bluuebunny/crossref_metadata_2025 huggingface dataset:
The application of artificial intelligence technology in game design
Authors: Zhang Heyi
Published: October 2023

With the continuous development of both games and artificial intelligence (AI) technology, their integration is inevitable. Traditional game design faces issues such as simple character behavior patterns, fixed gameplay, and a lack of immersive game scenes, which have found better solutions with the incorporation of AI technology. This paper focuses on the application of AI technology in game design from three aspects: character behavior patterns, environmental interaction and scene changes, and emotional perception and feedback. Firstly, two game cases are analyzed to highlight the important role of AI character behavior patterns in games, and two design schemes for character behavior patterns are summarized. Secondly, the dynamic changes in environment are analyzed through two game cases, demonstrating the qualitative impact of dynamic environment changes on a game. Thirdly, this paper focuses on emotional perception and feedback, specifically analyzing the automatic adjustment of game effects through facial recognition technology and physiological indicators. Then, the challenges and shortcomings that current artificial intelligence technology still faces in game design are analyzed and addressed. Finally, this paper concludes with a summary of the research and an outlook for future development trends.
Research on the application of machine learning models in speech recognition in noisy environments
Authors: Tian Yujie
Published: October 2023

In a world increasingly reliant on verbal communication with machines, this paper undertakes a rigorous exploration of the application of machine learning models in speech recognition within noisy environments. An important focus of this research lies in demonstrating the viability and robustness of these models when challenged by real-world noise interference. In particular, the utilization of deep learning models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) is thoroughly examined. Using concrete data and case studies, this paper uncovers a promising trajectory in the development and implementation of noise-robust speech recognition systems. In so doing, it lays a foundation for future research to enhance the practicality and effectiveness of machine learning models in this area. The ultimate goal is to ensure more reliable and accurate speech recognition that can withstand various noise conditions, thus expanding the potential for speech recognition applications in diverse and adverse environments.
Review of pedestrian detection technology based on deep learning
Authors: Zhong Zhaoyang
Published: October 2023

Pedestrian detection is one of the main research problems in the field of computer vision. With the development of deep learning technology, pedestrian detection algorithms have made great progress and breakthroughs. Currently, pedestrian detection technology using deep learning has become mainstream. The first type of algorithm is called two-stage detection algorithms, which are based on Region of Interest (ROI) and single-stage detection algorithms adopt an end-to-end training method. So this paper conducts extensive research on the basis of classification and comparison, studies the characteristics and development of the above two detection algorithms, respectively, and finally puts forward suggestions for their future development, such as the fusion of image background and target state.
Network security in multiple threats: A critical study
Authors: Joshi Kapil , Kumar Rajesh , Anandaram Harishchander , Diwakar Manoj , Singh Prabhishek , Shankar Achyut , E Sathishkumar V
Published: October 2023

Any organization nowadays needs a secure network. High-speed wireless networks and internet services are becoming increasingly unsafe as a result of the daily rise in security threats. Threats to information security come from within the company, in close to 80% of cases. With the latest trend of mobility (portable devices such as laptops and smartphones etc.), ubiquitous 3G connectivity and this trend increases but as more web-based applications are manufactured and available over the Internet, the instances of insider threats have been rising at an alarming rate. Current or former workers, contractors, or business partners who have permission to access the organization's network and servers are typically the source of insider threats. Confidential information is frequently stolen for monetary gain or wellful harm. Access to hacking tools via the Internet, USB drives, and wireless connectivity make break-ins simple. Here, Millions of dollars in losses in the event of IP theft, and customer / individual information theft are yet too considered. This article provides insight into Insider dangers, attackers, and their rationales and recommendations for organizational-level mitigation methods.
Linguistic mastery: Advances in natural language processing
Authors: S. Sujitha , L. Shree Ganesh , Priyatharsini G. Soniya , Geetha S.
Published: October 2023

NLPstands for Natural Language Processing, it is a kind of artificial intelligence. That demonstrate with scrutinize, understanding, and accuse natural human languages. In such a manner that analog procedure would get in touch and human language excluding computer-propel language like programming language such as c, c++, python, javascript. NLP field contains the intellect computer programming into an understanding language which is understandable by humans. So, powerful algorithms can even interpret one language into another language scrupulously. Natural language processing occasionally also known as computational linguistics, it uses semantics and syntax. It assists computers to acknowledge how humans talk or write and also it will know how to deduce meaning of words that they are saying. A language is designate as a synchronize of rules and symbols. These Symbol are amalgamated and used for disseminate the data as well as make contact to other resources. Symbols are potentiate by the rules and regulations.
A machine learning based wearable depression, anxiety and somatisation monitoring IOT system (WDASMS) for the prevention of suicides
Authors: Narayanan A. E. , Vijayadharshini D. , Ruthrabala P. , Ajay A. S.
Published: October 2023

Tamil Nadu reportedly is the alternate loftiest rates of pupil self-murder in India. According to report from the National Crime Records Bureau (NCRB) for 2020, about 46 people failed each day by self-murder in the state; further than two of these victims each day were scholars. The government of Indias domestic seminaries for meritorious pastoral children have reportedly witnessed 49 self-murders in just five times and half of the children who killed themselves on the premises were Dalits and Adivasis, Educationalists point at the pressure mounted on scholars for marks and competitive examinations as one of the main reasons. Utmost seminaries have councillors but going to the council has a smirch about it still. Domestic seminaries where these cases are passing more need to be covered more. Children dying by self-murder due to academic pressure are unheard of in other countries. Utmost of the A huge number of enquiries revealed depression and anxiety to be implicit risk factors for self-murder. Heart health is good among students between the ages of 15 and 25. A few physiological parameters are altered by anxiety and depression. Hence, current pulsation is the symptom for psychiatric diseases. Clinical trials have demonstrated that mental reasons, most frequently anxiety, depression, and somatoform illnesses, are present in cases with pulsation. Psychiatric problems like anxiety and somatization are linked to palpitation. A study found that individuals with high levels of both depression and anxiety are 54.77 times more likely to commit suicide, which is a significant increase above individuals with either high levels of anxiety or depression (2.46). (26.32).. Naturally students studying +2 and preparing for high marks are in under depression and it's dragged for two times. When the examinations are nearing and the results are nearing, the combined depression and anxiety factor increases the threat for committing self-murders for84.5. Hence, a system with literacy and decision making medium is needed to constantly monitor the students to take prompt conduct. A wearable IoT device transmits with scholars Id, the pulsation position constantly to the fellow knot. Logistic Retrogression machine literacy algorithm is used for decision making grounded on the probability of circumstance. The authorities are informed in their widgets and prompt conduct can be taken. This system will be more applicable to the boarding seminaries, where the children are far down from their parents and the number of self-murders are high particularly among +2 grade. This system can be used for colourful communities, where the working culture gives depression and anxiety.
Research advanced in deep learning-based licence plate recognition and localization
Authors: Li Jingrui
Published: October 2023

The purpose of licence plate recognition is to analyze pictures or videos of moving vehicles to read the plate and identify the vehicle's owner. Traffic data management and smart transportation systems rely heavily on licence plate reading technology. Initial picture capture, image preprocessing, licence plate analysis, character segmentation, and recognition are the building blocks of licence plate recognition. The present analysis centres on the examination of the above key steps. In this paper, we introduce the latest research progress in the implementation of licence plate recognition utilizing deep learning techniques, including the classic framework of licence plate location and character recognition, representative methods, and their advantages and disadvantages. We also perform a quantitative comparison of existing representative methods. Finally, we summarize the challenges in the research domain of licence plate recognition and discuss the future development direction from the aspects of neural network interpretability, more general small sample learning methods, and incremental learning.
Research on robots interacting effectively with autistic children
Authors: Xu Chenxuan
Published: October 2023

This paper focuses on designing a robot that can efficiently relieve pressure and heal children with autism. autism, a common and unavoidable disease, happens in 1 in 100 children worldwide, and is not easy to recover, especially for children. While many organizations have explored the effectiveness of social robots, only a few robots are available that encompass helpful functions. In this paper, a few new functions or changes, such as portable design and more attractive and complex interaction design, can significantly improve the possibility of healing. The research incorporated the detailed design of the robot and meta-analysis as the data analysis method. Our data come from previous research from different areas of the world. The robot reveals that current robots are still incapable of comprehensive interactions and what future designs are expected to be included.
An overview of algorithms for discovering new categories of problems based on unlabeled data
Authors: Tong Chuanzhi
Published: October 2023

In traditional machine learning, supervised and semi-supervised learning is designed to be used in a closed-world setting where the training data is fixed and does not change over time. Unfortunately, these methods still require a large number of labels for the categories to be categorized, which is expensive and impractical. A new category discovery algorithm is designed so that it can discover new categories while classifying and recognizing labeled images. In this case, the machine can automatically identify new categories without manual marking of image feature categories, which can greatly reduce the cost of image classification. Kai Han et al. named this problem a new category discovery problem and proposed that deep clustering can be used to solve it well. This paper focuses on the comparison of two commonly used robust baselines in the new category discovery and proposes that adding a post-processing model can better improve the accuracy of the model result. This paper applied the relaxed contrast learning method to the Ranking Statistics, and the accuracy of CIFAR-100 is improved by 6%.
Effectiveness of masked autoencoder in vision transformer models for image classification
Authors: Li Zhicheng , Liu Yingze , Wang Xinran
Published: October 2023

This paper uses the literature reading method to systematically sort out and introduce the basic principles of the three algorithms of the transformer model and their application in the field of image classification, which has high theoretical value and social value, and has strong reference for the development of the transformer model in the future. ViT is simply an innovation in computer vision based on the transformer model. It first separates an image into several local patches (16x16), and then maps each one to a feature vector. These vectors will be delivered to an encoder for polishing. Finally, a special token is appended to these vectors for integrating location information. The final prediction is based on these tokesn. Swin-T is a new Transformer architecture, which is proposed by Microsoft Research to improve the performance of computer vision tasks. It adopts a new windowed feature extraction strategy, which can maintain high accuracy while significantly reducing the amount of computation and memory consumption. It has achieved leading performance in multiple computer vision tasks, becoming one of the most advanced visual Transformer models. In computer vision image classification, the information is highly redundant, the lack of an image piece, may not make the model produce much confusion, the model can be inferred from the surrounding pixel information, masked autoencoder (MAE) is to mask a high proportion of image pieces, create a difficult learning task, the method is simple but extremely effective.
Research on the relationship between CO2 emission and GDP growth with data visualization methods
Authors: Fu Xiaoyu
Published: October 2023

Climate change is a major global challenge, and CO2 emissions are a significant contributor to this issue. This paper uses data visualization methods to explore the relationship between CO2 emissions and GDP growth. To ensure the reliability of research results, data is collected from some authoritative institutions, including the World Bank and the Intergovernmental Panel on Climate Change (IPCC). The paper uses regression analysis and data visualization techniques to analyze the relationship between CO2 emissions and GDP growth. The results indicate that there is a positive correlation between GDP growth and CO2 emissions, which suggests that economic growth is linked to an increase in emissions. The findings of this paper can contribute to a better understanding of the relationship between economic growth and environmental sustainability.
Transformer model: Explainability and prospectiveness
Authors: Nuobu Gengpan
Published: October 2023

The purpose of Artificial Intelligence(AI) is to simulate learning process of human brain by strong computing power and appropriate algorithm, so that the machine can develop judging ability at work as human. Current AI mainly relies on Deep Learning model which is based on artificial neural network, like Convolutional Neural Network(CNN) in computer visualization, but that also takes with some defects. This paper introduces defects of CNN and discusses Transformer model in solving unexplainability of traditional CNN algorithm. To discuss why the Transformer model and attention mechanism are considered as the way to AI intelligibility.
The application of virtual reality: Authenticity and interactivity
Authors: Li Aimeng
Published: October 2023

In recent years, with the continuous advancement of science and technology, virtual reality technology has also been further developed and gradually applied to various fields. In the field of games, virtual reality technology has been fully applied, and virtual reality games provide the public with a more playable virtual space. At present, the research and analysis on virtual reality games is not sufficient enough to fully analyze and summarize the uniqueness of virtual reality games. Therefore, this paper makes the following research. First analyzes the characteristics of traditional computer games, then focuses on the application of virtual reality technology in games, elaborates from three aspects of game quality, authenticity and interactivity, and finally summarizes some shortcomings of the game at current virtual reality. Game quality refers to the improvement in game design, and the entire virtual reality game provides players with more realistic pictures and different interaction methods. At the end of the article, this article summarizes the full text and looks forward to the future research direction.
Analysis of transformer-based models for time series data, natural language processing, and computer vision
Authors: Chen Huanzhang , Hou Yunfan , Miao Tianhao , Xue Jiayu
Published: October 2023

The birth of the Transformer revolutionarily signalled the start of a new epic chapter in the deep learning era. Through an encoder-decoder architecture, including residual connection, multi-head self-attention, etc., it completely reformed the deep models and unified the models used in traditional computer vision (CV) and natural language processing (NLP) problems. In recent years, many papers published have adapted the original Transformer model to better complete tasks in time series analysis, CV, and NLP. In the area of natural language processing, Bidirectional Encoder Representations from Transformers (BERT) employs a two-way transformer structure to learn context-based language representation, whereas Generative Pre-trained Transformer (GPT) employs a one-way transformer but enhances corpus training to enhance the model effect. The Vision Transformer model is the cornerstone of computer vision. It separates the input image into various patches, projects each patch into vectorized features, and then passes the them to Transformer. Based on the idea of the Vision Transformer, Swin Transformer and Biformer further optimized the Transformer and achieved better results. Time series combines the ideas embodied in CV and NLP, and in doing so, improves the specificity and various difficulties of time series problems to lower algorithm complexity and increase prediction accuracy. This article summarizes the uses and improvements of the Transformer in NLP, CV and time series, explores the development history and ideas on algorithm optimization, and predicts the potential developments of Transformer in these three fields.
Comparison and analysis of deep learning models for vehicle re-identification
Authors: Tang Liyuan
Published: October 2023

The rapid urbanization and increasing number of vehicles on the roads demand efficient and accurate vehicle re-identification (Re-ID) techniques for intelligent transportation systems, traffic monitoring, and surveillance applications. This paper offers a detailed analysis of deep learning approaches to vehicle Re-ID, covering feature learning, attention mechanism, unsupervised learning, self-supervised learning, and specialized loss function. The efficiency of these methods is assessed using VeRi-776 and VehicleID datasets. Key metrics, such as mean Average Precision (mAP) and Rank-n accuracy, are employed to gauge their success. Results show that feature learning, attention mechanism, and specialized loss function play pivotal roles in achieving high performance in vehicle Re-ID tasks, with unsupervised and self-supervised learning methods displaying potential for practical applications due to their scalability. The paper also highlights several challenges, including enhancing the interpretability of attention mechanisms, exploring the relationship between popular loss functions, and addressing the infeasibility of existing methods for real-time applications. The paper concludes with several recommendations for the prospects of vehicle Re-ID, including developing advanced real-time algorithms, enhancing deep learning techniques, investigating innovative approaches, and addressing existing challenges. These proposed advances could significantly improve the efficacy of feature learning and spark fresh innovations in this field.
Analysis of machine learning-based applications for intelligent construction of shield tunnels
Authors: Cheng Maolin
Published: October 2023

With the quick advancement of the level of information science in shield tunnel construction, the monitoring methods of shield equipment during tunnel boring work are increasingly improved and the recorded construction data includes not only information on the internal workings of the shield equipment, but also on its interaction with the external strata. Machine learning data analysis is powerful and has a wider range of applications and scope than traditional data analysis methods in the civil construction industry. Through the use of machine learning methods, the data and information collected can be mined and analysed in depth to find the intrinsic connections and linkages that can help improve the safety and efficiency ragarding shield tunnel construction. This work presents a literature analysis of current situations of machine learning for shield tunnel construction at home and abroad, briefly describes the basic principles of machine learning methods, summarises and analyses the research situation in shield tunnel construction, reviews the progress of machine learning-based shield equipment condition analysis, intelligent prediction and control methods for shield tunneling parameters and shield tunneling surface deformation prediction, and summarises the current research The study also summarises the shortcomings of current research. Finally, an outlook on the development of shield tunneling towards intelligence is presented.
Predicting and analyzing long-term deposit possibility leveraging logistic regression and visual analysis
Authors: Xu Heying
Published: October 2023

Keeping balanced deposits in commercial banks is of great importance for the rational use of money. This article focuses on logistic regression to forecast the long-term deposit activity of banks. And the visual analysis is helpful to reveal a strong association between people's likelihood of long-term deposits and their age and happiness. In detail, people are less likely to make long-term deposits after the age of 65, i.e., after the age of retirement. In addition, people are more likely to make long-term savings when they have a high life expectancy index, such as when they are married. Finally, the whole logistic regression model is analyzed by using precision, recall, and f1-score. It is concluded that this logistic regression model is very accurate in predicting the long-term deposits of bank customers. However, the concluding section also explores the idea that the overall logistic regression model should use a broader data set for its effectiveness and high accuracy.
Prediction of air pollution caused mortality exploiting machine learning models
Authors: Li Jianfei
Published: October 2023

Air pollution is one of the most serious problems facing all mankind. It can not only cause various diseases and even lead to premature death of human beings. Especially, it could cause more harm to children, the elderly, and people who have been ill. Air pollution mainly occurs in developing countries with low income. Therefore, in order to better deal with the harm caused by air pollution, people need to predict the air pollution deaths. Based on the data set of air pollution deaths from 1990 to 2017, this paper investigated the performance of four machine learning models in predicting air pollution deaths. After the evaluation and comparison of four indicators, linear regression model and Ridge regression model are selected for further prediction considering their superior performance. This study finds that the overall trend of air pollution deaths worldwide is declining and will fall to zero by 2059 without other interference.
Artificial intelligence and autonomous driving
Authors: Jin Cance
Published: October 2023

With the rapid development of science and technology, artificial intelligence and autonomous driving technology are becoming a hot topic in today's society. The integration of artificial intelligence and driving systems satisfies this requirement effectively and produces a new driving technology for the sake of traffic safety and a better driving experience. To increase driving safety and comfort, computer calculations are utilized to aid the driver or eliminate the interference of human variables. First, this study introduce the basic concepts and principles of artificial intelligence and autonomous driving, as well as their importance in practical applications. Secondly, this paper discuss in detail the key technologies of artificial intelligence in autonomous driving, including the application of perception, decision making, and control. Finally, his essay explore the challenges facing artificial intelligence and autonomous driving, including technical challenges, legal and ethical considerations. Through the research of this paper, we can better understand the relationship between artificial intelligence and autonomous driving, and provide reference and guidance for future development.
Application analysis of machine learning and data visualization in heart failure prediction
Authors: Zhou Zheng
Published: October 2023

Heart failure is a non-eligible global health challenge, characterized by increased morbidity, mortality, and health expenditures. Early detection of heart failure can help prevent disease progression and improve outcomes. Logistic regression is a machine learning technique widely used in binary classification problems. In this paper, the patient record dataset was used to predict heart failure using logistic regression. The most important features for predicting heart failure are also determined through detailed analysis, mainly including age, serum creatinine, and ejection fraction. The results suggest that logistic regression can be a valuable tool for predicting heart failure and improving patient outcomes. Further research could explore other machine learning algorithms and more sophisticated feature selection techniques to further improve the prediction of heart failure.
Spiking neural network research based on brain-inspired computing
Authors: Geng Yichen
Published: October 2023

With the shortcomings of deep learning in training cost, generalization ability, interpretability, and reliability, increasing attention was concentrated on the novel neuromorphic devices. A spiking neural network can better simulate the information transmission mode of biological neurons, and has the characteristics of strong computing power and low power consumption. Therefore, this study provides an overview of the pulse neural network from the perspective of its fundamental structure and operating principle. In terms of structure optimization, there is a summary of the aspects of the encoding mode of a spiking neural network and topology structure. In addition, the deficiency and development of the spiking neural network are analyzed. Pulsed neurons work by integrating the mechanism of firing, exchanging information with the time of the signal, and transmitting signals to adjacent neurons only when the membrane potential reaches a threshold. Spiking neural network (SNN) still lags behind traditional Artificial neural network (ANN) in accuracy, has large computational capacity and is inefficiently trained due to the complexity of pulsed neuron models. Future developments lie in bionics, deeper studies of the complex dynamics of the human brain, and synaptic connections in the human brain.
Prediction of the age of abalones based on machine learning algorithms
Authors: Li Muzi
Published: October 2023

Abalone is an important seafood, widely used in food, medicine, and other fields. The age of abalone is one of the important factors that determine its quality and market value. However, the traditional age determination method requires the dissection of abalone, which is time-consuming and expensive. Therefore, it is important to find a fast and work out age prediction method. This article uses a machine learning algorithm to predict the age of abalone. The authors collected data on characteristics such as sex, length, diameter, height, and weight for 4177 abalone observations. This data set is admirably large. In the following study, the authors compare the effects of prediction using different machine learning algorithms, including linear regression, decision trees, random forests, and support vector machines. It is worth mentioning that the authors have done sufficient research and evaluation of these algorithms to find out the best prediction scheme. The results show that the random forest algorithm is the best, and its average absolute error is only 1.44 years. The performance of random forest algorithm is the best.
A flight control system for quadrotor plant protection UAV based on RBF-PID algorithm
Authors: Liu Chenyu , Xu Jiawang
Published: October 2023

As a powerful tool for modern agricultural protection, unmanned aerial vehicle (UAV) has the advantages of good adaptability and high efficiency in spraying and other operations compared with traditional manual operations The attitude and control of plant protection UAV are greatly affected by external factors during operation. However, the traditional proportional-integral-derivative (PID) controller commonly used at present cannot adapt to the complex environment because of the fixed parametersTherefore, a more sensitive method is needed to achieve adaptive balance of the aircraft attitude. In this paper, we establish the dynamic mathematical model of quadrotor UAV, and the influence of air resistance and rotational torque is considered in the process. Adopting the combined control method of Radial Basis Function (RBF) neural network and PID, the dynamic tuning of system control parameters is achieved through the self-learning of neural network and the feature of nonlinear mapping. The control system not only has the advantages of simple PID control structure and clear physical meaning, but also has the ability of neural network self-learning and adaptation. Matlab/Simulink was used as the experimental platform to respectively simulate the RBF-PID control system and the traditional PID control system. The simulation results show that the control system based on RBF-PID algorithm has a shorter response time and stronger robustness, which enhances the systems self-adaptability
Exploiting ensembled neural network model for social platform rumor detection
Authors: Huang Bowen , Feng Ruoheng , Yuan Jiahao
Published: October 2023

With the spread of the internet and social media, it has become difficult to detect rumors from the vast amount of event information. In order to improve the accuracy of rumor detection, deep learning neural network models are often used in rumor detection tasks. First, this paper reproduces the rumor detection experiments of four single neural network models: Long Short-term Memory Networks (LSTM), Text Convolutional Neural Networks (TextCNN), Text Recurrent Neural Network with Attention Mechanism (TextRNN_Att), and Transformer. On this basis, a model based on pre-trained feature extractor and ensemble learning is proposed, and a weighted average ensemble algorithm is adopted. The results show that the rumor-detecting ensemble learning model is better than the single model in all indicators. Then, aiming at the problem that the weighted average ensemble method cannot determine the optimal ensemble parameters, this paper proposes to improve the adaptive ensemble model. Multilayer Perceptron (MLP) is selected as the metamodel, and the weight parameters are automatically trained finetuning on the predicted output of the base model by weighted summation and MLP neural network is used, which improves the traditional integrated weighted average model and realizes the function of automatic weight adjustment. Finally, the Fast Gradient Sign Method (FGSM) algorithm is used to train the model adversarily. The results show that the ensemble model after adversarial training obtains stronger generalization, robustness and attack resistance under the premise of ensuring that the classification performance is not reduced.
Statistical analysis and risk prediction of lung cancer exploiting logistic regression
Authors: He Weiqi
Published: October 2023

Cancer is a disease with a high mortality rate. Early prediction of cancer is an important means to completely cure the disease. Therefore, there is an ever-increasing demand for technologies to detect early cancer nodules. As an easily misdiagnosed disease, the mortality rate of lung cancer has reached the highest level in recent years. Early diagnosis of lung cancer can save many lives. The study used logistic regression to predict cancer risk by collecting and analyzing patient data. Logistic regression, referred to as logistic regression analysis, is a general linear regression analysis model that relates to supervised learning in machine learning. First, n sets of data are given to the model as a training set for training, and then the model could be leveraged to classify other data (test sets), and finally the classification results could be achieved. P indicators make up each batch of data. This study used a dataset from Kaggle, which contains information from one thousand patients with lung cancer, and it has 23 eigenvalues. This dataset classifies the predicted values as high, medium, and low. The regression results show that the precision value of the low prediction value is 84%, the precision value of the middle prediction value is 88%, and the precision value of the low prediction value is 93%. The result is that as the number of samples with different predicted values increases, the precision values increase.
Building motivation above trust: A blockchain-based NFT GameFi model for construction workload assessment
Authors: Yang Mingqi , Zhao Manli
Published: October 2023

In the VUCA (Volatile, Uncertain, Complex, Ambiguous) era, construction management faces unique challenges, particularly in the realm of workload assessment. This paper proposes an innovative solution to address these challenges: a blockchain-based GameFi model for construction workload assessment. This model leverages blockchain Non-Fungible Token (NFT) technology to issue various badges, reliably representing employees' workload. The technology ensures the authenticity and tamper-proof nature of the badges, thereby fostering employee motivation through a distributed trust system. This study provides fresh insights into the construction industry's digital transformation and is anticipated to lay the groundwork for similar applications in other sectors.
Facial expression recognition based on Feature Pyramid Network
Authors: Huang Yongcheng
Published: October 2023

Facial expression recognition with significant implications across fields such as psychology, computer science, and artificial intelligence. This paper proposes a combination of a Feature Pyramid Network (FPN) and a Residual Network (ResNet) to construct a recognition model. The main objective of the proposed model is to refine the multi-level feature representation of facial expressions. This approach aims to provide a more holistic understanding of the diverse and complex nature of facial expressions, recognizing the intricate interplay between macro and micro-expressions. Experimental results underscore the model's considerable superiority over traditional methods, particularly in terms of accuracy and adaptability to objects of varying sizes and complexities. This comprehensive approach to facial expression recognition showcases the potential of integrating different neural network architectures, furthering our understanding of the subtleties of facial expressions. The research, therefore, presents a significant contribution to the field of facial expression recognition, demonstrating the efficacy of integrating multi-scale feature extraction techniques to improve model performance. It sets the stage for future research directions in this domain, paving the way for more sophisticated emotion recognition systems that can be deployed in real-world applications.
Face-emotion classification guided by deep convolutional neural network
Authors: Wu Shujie
Published: October 2023

With the rapid development of computer vision and convolutional neural networks, the task of automatic face emotion classification has become a reality. The aim of this study is to improve the underlying neural network model to achieve effective face emotion classification. By presenting a simplified network to generate the recognition model, the author enhances the underlying neural network architecture. The model, in particular, augments the underlying neural network with a convolutional layer, a maximum pooling layer, and a discard layer, and increases the number of neurons in the dense layer from 25 to 128. The convolutional layer allows for the automatic extraction of sentiment features. To decrease the parameters in the feature maps, the maximum pooling layer is applied. The experiments are constructed on the Facial Emotion Recognition 2013 dataset (FER-2013). The streamlined network model improves performance by 6% to 56.32% as compared with the basic neural network model. Numerous experiments show that the proposed streamlined network model can effectively recognize facial emotions. In addition, the author analysis the confusion matrix and finds that the model has weak feedback for aversive emotions. Future research will focus on improving the representation of unclear features such as aversive emotions to enhance model generalization.
Estimating diabetes risk using Nave Bayes classifiers
Authors: Li Juntong
Published: October 2023

Diabetes is now a common disease for modern people. Diabetes will cause some serious symptoms. Diabetes patients will face a painful life, high cure cost, and even death. So, it is necessary to correctly diagnose diabetes and analyze the factors that mainly cause diabetes to prevent the happen of diabetes. This essay mainly focuses on training the computer to help the doctor to train the computer. Three Naive Bayes classifications will be used to train the computer to do the prediction, including the Gaussian Nave Bayes, Bernoulli Nave Bayes, and Multinomial Nave Bayes. To compare each methods result, accuracy will be the main index to measure whether a method is good enough to put into use. Not only the accuracy, classification report, and confusion matrix also assist to measure the prediction. Finally, the Gaussian Nave Bayes has the highest accuracy and when combined with the confusion matrix and the classification report, the Gaussian Nave Bayes has a huge advantage over the other two models. The accuracy of these models still does not satisfy medical demand. Some deep learning and high-level model are expected to optimize this project.
A Comparison of LSTM and BERT model for sarcasm prediction
Authors: Ding Yizhong
Published: October 2023

Sarcasm prediction is a text analysis task that aims to identify sarcastic and non-sarcastic statements in text. Sarcasm is a figure of speech that uses opposite or contradictory language to express a certain meaning or idea. Sarcasm is usually cryptic, vague, and suggestive, which makes sarcasm prediction a challenging task. In sarcasm prediction projects, techniques of natural language processing are usually leveraged to analyze and classify the text. The main challenge of this task lies in the fact that sarcasm usually has multiple manifestations and needs to consider the contextual and semantic information of the text. The prediction of sarcasm holds significant application value in natural language processing, such as social media analysis, public opinion monitoring, sentiment analysis and so on. In this paper, by controlling variables, the influence of adding the long short-term memory (LSTM) layer and changing the grid structure of the model on the accuracy of prediction results is explored. Moreover, accuracy of the LSTM prediction performance is compared with that of the bidirectional encoder representations from Transformers (BERT) model. At the same time, this paper analyzed and discussed the phenomenon that adding the number of LSTM model layers could not obtain higher prediction accuracy, and the accuracy gap of prediction results between LSTM model and BERT model, and finally obtained relevant conclusions.
Advancements in machine learning algorithms for brain tumor detection and segmentation: A comprehensive analysis
Authors: Wu Yuanfeng , Zhang Brandon
Published: October 2023

Brain tumors pose a substantial health challenge globally. Their accurate detection and segmentation are crucial for effective treatment, and recent advancements in machine learning (ML) present a promising solution to these tasks. This paper provides a comprehensive analysis of traditional and modern ML algorithms for brain tumor detection and segmentation. It highlights the pivotal role of ML in advancing brain tumor analysis and how it can potentially mitigate the impact of malignant tumors. Traditional image processing techniques have shown their value but face limitations in dealing with the complexity of brain tumors. The integration of ML has substantially enhanced the capabilities of traditional detection techniques, with architectures such as convolutional neural networks (CNNs) providing improved results. Moreover, brain tumor segmentation techniques have also seen significant enhancements, with the transition from conventional techniques like Region Growing and Watershed methods to state-of-the-art deep learning methods, such as U-Net. Despite these advancements, great challenges remain. Ongoing researches are necessary to further harness the potential of ML in brain tumor diagnosis and treatment. The findings of this review underscore the significance of ML in brain tumor analysis and its profound potential impact on patient outcomes and the overall landscape of cancer treatment.
Comparison and analysis of deep neural networks in facial expression recognition
Authors: Li Jiahang
Published: October 2023

Recognizing facial expressions automatically is of importance to interactive computer systems since facial expression is an efficient means of conveying emotions. Over the past few years, many researchers have attempted to use deep learning for expression recognition. The advantage of deep learning lies in its ability to learn features from datasets automatically, without relying on hand-crafted features. The paper analyzes what mechanism is useful for expression recognition in deep learning by comparing the performance of different popular models and algorithms from recent research. The paper trains the models on the Facial Expression Recognition Dataset (FER-2013), which is a relatively small and imbalanced dataset. After that, model performance is assessed on the private test dataset of FER-2013. Specifically, Residual Network (ResNet), Visual Geometry Group Network (VGGNet), and MobileNet are evaluated in the experiment. The evaluation is based on running time, the number of parameters, and the accuracy. Squeeze and Excitation (SE) block is utilized in the ResNet, which enables the models to learn useful features from global information. In the paper, ResNet34 inserted with SE block (SE-ResNet34) get the highest private test accuracy of 70.80%. Experimental results show that the residual learning enables models to go deeper without degradation and the SE block is beneficial for the model to learn global information.
An overview of research results and applications in the field of semi-supervised semantic segmentation
Authors: Shi Dingrui
Published: October 2023

Traditional segmentation approaches, supervised deep learning methods, and semi-supervised deep learning methods have all found widespread use as the field of semi-supervised semantic segmentation has advanced. These methods have developed and progressed over time, opening up novel avenues of research in the field of image segmentation and giving potent resources for tackling difficult practical issues. These developments have deepened our understanding of image segmentation and provided flexible and efficient solutions to challenges in practical applications, ranging from classical traditional approaches to supervised methods based on deep learning, and beyond to semi-supervised methods that leverage both labeled and unlabeled data. Focusing on their specialized applications in medical and remote sensing image processing, this paper presents a complete overview of the development status of these methods. This study's image segmentation solutions can help tackle actual-world issues where annotated data is rare or expensive to some extent.
Investigating MIDI data simplification by AI models
Authors: Ou Braden
Published: October 2023

According to the Smithsonian Institution, the art of making music has existed for over 35,000 years. As musical technology has improved, the music of the time has also improved and adapted to the new technology. In the recent expansion of technology from generative AI, text and image generation have become not only possible but also competitive with human-created text and images. As such, the development of AI-generated music is increasingly sparking considerable interest among musicians and developers alike, raising questions about the potential of AI to enhance or even replace human musical creativity. This paper will first explore the advancements of AI-generated music. Next, it will delve into the technologies and methodologies involved in generating music, as well as its current limitations using a basic LSTM (Long Short-Term Memory) model. Finally, it will explore the implications of this music for the whole music industry. By examining these various facets of AI-generated music, this research provides insights into AI's potential role in shaping the future of music. According to the analysis, a rudimentary AI model trained on complex music can produce music that is fairly elementary. Overall, these results shed light on guiding further exploration of the interaction between artificial intelligence and music.
The Spirit Train: A VR game for immersive exploration
Authors: Jiang Yifan
Published: October 2023

As VR technology advances, VR games have gradually become a research hotspot. This paper investigates a VR virtual game called Ghost Train, which was developed for missing persons events. In the game, players can play as ghosts, open related feature platforms, and also play different roles to experience different lives. By doing so, they can experience the disadvantaged groups that need help and the masses that can help them. The purpose of the game is to encourage people to calmly help strangers they encounter in trouble on the road, rather than leaving without a trace. Extensive testing showed that the game design has good performance in terms of interaction and immersion, and can also provide better help for the physical and mental health of missing persons. Overall, this papers research provides valuable insights for the development of VR games that can have a positive impact on real-world situations.