bluuebunny/crossref_metadata_2025 huggingface dataset:
An empirical analysis of the growth of artificial intelligence in socio economic environment
Authors: S.K.B Sangeetha , Immanuel Rajeswari Rajesh , R. Sukumar , M. Sandeep Kumar
Published: July 2023

Artificial Intelligence (AI) is very evident in our daily lives and our economy, and it has had an impact on our environment in various ways. The competition to reap its benefits is growing worldwide, and world leaders - the US and Asia - are already taking action. Many people see AI as a way to improve productivity and economic development. It can improve process efficiency and greatly improve decision-making processes by examining large amounts of data. It can also lead to the creation of new products, services, markets, and sectors, leading to increased customer demand and new sources of revenue. Some are concerned that it could lead to the development of large corporations - institutions of wealth and knowledge - that would hurt the entire economy. It has the potential to widen the gap between developed and developing countries, as well as to increase the need for highly skilled workers while firing others; the latest trends can have a profound effect on the labor market. Experts also worry that inequality could worsen, reduce wages, and lower the tax base. The aim of this study was to learn how artificial intelligence is used in today's economy and what society thinks about its use.
Creating a computer mediated equity crowdfunding mechanism based on regression and sentiment analysis
Authors: Wang Weiwei
Published: July 2023

The rise of crowdfunding provides a viable alternative to the traditionally opaque capital market and significantly reduces the cost due to information asymmetry among founders, investors, and intermediaries. Numerous research studies have been conducted to predict the success of a crowdfunding campaign, including subjectivity vs objectivity, overstatement, keywords, frequency, length of interactions between founders and investors, etc. However, very few of them, if any, shed lights on actionable implementations to offer suggestions and aid entrepreneurs, especially those from disadvantaged backgrounds and dont have access to financial advisors, navigating through the fundraising process. Through analysing two studies examining founder and investor communication, one focusing on unilateral communication and message conveyed by founders to potential investors, the other focusing on bilateral interaction between the two parties, using sentiment analysis, this paper aims to provide a reference for creating a computer-mediated mechanism, offering entrepreneurs suggestions for future campaigns, and bringing equity to the capital market across different geographies.
Self-correction of automatic speech recognition
Authors: Lin Zihao
Published: July 2023

The rapid development of modern technology and society has raised the need for a large number of real-time translations, such as Automatic Speech Recognition (ASR) technology. One of the important factors determining the fluency of cooperation is that ASR translates the input speech into readable text so that both parties can understand each other's meaning. There is also a huge demand for and reliance on automatic speech recognition technology for modern applications. This article focuses on the word matching algorithm for backward search (WMA-BS), which requires two separate steps, one for the word matching algorithm and the other for the backward search step, for the first step, which can also be done by applying the word-level N-Gram language model. n-Gram model is a language model in which when n words appear in a text, we can use these n words to predict the structure of the text. we can use these n words to predict the structure of the text. In order to make the word-level N-gram language model suitable for the method, it is more useful for us to integrate it into shallow fusion. One of the most important parts to be addressed in the word matching algorithm is the boundary, or distance, between two words in English.
The comparison of Chinese and Australian aviation networks under the most important node attacks
Authors: Zhang Aiwen
Published: July 2023

Air traffic plays an important role in the economy and daily life both in developing and developed countries, The shutdown of the crucial airport will lead to large financial losses. In this study, the data have been collected from Openflight.org with information of flights and routes. Then the characteristics of the Chinese aviation network and Australian aviation network have been computed. The results indicate that the Chinese aviation network and Australian aviation network are both scale-free networks of power-law degree distribution with long-tail attributes. Then, the centrality analysis of the top 10 airports in the Chinese aviation network and Australian aviation network have been assessed, such as betweenness centrality analysis and closeness centrality analysis. Lastly, the study looks into how the characteristics of the Chinese and American aviation networks have changed as a result of node attacks and offers helpful advice for both the Chinese and Australian aviation networks after the most important node attacks.
The use of social network analysis in different fields
Authors: Zhou Jiaqi
Published: July 2023

Social network analysis is a well-established research method in sociology. Some scholars also refer to this quantitative approach to graph theory as network science. As research intensified in the 1970s, it gained the attention of researchers in many other fields and has become an interdisciplinary research method. Social network analysis collects data from multiple sources and unearths new information by building network structures.The constructed network topology diagram and the calculated network properties are eventually analysed and generalised to produce further research results. The use of social network analysis (SNA) in a few different fieldstourism, society, transportation, citation, viral communication, medical education, and economic geographyis reviewed in this essay. Social network analysis is a quantitative analysis method that blends multiple disciplines. Various relationships are quantified through the construction of networks and property analysis. The paper is categorized according to the different fields of application. The different applications of social network analysis in various fields show its general and specific analytical approach.
Analysis of sentiment analysis model based on deep learning
Authors: Wang Guanchao
Published: July 2023

A traditional yet important topic in the study of natural language processing is sentiment analysis. Deep learning models have gradually taken over as one of the primary techniques for resolving sentiment analysis issues over the last ten years. Common deep learning models targeting sentiment analysis tasks include both recurrent and convolutional neural networks, as well as the BERT model. The current research examines the classificational accuracy of numerous deep learning models with diverse structural types in order to compare their performance in sentiment analysis. Results from the experiments suggest that the pre-training BERT model achieves the highest accuracy, while the convolutional neural network appears to sustain better results on sentiment analysis than standard recurrent neural networks.
Comparison of Terahertz channel and SAGINs channel of 6G channels
Authors: Ge Yan , Wu Wenqiu , Zhang Yemin , Zhao Chencheng
Published: July 2023

This paper is mainly about researching some different types of 6G channels. The article introduces two common types of 6G channels: Terahertz channel and SAGINs channel, and studies and summarizes their advantages, problems, and development prospects, and compares them. The paper also introduces three small-scale 6G channel models.
Improve elderlys online healthcare experience based on interactive design
Authors: Mao Lijun
Published: July 2023

1) Research Background; It has been suggested that improve elderlys online healthcare experience has become one of the most important social issue, researchers have found that there is a trend that elderly have become more likely to use online information while solving their medical problems. However, the accessibility of the specific website still exists great gap to fill on. 2) Research Purpose; Due to that, the research topic of this paper is about to design a developed interactive website to improve elderlys healthcare experience. 3) Research Methods This paper includes the following sections: 1. introduce data collected from user experience survey, 2. analyse these data by thematic analysis, 3. discuss the possible formation of the result and extend it for suggestions, 4. analyse the insufficiency of the research and show how to improve it. 4) Research Meaning; According to the interview, most existed healthcare website present various problems and cause inconvenience for elderly to use. Besides, most elderly wish to add some practical functions like locate nearby hospital or get simultaneous reply from the doctor on the website. Due to that, designer should focus more on accessibility of the website instead of its appearance.
Path planning of robot in epidemic situation
Authors: Ma Yuxuan , Peng Yufei , Rao Chengxi , Zhang Yuyang
Published: July 2023

With the development of information technology in the world, human life is becoming more and more intelligent, which leads to the robot path planning algorithm becoming more and more important. Combined with the environment of new coronal pneumonia in China, this paper first introduces the environment of the cabin mobile hospital, and then deduces the role that the automatic obstacle avoidance robot with path planning ability can play in the mobile cabin hospital, and introduces the construction of the whole system model in the virtual simulation platform. This paper discusses the improvement and application of breadth first search algorithm, including the explanation of algorithm reference formula, and the experiment and subsequent analysis on the webots platform, and successfully proves its feasibility. Finally, the existing problems are explained and the parts that can be improved are discussed. Then, the research on robot path planning under the environment of new coronal pneumonia is summarized and prospected.
Non-contact automatic COVID-19 nucleic acid detection robot
Authors: Wu Fei , Zhao Yueyang
Published: July 2023

This product is mainly used to realise the contactless automatic COVID-19 nucleic acid detection robot, which can navigate to the workstation or location of the set personnel through the camera and radar and is mainly applied to the autonomous movement function of the existing automatic nucleic acid detection robot. The robot will conduct intelligent navigation according to the position of the nucleic acid person, avoid obstacles autonomously through radar and camera, and use clever navigation to plan the nearest route to the position part of the needed nucleic acid. This product carries a camera and laser radar based on SLAM technology for obstacle avoidance of autonomous mobile robots, through the experiment of advanced algorithms and the team constantly optimised. The algorithm makes its universality, compatibility, real-time and precision to achieve the desired effect. These autonomous mobile robots in the industrial production industry are essential to the application.
Skin lesion segmentation of dermoscopy images using U-Net
Authors: Wang Duan
Published: July 2023

Skin cancer is one of the most threatening cancers as reported and has been on the increase over the past 10 years. The traditional methods of skin cancer segmentation are time-consuming and inefficient. U-Net is a powerful and accurate way of self-segmentation in the medical field. In order to solve this problem, this paper proposes a U-Net skin cancer segmentation system that can provide results and feedback quickly, accurately and intelligently. It is composed of two parts: Skin Image Analysis Module and Skin Image Segmentation Module. In the skin image analysis module, the system learns segmentation from the training set images, and verifies the correctness of learning from part of the images. In Skin Image Segmentation Module, the system segments all the images in the test set folder. Among several experiments, the system using GPU training and learning with 100 images of ISIC dataset after 10 epochs has the training accuracy of 0.9085, while the validation accuracy is 0.9536. The system allows users to upload their lesion images to a test folder to obtain reliable segmentation results in a timely manner, thereby improving the survival rate of potential patients.
New hide-in-dark image encryption algorithm
Authors: Zhang Hao
Published: July 2023

In order to protect image information, existing encryption methods pay more attention to transforming images into noise-like images with random pixels. It is acknowledged that attackers can easily notice these encrypted images for there is a striking difference between them and unencrypted ones visible to the naked eye. As a result, these noise-like images may suffer a lot of attacks which could increase the probability of information leakage. For this problem, a method based on discrete wavelet transform and previous encryption methods has been proposed which can make the encrypted images finally look like other pictures. However, the results do not have a high quality, for some obvious noise can be found in the details of the final images. In this paper, a new method called HID is introduced in which the information is hidden in a dark area. Both experimental results and security analysis prove that our method can not only transform images full of noise into meaningful images but also produce images with higher quality.
Analysis of network resilience global air transportation
Authors: Liu Yaoxuan
Published: July 2023

Small-world network is a very common network structure, which is characterized by low average degree, small average path length and high centrality. At the same time, small-world networks have high resilience to random errors and low resilience to targeted attacks. In this study, the importance of nodes is represented by attributes such as degree and centrality, and attacks refer to the removal of important nodes. The network is attacked according to the degree, betweenness and closeness centrality to observe the power distribution. The data is mainly obtained from the open source OpenFlight. Gephi, Python, and Excel are used as tools. Gephi is used for network visualization and analysis. The third-party python libraries Pandas, Matplotlib, and NetworkX were used in this study to deal with the things that Gephi can't compute or represent well, and then plot the corresponding graphs with Matplotlib. The work of cleaning data is mainly done by excel.
The history of neuromorphic computing and its application on recognition systems
Authors: Jing Zhizhi
Published: July 2023

Since the invention of the memristive device with a nano-scale footprint, a lot of scholars have started to focus on the area of recognition systems based on the Complementary Metal-Oxide Semiconductor chips (CMOS) integrated with memristive devices. This papers goal is to compare and analyze the advantage and disadvantage on the near research on the cognitive machine. Start with the construction of a simple dynamic model of neurons in Section 2, the history of the development of the recognitive machine is introduced in Section 3. Section 4 focusing on the comparison and analysis of researches done by different scholars. Through the comparison of multiple scholars work, improvement of the memory system would be a potential way to improve the recognition system nowadays, and it is discussed in the conclusion.
Vehicle fault diagnosis using FTA and FMEA method
Authors: Zhao Zhihan
Published: July 2023

In recent years, users requirements for the richness of car functions have gradually increased. The application of a large number of electronic components in the automobile makes the degree of complexity of the automobile higher, so the user has a higher demand for the stability of the automobile system. Especially once a key functional component fails, driving safety will be seriously affected. Because in the design process, we find and analyze the possible causes of failure, so as to avoid the impact of failure in the design as much as possible. We use FMEA (Failure Mode and Effect Analysis) Analysis method and FTA (Fault Tree Analysis) analysis method to analyze the car headlight fault. These two analysis methods can efficiently and accurately analyze faults and determine the most basic events leading to failure. Based on the above analysis results, the proposed solution to improve the system can be given, so that the system can meet the functional safety requirements.
Face recognition with masks in the lasting era of COVID-19
Authors: Fang Longjie
Published: July 2023

At the end of 2019, the pandemic began to have a huge impact on people's lives around the world. In order to control and prevent the virus from spreading, world-wide governments have encouraged all residents to wear masks in their daily lives. Since the 1960s, face recognition technology has gradually emerged and developed. In the 21st century, it has been widely used in security, finance, transportation, and even retail, advertising, smart devices, education, health care, entertainment and other fields, playing a pivotal role in everyone's life. In the era of COVID-19, the public wearing face masks has undoubtedly hindered the smooth technology application of face recognition. The technology of recognizing human face in the case of wearing masks should be developed, followed by the adjustment of the original technology and data. The development of technology also brings certain social impact. Our research reviews the adjustment and progress of face recognition technology in the era of COVID-19, summarizes the impact of technological progress on society and people's lives, and provides help for the government to better prevent and control the epidemic.
Common lossless compression algorithms and their error resiliency performance
Authors: Zhang Mingchu
Published: July 2023

With the development of communication technology and computer technology, many related industries, such as multimedia entertainment, put forward higher requirements for storing and transmitting information data. The research of data compression technology has attracted more and more attention. Therefore, the error resiliency ability of data compression algorithm is particularly important. How to enhance the error resiliency of data compression communication systems has been a hot topic for researchers. This paper mainly introduces the lossless data compression technology and its basic principle and performance index. Two typical lossless compression codes, Huffman and Arithmetic coding are deeply studied, including the principle of coding and the problem of error resiliency. Huffman coding and Arithmetic coding are two very important lossless compression codes widely used. The ability to resist channel error is an important index for data compression in communication. It is of great significance to further improve the channel adaptability of data compression to study the above two kinds of codes and their ability to resist channel error.
Basic things about reinforcement learning
Authors: Zhang Ziyu
Published: July 2023

Artificial Intelligence has been a very popular topic at present, machine learning is also one of the main algorithms in AI, which consisted of Supervised learning, Unsupervised learning and Reinforcement learning, and Supervised learning and Unsupervised learning have been relatively mature. Reinforcement learning technology has a long history, it wasn't until the late '80s and early' 90s that reinforcement learning became widely used in artificial intelligence, machine learning. Generally, Reinforcement learning is a process of trial and error, agent will choose to make an action according to the feedback from the environment, this step will repeat a lot of times until it find the best policy, mapping it to reality, it can help human to fulfill some missions which are nearly impossible before. However, there is still some potential problems in Reinforcement learning. This essay compares some basic algorithms related to RL to help reader to have a basic understanding of RL and propose some exsiting defects about it.
Designing an inclusive home self-management website for seniors
Authors: Chen Zuonong , Shen Chenxin , Zhang Yikun
Published: July 2023

In the modern society, chronic diseases are a major problem plaguing many elderly people. The problem that comes with chronic diseases is how to do proper self-management. Therefore, this study will research this issue from the perspective of the elderly to explore the root of the problem. At the same time, this study will discuss three aspects of health care, diet, and exercise. This study will research existing apps and websites, identify problems and propose solutions. At the same time, this study will design a database on the self-management of the elderly at home based on the survey results and survey data on the elderly. This study got inspiration from the DNA database classification project. This study hoped to integrate the information on the use of the drug, the number of daily use, and taboos according to the instructions of the drug, and finally integrate it into a general database.
An M/M/2 heterogeneous service encouraged arrival with feedback, reverse balking, reverse reneging and keeping of reneged customers
Authors: Ismailkhan E , Sathishkumar Veerappampalayam Easwaramoorthy , Rajendran P , R. Sukumar , R. Vijay Anand
Published: July 2023

An infinite capacity This study develops a Markovian 2-server heterogeneous service encouraged arrival queuing model with the present challenges of customer feedback, customer impatience, customer keeping, and reverse balking. The model is then iteratively solved to produce several encouraged arrival probabilistic estimates and system performance measures. The results of the prescribed model , with regard to the modern scenarios described above are presented in this work that are having great value for developing strategies for the smooth operation of the system.
An exploration to random walk
Authors: Liu Tongtong
Published: July 2023

There is a lot of uncertainty in many things that happen in real life, which leads to many events occurring randomly, and the quantitative description of the relationship between this sequence of random events is the stochastic process. Stochastic process is an important part of Probability. The stochastic process is a set of random variables, which is {Xt} t belongs to the T, The variable t is the parameter known as time, The parameter in a stochastic process are uncertain, each t corresponding to a random variable. T is the parameter set, and is a discrete parameter process when T is a finite set or a countable set, otherwise it is a continuous parameter process. The stochastic process can be divided into discrete state and continuous state according to the state space. If the random process x(t) takes countable values, then it is a discrete state process, otherwise it is not countable which is a continuous state process. The values of random variables change according to the results of random trials, and the pattern of change is also known as probability distribution. Stochastic process, which belonged to the field of physics in the early days of research, have developed into a powerful tool for research in many fields such as natural sciences, engineering sciences, and social sciences. Stochastic process use formulas to derive the probability of future events, find the inner laws from the apparent chance of events and describe the laws through probability. It can also be used as a statistical model to predict and deal with the development of some complex things in nature.
The application of probabilistic programming in statistics
Authors: Zhang Yi
Published: July 2023

This article focuses on three aspects of Probabilistic reasoning systems for applications, including Predict future events, Infer the cause of events, and learn from past events to better predict future events. In addition, the article explains that the data will become more accurate as the number of samples increases, and finally, the article describes the application of Turing-Completed Systems in statistics, a kind of probabilistic programming system. This article starts with a soccer competition as an example, then will explain the concepts in depth behind the example.
Mask recognition in computer vision technology
Authors: Lu Yaoyun , Ma Yueyang
Published: July 2023

In order to lessen the strain on employees and the potential for carelessness, intelligent identification is required in China where there is a scarcity of staff to monitor the wearing of masks in public areas. In this work, we use a mask recognition technique to determine which members of the population weren't wearing masks by using the CNN and the VGG16 model. The ideas of data augmentation, dropout, non malicious, and transfer learning are used in the proposed study. This method may be used at hospitals, retail centers, transit hubs, dining establishments, and other community gatherings that require monitoring.
Design and implementation of STM32 based push box game
Authors: He Weihang
Published: July 2023

Over the past decade, China's gaming industry has continued to show stepwise growth. There are over 370,000 gaming-related enterprises in China, and over 20,000 gaming-related enterprises were established last year, but there is still a gap between the level of gaming development in Europe, America and Japan. The importance of portability for game carriers needs no further explanation, people can enjoy the joy of games anytime and anywhere. The project is to design an STM32 based Sokoban game that is portable, educational, and can be played anywhere and anytime by players. This paper introduces the technology and methodology of developing a STM32-based Sokoban game. Sokoban is a classic game that can be enjoyed by young and old alike, which is both brain exercise and physical and mental pleasure. The system uses the STM32L476RG board as the hardware platform, based on the Keil software development system and the C language, to complete the game design and implementation. Players can control the game through remote sensing and display. The game is accompanied by background music and game sound effects.
Deep reinforcement learning in stock portfolios
Authors: Quan Yue
Published: July 2023

This paper investigates stock portfolios by application of Deep Reinforcement Learning (DRL) Models to achieve an optimal tactical asset allocation. The research problem is described as an optimization scenario that seeks to maximize the portfolio risk adjusted returns for a given portfolio asset allocation. The problem is set up with an initial capital investment which is invested in a set of assets. The initial strategic allocation is determined, which in our case is the equal weight allocation, and all of the capital is invested in the set of assets. At each point in time, the assets are reallocated according to the allocation which will increase the portfolio value. Two DRL models are implemented. The performance of the DRL models is compared with the uniform weights portfolio. The results show that, generally, two DRL models have higher cumulative returns.
Comparative analysis between application of transformer and recurrent neural network in speech recognition
Authors: Liao Zhehao
Published: July 2023

Transformer is a deep learning model applying self-attention mechanism which is widely used in solving sequence-to-sequence questions, including speech recognition. After Transformer was proposed, it has been greatly developed and made great progress in the field of speech recognition. Recurrent Neural Network (RNN) is also a model that can be used in speech recognition. Speech recognition is a kind of sequence-to-sequence question that can transform human speech into text form. Both RNN and Transformer use encoder-decoder architecture to solve sequence-to-sequence questions. However, RNN is a recurrent model, weak in parallel training, and it will not perform quite well as Transformer in sequence-to-sequence question, which is a non-recurrent model. This paper mainly analyzes the accuracy Transformer and RNN in automatic speech recognition. It shows that Transformer performs better than RNN in speech recognition area, having higher accuracy, and it therefore provides evidence that Transformer can be an efficacious approach to automatic speech recognition as well as a practical substitution for traditional ways like RNN.
A survey on the method of image segmentation based on deep learning
Authors: Zhu Zengbin
Published: July 2023

Image segmentation is a widely used technology, such as autonomous driving, smart factories, smart cities, computer vision, medical image segmentation, robot perception, and augmented reality. The success of Convolutional Neural Networks (CNNs) has contributed greatly to the field of recent computer vision, including image segmentation. This article have conducted a review of some of the most important recent literature in the field of semantic segmentation based on deep learning (DL), which is divided into CNNs, Fully Convolutional Models, Encoder-Decoder Based Models, Pyramid Network Based Models, R-CNN Based Models. At the end of the article, the main features of these models are discussed and future directions for development are proposed.
A new method for mandala image synthesis based on WGAN-GP
Authors: Cao Zhengyu , He Wei , Lin Fengsheng , Liu Changyi
Published: July 2023

Mandala, as an ancient image art, has been found to have many unexpected applications in recent years, including use in art therapies, meditation induction and human body state assessment. Despite the omnipresent applications of convolutional neural networks in imaging Synthesis, it can be found that there is no work on Mandala Image Synthesis yet. To fill this research gap, deep learning algorithms were considered in this study. With existing research on Generative Adversarial Network (GAN), a typical GAN network called WGAN-GP was used to produce Mandala images. The generator and discriminators in the WGAN-GP network are fully trained in this study. To prove the robustness of the employed method, this study also investigates and compares the performances of WGAN-GP and other GANs. The experimental results demonstrated the effectiveness and satisfactory performance of the employed method. In brief, our completed work can effectively help provide Mandala images for further research on it.
Algorithm structure for real-time general game playing agents
Authors: Ge Chenming
Published: July 2023

General Game Playing is an examination of AI agents that are designed without any explicit knowledge of a particular game. Instead, they are required to play a variety of games that they have never seen before by merely reading the description of the rules at runtime, without any intervention from a human being. Previous successful agents have concentrated their efforts on turn-based games, employing either generic heuristics or the Monte Carlo/UCT simulation technique. Real-time games have dominated the market in recent years, and as a result, agents that have more reflexibility have emerged as an important area of research. In this paper, the author evaluateswhether or not it is possible to implement the already-existing algorithms into a real-time playing GGP agent, and we present a potential algorithm structure that, on the basis of this evaluation, could be able to solve the challenge.
Dual-antenna multidimensional positioning algorithm based on positioning of disaster relief personnel
Authors: Zhang Songnian
Published: July 2023

When facing a major disaster, firefighters and other rescue workers may also encounter certain dangers, which means certain protective measures are needed to ensure the safety of disaster relief personnel. Due to the complexity of the rescue environment, such as being blocked by obstacles or due to the influence of magnetic field, satellite positioning may not be used, and the position of the personnel in need of rescue can not be accurately located. On account of the above issues, we used search and rescue equipment and handheld terminals, and the dual antenna multidimensional positioning algorithm were also used to determine the position of firefighters in real time. Matlab was used for simulation, and error sources and error sizes were estimated,which ensured the safety of firefighters and provided timely help and rescue in case of firefighters' distress. After simulation, it can be concluded that in the case of large signal-to-noise ratio, the positioning accuracy is high and the positioning effect is pretty well.
Optimization of linear regression in house price prediction
Authors: Zhu Liangji
Published: July 2023

House price prediction plays a very important role in housing transactions. Linear regression based algorithms show good effects in predicting house prices. They have strong interpretability and fast operation speed. However, people ignore the estimation of deviations in linear regression (LR) algorithms. In this paper, k-nearest neighbor (KNN) algorithm is supposed to estimate deviations that are added to the result of linear regression to predict house prices accurately. Furthermore, deviation regression (DR) algorithm is supposed to make the prediction result more accurate. By utilizing Boston House Price data from Kaggle, extensive experiments are conducted and demonstrate the superior performance and compatibility of DR.
A deep learning model for accurate and robust internet traffic classification
Authors: Fu Tianhao , Sun Boyuan , Zhang Chenyue
Published: July 2023

Network traffic classification is significant due to the fast growth of the number of internet users. The traditional way of classifying the large number of traffic generated by these users is becoming less effective. Therefore, many researchers made a network traffic classifier based on deep learning. However, those classifiers do not provide far better results and perform poorly when dealing with encrypted information. This paper tries to approach highly accurate and robust results in both encrypted and unencrypted networks by using machine learning algorithms. The algorithm used is the convolutional neural network (CNN). The performance of the proposed CNN is compared with that of the classical LeNet-5 network. Experimental results show that the classifier based on the proposed CNN performed better when dealing with both encrypted and unencrypted datasets, achieving a maximum average accuracy of 83.55%. Moreover, it is not sensitive to hyper-parameter choices, indicating its superiority in robustness. Compared with traditional network classifiers, the network classifier based on CNN can improve accuracy and improve stability.
Studies advanced in face recognition
Authors: Wang Haoyu
Published: July 2023

Face recognition has always been a hot topic in the field of computer vision research. Its purpose is to enable computers to have the ability to recognize people through face images like humans. The early face recognition technology was mainly based on artificial features, mainly including five parts: face image acquisition, face image detection, face image preprocessing, face image feature extraction and matching recognition. Thanks to the rapid development of convolutional neural networks, face recognition technology based on deep learning has gradually become the mainstream method. Focusing on the above two categories of frameworks, in this paper, we introduce representative algorithms for face recognition, including their design ideas, basic processes and key steps. We also discuss existing problems in the field of face recognition and predict future directions for this topic.
A real-time automatic pothole detection system using convolution neural networks
Authors: Bharat Ricardo , Ikotun Abiodun M , Ezugwu Absalom E. , Abualigah Laith , Shehab Mohammad , Zitar Raed Abu
Published: July 2023

Detecting a pothole can help prevent damage to your vehicle and potentially prevent an accident. Different techniques, including machine learning, deep learning models, sensor methods, stereo vision, the internet of things (IoT), and black-box cameras, have already been applied to address the problem. However, studies have shown that machine learning and deep learning techniques successfully detect potholes. However, because most of these successful attempts are peculiar to the location of the study, we found no study which has addressed the peculiarity of potholes in South Africa using a tailored-trained deep learning model. In this study, we propose using a convolutional neural network (CNN), a type of deep learning model, to address this growing problem on South African roads. To achieve this, a CNN model was designed from scratch and trained with image samples obtained from the context of the study. The classifier was adapted to distinguish between a binary class which identifies the presence or absence of potholes. Results showed a significant performance enhancement at a classification accuracy of 92.72%. The outcome of this study showed that this machine learning approach holds great potential for addressing the challenge of potholes and road bumps in the region and abroad.
Exploring hippocampus segmentation on unbalanced data set using U-Net-based models
Authors: Wu Kaiyuan
Published: July 2023

This paper studies the performance of training the U-Net-based models on an unbalanced data set for hippocampus segmentation. It investigates through a series of ablation studies the effect of the weights in loss function, sampling method, model architecture, and learning rate type, and compare across different trials regarding their dice score and accuracy to identify the best strategy under class imbalance. Lastly, it displays numerical and graphical results before discussing potential implications and future directions.
Performance of NeRF and its optimized models on generating images from 2D to 3D of animation or game character
Authors: Xie Yuchen
Published: July 2023

Neural Radiance Fields (NeRF) has recently emerged as a promising approach for single-image 3D scene reconstruction. Despite this, most studies on NeRF have focused on real-world data or items with established camera projection connections, rather than snapshots of video game characters that may not have a distinct camera projection connection. Although some research has been conducted on the use of animatable mannequins, the majority of these studies have used photographs of real people as their data source. To date, there has been limited research into the use of NeRF for animation or game characters. In this paper, we compare and evaluate a range of NeRF models on a dataset of animated game characters. The models considered include original NeRF, Block-NeRF, Mip-NeRF, and Pixel-NeRF, each of which has its own design choices and trade-offs. Our results show that Mip-NeRF models are effective at generating high-quality images of game characters on our dataset, while Pixel-NeRF performs poorly on our dataset. Despite these findings, our research demonstrates the feasibility of using NeRF-based models to create realistic character models or animations for video games, with potential applications in game development and virtual reality.
Heart disease prediction based on machine learning algorithms
Authors: Xu Mingyuan
Published: July 2023

Heart disease is a medical research field in which the outcome can benefit lots of people. Because there are several factors that might raise the risk of heart disease, it is useful to build a prediction model to assist people in assessing their health. This paper makes use of a Kaggle dataset that was derived from CDC (Centers for Disease Control and Prevention). First, 8 components are analyzed using diagrams, and then the dataset is used to train classifiers in machine learning models. This paper conducts a comparative study between different algorithms, including Decision Tree, Logistic Regression, SVM (Support Vector Machine), and Random Forest. Besides, the factors taken into consideration while evaluating performance include accuracy, precision, recall, and f1-score. As a result, the maximum accuracy is reached by SVM with a linear kernel, and logistic regression achieves the highest precision. In addition, the highest recall and f1-score are obtained from the model SVM with an RBF kernel.
Fractionally spaced frequency domain equalization system based on FQPSK modulation
Authors: Wu Junyi , Liu Zhiwen , Yang Ziyi , Du Changhao
Published: July 2023

Severe multi-path effects in the process of high-speed data transmission cause inter-symbol interference (ISI). The single carrier frequency domain equalization (SC-FDE) method combining the benefits of Feher-patented quadrature phase shift keying (FQPSK) modulation is raised to solve this problem, including the constant envelope, high spectrum efficiency, and insensitivity to nonlinear distortion. Furthermore, a fractionally spaced frequency domain equalization (FS-FDE) system based on FQPSK modulation is modeled based on the proposed algorithm. Simulation results confirm the superior performance of the proposed model on erasing ISI, comparing to the symbol-spaced FDE (SS-FDE) system based on FQPSK modulation and the FS-FDE system based on QPSK modulation.
Automatic dog breed classification using deep learning
Authors: Kumaravel T , Natesan P , Sathishkumar V E , N Sathiya Shri , G Swathy , V Uvetha
Published: July 2023

Dogs are a common type of animal that can present various problems, such as issues with population control, controlling rabies outbreaks, administering vaccinations, and legal ownership, due to their large numbers. Understanding the breed of the dog can help the owner identify potential health issues and determine their lifespan. There are over 120 different breeds of dogs, each with unique characteristics and health concerns. To properly care for and train a dog, it is important to know their breed. This study discusses methods of classifying dog breeds and presents a method using a CNN (Convolutional Neural Network) to accurately identify different breeds by analyzing dog images. This approach, using DenseNet201, achieved an accuracy of 87.34% on the Dog Breed Images dataset and is more effective than other methods found in literature.
Prediksi Unsur Hara Sampel Tanah Menggunakan Near Infrared Spectroscopy
Authors: Wening Opal Priya , Kuswurjanto Risvan
Published: July 2023

Penentuan karakteristik dan konsentrasi unsur hara tanah perlu dilakukan metode analisa untuk perlakuan lahan serta ketepatan dosis dan jenis pemupukan dalam optimalisasi produktivitas tanaman. Metode alternatif analisis kandungan sampel secara cepat, efektif, ramah lingkungan, dan tidak merusak sampel dapat menggunakan near infrared spectroscopy (NIRS). Sampel yang digunakan berasal dari Laboratorium Tanah, P3GI. Parameter yang digunakan sebagai perkiraan kandungan yaitu pH, nitrogen (N, %), fosfat (P2O5, ppm), dan kalium (K2O, ppm). Sebanyak 64 sampel tanah digunakan untuk kalibrasi dan 30% untuk validasi. Spektrum NIRS diperoleh dari FOSS XDS rapid content analysis (400 – 2.400 nm, reflectance mode) dan pengolahan data statistik menggunakan vision software dengan model yang dibangun berdasarkan partial least square (PLS). Penelitian ini didapatkan hasil evaluasi bahwa model yang dibangun untuk memprediksi parameter pH (R2 = 0,951; r2 = 0,511; SEP = 1,428; RPD = 1,440; consistency = 22,321%; dan  = 0,1776) dan nitrogen R2 = 0,767; r2 = 0,665; SEP = 0,025; RPD = 1,749; consistency = 97,745%; dan  = 0,0131) performanya masih cukup baik, sedangkan lainnya masih kurang. Berdasarkan hasil evaluasi tersebut, NIRS memiliki potensi sebagai metode analisa kualitas tanah namun model yang dibangun masih perlu ditingkatkan keakuratanya.
CRISPR-Cas9: The Future of Precision Genome Engineering
Authors: Akram Tariq Muhammad
Published: July 2023

CRISPR-Cas9, a revolutionary gene-editing tool, has garnered significant attention in the scientific community and beyond. This innovative technology holds immense promise for precision genome engineering, enabling scientists to modify DNA with unprecedented accuracy. The potential applications of CRISPR-Cas9 are vast, ranging from treating genetic disorders to enhancing agricultural crops. As we delve into the era of personalized medicine and genetic advancements, CRISPR-Cas9 emerges as a game-changer in the field of biotechnology. At the core of CRISPR-Cas9 lies its ability to precisely target and edit specific genes within the genome. This powerful tool utilizes RNA molecules as guides to direct the Cas9 enzyme to the desired DNA sequence, where it makes precise cuts. By introducing modifications or repairs to the DNA at these targeted sites, researchers can potentially correct genetic mutations responsible for diseases. The ability to edit the human genome with such precision offers hope for treating previously incurable genetic disorders and improving patient outcomes.

Moreover, CRISPR-Cas9 has implications beyond human health. It has the potential to revolutionize agriculture by enhancing crop resistance to pests, improving yields, and reducing the need for harmful pesticides. By modifying specific genes in plants, scientists can develop crops that are more resilient to environmental stressors, ultimately contributing to global food security and sustainability efforts. Despite its immense potential, CRISPR-Cas9 also raises ethical considerations and concerns. The technology's power to edit the human germline raises questions about the potential for unintended consequences and the need for responsible use. The scientific community and policymakers must engage in thoughtful discussions and establish guidelines to ensure the ethical and responsible implementation of CRISPR-Cas9.

CRISPR-Cas9 represents a landmark advancement in precision genome engineering, offering unprecedented opportunities to reshape the future of healthcare and agriculture. By harnessing its potential, we can envision a world where previously untreatable genetic diseases are curable, crops are more resilient and productive, and our understanding of the genetic basis of life is deepened. However, as we embrace the future of CRISPR-Cas9, it is crucial to navigate the ethical implications and ensure responsible use to maximize the benefits and minimize potential risks. The potential of this transformative technology is immense, and its impact on society will be profound.
Comparing Satisfaction of Undergraduate Nursing Students`: Mini-CEX vs CIM in Assessing Clinical Competence
Authors: Saghir Somia , Ali Anny Ashiq , Khan Kashif , Bibi Uzma , Ullah Shafaat , Ullah Rafi , Khan Zaifullah , Khan Tahir
Published: July 2023

Assessing clinical competence is crucial to medical education, ensuring that future nurses possess the necessary skills and knowledge to provide high-quality patient care. Two widely utilized assessment methods in this domain are the Clinical Integrated Map (CIM) and the Mini-Clinical Evaluation Exercise (Mini-CEX). Both approaches offer valuable insights into the clinical performance of health professionals. Objective: To assess the satisfaction level of the students between the Mini-CEX assessment versus the CIM assessment. Methods: A total of 12 BSN second-year semester-IV nursing students were enrolled in the project. The participants were randomly divided into two groups, with six students in each group. One group was assessed on the CIM tool, whereas the other group was assessed on the Mini CEX tool. The study was carried out at a private nursing college. Results: The age of the participants was between 18-25 years and most were between 18-20 years of age. Most of the participants were female and all were undergraduate’s students of semester four year two of Shifa College of Nursing. The results revealed that most students are satisfied while using Mini-CEX compared to CIM. Conclusions: Students' abilities and interests are vital for implementing Mini-CEX successfully. It has been highlighted as a useful formative assessment tool that provides timely feedback and enhances learning outcomes.