bluuebunny/crossref_metadata_2025 huggingface dataset:
Transparencia pública: análise das licitações e contratos administrativos realizados na Sudam - 2015 a 2022
Authors: Almeida Marcos Rodrigues de , Toma Marina Yassuko , Fernandes Rosana Pereira , Chaves Marcelo Santos , Pena Heriberto Wagner Amanajas
Published: July 2023

A Administração pública tem o dever de publicizar seus atos de compras, sobretudo com o advento da Lei nº 12.527/2011, a chamada de Lei de Acesso à Informação (LAI), que estabeleceu como diretrizes da governança pública o uso da publicidade como preceito geral no cumprimento dos dispositivos legais relativos às práticas da transparência em seus processos licitatórios e na gestão de suas compras e contratações. Em termos gerais o artigo pretende analisar no âmbito da administração pública os níveis de transparência ativa nos contratos realizados na SUDAM no período de 2015 a 2022. A metodologia empregada se fundamenta na representação matemática sustentada pela teoria dos números, os índices propostos neste levam em conta a formalização de equações. Os resultados da aplicação do modelo demonstraram incialmente que, para a aferição do nível de transparência das contratações na Superintendência de Desenvolvimento da Amazônia SUDAM, o índice ITC com maior transparência foi verificado em 2017.
Rede de cuidado em saúde e transexualidade: estudo de caso
Authors: Costa Cristiane , Bitancourt Nascimento Gabriella , Leyse Abrahão da Silva Julia , Alves de Souza Carvalho Gabriele , Deleon de Melo Laércio , Spindola Thelma
Published: July 2023

Este estudo tem como objetivo geral, analisar a experiências de uma pessoa transexual como usuária do SUS, através de sua vivência com a rede de cuidado do sistema de saúde do Brasil (SUS), apontando as barreiras vividas e seus motivos. Realizou-se uma entrevista, através de uma plataforma online, com uma mulher transexual de 25 anos, moradora do Rio de Janeiro. Na análise da entrevista, a invisibilidade dada as necessidades específicas de saúde de mulheres transexual, com grande destaque ao desrespeito de muitos profissionais em utilizar seu nome social, e a transfobia institucional, gera sentimento de exclusão social e negação de direito à saúde. Salienta-se que os profissionais da enfermagem, como precursores da promoção de saúde, devem estar aptos a cuidar das demandas e necessidades de saúde das pessoas transexuais, primando pelos princípios basilares do SUS. O estudo buscou evidenciar, aos profissionais de saúde, as consequências de atos transfóbicos, que se traduzem em (des)cuidado às pessoas transexuais, além de contribuir para os estudos referentes a diversidade de gênero, agregando conhecimento para a área.
Exploring the diversity, physiochemical analysis and morphometric measurement of fish fauna of Khudo Khail stream District Buner Pakistan
Authors: Ahmad Shafiq , Qasim Muhammad , Sirajvi Muhammad Mahboob Ahmad , Zafar Hasnain , Achakzai Wali Muhammad , Saddozai Shagufta , Masud Samrah , Bukhari Syed Zain Zaffar , Rehman Hameed Ur , Saeed Kausar
Published: July 2023

The aim of this research to investigate the diversity, physiochemical analysis, and morphometric measurement of fish fauna of Khudo Khail stream District Buner Pakistan. The present study was conducted on the Khudo Khail stream District Buner during the period of October 2019 to October 2020 to explore the fish fauna of Khudo Khail stream. The study was conducted in 7 different localities in Khudo Khail stream that is Bato, Kalaan, Dambaray, Dhandar, Baba Berr, Qasam Khail and Totalai. During the study 94 specimens were together from the collection points. The collections were taxonomically treated in the result of 9 species of fish under 5 orders, 6 families and 9 genera. The species were Barilius pakistanicus (Mirza and Sadiq, 1978),Cyprinus carpio (Linnaeus, 1758), Channa punctata (Bloch, 1993), Cyprinion watsoni (Day, 1872), Mystus bleekeri (Francis Day, 1877),Oreochromis mossambicus (W. K. H. Peters, 1852),Pinctus ticto (Hamilton, 1822),Wallagu attu (Bloch and Schneider, 1801) and Matacembelus armatus (Lacepede, 1800). The water quality parameters of the Khudo Khail stream were also analyzed and were found to be suitable for the survival of the fish fauna. It was found that the fish fauna of Khudo Khail stream is very rich.
The First Subregional Forum of Indigenous and Local Community Women in Central Africa and the Congo Basin: Declaration
Authors:   Rights and Resources Initiative,   REPALEAC
Published: July 2023

On May 8–12, 2023, Indigenous and local community women leaders joined international donors and philanthropies, and African ministers and political actors in Brazzaville, Republic of Congo, to strengthen and promote the role of the region’s Indigenous and local community women and girls in climate resilience and biodiversity conservation.  The Congo Basin’s ecosystem in Central Africa is the world’s second lung after the Amazon rainforest, home to extraordinary biodiversity of crucial importance to global climate goals. Indigenous and local community women play a vital role in the management and protection of the natural ecosystems that store biodiversity, sequester carbon, sustain local livelihoods, and safeguard their cultures and traditional knowledge.   In order to value their often-ignored contributions to nature conservation and climate resilience—and above all to fight against widespread injustices and discrimination—REPALEAC and Indigenous women leaders partnered with RRI to host the First Forum of Indigenous and Local Community Women in Central Africa and the Congo Basin. This Declaration is a global call to action.
Interaction between trees and ground flora in different aged chirpine stands of sub-tropical region in India: III. Biomass of Herbage and LAI
Authors: Dutt Vaishnu , Gupta B.
Published: July 2023

The present study was conducted to investigate the variations in herbage production under Chirpine stands at four locations viz. Barog, Deothi, Dharampur and Tatul of Solan Forest Division, Himachal Pradesh, India during 1998. Each location had four sites three representing different ages of Chirpine stand viz. sapling, pole and tree whereas fourth one represented an open grass land. Herbage vegetation characteristics viz. biomass production and LAI increased from July to August/September and thereafter showed a continuous decrease up to November in all the sites. Among different species, Themeda anathera, Panicum maximum, Chrysopogon montanus and Pollinia argentea were found to be the major contributors to the biomass production. Values for biomass production and LAI of herbage were higher in open grasslands as compared to Chirpine inhabited sites. LAI was significantly related with the aboveground biomass.
Neutrosophic Mathematical Model of Product Mixture Problem Using Binary Integer Mutant
Authors:   Faculty of Informatics Engineering, Al-Sham Private University, Damascus, Syria, admin admin
Published: July 2023

Linear programming problems with integers, are issues in which some or all decision variables are restricted to be their values are correct values, and here these models can be solved by neglecting the constraints of integers and then rotating the fractional values of the optimal solution to obtain correct values, paying attention to the need for the resulting solution to belong to the accepted solutions area, but this procedure can lead to the desired purpose if the number of variables is small, but if there is a number We do not guarantee to obtain an optimal correct solution for the model, even if all the solution combinations are tested, knowing that in the model that contains a variable n a set of solutions 2n must be tried, and if we can rotate, the correct solution will be an approximate solution, in order to obtain more accurate integer values, we present in this research a study in which we use binary integer variables to build the neutrosophic linear mathematical model. The importance of this study lies in providing solutions to many practical problems that require solutions with integers and we will clarify all the above by building a mathematical model for the problem of the mixture of products using binary integer variables and neutrosophic values.
Computer vision models application in the current system on object detection tasks
Authors: Huang Feilian
Published: July 2023

The implementation of object detection algorithms would be helpful to the various fields of the current time. When object detection is applied to the surveillance camera system, it will be more efficient to locate crimes or find lost kids. This paper will investigate the performance of different object detection algorithms in a real-world scenario. With experimentation, CenterNet++ outperforms YOLO and MaskRCNN, two traditional and classic object detection algorithms, on the MS COCO dataset, which concludes that CenterNet++ can ensure both accuracy and speed.
Application of wireless charging in UAV line patrol
Authors: Hong Yue
Published: July 2023

With the development of the times, unmanned aerial vehicles (UAVs) are increasingly used in power systems, especially in power line patrol. At the same time, The charging method of UAV power patrol system has attracted more and more attention. Therefore, this paper analyzes the different ways of wireless charging of UAVs applied in power circuit system. It includes self-resonant ZVS topology, Double LCCL topology, LCL-LCL /S hybrid self-switching resonant wireless charging system and wireless charging method using solar energy. At the same time, the future development direction of UAV wireless charging in this field is prospected.
Military AIs impacts on international strategic stability
Authors: Yu Yifan
Published: July 2023

Technological revolution brought major changes in the system framework of strategic stability. Artificial intelligence (AI) thrives internationally as a disruptive technology and is applied to many fields in the 21st century. This paper evaluates the strength, limitations, and impacts of AI-empowered military application on international strategic stability. Applications of AI technology for military purpose brings both positive and negative impact on nations defending and offending, so the international strategic stability. However, the impact of AI on international strategic stability is mainly negative. For facing stability challenges, nations shall formulate systematic governance of military AI; the global community shall promote friendly multilateral cooperation between each other. In the end, this view offers significant implications for maintaining international strategic stability and improving AI governance capabilities in the foreseeable future.
Analysis and research of EMI filter based on SiC power device
Authors: Liu Zheng
Published: July 2023

The new electronic devices represented by SiC have brought new opportunities to improve the efficiency and power density of the drive circuit system. However, the complex circuit drive system using the new electronic devices still faces many challenges to give full play to its advantages. This paper focuses on the electromagnetic interference of drive circuit. Research on Filter Circuit, through in-depth study of the filter circuit, several second-order active circuits are proposed to filter the signal interference of the conduction path. Finally, an adjustable quality factor bandstop circuit is designed. The proposed circuit has good adjustment and feedback functions when filtering interference signals.
Visualization of data analysis platform Taking QQ music recommendation system as an example
Authors: Li Xinyue
Published: July 2023

With the rapid development of big data technology, people's demand for personalized music recommendation systems is growing more and more urgent. However, the current music recommendation system still has some problems, such as inaccurate recommendations and too slow recommendation speeds, as well as cold starts and data sparsity caused by massive data. In order to design and implement a music recommendation system for the recommendation system storage caused by the continuous increase of data, insufficient storage, and computing power, this paper improved the QQ music recommendation system based on the collaborative filtering recommendation algorithm of the offline data warehouse technology project. After testing, the music recommendation system designed in this paper has good stability, scalability, and efficiency.
Machine learning and emotion recognition in video clips
Authors: Chang Yichen
Published: July 2023

The development and application of machine learning has been a hot topic in recent years, and enabling machines to read people's emotions is of great importance in many aspects of social development, such as healthcare, products development and human-computer interaction. This study recognizes seven human emotions by building a model of a neural network and training dataset. Using a large-scaled video dataset, Dynamic Facial Expression in-the-Wild (DFEW), trains the ResNet50 model and outputs the precisions. There are three possible training results, if the accuracies are all above 80%, then the neural network is well-performed; if partial classification accuracies is above 80% while others are above 50%, the neural network is partial well-performed; if the accuracies are all under 50%, the neural network is poor-performed.
Machine learning algorithms review
Authors: Ling Qingyang
Published: July 2023

Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machine learning and its algorithms. It will examine different types of machine learning models and introduce their most popular algorithms. The methodology of this paper is a literature review, which examines the most commonly used machine learning algorithms in the current field. Such algorithms include Nave Bayes, Decision Tree, KNN, and K-Mean Cluster. Nowadays, machine learning is everywhere and almost everyone using a technology product is enjoying its convenience. Applications like spam mail classification, image recognition, personalized product recommendations, and natural language processing all use machine learning algorithms. The conclusion is that there is no single algorithm that can solve all the problems. The choice of the use of algorithms and models must depend on the specific problem.
Fast CNN enhancement using channel attention and residual networks for image super-resolution
Authors: Qu Haoning , Ruan Yifeng , Wan Zerui , Zhu Ming
Published: July 2023

Single image super-resolution (SISR) refers to the process of reconstructing a high-resolution (HR) image from a low-resolution (LR) input image. Deep learning super-resolution algorithms have widely been used to solve SISR tasks. However, the demanding computation cost and memory occupation incurred through training the deep learning models has been hindering its real-world application. In this paper, we rebuild FSRCNN and apply it to solve SISR tasks. Firstly, we change the original training dataset to RealSR, a larger dataset consisting of real-world images. Secondly, channel attention and residual blocks have been applied to the mapping layers and important parameters including learning rate and optimizer have been reset. Thirdly, we change the cost function from loss to loss and replace the activation function from parametric rectified linear unit (PReLU) to exponential linear unit (ELU), to verify the discrepancies between different loss functions and activation functions. Finally, we compare the rebuilt models with the official FSRCNN based on the Peak signal-to-noise ratio(PSNR) and thestructural similarity index measure(SSIM) on three common test datasets. The original model achieves better performance on all the test datasets across different scale factors while the rebuilt models show better generalization capability. Our analyses illustrate that residual blocks can slightly promote model performance while different loss functions and activation functions do not generate an evident impact on the rebuilt model.
Predicting implicit operators among solutions for certain differential equations systems with DeepONets
Authors: Ji Haichao
Published: July 2023

Solving multivariate ordinary differential equations (ODE) systems and partial differential equations (PDE) systems is the key to many complex physics and chemistry problems, such as the combustion in process of reacting flow. However, the traditional numerical methods in solving multivariate ODE and PDE systems are limited by computational cost, and sometimes its impossible to obtain the solution due to the high stiffness of ODE or PDE. Coincident with the development of machine learning has been a growing appreciation of applying neural networks in solving physics models. DeepM&M net was proposed to address complicated problems in fluid mechanics based on another neural network: DeepONet, which is used to predict functional nonlinear operators. Inspired by these two nets, a machine learning way of solving certain ODE and PDE systems is proposed with a similar framework to the DeepM&M net, which takes inputs of the initial conditions and outputs the corresponding solutions. The main ideas of this framework are first to explore the relations among solutions of the system by DeepONets and then to train a deep neural network with the assistance of trained DeepONets. The implicit operators between variables in certain ODE systems are verified to have existed and are well predicted by the DeepONet. The feasibility of the proposed framework is implied by the success in building blocks.
Melanoma skin cancer cell detection using image processing: A survey
Authors: Sivaranjani M. , E. Sathishkumar V , Shanmugavadivel Kogilavani , Subramanian Malliga
Published: July 2023

In the current day, melanoma, basal cell carcinoma, and squamous cell carcinoma remains to be the three most common kinds of skin cancer. The likelihood of survival is exceedingly poor for patients with melanoma, making it the most dangerous type of cancer. The victim's chances of survival can only be boosted by detecting the melanoma cancer cells at an early stage. Modern skin cancer detection equipment classifies cancer cells using different algorithms based on machine learning and deep learning. In-depth learning examines the findings state-by-state and provides an in-depth analysis of the technology research for skin cancer diagnosis.
A review on BCI and VR: From past interactions to future development
Authors: Hao Shuyan
Published: July 2023

A cutting-edge method in the fields of game entertainment and medical rehabilitation is the fusion of virtual reality (VR) and brain-computer interfaces (BCI). However, as the technology is still in its infancy, along with its development, there are many limitations as well that could be improved in the future. This paper reviews the achievements of BCI-VR in two of the most promising fields: neurorehabilitation and game entertainment in the past ten years. Meanwhile, it also summarizes the current limitations in these fields coupled with potential pathways for further research in the future. To conclude, in the realm of neurorehabilitation for the use of BCI-VR, there are technologies from a hybrid BCI-VR system to a closed-loop real-time BCI-VR system for gait therapy system, and even a multi-model BCI-VR. There are also studies on the utility of BCI-VR in clinical medicine. While for the application of BCI-VR in the game entertainment industry, on the one hand, there are quite a lot of achievements ranging from using a multimodal BCI to control the protagonist of a VR game to allowing players to do multitasks, as well as the later improvements on the real-time operation and feedback experience for the user. On the other hand, there are still some limitations such as low accuracy and a limited number of action demands. In the future, the investigation will still be continued.
A variable neighborhood search framework combining improved simulated annealing search to solve the one-dimensional crating problem
Authors: Qiao Gaoyang
Published: July 2023

The N-P problem, similar to bin packing, has a great impact on us in real life. Similar to the classical combinatorial problem, with the expansion of the problem size, usually there is no way to solve it by enumeration. The common solution is to solve by heuristic methods, in addition to the improved local search method by forbidden search or simulated annealing to solve. However, due to the multivariate nature of the bin packing problem, it is difficult to solve most of the bin packing problems by the above single method. In this paper, a variable neighborhood search combined with an improved local search is used to solve the one-dimensional bin packing problem. The method in this paper is generalizable and can easily improve the existing method to suit new problems, and the results are acceptable.
Quantization aware training for efficient reinforcement learning network
Authors: Li Qiaolin
Published: July 2023

In recent years, reinforcement learning has been applied to many areas, especially deep reinforcement learning. Deep learning-based methods use a large computing method known as Quantization Aware Training (QAT). For this project, QAT will be used to investigate the performance of Deep Q-Networks. This method of QAT can reduce memory usage of the network, but may affect the training performance of the network with some games.
The application of artificial intelligence in board games
Authors: Jiang Chengyi
Published: July 2023

The development of artificial intelligence in games has been going on for a long time. In the case of board games, artificial intelligence helps people to play against computers alone. While there have been instances where computers have beaten real people, artificial intelligence is still being refined and modified. In this work, the aim is to investigate how algorithms are used to create artificial intelligence in four different board games. For existing tic-tac-toe, gobang, go game, and chess research, different algorithms or systems are applied to different chess games. From simple to difficult, researchers have come up with more algorithms to screen out the most suitable ones to be applied. For the simpler tic-tac-toe, the researchers also adopted a simpler search tree. As the board size improves in gobang game, the researchers increase more algorithm tools in ordinary search trees to save time. In the later more complex go and chess, the researchers compared some existing techniques and analysed the advantages and disadvantages of various algorithms, and finally concluded the best application for a particular algorithm. Even though current algorithms can help AI run fast and compete with real people, it is believed that more efficient algorithms will emerge in the future to further enhance the power of AI.
Analysis of the survey of voice synthesis technology
Authors: Zhou Zhangyao
Published: July 2023

The original purpose of speech synthesis was to complete the task of converting from text to speech (TTS). With the application of deep learning models in this field of speech synthesis, the results of speech synthesis have gradually reached the level of human voice, which makes it widely used for voice assistant, navigation, reading, intelligent customer service, and many other aspects. In order to help readers expand more research ideas and understand the development process of speech synthesis technology and the ethical choices faced, this article introduces as much as possible in the development process of speech synthesis technology, several frameworks for optimization, as well as interpreting their respective advantages and disadvantages, summarize their approximate results obtained in the MOS test method and analyze the ethical issues that may be brought about by voice cloning technology.
Review on the FPGA design optimization for ASIC
Authors: Lei Xiaohaoyang
Published: July 2023

FPGA is a product of further development on programmable devices such as PAL and GAL. It is not only a chip, but a design pattern. With the rapid growth in FPGA performance and density, its complete top-down model can be applied to the development of similar ASICs while improving the technology itself. This paper shows that a summary analysis of previous articles can conclude that the application of FPGA design to ASICs can lead to optimization of many aspects. Architecture-level embedding and mapping transformation are the current mainstream application methods, both of which can bring strong economic and time benefits. Meanwhile, there are various issues that need special consideration in the application process which is related to the question of whether or not to map and convert. Finally, according to the analysis, we still need to solve two important issues which are the lack of automatic mapping tools and the lack of a unified function library in the future.
Computer network information security and protection strategies in the era of big data
Authors: Sun Yuxuan
Published: July 2023

The Internet has entered the big data era, and as more and more businesses employ big data, cloud computing, the Internet of Things, artificial intelligence, and other technologies, the amount of data on the Internet as a whole has also grown dramatically. While Big Data technology has improved the efficiency of many companies, it has also brought challenges for computer network information security protection technology, including massive data privacy leaks that seriously jeopardize the privacy of Internet users. This paper synthesizes literature review findings proving that computer network information security in the big data era has the characteristics of universality, suddenness, concealment, and intelligence in order to address this issue. It also studies the main network information security technologies, such as network situational awareness, vulnerability scanning, intrusion detection and firewall, and identity authentication. In addition, the paper finds that the current problems of network information security mainly include phishing attacks, computer viruses, ransomware, denial of service attacks, APT attacks and threats from inside the network. Finally, the paper explores a number of protective measures to address existing security issues, including the development of network monitoring and threat intelligence systems, the implementation of access control and authentication technologies, network isolation methods, regular software scanning and updates, and organising internal staff security training. Through this study we are able to fully understand the overall pulse of current computer network information security technology and development trends in the era of Big Data, thus providing a more comprehensive and accurate reference for Big Data-related participants to develop responsive information security protection measures and strategies to provide designation.
A review of techniques used in e-commerce recommendation system
Authors: Yan Ke
Published: July 2023

In the age of the internet, the explosive growth of information makes the e-commerce platform users need more and more requirements for the shopping experience. Recommendation systems are developed to retrieve information efficiently and provide a personalised shopping experience for users. There are three types of algorithms that are most frequently employed in recommendation systems: content-based filtering, collaborative filtering, and hybrid techniques. In this paper, content-based filtering and collaborative filtering are firstly explained in terms of working principles, limitations, and advantages. Then, some hybrid techniques that combine content-based filtering and collaborative filtering are proposed to overcome the problems of these two conventional methods. It is found that while collaborative filtering may struggle with scalability, sparsity, and cold start issues, content-based filtering may have quality issues and a lack of variation. The hybrid techniques are introduced to address the drawbacks of these two algorithms. It is, however, more complex and requires more memory resources to use hybrid techniques. Implementing deep learning in recommendation systems would be a promising area in the future. Through the analysis above, this paper would provide a systematic review of the development of the recommendation system and inform the future study. Students or researchers who are interested in big data can quickly grasp the concepts of techniques used in e-commerce recommendation systems as well as the benefits and drawbacks of different techniques.
Operation and algorithm optimization of short video recommendation algorithm
Authors: Wu Yilin
Published: July 2023

Now that technology is developing rapidly, the field of computing has been showing exponential progress, and scientific and technological civilization of mankind is making continuous progress. Games, live broadcasts, and short videos are all new products under big data. Especially for short videos, the huge database records the preferences of billions of users. With the combination of collaborative filtering algorithms and content-based recommendation algorithms, computers can always accurately recommend suitable videos to various users. In this paper, improvement of this method is aimed at the item-based algorithm in the collaborative filtering algorithm. For the item-attribute matrix, first, use the Jaccard distance to calculate the similarity, and then use this similarity value instead of the Euler distance formula to bring it into the k-means clustering, and use iteration to obtain countless different clusters. Finally, set a threshold x, which is the distance between each cluster center. Whenever there is a new matrix to be classified, the similarity y corresponding to this matrix is calculated first. If y<x, the matrix is classified into the corresponding cluster. This approach can improve the diversity of recommended videos and tap the potential interests of users. Such improvements to the matrix can improve the accuracy of the algorithm and user stickiness.
Review on FPGA-based accelerators in convolutional neural network
Authors: Li Yiming
Published: July 2023

In current research, an important means to realize artificial intelligence is artificial neural network. Many tedious problemscan be solved by artificial neural network, such as image classification, speech recognition, natural language processing. Among neural networks, although convolutional neural network has significantly better performance in the field of image recognition, it requires many parameters and a large amount of computation, and the number of network layers is gradually increasing with the progress of the algorithm, which leads to model sizes are getting larger and more difficult to handle, which has more stringent requirements on various aspects of hardware capabilities, such as computing power, data storage and memory bandwidth. Therefore, the research of acceleration is particularly important, especially in the field of hardware acceleration. And as an emerging hardware platform, field programmable gate array has the characteristics of rapid customization and programmability, which can greatly improve the efficiency of accelerators design, implementation, and verification. As a result, it can be widely used in the hardware-accelerated design of neural networks. Although field programmable gate array itself has some defects, many research results have proved its huge advantages and development potential. This paper mainly summarizes the acceleration of convolutional neural network based on field programmable gate array platform, and discusses its characteristics and application space by comparing with other platforms, showing its high applicability to convolutional neural network, and finally looking forward to the progress of related research work.
Research on the evolution of neural networks models in NLP
Authors: Zhao Ziang
Published: July 2023

Since their first emergence, neural networks have produced significant results and have been an essential tool for solving natural language processing (NLP) problems. This paper aims to provide insight into the development of neural network models and their application in various NLP problems. The efficiency of the different approaches would be analyzed and insight into the different models would be provided. Overall, neural networks have been proven to be essential for solving problems related to NLP, and it is believed that further advancements in newer models would see a surge in performances of questions that were currently considered hard.
Machine learning for Covid-19: A review on forecasting models
Authors: Su Shichen
Published: July 2023

The accurate forecasting of Covid-19 cases remains a challenge even for comprehensive statistical models due to the complex dynamics inherent in largely urbanised populations. This difficulty, coupled with recent developments in artificial intelligence (AI), has led to a burgeoning interest in implementing machine learning (ML) algorithms to aid such predictions. Ever since the outbreak, various ML-based forecasting approaches have been trained and tested to support the design of intervention and healthcare strategies to protect the population. It is thus vital to assess the performance of these algorithms such that their accuracies, along with strengths and weaknesses, are known. This paper compares the forecasting performance of six commonly used ML models for Covid-19 forecasting. A brief analysis of the algorithms suitability for forecasting is also provided as part of the review. It is found that different algorithms were able to predict with varying degrees of accuracies in different scenarios, and that there is yet to be a definitive algorithm that is best for forecasting the Covid-19 outbreak.
Diabetes classification and prediction using artificial neural networks
Authors: Wu Jiajie
Published: July 2023

Diabetes is a chronic disease which threatens the global human health. Over time it could lead to other serious medical problems and does not have a permanent cure. Hence if we could diagnose or predict it early, then it might be possible to prevent it. Several researches have shown that computer technology can effectively assist in the diagnosis of diseases. And neural network could be used for classification and prediction. In order to identify the most significant element that has the greatest impact on the classification and prediction, this paper applies artificial neural networks to the PIMA Indian diabetes dataset. This study's neural network is a fully connected neural network. We used the fully connected neural network discussed above to the dataset that deleted one factor column per time, and we compared their accuracy to determine which factor was the most significant in this dataset. Finally, this study discovers that "Glucose" level, followed by "BMI," is the most crucial component for diabetes in this dataset, with a fully connected neural network having an accuracy of 84%.
Review of application on improved ant colony algorithm
Authors: Ding Zihan
Published: July 2023

A heuristic global optimization algorithm is the Ant colony algorithm, with several advantages, such as robustness, so the algorithm can be used in many fields of our daily life. This article briefly explains some of the principles of the basic ant colony algorithm, and a detailed analysis of the representative improved algorithm models, is carried out. Moreover, the research status of Ant Colony Algorithm in several fields, like travelling salesman problem, path planning problem, routing problem are also summarized in this paper.
Deep learning in automatic music generation
Authors: Zhang Yizhen
Published: July 2023

Music is a grouping of musical tones from various frequencies. While artists composed through a deliberate arrangement of different notes, nowadays, A.I. programs learn to automatically generate short music through a machinal sequence of distinct notes. This essay compared the utility and efficiency of traditional machine learning (Regression Model) and deep learning methods (LSTM). This research only focused on instrumental classical music and used the MusicNet collection as the primary dataset. The comprehensive experiments are conducted from these two models, which suggests two results. Firstly, the LSTM model generates melodies that better fit the training styles. Secondly, models are better fitted on single music data than on the entire dataset.
Generative adversarial network based image inpainting
Authors: Gao Jingjing
Published: July 2023

Image inpainting, which is the repair of pixels in damaged areas of an image to make it look as much like the original image as possible. Deep learning-based image inpainting technology is a prominent area of current research interest. This paper focuses on a systematic and comprehensive study of GAN-based image inpainting and presents an analytical summary. Firstly, this paper introduces GAN, which includes the principle of GAN and its mathematical expression. Secondly, the recent GAN-based image inpainting algorithms are summarized, and the advantages and disadvantages of each algorithm are listed. After that, the evaluation metrics, and common datasets of deep learning-based image inpainting are listed. Finally, the existing image inpainting methods are summarized and summarized, and the ideas for future key research directions are presented and prospected.
Text classification algorithms exploration on sentiment analysis
Authors: Zhang Xupeng
Published: July 2023

Internet provides us with an abundance of useful tools and data. However, it also generates a vast quantity of data that may bewilder us. There must be a technique for automatically processing these data. Here, text classification becomes useful. Text classification is the algorithm-based process of categorizing data inputs into distinct labels. For instance, email software utilizes it to assess if an email should be filtered into the spam folder, social media forums use it to classify postings into labels that are relevant to the topic, etc. Text categorization is utilized in a variety of businesses, including search engines, sentiment analysis, emergency response systems, chatbots, etc. Review websites have emerged in recent years where customers may share their opinions on a business or a product. The review is extremely emotive but crucial to the company. It is possible to accurately assess the reviews for the sentiment they present through text classification. This paper compares the efficacy of various text classification algorithms for sentiment analysis.
Analysis of the practical application value and practice path of big data in epidemic warning
Authors: En He
Published: July 2023

Late in 2019, the unique viral disease coronavirus disease, or COVID-19, initially appeared. On March 11, 2020, the World Health Organization (WHO) proclaimed the COVID-19 outbreak a pandemic. It rapidly spread to every corner of the globe. This paper examines the use and actual application of big data in epidemic early warning. Based on the analysis of the value of big data epidemic early warning mechanisms, this paper divides the current big data epidemic early warning systems into three main categories according to the various channels of data acquisition: early warning systems based on the Internet and communication systems, early warning systems based on electronic medical information, and early warning mechanisms based on the Internet of Things information collection.
A machine learning algorithm for event recognition and classification in -OTDR system based on lenet neural network
Authors: Liu Tianyang
Published: July 2023

The full English name of OTDR is Optical Time Domain Reflectometer. The main principle of its work is the use of light in the optical fiber transmission of Rayleigh scattering and Fresnel reflection generated by the backscattering of precision photoelectric integration instrument. In recent years, deep learning methods for classifying events become more and more popular on account of its importance. But the conventional classifiers perform not that well in accuracy. In this paper, we show how conventional machine learning algorithms performs in our dataset and compare them to our new network basing on Lenet, which aims at increasing the accuracy of classification in -OTDR systems by introducing the residual module on Lenet.
Comparison of algorithms that use deep learning to classify spam
Authors: Dai Congying
Published: July 2023

Although the application of network security protocols and cryptography provides a certain security guarantee for Internet surfing, it is difficult to cure the persistent security problems. Driven by the promotion of e-mail technology and benefits, bad businesses will also issue promotional emails indiscriminately to a large number of mailboxes, and even drive the underground industry of private mailbox information trading. The existing spam filters use black and whitelists, sensitive word matching and other technologies, but they can not effectively filter all forms of spam, and non-spam is often filtered, which brings more trouble to users. With the rise of artificial intelligence, machine learning algorithms have been applied to spam recognition, such as decision tree algorithm, Boosting algorithm, K nearest neighbour algorithm, SVM support vector machine algorithm, Bayesian principle related algorithms, etc. These methods based on traditional statistics can intelligently classify data sets with large differences and are often used together with expert systems with certain rules to classify spam. However, with the diversification of spam types, the old classification rules are relatively rigid, and new types of mail will be misjudged. In addition, statistics based natural language processing method is based on pre trained fixed dictionaries. For new words and polysemy words, it is impossible to give word vectors with accurate semantics, which brings difficulties to classification. This paper mainly studies the application of five machine learning algorithms in spam detection: improved naive Bayes algorithm, A Lite Bidirectional Encoder Representations from Transformer (ALBERT) dynamic word vector algorithm, Bidirectional Gating Recurrent Unit (BiGRU) algorithm, the Inverted Multi-Index with Weighted Naive Bayes (IMI-WNB) algorithm and clustering analysis algorithm.
Performance analysis of using multimodal embedding and word embedding transferred to sentiment classification
Authors: Zou Zhuo
Published: July 2023

Multimodal machine learning is one of artificial intelligence's most important research topics. Contrastive Language-Image Pretraining (CLIP) is one of the applications of multimodal machine Learning and is well applied to computer vision. However, there is a research gap in applying CLIP in natural language processing. Therefore, based on IMDB, this paper applies the multimodal features of CLIP and three other pre-trained word vectors, Glove, Word2vec, and BERT, to compare their effects on sentiment classification of natural language processing, to test the performance of CLIP multimodal feature tuning in natural language processing. The results show that the multimodal feature of CLIP does not produce a significant effect on sentiment classification, and other multimodal features gain better effects. The highest accuracy is produced by BERT, and the Word embedding of CLIP is the lowest of the four accuracies of word embedding. At the same time, glove and word2vec are relatively close. The reason may be that the pre-trained CLIP model learns SOTA image representations from pictures and their descriptions, which is unsuitable for sentiment classification tasks. The specific reason remains untested.
Application of wireless sensor network in telemedicine monitoring
Authors: Ni Haoyu
Published: July 2023

Wireless sensor networks have developed rapidly and have been applied to various fields of life, especially in the medical field, which has made great progress, especially in the telemedicine monitoring system. This paper analyzes and summarizes the application characteristics of wireless sensor in telemedicine detection. Then it points out the challenges faced by the promotion and popularization of telemedicine based on wireless sensor networks, and analyzes the energy consumption and security of wireless sensor networks in detail. Finally, the development of telemedicine monitoring system based on wireless sensor network is summarized and prospected.
Global and local path planning for mass point robots in two-dimensional environments
Authors: Yang Yao
Published: July 2023

Robot path planning is a popular research topic due to its wide application. There are various approaches to apply to path planning, such as artificial potentials-based approach, probability-based approach, retraction-based approach, and heuristic approach. In this paper, the heuristic approach is focused on due to its convenience. In heuristic approach, considering the application, the author chooses fuzzy logic and neural network to apply to path planning. By conducting experiments both on PowerBot and the simulation environment, the methods proposed in the article are feasible. The environment data is collected by the sensors. Next, the fuzzy controller or neural network will process the data, and generate an appropriate path to get to the target without hitting any obstacles or walls.
Reddit sentiment analysis for natural language processing
Authors: li Ang
Published: July 2023

In the Internet age, social media has fully penetrated into people's lives. As one of the well-developed online platforms with a large user base, Reddit allows users to independently publish current news, life experiences, and interesting life stories. However, sometimes it sends a negative tone that affects the brand of a company or individual and destroys profits and it is necessary to prevent Twitter by identifying hate words. The biggest innovation of this post is that we use reddit data to compare various methods simultaneously. As we process more data, trying deep learning will yield good results. Compared to other machine learning classifiers, the transformer classifier achieves the best results.
A lightweight plant disease recognition network based on ResNet
Authors: Duan Yunlong , Han Ziyu , Tang Zhening
Published: July 2023

Identification of foliar diseases is very important for the cultivation of plants. If no diseases are found, the cultivation results may decline, resulting in serious losses of related industries. Most of the early automatic recognition methods of plant leaves are based on manual features and classifiers, and the recognition performance is often unable to meet the actual complex application scenarios. Thanks to the rapid development of convolutional neural networks, such as ResNet, the accuracy of plant disease identification based on deep learning has made a breakthrough. However, convolutive neural network tends to have too many parameters, large amount of calculation and slow training speed, which is difficult to be used in various small and medium-sized plant cultivation industries, especially in small edge computing devices deployed in the field. This paper designs a new lightweight Resnet network structure, namely Resnet-9. The number of network layers in traditional Resnet is reduced. Compared with other commonly used plant disease recognition methods, the accuracy of Resnet is guaranteed and the network is more lightweight. The parameter of this model occupies only 6.6M memory and achieves 99.23% accuracy on public datasets. Even in the other data sets, the accuracy was still 95.15%. The effectiveness of the method is verified by comparative experiment.