All of the articles in this proceedings volume have been presented at the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023) during August 11 to August 13 in Singapore. These articles have been peer reviewed by the members of the Technical Committee and approved...

In light of China’s aging population and improved living standards, there is an increasing focus on health issues. Parkinson’s disease is a commonly-seen neurodegenerative disorder that greatly impacts patients’ quality of life, and its prevalence is on the rise. The traditional approach to Parkinson’s...

Mental health accompanies every human being inevitably and has great significance in helping people address life stress and realize their abilities. However, mental health is also a double-edged sword, which mental health issues can hinder people from carrying out daily activities normally and keeping...

Sentiment analysis, often referred to as opinion mining, plays a crucial role in the field of natural language processing by computationally analyzing text to extract sentiments or opinions. It has become essential in understanding public sentiment, evaluating customer feedback, and identifying market...

Recently, global economy has recovered from the COVID impact. Stock index is reasonable indicator of economy. The prediction of stock index contributes to both forecasting trend of global economy and portfolio construction. According to the characters of price factors, this paper proposes a hybrid model,...

Road crack recognition and detection is one of the fundamental tasks in the fields of autonomous driving and intelligent transportation, which has attracted a lot of research interest in recent years. Thanks to the rapid development of Convolutional neural network, the accuracy of road crack recognition...

Pattern recognition algorithms have achieved great success in medical image analysis, especially in terms of lesion area segmentation in magnetic resonance images. Different from the labor-intensive manual segmentation and diagnosis, image segmentation technology based on deep learning has made breakthroughs...

Cryptocurrencies have gained significant importance in the financial market, and the emergence of quantum computers has raised concerns about their impact on cryptocurrency systems. This article aims to analyze these impacts, focusing primarily on the effects on the mining process, threats to blockchain...

This manuscript elucidates the fundamental principles of quantum computing and its groundbreaking applications within the realm of artificial intelligence. An initial section of the paper is devoted to providing a comprehensive overview of quantum mechanics, exploring the concept of quantum bits or 'qubits'...

The field of virtual reality technology plays a pivotal role within the automotive industry, predominantly aimed at augmenting the efficacy of driving simulations and training programs. This evolution is fueled substantially by the hefty investments poured into VR technology by leading automakers, leading...

In many sectors, network traffic categorization is a crucial duty, including network security, quality of service, and traffic engineering. Deep learning models have demonstrated potential in this area. This study used a comparative analysis method to evaluate and compare how well various models performed...

The rapid advancements in science and technology have had a profound impact on how people perceive themselves and communicate with others. As a result, personality tests have become increasingly popular for individuals seeking self-awareness and a deeper understanding of others. This study focuses specifically...

With the increasing importance of social media platforms such as Twitter, understanding the emotions expressed in text data has become crucial for various applications. Manual analysis of the vast amount of user-generated content is impractical, highlighting the need for automated classification techniques....

Memes are an important medium of expression in online communication, yet traditional methods such as collaborative filtering (CF) have limitations in processing multimodal data, especially when analyzing memes has limitations in processing large-scale datasets and are sensitive to data noise and sparsity....

This paper embarks on an extensive exploration into the application of OpenAI’s ChatGPT model within Question and Answer (Q&A) systems. With the advancement of natural language processing (NLP) and machine learning technologies, a new era of conversational artificial intelligence (AI) has emerged....

In the era of large models, traditional training methods can no longer meet the massive requirements of computing power and data sets. Using distributed training can alleviate this problem to some extent. However, in distributed training, the challenge and complexity of hyperparameter adjustment will...

Consortium Blockchain (CB) technology has witnessed significant growth in popularity across various industries, such as finance, supply chain management, and healthcare. This paper introduces a housekeeping services platform that leverages the capabilities of CB. By capitalizing on the decentralized...

Offline Reinforcement learning (RL) uses the collected data to train agents without interaction with the environment. However, due to the inconsistent distribution between the data set and the real world, the training samples collected in the real environment cannot be well applied to offline RL. In...

Multi-agent path finding (MAPF) problem has long been a focus of reinforcement learning researchers due to is potential applications to real world robot deployment. Nowadays, many efforts have been made to develop decentralized MAPF algorithms since decentralized ways tend to scale better in larger robot...

With the progressive advancement of technology in the gaming industry and the growing maturity of Reinforcement Learning (RL) techniques, the utilization of RL in gaming has significantly evolved. However, there is a noticeable dearth of comprehensive overview and summary in this domain. Thus, this paper...

With the continuous development of reinforcement learning, artificial intelligence has reached a level of sophistication where it can effectively handle complex situations, making it a valuable component in video game development and gameplay experiences. Recent applications have demonstrated the potential...

This paper designs experiments to investigate the impact of different GPU counts on occupancy, accuracy, loss values and time of the PyTorch distributed data parallel and model parallel module in LeNet VGG16 and ResNet models respectively. PyTorch is a powerful deep learning framework but due to the...

This paper introduces an optimized version of the MOON algorithm for federated learning, which is a distributed machine learning approach designed to tackle challenges related to data privacy and decentralization. However, the performance of traditional federated learning methods is hindered by the non-uniform...

With the inherent complexity and variability of handwritten symbols, accurate recognition is crucial for various applications. This research aims to enhance the recognition of handwritten math symbols through a deep learning model named Convolutional Neural Network (CNN). In particular, the study utilizes...

Social media platforms such as Facebook, Twitter, and Reddit have experienced a substantial surge in user base and popularity over the past decade, facilitating global connectivity among billions of individuals. The major platforms have also served as a place for users to freely spread hate speech, which...

After years of extensive development, the accessibility of Virtual Reality (VR) technology has reached a point where it is now within reach of ordinary individuals, allowing them to engage and derive pleasure from immersive experiences. This rapid proliferation of VR brands in the market has prompted...

This study aims to address the practical problem of providing an optimal and cost-effective short-distance route for tourists by employing an enhanced version of the Ant Colony Optimization algorithm (ACO). The research begins by presenting a comprehensive overview of the evolution of ACO algorithms....

This study embarked on a mission to solve a complex issue in the field of maritime logistics - understanding and classifying the operational patterns of Liquified Natural Gas (LNG) terminal points across the globe. Given the significant role of LNG shipping in global energy transport, identifying these...

The utilization of machine learning methods for predicting student grades has emerged as a valuable approach to assessing the educational advancement of academic institutions, driven by the rapid evolution of these techniques. While prior studies have predominantly encompassed data from various facets,...

This research investigates the effectiveness of various preprocessing methods and parameters on Acoustic Scene Classification (ASC) using the K-means clustering algorithm. Utilizing the ESC-50 dataset, a combination of Principal Component Analysis (PCA) and StandardScaler was employed for preprocessing....

Film reviews play a pivotal role in influencing audience decisions, necessitating accurate classification of sentiments as positive or negative, which holds significant importance for the film industry. To address this, the present study introduces an innovative ensemble learning approach that integrates...

In this research, the determinants of happiness scores across countries are explored using a data-driven, machine learning-based approach. The study employs a dataset comprising variables such as GDP per capita, social support, healthy life expectancy, freedom to make life choices, etc. to predict the...

Algorithms designed for quantum computing, such as Harrow-Hassidim-Lloyd (HHL), have shown significant potential in solving linear equations, with the possibility of achieving exponential acceleration. However, there are still several areas that need to be further developed and improved. One of the critical...

Quantum computing, even though it is still in its early stages, holds the potential to revolutionize the finance sector by providing efficient solutions to complex problems. This potential is evident in the direct correlation between key financial issues and the principles of quantum mechanics, such...

Image-based virtual try-on, as a challenging and practical real-world task, is one of the interesting research topics in recent years. Virtual try-on will become a common way to buy fashion products in the future, however, with the development in recent years, relevant review articles are not sufficient....

Contemporary recommendation systems encounter the challenges posed by information overload and personalized user needs. Recently, there has been a widespread application of deep reinforcement learning algorithms (DRL) to tackle the aforementioned issues. The paper provides a detailed introduction to...

Reinforcement Learning (RL), a pivotal technique in AI, finds extensive applications in game AI, ranging from elementary board games to sophisticated strategy games. This application not only carries significant practical implications but also catalyzes theoretical advancements in AI. The objective of...

RNA-Binding Proteins (RBPs) have a great impact on Ribose Nucleic Acid (RNA) stability, transport, translation, splicing and other functions. Predicting the function and mechanism of the action of RNA-binding proteins is of great significance for the fields of cell signaling and metabolic regulation,...

In the realm of present multi-agent research, the presumption is often that two competing groups possess equivalent strength. This article, however, ventures into unexplored territory by contemplating the strategy a significantly weaker group should adopt in its struggle for survival when faced with...

This paper provides a theoretical exploration of queueing theory and its intersection with the rapidly expanding field of artificial intelligence (AI). In an effort to employ AI methodologies in addressing queueing theory problems, an innovative approach is proposed that blends simulation techniques...

The introduction of 5G network technology has not only provided a significant boost but also a solid foundation for the advancement of the Internet of Things (IoT). In the future, the 5G IoT promises to realize the full potential of IoT in society. The emergence of 5G has revolutionized the landscape...

The advent of advanced artificial intelligence(AI) models such as ChatGPT has brought a transformative impact on various sectors, with the field of education being a notable one. Given the system’s impressive efficiency and accuracy in answering questions, ChatGPT has garnered significant attention as...

Amidst the rapid progression of information technology and the onset of the digital age, big data models have undergone extensive evolution and exploration. This has been stimulated by a blend of factors including an exponential data surge, technological advancements, cost reductions, data-centric decision-making...

The research of UAVs(Unmanned Aerial Vehicle) has received a lot of attention in recent years, and more and more UAV manufacturers have emerged in China and abroad, such as DJI, 3D Robotics, Parrot and so on. The advantage of UAVs over manned vehicles is that they can perform missions in dangerous and...

Computer queueing theory is a vital mathematical discipline that focuses on the study of queue behavior in computer systems. Its significance has grown substantially in recent years due to the increasing demand for high-performance computer systems. This paper aims to provide a comprehensive analysis...

Queuing theory is a mathematical discipline that studies the behavior of queues or waiting lines. This article aims to provide a comprehensive examination of the core principles of queuing theory and explore its applications in various industries such as transportation, manufacturing, and telecommunications....

The scale of the global power system is constantly expanding. In order to ensure the safe operation of transmission lines, power line inspection and insulator cleaning are essential. The traditional power line inspection and insulator cleaning methods are low efficiency, high cost and low safety. With...

With the progress and development of modern science and technology, people’s productivity and work efficiency in agriculture has been constantly improving. One reason for this is the use of drones. Over the years, people have been optimizing the various structures of drones to ensure that they can better...

With the evolution and maturity of cloud computing and big data technologies, the shift from traditional libraries to digital libraries has emerged as a prevailing trend. To comprehend the current scheduling strategies employed within digital libraries, and to address challenges such as server congestion...

Multimodal emotion recognition involves the identification of human emotional states utilizing a combination of sensory information, such as audio, video, and physiological signals. This field, straddling the domains of computer science and psychology, holds significant commercial potential in areas...

With the increasingly widespread application scenarios of drones, higher requirements have been put forward for the autonomous flight capability of drones. The autonomous obstacle avoidance technology of unmanned aerial vehicles plays an important role in various environments that are inconvenient for...

Queuing theory is a mathematical field concerned with the dynamics of waiting lines, or queues. Its applications are pivotal in analyzing and optimizing systems where the timing of customer arrivals and service is of utmost importance, such as in banks, hospitals, airports, and call centers. By employing...

The rapid advancement of quantum computing unveils significant potential in addressing various real-world challenges. Notably, the Quantum Key Distribution (QKD) protocol emerges as a promising solution for enhancing network communication security through its distinctive anti-eavesdropping mechanism....

Currently, the entire global population is experiencing the profound impact of the COVID-19 virus. In response, the Chinese government has implemented various measures to enforce social distancing, aiming to minimize the spread of the disease. Consequently, an increasing number of individuals are turning...

The objective of this research endeavor is to propose an innovative methodology for diagnosing lung cancer through a sophisticated approach to data augmentation. In essence, the proposed method harnesses the potential of the Swin-Unet model, a unique architectural design for feature extraction and classification....

As the internet continues to expand, demands for efficient multi-data transmission and heightened security grow ever stronger. However, traditional point-to-point systems fall short in meeting the increasing requirements for secure links among multiple users. This is where the hybrid Quantum-classical...

The performance of the Naive Bayes model in text classification is constrained by its assumption of feature independence, which does not hold true for textual data, as well as its reliance solely on word frequency information, disregarding word order and relationships and hindering its ability to capture...

This study delves into advancements in the realm of email spam filtration, a critical pillar in augmenting email security infrastructure. Given the unceasing challenges presented by unwarranted spam, the deployment of efficacious spam filtration methodologies remains imperative. Contemporary strategies...

Email becomes a main way for people to communicate or send information to each other. However, spammers send people unwanted and harmful information using emails. Therefore, useful email filtering needs to be used for our email. This paper shows a comprehensive review and comparative concept of various...

The Myers-Briggs Type Indicator (MBTI) is a well-known personality assessment designed to classify individuals into different personality types. It is a widely used personality test that helps people understand others’ cognitive, decision-making, and behavioral preferences. The assessment attempts to...

With the advent of advanced techniques for training large models, the research community has become increasingly interested in exploring various methods to enhance the efficiency of model training. The Vision Transform (VIT) model represents a novel approach in the field of image processing, being the...

The escalating prevalence of Convolutional Neural Networks (CNNs) coupled with the incessant growth in both model variants and datasets necessitates the formulation of a judicious data parallelism approach to effectively enhance the pace of model training. This imperative arises as a significant challenge...

Agents in reinforcement learning relies on reward to make movement, improve algorithms, and reach the final goal. However, reward setting is a subject that requires much engineering skills and experiences. Two types of reward, extrinsic reward and intrinsic reward, are totally different in ways of setting....

In contemporary times, there has been a growing inclination among individuals to engage in photography and employ uncomplicated filters to enhance their visual outputs. Although these seemingly straightforward and aesthetically enhanced images are favored by many, they can inadvertently lead to erroneous...

Neural networks, which are highly effective models for addressing various real-world challenges, face the challenge of excessive computational requirements caused by the substantial size of the models and the extensive datasets employed during training. In this paper, the purpose is to examine the effects...

Recognizing handwritten mathematical symbols presents a significant obstacle due to the inherent variability in individuals’ writing styles. In order to enhance the accuracy of symbol recognition, this scholarly article introduces a pioneering methodology that synergistically merges the capabilities...

The primary focus of this research paper is addressing the difficulty of identifying handwritten mathematical symbols, which holds significant importance in diverse fields including education, scientific research, and data analysis. The recognition of these symbols is challenging due to their diverse...

Given the escalating diversity and intricacy of handwritten samples, it remains challenging to enhance the accuracy and robustness of recognition algorithms. This study proposed a solution to this problem by optimizing a pre-existing Convolutional Neural Network (CNN) model for handwritten recognition...

In recent years, the practical application of machine learning and natural language processing models, such as the random forest model and decision tree model, has become widespread. These models have been successfully employed in various domains, including economic forecasting and sentiment analysis,...

This study uses three different machine learning algorithms to build model for diabetes prediction and compares the accuracy of each model, and these algorithms are K Nearest Neighbors (KNN), Logistic Regression, and Extreme Gradient Boosting (XGBoost). The goal for this study is to find a precise algorithm...

This study advances diabetes prediction by introducing a nuanced, six-class classification system and examining the interaction effects of various risk factors. Rather than the traditional binary classification, this research proposes six distinct diabetes classes: normal, pre-diabetic, diabetic under...

In recent years, there has been a steady increase in the incidence of breast cancer, positioning it as the leading form of malignant tumors among women. Consequently, leveraging artificial intelligence (AI) technology to accurately classify and diagnose breast cancer has emerged as a crucial field of...

Sentiment classification plays a crucial role in understanding and analyzing text data, particularly in domains like social media and online reviews. In this study, the influence of three key parameters on the accuracy of sentiment classification was investigated by applying Naive Bayes classifier to...

Advancements in conversational Artificial Intelligence (AI) models, exemplified by ChatGPT, New Bing, and Claude, have substantially improved real-time interaction and virtual assistant capabilities of chatbot systems. The growing recognition of the significance of conversational AI has led to an increased...

In today’s society, the application of artificial neural networks in the field of image classification is becoming increasingly widespread. However, the exploration of how to improve the classification accuracy of neural networks has never stopped. This paper is based on the most classic neural network...

Multi-agent systems (MAS) are composed of multiple agents that have the ability to learn and make decisions autonomously, while interacting with each other and a shared environment. The collaboration of multiple robots within complex spaces inevitably gives rise to potential conflicts, making the development...

This paper introduces a proposed Near Field Communication (NFC) based smart access control system. The system utilizes a server built on Spring Boot, an Android-based client, and a hardware lock powered by Raspberry Pi to create a robust smart access control mechanism. Through innovative system design,...

With the continuous development of digital technology, the application of electronic documents(e-document) has also penetrated into various industries. Electronic document is the best alternative for paper documents, and various industries can better assist in the storage and transmission of data through...

This paper provides a thorough examination of diverse encryption techniques, algorithms, and protocols, offering a comprehensive understanding of the present landscape of encryption technology, its applications, challenges, and future possibilities. By meticulously analyzing the strengths and weaknesses...

The rapid development of logistics industry provides a broad prospect for the application of blockchain technology, such as logistics information tracking, supply chain transparency, and de-trusted transactions, which have been widely used because of its advantages such as data sharing, labor cost reduction,...

Video style transfer is an emerging research hotspot in the computer vision community, which aims to apply artistic styles to videos to generate visually appealing and stylized video sequences. Compared to image style transfer, video style transfer mainly involves adjusting temporal consistency, which...

Recently, thanks to the rapid development of deep learning, there are many methods to remove shadows in images by using generative adversarial networks. Most of them can learn the relationship between different domains, like shadow and shadow-free areas, to transform the shadow areas into areas with...

As Blind Box has gained explosive popularity among young people in China and POP MART is the most successful company in this industry, its operation and marketing strategies arouse the wide attention of the market and are copied by its competitors. Therefore, this paper focuses on POP MART s development...

In the digital era, digital transformation is in rapid development. At the same time, Digital transformation also brings many opportunities for the retail industry. The world’s largest retailer of sporting goods is Decathlon, and it has a leading position in the international sports brand retail market....

In the past two decades, the international and domestic electronic information industry has developed rapidly, resulting in great changes, and more and more mobile phone brands have been noticed by the public. The subject of this study is to study Apple mobile phones and Meizu mobile phones, aiming to...

With the rapid development of the internet and e-commerce, the scale and influence of internet platform companies are gradually expanding, bringing new categories and challenges to corporate social responsibility. The emerging internet platforms have the characteristics of multi-level and nested nature,...

It has gradually become a general trend for manufacturing industry to take Digital transformation as its strategy. This is because the realization of digital transformation can bring many benefits to enterprises. Nevertheless, many manufacturing enterprises still face many problems when carrying out...

With the growing demand for dairy products, the attention of the dairy industry has increased, and the brand-building task of Mengniu Dairy, as one of the leading companies in China’s dairy industry, is a top priority. This paper uses the SWOT model to study the current situation of Mengniu Dairy’s brand...

With the advent of digital age, digitalization has brought significant changes to human life, such as people’s communication means becoming more diversified. Therefore, relevant enterprises’ management methods in the digital development process also need to be reformed. In order to maintain core competitiveness,...

With the development of science and technology and the advances of the day, people are moving into the age of big data, many enterprises need to follow the footsteps of the times and carry out digital transformation. Digital transformation is indispensable for the survival of enterprises. By analysing...

By introducing new digital technologies such as big data analysis, cloud computing and artificial intelligence, the traditional business model, industry structure and market operation process will be gradually reconstructed in the near future. As an e-commerce platform, Poizon adopts the dual business...

China Mobile is one of the largest operators in China and has strong dominance in communication. China Mobile is a central enterprise established in 2000 in accordance with the overall deployment of the national telecommunications system reform. China Mobile has a relatively long history and is the earliest...

With the global wave of digital transformation in recent years, the dairy industry, one of the most important traditional manufacturing industries in China, is also gradually carrying out digital transformation. In this process, most companies have faced problems and challenges in terms of lack of technological...

The application of mechanical engineering management is a management method and strategy adopted to effectively coordinate and control mechanical engineering project activities. The current research and practice have made some progress in this field. Research has shown that the complexity of mechanical...

Disease area segmentation is an important task in the field of smart agriculture, which is of great significance for analyzing the fine-grained information inside disease spots and supporting prevention and control decisions. Early disease area segmentation mostly relied on image processing or manual...

The computer vision community has become increasingly interested in Low-Light Image Enhancement (LLIE), which tries to transform low-light photos into typically exposed images. The convolutional neural network has advanced quickly, and this has helped the deep learning-based LLIE approaches make a breakthrough...

In recent years, adversarial learning has gradually attracted a lot of research interest, which aims to understand the attack behavior and design various algorithms that can resist the attack. The design of adversarial learning algorithms mostly revolves around the generation of adversarial examples,...

This paper introduces the importance of image classification in computer vision. It aims to classify input images into different categories. Traditional image classification methods use manual feature extraction or feature learning to describe images but it is difficult to reveal deep semantic abstract...

The convolutional neural networks (CNNs) are widely used for image classification tasks because CNNs can successfully capture spatial hierarchies and patterns in images. A dataset can be utilized to evaluate the performance of various types of CNNs. To compare the effectiveness of four CNN models for...

This paper investigates the factors influencing data-parallel convolutional neural network (CNN) models in the context of image classification. This paper analyzes various factors, including batch size, learning rate, loss function, and regularization techniques, to understand their impact on the performance...