bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing fragility functions of unreinforced masonry buildings
Authors: Djaalali Fouzia , Bensaibi Mahmoud
Published: April 2024

The seismic vulnerability entrenched within the historical urban landscapes of Algeria serves as a poignant reminder of the pressing need for comprehensive risk management strategies, particularly concerning low and medium height unreinforced masonry (URM) structures. These architectural gems, rich in cultural significance, stand as tangible embodiments of Algeria's storied past. However, their susceptibility to seismic disturbances presents an immediate threat, demanding meticulous attention and innovative methodologies for effective risk assessment and mitigation. In a pioneering scholarly pursuit, this research embarks on an ambitious exploration, melding fragility functions and a sophisticated vulnerability index method to unravel the intricate tapestry of seismic risk assessment. Through the lens of fragility functions, the study delves into a nuanced analysis, probabilistically delineating the vulnerabilities entrenched within the complex framework of URM buildings. Furthermore, the meticulous derivation of vulnerability functions via the vulnerability index method enhances the granularity of risk assessment, providing a multifaceted perspective to discern vulnerabilities accurately. Central to this investigation is the Belouizdad district, a microcosm of historical significance nestled within the vibrant metropolis of Algiers. By scrutinizing seismic scenarios spanning a spectrum of intensities (ranging from VII to X), the study orchestrates a symphony of empirical data and analytical prowess, unraveling the potential seismic reverberations poised to impact the district's architectural heritage. In essence, this scholarly endeavor transcends the confines of mere academic pursuit, serving as a guiding light illuminating the path towards tailored seismic reduction policies. It stands as a clarion call to safeguard Algeria's architectural patrimony, fostering resilience amidst the tremors of uncertainty and ensuring the preservation of its cultural legacy for generations to come.
Simultaneous versus consecutive administration of cervical traction and neural mobilization in patients with cervical radiculopathy: A randomized controlled trial
Authors: Masood Tahir , Afridi Sabah , Malik Arshad Nawaz , Osama Muhammad , Amir Muhammad
Published: April 2024

Background & Objective: Cervical traction and neural mobilization are frequently utilized in the management of cervical radiculopathy. However, there is a paucity of literature concerning the best order of application of these techniques. The aim of this study was to compare the effects of simultaneous and consecutive administration of cervical traction and neural mobilization on pain and function in cervical radiculopathy.
Methods: Thirty patients were randomly assigned to two equal groups: consecutive (CON) and simultaneous (SIM) cervical traction and neural mobilization in this single-blind randomized controlled trial. The inclusion criteria comprised age 20-60 years, unilateral cervical radiculopathy ≥ 6 months, and positive upper limb neural tension tests of radial, median or ulnar nerve, and positive Spurling test. Outcome measurement tools were numeric pain rating scale, cervical goniometry, neck disability index, and short-form health survey. Data was analyzed with SPSS 23.
Results: Significant improvement was evident in all parameters in CON after 1-week rehabilitation (P≤0.001 for seven of the ten variables). Similarly, all parameters showed significant improvement in SIM (P≤0.001 for all variables except mental component score of SF-12 with P<0.05). After rehabilitation, SIM was significantly better with respect to pain (P<0.05), flexion-extension mobility (P<0.001), and quality of life (P<0.01). No differences were found in other variables.
Conclusion: Simultaneous use of cervical traction and neural mobilization resulted in significantly better outcomes with regards to pain, flexion-extension mobility, and quality of life. The two groups performed similarly on other outcome measures.
Evaluation of cognitive functions and dementia two years after COVID-19 infection
Authors: Yuksel Hatice , Dirik Ebru Bilge , Tutal Gursoy Gorkem , Bektas Hesna , Yamanel Levent , Guner Rahmet
Published: April 2024

Background: The Coronavirus disease 2019 (COVID-19) causes high morbidity and mortality in the acute period. Some patients with COVID-19 continue to have symptoms for a long time. However, data on the long-term effects of COVID-19, particularly on cognitive disorders is scarce. We aimed to evaluate cognitive functions and dementia in patients who survived two years after discharge from hospital due to acute covid-19 infection.
Methods: Two hundred and five patients who had neurological symptoms during index hospitalization for COVID-19 infection were evaluated two years after discharge, and 64 patients were included in the study. According to cognitive assessments, patients were grouped as having normal cognitive levels (48 patients) or impaired cognitive levels (16 patients).
Results: The impaired cognitive level was found in 16 (25.0%) of patients who underwent cognitive tests. Age, the pre-existence of neurological comorbidity, mean intensive care unit day, and Beck’s Anxiety Inventory and Beck’s Depression Inventory scores in the impaired cognitive level group were significantly higher than the normal cognitive level group (p:0.001, p:0.013, p:0.015, p:0.004 and p:0.008). The frequency of cognitive impairment in patients who developed altered mental state during hospitalization was 36.0%, while it was 17.9% in patients with normal mental state. Age, the pre-existence of neurological comorbidity, and anxiety were determined as independent risk factors for impaired cognitive level. In addition, we detected new-onset dementia in 8 patients (12.5%).
Conclusion: We found a high frequency of cognitive impairment and new-onset dementia among COVID-19 survivors who developed neurological manifestations during the acute infection phase.
The intronic variant of SATB2 gene observed in an Indian Glass syndrome family
Authors: Murugasamy Pradeepkumar , Mohan Gomathi , Kaviya Mohandass
Published: April 2024

The Glass syndrome is a rare genetic disorder that is associated with a multisystem disorder due to a SATB2 gene mutation. The intellectual development delay and the delay in speech are the predominant characteristics of the disorder. The reports on Glass syndrome are very few and have not been reported in India. In the present study, we report the Glass syndrome disorder in an Indian mother and her child, both affected by the same likely pathogenic mutation c.1741-2A>C intronic variant at intron 11 of the SATB2 gene. The mutation was observed as a heterozygous and autosomal dominant missense mutation through focused exome sequencing analysis. The variant identified has not been observed in the general population, and the clinical features like cleft palate, speech delay, and intellectual delay match the causative mutation. The present study is novel in exhibiting the intronic variant and identifying Glass syndrome in the Indian population.
Childhood vertigo: A retrospective series of 791 cases
Authors: ÖZDEMİR USLU Zülal , Ardıçlı Didem , Yılmaz Deniz
Published: April 2024

Background: Vertigo is one of the most common complaints leading to admissions in pediatric neurology clinics. Diagnosis of vertigo in children is often challenging due to limited communication capacity in defining symptoms, the unreliability of neurovestibular investigations in children and the variability of etiologies. The aim of this study is to evaluate clinical and etiological profile of vertigo in childhood and to assess the diagnostic yield of neuroimaging and electroencephalography (EEG).
Methods: All patients under the age of 18 years presented with vertigo to the pediatric neurology outpatient clinic of Ankara Keçiören Research and Training Hospital were included in the study. Demographic data, accompanying symptoms, family history, physical and neurological examination findings, neuroimaging, and EEG results were retrospectively analyzed. The etiological spectrum was also evaluated according to age groups.
Results: A total of 791 patients (female/male=2.1) with a mean age of 13.1 years were included in the study. The most common etiologies were vestibular migraine (27.3%) and psychogenic vertigo (14.9%) in the entire cohort; whereas benign paroxysmal vertigo of childhood was the top diagnosis in children ≤12 years (25%). The most common accompanying symptom was headache (36.7%) which was most frequently associated with vertigo attacks in migrainous patients. In most cases there was no correlation between neuroimaging findings and vertigo. Additionally, the contribution of EEG to the diagnosis was found to be limited.
Conclusion: Most of the childhood vertigo cases can be diagnosed accurately by a detailed medical history, physical and neurological examination. A multidisciplinary approach is essential for evaluating childhood vertigo and all the signs and symptoms accompanying vertigo should be considered while planning further investigations including neuroimaging and EEG.
Angong niuhuang pill improves the neurological function of younger patients with basal ganglia and cerebral lobe intracerebral hemorrhage: A randomized controlled trial
Authors: Chen Weiliang , Yao Chunyu , Zhang Taotao , Wang Guanjun
Published: April 2024

Background & Objective: Spontaneous intracerebral hemorrhage (SICH) is one of the stroke forms with the highest disability and mortality rates and significant differences in prognosis among patients with different locations of SICH. Angong Niuhuang Pill (ANP) 安宫牛黄丸 is one of the most well-known traditional Chinese patent medicines used in the clinical emergency treatment of cardio-cerebrovascular diseases. This prospective study was performed to determine the therapeutic effect of ANP in different locations of SICH.
Methods: Patients with basal ganglia(n=81) and cerebral lobe (n=67) hemorrhage were randomly assigned into two groups: the standard medical management (SMM) group and the combined ANP as well as medical management group (ANP group). Fisher exact test and Mann- Whitney U test were used for comparing the differences between two groups. Primary and secondary outcomes were the 6-month modified Rankin Scale (mRS) scores and the 2-week Brunnstrom stage ratings.
Results: For patients with basal ganglia hemorrhage and cerebral lobe hemorrhage, there were no significant differences observed between the ANP group and the SMM group in terms of patient age (53.1±5.3 vs 52.7±6.4; 54.7±4.8 vs 52.7±5.9), sex (male:64.3% vs 53.8%; 63.6% vs 58.8%), body mass index (25.1±2.1 vs 24.2±2.6; 25.6±2.0 vs 25.0±2.4), mean hematoma volume (17.1±6.6 vs 18.6±5.7; 18.4±7.4 vs 18.9±6.0), as well as the proportions of hypertension (88.1%vs71.8%; 90.9% vs 76.5%), alcoholism (40.5% vs 51.3%; 54.6% vs 41.2%), smoking (40.5% vs 38.5%; 36.4% vs 38.2%), and diabetes mellitus (21.4% vs 18%; 12.1% vs 17.7%). The proportion of basal ganglia hemorrhage patients with a 6-month mRS score of 0-2 in the ANP group was significantly higher than that in the SMM group (26.2% vs. 2.6%, P=0.004), but it was not significantly improved in patients with cerebral lobe hemorrhage (57.6% vs 38.2%, P=0.145). The 2-week Brunnstrom stage ratings of patients with basal ganglia hemorrhage were significant difference (P=0.048). But for patients with cerebral lobe hemorrhage, there was no significant difference (P=0.164). The incidence of liver dysfunction, renal dysfunction, gastrointestinal dysfunction, hypothermia and allergy in the two groups of patients was not significantly different.
Conclusions: ANP has different therapeutic effects on different locations of SICH, and has the most obvious effect on improving the long-term neurological function of patients with basal ganglia hemorrhage.
Um novo tomógrafo por micro-ondas para imageamento da mama utilizando braços robóticos
Authors: Costa Lucas Gallindo , Campos Thiago , Oliveira Júnior Everaldo Maciel de , Mendes Hugo Abreu , Liaqat Maryam , Lins Emery Cleiton Cabral Correia
Published: April 2024

Este trabalho apresenta um novo sistema para imageamento da mama por micro-ondas que consiste de um par de antenas sendo automaticamente posicionadas ao redor da mama por braços robóticos, o que proporciona um maior conforto e flexibilidade a pacientes com mamas de tamanhos diferentes, além de possibilitar um número maior de medições. O sistema utiliza uma câmera de profundidade para obter o formato externo da mama e o utiliza para o posicionamento das antenas. O protótipo foi capaz de decidir o posicionamento das antenas para dois fantomas de forma semiautônoma, com mínima intervenção de um operador.
Empreendedorismo acadêmico: uma experiência de empreender na Pós-Graduação
Authors: Rocha Lindalva de Moura , Silva Rafael do Nascimento , Sousa Leanne Silva de , Silva Pedro Wesley Rodrigues da , Oliveira Mirele Alves de , Araújo Elys Rejanne Marques Cruz , Araújo Wesley Fernandes , Paula Gabriela Almeida de , Santos Júnior José Ribeiro dos
Published: April 2024

O empreendedorismo acadêmico faz parte da terceira missão das universidades, no qual envolve diferentes atividades empreendedoras realizadas no meio acadêmico. Faz parte do empreendedorismo a geração de novos produtos ou serviços, que são partes fundamentais da inovação. Uma vez que novos produtos são o ponto de partida para implementar novas empresas. A criação de novas empresas entre estudantes universitários é um elemento importante para o fortalecimento da inovação, especialmente quando as novas empresas criadas são frutos   de   resultados de pesquisa acadêmica realizada na universidade. Desse modo, o presente estudo objetiva apresentar um relato de experiência da trajetória de empreendedorismo acadêmico de um projeto de pesquisa do Programa de Doutorado em Biotecnologia – Rede Nordeste de Biotecnologia (RENORBIO), da Universidade Federal do Piauí (UFPI). O presente trabalho é de caráter qualitativo, tendo por base a descrição das experiências da doutoranda. Um relato de experiência é uma produção de conhecimento cujo texto se refere a uma experiência acadêmica e/ou profissional em um dos pilares do ensino universitário. Os resultados foram gerados a partir da participação da doutoranda nos diversos Editais de inovação. O resultado da participação apresenta a trajetória da pesquisa científica em diferentes programas e editais de fomento, tais como:  CENTELHA/PI, PEIEX, CATALISA ICT - Etapas 1, 2 e 3, Programa IDEIAZ, Rede de Mulheres Empreendedoras - Acelera 2021, INOVATIVA, Startup Nordeste e Fundeci/BNB (Banco do Nordeste do Brasil). Com a conclusão da participação nos programas e editais de fomento foi possível transformar uma ideia de projeto de pesquisa, desenvolvido em laboratório, em uma startup com produtos testados e validados em ambiente relevante de produção, com clientes pagantes.
Judicialização de benefícios do instituto nacional do seguro social no estado de Pernambuco entre 2014 e 2018
Authors: Souza Rodrigo Cézar de , Nogueira João Nicole Tupiná , Félix Giselle de Medeiros , Dourado Ana Claúdia Amorim Gomes , Martins Victor Hugo da Silva
Published: April 2024

Objetivo: caracterizar as decisões judiciais julgadas pela perícia médica judicial acerca dos benefícios do Instituto Nacional do Seguro Social entre os anos de 2014 e 2018 nos juizados especiais federais de Pernambuco. Metodologia: Delineado como estudo transversal, retrospectivo, a pesquisa utilizou o banco de dados da Justiça Federal. As variáveis foram coletadas em processos julgados entre 2014 e 2018 e os dados foram utilizados para testar relações significativas entre os dados. Foram avaliados 422 processos de 3 diferentes Juizados. Resultados: O INSS foi réu em 100% dos processos e benefício em espécie foi o objeto de todas as ações judiciais (100%), sendo o mais frequente o auxílio-doença previdenciário (35,1%). Houve discordância de 48,1% entre a avaliação da capacidade laborativa realizada pela perícia médica judicial e a avaliação da perícia médica administrativa (p<0,001). Observou-se uma concordância entre a hipótese diagnóstica concluída pela perícia médica judicial e a perícia médica administrativa de 69,9%. A perícia médica judicial concluiu haver incapacidade laborativa em 54,0% dos processos. Nos 228 processos com periciados incapacitados, as principais classes das doenças incapacitantes foram doenças do sistema osteomuscular e do tecido conjuntivo (29,4%), doenças do aparelho circulatório (18,0%) e transtornos mentais e comportamentais (17,1%). Nos 125 processos com conclusão de incapacidade permanente, as principais classes das doenças incapacitantes foram transtornos mentais e comportamentais (28,8%), doenças do aparelho circulatório (21,6%) e doenças do sistema osteomuscular e do tecido conjuntivo (17,6%). Foi registrada associação significativa (p < 0,001) entre o tipo de incapacidade laborativa e as variáveis situação laboral, tipo de vínculo e histórico previdenciário. A sentença do magistrado considerou a conclusão da perícia médica judicial para fundamentar a decisão judicial em 99,3% dos processos. Conclusão: são múltiplos os fatores determinantes do aumento progressivo da litigiosidade em processos judiciais contra o INSS nos Juizados Especiais Federais. Existe divergência significativa na avaliação da capacidade laborativa entre a perícia médica judicial e a perícia médica do INSS. A fundamentação jurídica das sentenças judiciais converge com melhor avaliação de cada caso, bem como corrobora na elucidação dos mesmos diante do aumento progressivo no número de processos judiciais. A perícia médica judicial tem importância significativa na decisão judicial.
Atenção primária à saúde e apoio matricial: a nasf e as contribuições do profissional de educação física para a saúde da mulher
Authors: Carvalho Cleyton Dias de , Kamimura Quésia Postigo , Oliveira Edson Aparecida Araújo Querido , Postigo Sóstenes , Cornetta Vitória Kedy
Published: April 2024

A Atenção Primária à Saúde (APS) é reconhecida nacional e internacionalmente como princípio norteador de sistemas de saúde integrais que visam ampliar o acesso e a utilização dos serviços e das ações para alcançar a saúde universal.  O estudo, desenvolvido no período de pandemia, tem como objetivo analisar a contribuição do profissional de Educação Física na qualidade de vida das mulheres cadastradas no NASF (Núcleo de Apoio à Saúde da Família), na cidade de Imperatriz, Estado do Maranhão, Brasil. O estudo, de abordagem qualitativa, foi realizado durante o período de pandemia, por meio de entrevista semiestruturada via plataforma zoom, aplicado por quatro profissionais de educação física que trabalham nos NASF, por ser um período de pandemia Covid-19. Para a análise dos dados utilizou-se da técnica análise temática e contou com o apoio do software Iramuteq. Os principais resultados mostraram a importância da atividade física à saúde da mulher, no entanto, os profissionais apontam para a necessidade de estrutura física para essas ações, que se utilizam das academias da saúde, pátios dos postos de saúde e das igrejas e durante a pandemia as atividades foram limitadas às atividades ao ar livre. Conclui-se que é primordial aprimorar políticas de saúde que ampliem o atendimento efetivo do NASF em ações de promoção de saúde e prevenção de doenças, por meio de equipe multidisciplinar que contempla o profissional de educação física, podendo desenvolver abordagens de cunho prático no que se refere ao suporte para a gestão da saúde da mulher e ao desenvolvimento local.
Proposition of empirical equations to describe the kinetics of water absorption by film-coated maize seeds during germination
Authors: Gomes Michael Marcos de Aquino , Silva Wilton Pereira da , Melo Bruno Adelino de , Gregório Mailson Gonçalves , Saraiva Maria Monique Tavares , Gomes Josivanda Palmeira , Silva Rosilene Agra da
Published: April 2024

Maize is a crop widespread in various parts of the world, serving as a source of food for humans and animals and also as a source of energy. Seeds can be attacked by pathogenic microorganisms and, a way to protect them against these attacks, it is to coat them with suitable products. In this paper, empirical equations with 2 and 3 parameters are proposed to describe the kinetics of water absorption by film-coated maize seeds during germination at temperature of 10 °C and atmospheric pressure of 71 cmHg. A total of 14 experiments involving different types of film on the seeds were carried out, plus a control experiment without film coating. A computer program, which automatically fits about 200 functions available in its library to the experimental data, was used for each of the 15 datasets. This program uses non-linear regression to generate a classification of the best-fitted functions, based on the value of the reduced chi-square of each fit performed. The best equations with 2 and 3 parameters obtained in this study were compared with each other, and the one that best described the kinetics of water absorption by the seeds was chosen. Also, the equation chosen in this study was compared with other empirical equations available in the literature. In all comparisons, the equation obtained and proposed in this study showed better statistical indicators than the empirical equations available in the literature. It was also possible to conclude that the film coating treatments did not significantly interfere with the value of the final moisture content (about 35%, w.b.), but influenced the time of the seed germination process (between 24 and 40 days).
Risk Analysis Research on SMS Verification Code and Biometric Recognition Technology
Authors: Yu Likun
Published: April 2024

This paper delves into the ongoing transformation in the domain of digital identity security. It specifically focuses on the paradigm shift from traditional methods of authentication, such as passwords and text message verification codes, towards the use of biometric recognition technology. By scrutinizing the inherent weaknesses linked with the utilization of text message verification codes, the paper underlines the demand for more trustworthy and secure solutions for authentication. Following this, biometric authentication is thoroughly examined, with a highlight on its unique features, the convenience it provides to users, and its capacity for ongoing verification. The discussion extends to practical applications of biometrics in a variety of sectors, including healthcare and high-security workplaces, as well as in situations where employing text message verification is unsuitable. A focal point of the discussion is multi-modal biometric authentication. While acknowledging the potential hurdles and future possibilities tied to the adoption of biometric technology, the paper offers an objective perspective. The overall intent of this research is to furnish insights that will enable informed decision-making regarding the deployment and improvement of digital identity verification methods across a wide range of industries.
The Investigation Related to Application of Federated Learning’s in Chest X-ray Detection
Authors: Tan Haotian
Published: April 2024

Following the global emergence of COVID-19 in 2020, the significance of Chest X-ray detection has grown exponentially as it plays a crucial role in diagnosing respiratory conditions. Concurrently, the evolving federated learning framework has been progressively integrated into the medical field, particularly in conjunction with Chest X-ray detection. This integration reflects a promising trend in enhancing collaborative diagnostic capabilities and leveraging collective knowledge across diverse medical institutions. Based on this background, this article provides a thorough review of medicine detection related federated learning frameworks and federated models, summarizes the characteristics and methods of federated models that have been widely used in various experiments in recent years, discusses and analyzes their advantages and disadvantages, and compares their performance with existing other machine learning models. In conclusion, the federated model outperforms non-federated machine learning models when it comes to analyzing Chest X-ray images and predicting symptoms. Lastly, this article outlines potential risks and offers improvement suggestions for the implementation of federated learning in chest X-ray detection.
Evaluative Comparison of Machine Learning Algorithms for Precision Diagnosis in Breast Cancer
Authors: Li Jiarui
Published: April 2024

Breast cancer remains a prominent issue in worldwide public health, exhibiting a gender disparity that primarily impacts women. This study systematically evaluates the diagnostic capabilities of various machine learning algorithms in predicting breast cancer recurrences. Utilising a dataset of 569 data points, the algorithms scrutinised include Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), XGBoost (XGB), Logistic Regression (LR), and K-Nearest Neighbours (KNN). Principal Component Analysis (PCA) was applied and employed with the algorithmic evaluation for selecting features and reducing dimensionality. The study utilised multiple evaluative metrics, focusing on Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values. The findings suggest that Logistic Regression and Support Vector Machines performed better than the other algorithms. Specifically, Logistic Regression achieved an AUC value of 99.77%, and Support Vector Machines achieved an AUC value of 99.74%. Additionally, these algorithms demonstrated an accuracy rate of 97.37%, precision of 97.62%, recall of 95.35%, F1 score of 96.47%, and Cohen's Kappa coefficient of 94.37%, consistent. The study suggests potential avenues for further investigation into the utility of machine learning algorithms and dimensionality reduction techniques in diagnosing breast cancer recurrence. These preliminary findings have the potential to make a valuable contribution to the current discourse around the use of machine learning technologies within healthcare environments
Data Storage and Reliability Analysis on the Internet of Things Based on Pyramid Code
Authors: Zhao Xudong
Published: April 2024

As the IoT (Internet of Things) develops, computers and devices generate a lot of data that needs to be processed and stored. Pyramid code, as an update of the Reed-Solomon code, is suitable for correcting data failure on various sides as an erasure code.  Not only does Pyramid code reduce the cost of computation, but it can also meet the reliability and security requirements of IoT. The convenience of the Pyramid code is evaluated by comparing the (15, 12) RS code to the (16, 12) Pyramid code in this paper. Then, section 4 investigates how Pyramid code can match the needs of reliability, security, cost-effectiveness, etc. After that, the following section aims to give the applications of Pyramid code in IoT. There are many applications and this section concludes the several outstanding scenarios, such as healthcare, smart city, etc.
Research Advanced in Technology and Applications of Image Recognition Based on Deep Learning
Authors: Lu Zihan , Qiao Guanyu , Zhang Shaozhe
Published: April 2024

Image recognition is a fundamental research problem in the field of computer vision, which aims to build models to recognize the category of a given image. Early image recognition methods mainly relied on artificially designed features, and their performance could not meet actual application needs. Thanks to the rapid development of deep learning technology, convolutional neural networks can extract deep information from images to better represent image semantics. Therefore, image recognition based on convolutional neural networks has attracted a lot of research attention in recent years and has been widely used in many fields. Focusing on image recognition and its applications, this paper first introduces common image recognition frameworks, and focuses on the latest research progress in image recognition based on deep learning. Then, this article summarizes the scientific research results of deep learning in agriculture, medicine, transportation and other application fields in recent years. Finally, this paper discusses the development trend of deep learning in the field of image recognition, and expounds the future development trend of image recognition.
Different Types of Neural Networks and Applications: Evidence from Feedforward, Convolutional and Recurrent Neural Networks
Authors: Pan Yumin
Published: April 2024

Neural networks have achieved great process in the 90 years since they were officially introduced in 1943. Because of its wide application and huge research and development potential, this technology attracts more and more scientific and technological workers to the research of neural networks. Neural network technology is an essential component of AI development, and it is a significant indicator of a country's overall strength. In this paper, this study will demonstrate Feedforward Neural Network, Convolution Neural Network and Recurrent Neural networks and evaluate them through datasets from kaggle.com. and CSDN (China IT community). Through this paper, readers can have a better outlook and understanding of the operating principles of each type of neural network as well as their specific jobs (what kind of jobs they specialized in) and each application of these neural networks. So that this paper can promote readers' thoughts and help them start learning neural networks or be a supplement or reference for future scholars. In the end, this paper will present the outcome, which is the evaluation of the accuracy, loss curve, and accuracy curve of neural networks.
Exchange rate forecast between Euro and US dollar based on recurrent neural network
Authors: Tao Yunze , Sheng Xia
Published: April 2024

In the global financial market, the euro and the US dollar are the two most important currencies. The euro is the official currency of EU member states and the second largest trading currency in the world. Its exchange rate is determined by market supply and demand. The US dollar is the world's largest trading currency, and its position in international trade and financial markets is irreplaceable. They play an important role in international trade, financial markets, and travel. It plays an important role in various aspects and has a profound impact on the global economy. The relative value of these two currencies is influenced by various factors, and predicting their exchange rate movements is of great significance for investors, economists, and policymakers. This paper introduces a method to predict the exchange rate between Euro and US dollar using recurrent neural network (RNN). By processing and analyzing historical exchange rate data, the RNN-based model is trained and used to predict future exchange rate changes. Our model shows high accuracy, which proves the predictive ability of RNN in this respect. Deep-learning-based forecasting models and their applications in finance have attracted considerable attention in recent research (Fischer and Krauss 2018; Huck 2019; Kim et al. 2019).
Research on Federated Learning Algorithms in Non-Independent Identically Distributed Scenarios
Authors: Wu Jikun , Yu JiaHao , Zheng YuJun
Published: April 2024

Federal learning is distributed learning and is mainly training locally by using multiple distributed devices. After receiving a local parameter, a server performs aggregation and performs multiple iterations until convergence to a final stable model. However, in actual application, due to different preferences of clients and differences in local data of different clients, data in federal learning may be not independently and identically distributed. (Non-Independent Identically Distribution). The main research work of this article is as follows: 1)Analyze and summarize the methods and techniques for solving the non-IID data problem in past experiments.2) Perform in-depth research on the basic methods of federal learning on non-IID data, such as FedAvg and FedProx.3) By using the FedAvg algorithm, using the CIFAR-10 data set, the simulation method is used to simulate the number of types contained in each client, and the distribution of the data set divided according to the distribution of Dirichlet to simulate the non-independent identical distribution of data. The detailed data analysis is made on the influence of the data on the accuracy and loss of model training.
Comparative Analysis of ARIMA and LSTM Models for Agricultural Product Price Forecasting
Authors: Xia Chenxi
Published: April 2024

The fluctuations in vegetable prices can have an impact on the economy. Machine learning can identify price trend changes. This study investigates the performance of ARIMA and LSTM models in predicting price trends for agricultural products, focusing on greens and lotus roots. The objective was to ascertain the superior model in terms of accurately reflecting market oscillations—a critical aspect for stakeholders in the agricultural sector. The investigation contrasted the ARIMA model's adeptness at detecting linear tendencies against the LSTM's capacity to decode intricate nonlinear dynamics. Empirical assessment employing metrics such as RMSE, MAE, and SMAPE disclosed a consistent supremacy of the ARIMA model over the LSTM in both datasets. This was particularly evident within the lotus root forecasts, where the discrepancy in error metrics for LSTM was remarkably pronounced. The outcomes indicate that notwithstanding the sophisticated structure of LSTM, ARIMA models maintain their status as robust and precise tools for agricultural time series forecasting. This is especially pertinent in contexts demanding computational efficiency and model interpretability. This investigation evaluates the efficacy of ARIMA and LSTM models in forecasting time-series data for agricultural commodities.
Analysis Of the Principle and Application of Machine Learning for Music Composition
Authors: Sun Yingkai
Published: April 2024

Contemporarily, machine learning and artificial intelligence are undergoing rapid developments in the field of arts. This paper explores the principles and applications of machine learning in music composition, tracing back to its inception in 1950s, and taking a brief look at the first works in the field of computer-generated music. It delves into the key principles of music composition using machine learning, and discusses the theory behind major models of recurrent neural networks (RNNs) and Generative Adversarial Networks (GANs) and how they are utilized in music composition. This paper also discusses about the real-world applications and potentials of current models, and finally analyze key limitations present in this field and points out a few directions for future works. This paper gives a brief and simple overview of the current status of machine learning in music composition, and provides an assistance to individuals interested in exploring this field and a valuable insight for future innovations.
The Investigation of Performance Comparison for VGG, YOLO, and DINO in Image Classification
Authors: Chen Yanqi
Published: April 2024

The rise of artificial intelligence has led to a proliferation of deep learning models, yet there remains a noticeable shortage of comparative analyses, particularly among computer vision models rooted in different design philosophies. As such, this study seeks to delve into the strengths of various models through an examination of their structural attributes, with the aim of offering insights that can inform the development of more high-performing models in the future. This study first selects three representative models with different design ideas in their respective research directions, and preliminarily distinguishes the differences between different models. Then, through experiments on the dataset, the performance of different models is obtained, and the reasons for their current performance are analyzed. In this experiment, four models, VGG16, YOLOv5, YOLOv8, and DINOv2, were deployed and tested using the Fruit 360 dataset. The final accuracy was 0.955, 0.997, 0.998 and 0.986, respectively. The accuracy of YOLO model and DINO model was much higher than that of VGG model. The reason for this result may be related to the introduction of anchor boxes in the YOLO model and attention mechanisms in the DINO model, both of which indirectly increase the receptive area for feature extraction. The YOLOv8 model has a slight improvement in accuracy compared to the YOLOv5 model, possibly due to its use of a decoupled head, which reduces the impact of location information on classification tasks.
Research on multi-beam line measurement based on theoretical derivation and particle swarm optimization model
Authors: Chen Xuanliang , Liu Weiye , Chen Ruoyu
Published: April 2024

Starting from the geometric model formed by the multi-beam line measuring device for seabed mapping, this paper makes geometric analysis of the two-dimensional plane and three-dimensional map respectively and establishes the mathematical expressions of the relevant parameters, and calculates the mathematical function relationship of the relevant parameters according to the motion of the surveying ship on the sea level and the distribution of the survey lines. Two and three dimensional models are established to improve the accuracy of surveying submarine depth and the utilization rate of multi-beam line measuring device. By using geometric relation in two-dimensional plane, linear function relation is established and related parameters are solved. Based on the working principle of analyzing the solution method of the full coverage shortest path, a benchmark is established, the measured line is perpendicular to the sea boundary, the parameter values at this time are calculated, the dynamic programming model is established, the local optimal solution is obtained by particle swarm optimization algorithm, and the optimal solution is obtained by cyclic traversal. The minimum number of survey lines N and the minimum total measurement length L are obtained
Visual analysis of behavioral differences in online dating by age and gender
Authors: Hou Xirui
Published: April 2024

With the development of science and technology, people's dependence on the Internet and spiritual needs have increased simultaneously, so online dating has gradually become popular. Because high-quality online dating can increase people's happiness and benefit people's physical and mental health, and people's multifaceted preferences will affect the choice of dating partners and the success rate of online dating, so the research on users' behavior preferences in online dating is of great importance. This paper uses the method of data visualization to conduct research, focusing on the comparative study of behavioral preferences in online dating among different ages and genders. The analysis shows that the elderly and women in the dating market are relatively passive. At the same time, people still have certain prejudices against online dating, and there may be some people who do not aim to obtain a romantic relationship.
Enhancing User Experience Using a Framework Integrating Emotion Recognition and Eye-Tracking
Authors: Wang Jianyi
Published: April 2024

In a rapidly evolving digital landscape, ensuring a seamless and engaging user experience (UX) has become paramount. This study delves into the intricate realm of web interaction design, aiming to enhance user satisfaction and engagement. Through the integration of emotion recognition and eye-tracking technologies, a profound relationship between user emotions and web interface design is unveiled. The central theme of this research revolves around the integration of emotion recognition and eye-tracking technologies to evaluate web interaction designs. An experimental study involving 24 participants from diverse backgrounds and age groups was conducted. These participants navigated web interfaces that featured both emotion recognition and eye-tracking technologies. Three distinct tasks were carefully crafted to represent a spectrum of web interactions, ranging from basic navigation to complex decision-making. Data collection involved real-time valence and arousal scores, video recordings, visual attention patterns, task completion times, and questionnaires. The analysis revealed a series of significant discoveries. Users who engaged with simplified web versions consistently exhibited elevated valence values, indicative of heightened positive emotional feedback, diverging starkly from their counterparts navigating complex web iterations. The dynamic ebb and flow of emotional states, underscored by arousal levels, underscore the pivotal role of real-time emotional assessment in the critical evaluation of web interfaces. Moreover, the study unveiled a persistent preference for a state of emotional calmness during interactions, demonstrating a universal need for user-centered design principles that prioritize minimal cognitive load and emotional tranquility. In summation, this research contributes a robust framework to the design community and academia for the comprehensive evaluation of web interaction designs. The findings underscore the paramount significance of simplicity, real-time emotional evaluation, and unwavering adherence to user-centered design principles in the realm of web interaction. This study constitutes an invaluable repository for designers, developers, and researchers, steering their endeavors towards the relentless pursuit of optimized user experiences within the ever-evolving digital landscape.
Navigating the Path to Responsible AI: Interpretable Models and Ethical Implications
Authors: Zhu Alex
Published: April 2024

In the realm of responsible AI development, this study undertakes a thorough exploration of interpretable and transparent deep learning models, recognizing their pivotal importance in shaping the future of artificial intelligence. It rigorously investigates a broad spectrum of strategies, ranging from fundamental feature visualization and extraction techniques to advanced methods such as Local Interpretable Model-agnostic Explanations (LIME), Explainable AI (XAI) tools like SHAP and Integrated Gradients, and inherently interpretable architectures like decision networks. These multifaceted approaches collectively serve to demystify the inner workings of complex AI models, providing invaluable insights into their decision-making processes. Furthermore, this research extends its purview to encompass the ethical dimensions of AI, elevating its significance beyond technical prowess. It places a resolute emphasis on addressing bias mitigation and ensuring fairness, establishing robust mechanisms for accountability and transparency, conducting rigorous analyses of societal impacts, and bolstering data privacy and security protocols. These ethical considerations are recognized as critical pillars in the foundation of responsible AI development, with the potential to build and maintain public trust in AI technologies while simultaneously aligning these innovations with the values and expectations of society at large.
Dataset Augmentation for Counteracting Bias in Toxic Comment Classification
Authors: Cheng Senhao
Published: April 2024

Toxic comments are a prevalent issue on online social media and networking platforms. These comments contain offensive, malicious, hate speech, or other harmful content that negatively impacts audiences and communities. Effectively detecting and categorizing toxic comments is essential for maintaining order in the online environ ment, protecting user safety, and enhancing user experience. This is despite the fact that researchers and companies have developed various models to recognize toxicity in online chats and comments, achieving some success. However, many of the currently used models incorrectly classify non-toxic comments that contain certain identity terms as potentially toxic. This misclassification hinders the ability to accurately identify categorized comments. In this paper, the detection and classification of toxic comments were implemented using Term Frequency-Inverse Document Frequency (TF-IDF) and machine learning techniques. Additionally, two dataset-specific optimizations were proposed to mitigate the impact of bias on text classification by expanding the number of datasets. Comparative analysis of bias evaluation metrics demonstrates that this approach can effectively mitigate bias while maintaining the accuracy of the original model as much as possible.
An Optimized Deep Learning Model for Stock Price Prediction Using Bi-Directional LSTM with Multi-Inputs and Multi-Steps
Authors: Tian Zhuoqun
Published: April 2024

Predicting stock prices accurately is an inherently challenging task due to the dynamic and fluctuating nature of various influencing factors. However, with the advent and implementation of deep learning, achieving precise stock predictions has become feasible. This study employs Bi-Directional Long Short-Term Memory (LSTM) models to forecast the closing stock price of Tesla for the following day. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are selected as indicators to show methods' performance. A new variable has been created through open and close stock price. The original method uses close stock prices as the only input for prediction. In contrast, the modified method uses both the open stock price and the created new variable for calculating close stock price. Both methods' parameters are firstly trained and adjusted on training and validation dataset for their best performance. Finally, both methods are applied to test dataset and the value of both indicators depict their prediction performance.
Evaluation Of Sailing Boat Performance Based on Ridge Regression and Mathematical Model Optimization
Authors: Li Zhang
Published: April 2024

The traditional linear regression model can analyze and determine the coefficient between variables, reflecting the interpretation degree of variables in the equation. However, if the model is too complex, the training data is too few or too extreme, overfitting will occur, and the prediction effect of multicollinearity data is not good. The regularized ridge regression model can solve this problem. In this paper, Ridge regression analysis is carried out on the sailing performance data. Firstly, the data are preprocessed, and then the root-mean-square error RMSE=10.68 is obtained through the ridge regression model evaluation. The model fitting effect is good, and the regression coefficient corresponding to the variable Fr is the largest, indicating that the Froude number is the most obvious to the performance of the yacht. The variable beam draft ratio of B/Dr Is the least important to the yacht's performance. The conclusion of this paper can predict the performance of yachts more accurately, and provide corresponding improvement suggestions for enterprises.
The Latest Progress in Human-Computer Dialogue System
Authors: Feng Yanbo
Published: April 2024

The Human-Computer dialogue system allows a computer to make conversations with humans through natural languages, and some designs can accomplish tasks given by humans. The development of the Human-Computer dialogue system can significantly expand the range of users for computers. The profound ELIZA Program designed scripts containing keywords and their corresponding rule-based sentence transformation to respond to the user. As the computation power increases, Human-Computer dialogue systems are handling more varied and complicated scenarios by introducing deeper and more complex artificial neural networks. The designer of a task-oriented system can choose between the pipeline method and the end-to-end method. The pipeline method is basically a pipeline that lets the user input go through three parts handling Natural Language Understanding, Dialogue Management, and Natural Language Generation, to generate an appropriate response back to the user. The end-to-end method, however, uses joint models to allow the parts to interact with others and get more efficient in handling the information. With the development of ChatGPT, more general language models and more varied methodologies are becoming more prevalent in dialogue systems.