numbers and the Fibonacci numbers. Finally, identities concerning H_ns generating function and its relations with other existing {2,1,2} sequences are discussed, and a theorem that generalizes the generating function of all tiling sequences is proposed and proved.
Sentiment classification and visualization analysis of tourism comments: A canton tower example
Authors: Lin Yanping
Published: October 2023

As calculated by the Ministry of Culture and Tourism, there were 308 million domestic tourist trips in China during the Spring Festival in 2023, witnessing a year-on-year increase of 23.1%. And the satisfaction of tourists with certain spots can be partly reflected in the comments and scores they made on social media. Therefore, this research was aimed at mining useful information from the comment and scores of Canton Tower. After collecting detailed comment information from the web, this research used the plot module of Python to make data visualization to observe the distribution of users location, comment time, and comment label as well as the word cloud of remarks. Then the research used the data set to train three different sentiment analysis models including Nave Beyas, SnowNLP, and Bert, then compared their accuracy in predicting. This research shows that over half of the comments came from Guangdong Province, most of the tourists were content with Canton Tower, and the number of comments has increased obviously since 2023. In addition, the research found that the model having the highest accuracy of sentiment analysis is the Bert model, about 90%.
Research on algorithms based on object detection
Authors: Zhang Zeshun
Published: October 2023

Object detection technology is a hot research direction in computer vision field technology, which is broadly used in face recognition, vehicle navigation, aviation and other important fields, with broad development prospects. In recent years, object detection algorithms based on deep learning have also appeared as computer science technology has advanced quickly. Comparing modern object detecting algorithms to conventional ones, this algorithm has gradually highlighted the advantages of high precision and good real-time performance. This article reviews traditional object detection algorithms, and focuses on the HOG algorithm of traditional object detection, reviews deep learning-based two-stage and one-stage object identification systems and weighs the benefits and drawbacks of each. A summary and outlook for the future object detection algorithm development is also provided.
The application of intelligent tutoring systems and social robots in autism treatment
Authors: Gu Chloe
Published: October 2023

This study examines the utilization of intelligent tutoring systems (ITS) and social robots as innovative technologies to augment the treatment of autism. In the context of artificial intelligence, these interactive systems offer personalized and engaging interventions, holding great promise for individuals on the autism spectrum. While the research in this field is still evolving and lacks maturity, early findings indicate increased engagement and potential for improved learning outcomes among autistic children. Intelligent tutoring systems, capable of adapting to individual learners, exhibit promise for delivering personalized and effective interventions. Social robots, with their interactive functionalities, provide a distinct avenue for fostering social communication and skill development. Moreover, these technologies have the potential to deliver cost-effective intervention methods, thus enhancing accessibility for children with autism and their families. However, further research, development, and integration into existing therapeutic approaches are imperative to fully actualize their potential. By harnessing the power of intelligent tutoring systems and social robots, it is possible to pave the way for more effective, personalized, and engaging autism treatment, positively impacting the lives of individuals on the autism spectrum.
Review of image recognition systems for noisy environments
Authors: Lu Junkai
Published: October 2023

One of the major challenges faced by image recognition systems in practical applications is the presence of noise in the physical world. To address this challenge, this paper proposes two different approaches. The first approach involves constructing a noisy dataset and training the image recognition system to tolerate noise. The second approach utilizes a combination of denoising methods and pre-trained image recognition systems. Experimental tests and analyses are conducted to evaluate the advantages and disadvantages of each approach. Additionally, this paper investigates the relationship between different noise reduction methods and their impact on improving the recognition rate and resource consumption of images containing Gaussian noise, specifically when neural networks are used for recognition.
The application of virtual reality in art
Authors: Yang Shu
Published: October 2023

Virtual reality (VR) is an important technology applied in the field of art in recent years. VR has revolutionized the field of art with its unique immersion and interactivity. At present, there are very few articles that focuses on and discusses the development and limitations of the use of VR in the field of art. This paper will give readers a full understanding of the application of VR in art. Paper summarizes the application of VR in art, including VR exhibitions, VR scenic spots and historical sites, and 3D movie. We have cited the most representative examples in each field, discussed in detail how they are combined with VR, and demonstrated the advantages of VR in these fields. Then, this paper concludes some limitations of the use of VR in art and ends up with a discussion on the future development direction of VR in the art field.
Application of artificial intelligence in network security defense
Authors: Cao Yuqiang
Published: October 2023

With the development of today's social network, people use the Internet more frequently and become more reliant on it. However, as a result of the development of the network, network attacks are becoming more frequent, and people have an imperative need for a secure and dependable computer network. Meanwhile, the technology of artificial intelligence is progressively maturing, so the application of artificial intelligence in network security defense will be a more effective means of addressing network security's hidden dangers. This paper focuses primarily on the benefits of the application of artificial intelligence to network security defense, as well as summarizing and analyzing the application of artificial intelligence to network security defense. This paper concludes, by analyzing the most recent literature and evaluations, that AI plays a crucial role in resolving network security issues and can effectively withstand certain network attack threats.
Review on multi-modality medical image fusion using deep learning
Authors: Gupta Ishika , Gupta Satyam , Diwakar Manoj , Singh Prabhishek , Shankar Achyut , E Sathishkumar V
Published: October 2023

Multi-Modality medical image fusion is a method in which multiple images are merged having either single or multiple imaging modalities. This process is carried out to improve the quality of imaging while preserving all the essential and distinct features. Many areas such as Machine Learning, Artificial Intelligence, Image Processing, and Computer Vision are covered by Medical Image Fusion. This method has been adopted on a large scale by physicians to apprehend any damage or injury caused in organ tissues in clinical trials by performing a fusion of images with different modalities. In this review, Deep Learning methods carried out in the medical image fusion field have been discussed along with a comparison between their accuracies. The main objective of this paper is to list some of the most effective techniques in this domain and discuss their performance. At last, the paper concludes with the fact that although the development and growth in this area have increased over the years, many challenges have also come along the way.
Wireless sensor network for structural health monitoring using RFID based data mules
Authors: Singh Prabhishek , Maurya Ankur , Shankar Achyut , E. Sathishkumar V. , Diwakar Manoj , Pandey Happy , Anushka Anushka
Published: October 2023

The wireless system did the huge cost-cutting in monitoring the structure so, now it can be used permanently as an integral part of the system as a smart infrastructure that will give them real-time information the structure. The wireless devices transmit the collected data about cracks, displacement, and excess vibration in slab-tracks. The train which will collect the data and train will be used as a data mule in this paper which will upload the information to a remote-control centre. The data which will be collected stored in the database and to know the status of the track a query will be fired from an application. In this paper, many design for communication systems are proposed which are efficient, with fine accuracy, and most importantly it is a low-cost system.
A conventional and non-conventional analysis of SAR image despeckling technique
Authors: Singh Prabhishek , Maurya Ankur , Arora Sakshi , Shankar Achyut , E. Sathishkumar V. , Diwakar Manoj
Published: October 2023

The importance of high-resolution Synthetic Aperture Radar (SAR) imaging is undeniable in applications for Earth monitoring since it offers useful data for analysis. Post-image processing methods including edge and object detection, segmentation, and speckle noise removal are important to performed to find the information of images. The key technique that makes images aesthetically pleasing and understandable is despeckling. The presented study assays the latest trends & state of the art techniques for SAR image despeckling to gauge the performance & agility of existing techniques by performing a contemporary combination of theoretical & experimental analysis. This study analyses various techniques used in literature. The quantitative & qualitative analysis is done using Peak Signal-to-Noise Ratio (PSNR) and Universal Image Quality Index (UIQI) indices to find the better approach
A novel design of surveillance drone using Internet of Things with surface monitoring provisions
Authors: B. Mohanraju G. , G. Santhosh , S. Jeevankumar , P. Kumar
Published: October 2023

The Unmanned Aerial Vehicle (UAV) environmental monitoring system in an area is multipurpose and easy to use. With the use of targeted mobile networks and drone flight control, the relevant staff can effectively monitor regional temperature and humidity. This study draws on the author's previous expertise to outline the key points of a programme demonstration and system design, as well as to examine the software flow architecture of regional environmental monitoring. As a result of this work, users will be able to easily collect weather as well as other data by using a drone that can be controlled from an Android phone and flown to some faraway, inaccessible site. The mobile node MCU may receive commands from the WiFi module and relay them to the drone. The drone will be flown when the instructions have been sent via the IoT based NodeMCU module. From there, it will gather data via the network and communicate that information to the cloud database over WiFi. This setup has the potential to be implemented into a model for a weather prediction that can accurately foretell winds and temperatures in the immediate vicinity of the surface to an accuracy of 100 meters.
Feature analysis and model comparison of logistic regression and decision tree for customer churn prediction
Authors: Wen Ziqi
Published: October 2023

Customer churn has long been a concern for companies because it not only reduces the company's profit in the short term, but is also extremely detrimental to the company's growth in the long term. This paper focuses on the analysis of customer churn in banks by using two machine learning methods, namely logistic regression and decision tree, to predict the churn rate of customers and analyze the decision tree results based on the premise that decision trees are more accurate in prediction and do not have a large prediction bias for a certain group as logistic regression does. The results show that age, estimated salary and the number of products are important factors when predicting and customer groups with some specific characteristics will show a higher departure rate. To address this situation, this paper recommends that bankers continuously optimize their business systems and focus on user groups with high churn rates.
Feature statistical analysis and comparison of machine learning models for university admission prediction
Authors: Zhang Qianyu
Published: October 2023

In recent years, the pursue for academic degrees has intensified, leading to a surge in the number of undergraduate students applying for graduate programs at renowned universities worldwide. Consequently, universities have adopted a multifaceted approach to evaluate applicants, moving beyond traditional metrics like GPA to assess their overall potential. This study aims to comprehend the criteria employed by universities to select graduate applicants and assist undergraduate students in planning their academic trajectory. To achieve this, a diverse set of machine learning models are compared, including multiple linear regression and K-nearest neighbors, decision trees, support vector machines, and Bayesian classifiers. These models were trained with online admission probability data to predict the likelihood of admission and uncover the primary factors guiding university selection processes. The findings reveal that while research experience can enhance competitiveness in graduate admissions, academic indicators such as GPA, GRE scores, and language proficiency remain critical determinants of acceptance. Moreover, higher-ranked institutions exhibit a higher proportion of applicants with research experience. For candidates with strong GPAs, it is essential to demonstrate competitive language proficiency, augment research experience through well-crafted recommendation letters and personal statements. Conversely, applicants with lower GPAs should strive for outstanding GRE scores to compensate for academic performance.
An emotional expression framework based on social media style transfer
Authors: Zhou Chuying
Published: October 2023

Recently, artificial intelligence has great effort in social media. Style transfer is a subtask of deep learning, can transform images shared on social media into works with artistic painting style transfer through image recognition technology, which creating amazing effects. However, the current style transfer technology is still in the early stages of development, and can only convert the shared picture into the style of a certain artwork, but does not have the ability to convert the emotional expression of the picture. In this paper, we proposes an emotional expression framework based on social media style transfer, conducted an experiment in the LFW database where the same portrait photo can express different emotions by combining it with images of different styles and realizes the style transfer technology for emotion and emotional expression. Therefore, the application of style transfer technology in social media is of great value and can promote the intelligent development of social media.
Spam classification based on different artificial intelligence methods
Authors: Wang Xiaoke
Published: October 2023

The increased need for social communication has led to an increase in email users, and with it more spam is being spread. In this paper, by comparing and exploring the accuracy of some supervised machine learning methods and a deep learning method called Long short-term memory (LSTM) on the problem of spam classification, this paper aims to provide more solutions for the problem of spam filtering. This paper firstly conducts an in-depth understanding and analysis of the principles of different machine learning algorithm models, which is very helpful for the following research. Then the experimental comparisons after mastering the principles of different models are conducted. Regarding the process of the research, the data set was first pre-processed to facilitate the use of different algorithm models, and then the data set was put into different models for training. Finally, by comparing the accuracy and confusion matrix, it was concluded that LSTM was used in spam classification. problem is more advantageous.
Prediction analysis of cotton yield based on BP neural network and multiple linear regression
Authors: Liu Qiaomu
Published: October 2023

With the rapid development of the economy and agricultural production, cotton is an indispensable crop in people's production and life, and the average annual cotton yield is particularly significant in production. The unit yield of cotton is affected by many factors, so it is extremely important to analyze the factors affecting future cotton yield forecast. This article varies seven relevant indicators, including fertilizer cost,consumption of agricultural plastic, pesticide cost, seed cost, irrigation cost, chlorpyrifos control area, and labor input, and establishes a prediction model based on Back Propagation (BP) neural network. The model uses the function approximation ability of BP neural network and gradient descent algorithm to predict the nonlinear functional relationship between the unit output and the seven factors. Meanwhile, a multiple linear regression model is used to forecast the unit yield and the prediction accuracy of the two methods for unit output is compared. The experimental results show that compared with multiple linear regression, BP neural network has higher accuracy in predicting cotton unit yield, which can be used as an idea for future prediction.
Application of virtual reality technology in helping patients overcome treatment anxiety
Authors: Wang Maosheng
Published: October 2023

Patients undergoing surgery face several medical anxieties such as White Coat Syndrome, Iatrophobia, and Trypanophobia, which cause great inconvenience to both the medical staff and the patients. In recent years, with the advancement of science, virtual reality technology has been developed widely and plays a significant role in dealing with anxiety issues. This paper, with the theme of VR technology helping patients overcome treatment anxiety, summarizes and discusses patients' anxiety and fear during treatment, how VR technology helps patients overcome anxiety and fear, and some challenges and prospects, analyzing the effects of VR technology in relieving patient anxiety. This paper finds that virtual reality technology is currently in the development stage in the field of medical applications, and it plays a significant role in the field of psychotherapy. It can serve as a very effective tool to help patients get better treatment.
Comparing performance of neural network frameworks for image recognition: A review
Authors: Wang Luting
Published: October 2023

Spiking Neural Networks (SNNs) are increasingly recognized as a promising approach to simulating the biological behavior of neurons. This study conducted an in-depth performance comparison of different SNN models with supervised learning algorithms, particularly in the field of image recognition. Using datasets like MNIST, CIFAR-10, and ImageNet, the research analyzed the performance and accuracy of SNNs compared to traditional Artificial Neural Networks (ANNs). This research contributes to understanding SNNs' performance in supervised learning environments and offers insights into the optimization of SNN architectures, thus influencing future research and the development of next-generation neural network designs. Based on this comparative study, it was found that while SNNs perform admirably with smaller, less complex datasets such as MNIST and CIFAR, they encounter difficulties with larger, more complex datasets like ImageNet. Despite these challenges, advances in conversion techniques have led to models that more closely simulate the behavior of biological neurons. The study identified significant future work areas, including addressing issues of local learning methods, the limited scale of SNNs, hardware implementation difficulties, a lack of substantial benchmark datasets, and limited support for complex computations.
Research on image recognition based on the yolov3 model in an extreme environment
Authors: Jiang Haiyang
Published: October 2023

In recent years, the human demand for the exploration and application of extreme environment is increasing. However, the light, climate, object motion and other factors in extreme environments have great uncertainty and complexity, which makes the image recognition technology face great challenges. This study aims to investigate the image recognition techniques based on the yolov3 (You Only Look Once Version 3) model in extreme environments. For the problem of image recognition in extreme environments, this study compared the identification gap between the initial data set in the yolov3 model, and optimized the yolov3 model (The main way is to prune and quantify, and fine-tune the algorithm) to improve its accuracy and stability in extreme environments. In this study, the feasibility of yolov3 model for image recognition in extreme environment was verified by comparing the performance of yolov3 model before and after optimization. The experimental results show that the image recognition technology based on yolov3 model proposed in this study has high accuracy and stability in extreme environments.
Research and application of path planning algorithm based on deep learning
Authors: Jia Xulong
Published: October 2023

This paper mainly introduces the research and application of path-planning algorithms based on deep learning. Firstly, it introduces the background significance of path planning, the current status of domestic and international research and the content and methods, and then analyzes the application methods of deep learning in path planning, including deep reinforcement learning, convolutional neural network and recurrent neural network, and the comparison between different algorithms. Then, the implementation of a deep learning-based path-planning algorithm is introduced in detail, including system architecture and design, data preprocessing and model training, path-planning implementation and optimization, etc. In the Experiments and Results Analysis section, the analysis and comparison of experimental results, experimental conclusions and expansion directions are summarized. Finally, in the conclusion and outlook section, the research contributions and application values of this paper are described, and the research deficiencies and future expansion directions of deep learning in path planning are proposed. In addition, because of the continuous development of deep learning technology, there are many other possibilities for applying deep learning methods to build more optimal path-planning algorithms in the future.
Facial emotion detection based on improved VGG-16
Authors: Zhang Ran
Published: October 2023

In real life, facial emotion recognition is very important because it can convey information, build relationships, and facilitate communication. Therefore, emotion recognition technology is used in medicine, education, entertainment, security, and other fields. In the emotion detection field, the Facial Emotion Recognition 2013 Dataset (FER-2013) is a dataset that has been used in many places, that contains images of seven emotional expressions. In the area of detecting emotions from facial expressions, the deep learning structures, especially the convolutional neural networks (CNNs), have demonstrated significant potential since they have the ability to extract features and their computational efficiency. In this paper, the author constructs a model named Improved VGG-16 based on Visual Geometry Group Network of 16 weight layers (VGG-16). To be specific, first, the author adds two dense layers to improve the complexity and expressiveness; second, two dropout layers are used in order to reduce overfitting. An accuracy of 68.0% is achieved by this model on the test dataset of FER-2013. The result is better than some previous methods and shows that the improved VGG-16 model can recognize facial expressions effectively. In conclusion, this work aims to increase the accuracy and reliability of facial emotion recognition, providing support for research and application in related fields.
MLOffense: Multilingual offensive language detection and target identification on social media using graph attention transformer
Authors: Wang Grant
Published: October 2023

With the increasing use of social media in our daily lives, it is crucial to maintain safe and inclusive platforms for users of diverse backgrounds. Offensive content can inflict emotional distress, perpetuate discrimination towards targeted individuals and groups, and foster a toxic online environment. While natural language processing (NLP) has been employed for automatic offensive language detection, most studies focus on English only, leaving languages other than English understudied due to limited training data. This project fills this gap by developing a novel multilingual model for offensive language detection in 100 languages, leveraging existing English resources. The model employs graph attention mechanisms in transformers, improving its capacity to extend from English to other languages. Moreover, this work breaks new ground as the first study ever to identify the specific individuals or groups targeted by offensive posts. Statistical analysis using F1 scores shows high accuracy in offensive language classification and target recognition across multiple languages. This innovative model is expected to enable multilingual offensive language detection and prevention in social media settings. It represents a significant step forward in the field of offensive language detection, paving the way for a safer and more inclusive social media experience for users worldwide.
The moral problems brought by the development of artificial intelligence
Authors: Lin Zhaokun
Published: October 2023

With the rapid development of Artificial intelligence (AI), the rapid development of AI also brings many new problems. Among them, the ethical and moral issues brought by AI have attracted people's attention. Due to the rapid renewal of artificial intelligence, many moral problems that have never been envisaged have also emerged. To avoid such problems and provide solutions for the future, this paper mainly analyzes the possible moral and ethical problems brought by artificial intelligence in the future, and adopted solutions and the results after adopting solutions. The research data are taken from published research, reports, government legislation, etc. This research believes that AI should comply with the current moral principles and be released after having solutions that can solve possible moral and ethical problems.
FaceTell: A novel solution to diagnosing Parkinsons disease with facial expressions and deep learning
Authors: Shi Hanwen , Wu Ben
Published: October 2023

Parkinsons disease (PD) is a neurodegenerative disease afflicting over 10 million patients worldwide, most commonly the elderly, that causes tremors, stiffness, movement loss, and other symptoms. Since symptoms are often mild and difficult to notice in the early stages of the condition, it can be hard to notice and diagnose until the condition has already become more severe. An earlier diagnosis of PD will allow treatment to begin earlier and lessen the impact of the disease. The goal of this work is to develop an affordable, non-intrusive, and accessible way of diagnosing PD. This neurodegenerative disorder leads to loss of movement control and other symptoms. Since there is no known cure for PD yet, early diagnosis would allow timely treatment and prevent the symptoms from worsening too quickly. Doing so in an affordable and non-intrusive way will minimize costs and maximize efficiency removing the need for lengthy consulting with doctors and possibly expensive testing and medical equipment. This work presents the FaceTell system, which combines and optimizes traditional machine learning and deep learning to make predictions on the patients PD status based on video data of their faces. By analyzing a variety of attributes such as facial expressions and emotion prevalence/intensity, the model was able to achieve a more thorough examination of the patients condition and make predictions of similar accuracy compared to prior results. One main innovation was collecting data affordably: sampling publicly available videos from platforms like YouTube. This serves as a proof-of-concept to show that simple, affordable, and non-intrusive data collection methods can still produce viable results. Using methods and tools such as hyperparameter tuning, data cleaning, and Face++, the performance of the system readily improved. The ultimate results obtained include an F1 score of 0.86 and an accuracy of 89%, compared to prior results which reached up to 95% accuracy.
Application and analysis of text similarity in text clustering in the Chinese context
Authors: Fan Wentao
Published: October 2023

With the development of the Internet, information sharing is higher, and the amount of information that each user is exposed to is increasing. How to find the information peoples want from so much information is a very important question. The vast majority of these resources are related to textual information. The most intuitive manifestation of these problems is that when people usually use search engines, enter a piece of text, and search out the relevant website, if the algorithm is not good, the search results will be very unsatisfactory. Therefore, this paper studies the application of text similarity in text clustering in the Chinese context. First, the basic concept of text similarity is introduced. In addition, text clustering is explained/explained from three aspects: definition, application, and general processing process. Secondly, combined with the existing data, some mainstream clustering algorithms are comprehensively summarized. Then, combined with the above content, the similarity calculation method in text clustering is analyzed. Finally, the above methods are compared and analyzed according to the experimental results in the Python environment.
Research on soccer prediction model based on machine learning combined with domain knowledge
Authors: Song Junxian
Published: October 2023

With the global popularity of soccer and the increasing collection of data, more and more research focuses on using machine learning (ML) algorithms to predict match outcomes. However, accurately predicting soccer match results is a complex task that requires considering multiple factors such as team strength and player status. This paper aims to predict soccer match results using ML techniques in Python. To achieve this goal, a series of methods are employed to collect, analyze, and utilize historical match data, combined with knowledge and experience from the soccer domain, to identify factors relevant to predicting soccer match outcomes. By establishing appropriate feature extraction and selection methods, this paper is able to capture information closely related to match results. Based on this foundation, the paper proceeds with model development and enhances its performance through training and parameter tuning. Specifically, ML algorithms such as Support Vector Machines (SVM) and various techniques are applied to optimize the models and improve their predictive accuracy. Emphasis is placed on model stability and generalization, taking into account issues of overfitting and underfitting during the training process, with appropriate regularization and cross-validation techniques. Finally, a comprehensive performance evaluation is conducted on the established models, and a comparative analysis of different algorithms is performed. The experimental results demonstrate the excellent performance of the models proposed in this paper in predicting soccer match results, showcasing the significant potential of ML in this domain.
Development analysis and future of computer vision-based automobile license plate recognition technology and perception technology
Authors: Liu Leqi
Published: October 2023

With the increasing number of automobiles, there is a growing demand for the recognition and perception of license plates. As a unique identifier for each vehicle, license plates can help traffic management departments with vehicle tracking, real-time monitoring, and other tasks. License plate recognition and perception methods based on these technologies have been widely used. This paper aims to analyze and summarize the development of computer vision-based license plate recognition technology. The paper mainly discusses the two key steps in the license plate recognition process: license plate localization and detection, and character segmentation and recognition. These steps are classified into traditional methods and deep learning-based methods, and several methods are introduced for each category. Finally, by summarizing their advanced features and limitations, the paper compares them and predicts possible ways to improve them in the future.
Analysis of intention to major in computer science in post-secondary education based on random forest
Authors: Qiao Yu
Published: October 2023

Computer science and artificial intelligence have become an important sector of the world's economy and society. It is essential to determine what factors cause students to study and pursue a career in computer science-related fields to stimulate employment in the industry. In the past, researchers attempted to analyze the effect of different factors on students intention to study computer science using traditional statistical inference methods. This research, however, used a new way to assess the effects: the random forest model. This study collected two hundred ten responses from a survey posted on online forums. Using the random forest model, the study determined that career satisfaction and personality have the strongest influence on students intention to study computer science, and family background is less influential. Factors of the three listed categories averaged to have feature importance of 0.1551, 0.1528, and 0.1147 respectively. These results disagreed with prior studies and shed light on further exploration of this topic.
Transferability exploration of weight based on ImageNet for galaxy classification
Authors: Fu Xueying
Published: October 2023

The accurate classification of galaxies is essential for understanding the structure of the cosmos and establishing connections with host haloes. Manual classification methods are time-consuming, subjective, and prone to errors. This study investigates the efficiency of automated galaxy categorization utilising transfer learning with pre-trained models from ImageNet by utilising deep learning techniques. The Galaxy10 DECals Dataset is used in the study, and the DenseNet-121 network architecture is used for transfer learning. Experiments are used to assess the effects of various ImageNet weight configurations on the performance of the model. The findings reveal that the model trained with 20% ImageNet data achieves the highest classification accuracy of 79% for galaxies. Grad-CAM visualisation further highlights the impact of weight initialization on categorisation by displaying the different focal points of models trained with various ImageNet weights. The results indicate that using higher weights from ImageNet transfers advanced features, whereas using fewer weights transfers basic features, potentially producing inaccurate results. Prior to transfer learning, the study emphasises the significance of choosing the best weights from ImageNet. The knowledge acquired aids in the classification of galaxies and offers direction for further study.
ChatGPT v.s. media bias: A comparative study of GPT-3.5 and fine-tuned language models
Authors: Wen Zehao , Younes Rabih
Published: October 2023

In our rapidly evolving digital sphere, the ability to discern media bias becomes crucial as it can shape public sentiment and influence pivotal decisions. The advent of large language models (LLMs), such as ChatGPT, noted for their broad utility in various natural language processing (NLP) tasks, invites exploration of their efficacy in media bias detection. Can ChatGPT detect media bias? This study seeks to answer this question by leveraging the Media Bias Identification Benchmark (MBIB) to assess ChatGPT's competency in distinguishing six categories of media bias, juxtaposed against fine-tuned models such as Bidirectional and Auto-Regressive Transformers (BART), Convolutional Bidirectional Encoder Representations from Transformers (ConvBERT), and Generative Pre-trained Transformer 2 (GPT-2). The findings present a dichotomy: ChatGPT performs at par with fine-tuned models in detecting hate speech and text-level context bias, yet faces difficulties with subtler elements of other bias detections, namely, fake news, racial, gender, and cognitive biases.