bluuebunny/crossref_metadata_2025 huggingface dataset:
Depositional system and plant ecosystem responses to long-term low tempo volcanism, the Interbasaltic Formation, Antrim Lava Group
Authors: Beresford-Browne Adam , Jolley David , Millett John , Stevenson Carl , Watt Sebastian , Raine Rob , Carter Elliot
Published: November 2023

The Antrim Lava Group of NE Ireland comprises a volcanic sequence dominated by basaltic lava flows. Including subsidiary sedimentary interlayers and some evolved lavas and intrusions, the overall sequence reaches a cumulative thickness of ∼800 m. The tempo of eruption of the Antrim Lava Group is poorly constrained but can be evaluated via weathering patterns and environmental reconstructions derived from lava-flow interbeds. In this contribution, we present palynology from a newly identified and well-developed 2.0–2.5 m thick sedimentary sequence (interbed) at Ross's Quarry, Ballycastle, Co. Antrim, that helps elucidate the contemporary development of environments in a setting subject to periodic basaltic volcanism. The interbed is subdivided into geologically distinct subunits of cross-bedded and parallel-bedded sandstones and sandy siltstones, all rich in visible organic remains such as rootlets and fragments of wood and bark. A total of 19 samples was collected from the sequence and subsequently analysed for palynological content. The palynomorph data point toward a diversity of inputs ranging from estuaries, chalky soils, dry soils, swamps, lakes, floodplains, sand bars, wet soils, established bogs and fenlands. In contrast to current understanding, the palynological data and their inferred environments collectively reveal the presence of flora that favour a temperate climate rather than the subtropical climate that has previously been inferred from the lateritic interbeds of the Antrim Lava Group. By combining the Ross's Quarry observations with palynological data from other quarry sites and boreholes in Antrim, we provide new insights into the climate, weathering systems and eruptive history of the Antrim Lava Group.
Dear Diary
Authors: Neville-Neil George V.
Published: November 2023

While a debug log is helpful, it's not the same thing as a laboratory notebook. If more computer scientists acted like scientists, we wouldn't have to fight over whether computing is an art or a science.
Nudges to Mitigate Confirmation Bias during Web Search on Debated Topics: Support vs. Manipulation
Authors: Rieger Alisa , Draws Tim , Theune Mariët , Tintarev Nava
Published: November 2023

When people use web search engines to find information on debated topics, the search results they encounter can influence opinion formation and practical decision-making with potentially far-reaching consequences for the individual and society. However, current web search engines lack support for information-seeking strategies that enable responsible opinion formation, e.g., by mitigating confirmation bias and motivating engagement with diverse viewpoints. We conducted two preregistered user studies to test the benefits and risks of an intervention aimed at confirmation bias mitigation. In the first study, we tested the effect of warning labels, warning of the risk of confirmation bias, combined with obfuscations, hiding selected search results per default. We observed that obfuscations with warning labels effectively reduce engagement with search results. These initial findings did not allow conclusions about the extent to which the reduced engagement was caused by the warning label (reflective nudging element) versus the obfuscation (automatic nudging element). If obfuscation was the primary cause, this would raise concerns about harming user autonomy. We thus conducted a follow-up study to test the effect of warning labels and obfuscations separately.

According to our findings, obfuscations run the risk of manipulating behavior instead of guiding it, while warning labels without obfuscations (purely reflective) do not exhaust processing capacities but encourage users to actively choose to decrease engagement with attitude-confirming search results. Therefore, given the risks and unclear benefits of obfuscations and potentially other automatic nudging elements to guide engagement with information, we call for prioritizing interventions that aim to enhance human cognitive skills and agency instead.
Backstepping Sliding Mode Control Algorithm for Unmanned Aerial Vehicles Based on Fractional-Order Theory
Authors: Zhang Yanzhu , Zhuang Bo , Ma Chunhao , Zhang Cheng
Published: November 2023

Aiming at the problems of slow convergence speed and low tracking accuracy in attitude control and position tracking of quadrotor unmanned aerial vehicles (UAVs). This paper combines the fractional-order calculus theory with the backstepping sliding mode control algorithm, using the backstepping control to compensate for the nonlinearity of the system and the fractional-order theory to eliminate the jitter brought about by the sliding mode control, and proposes a new fractional-order backstepping sliding mode control strategy for the trajectory tracking control of the quadrotor UAV. The proposed fractional-order sliding mode surface increases the control flexibility and improves the robustness and anti-interference ability of the system to some extent. The stability analysis of the system is carried out using the Lyapunov stability theory, and the results prove the stability of the proposed controller. Finally, the effectiveness and feasibility of the proposed method are verified by comparing it with the traditional backstepping sliding mode controller. The simulation results show that the fractional-order reverse-step sliding mode control algorithm proposed in this paper is significantly better than other control algorithms in terms of convergence speed and also has a certain degree of superiority in terms of error elimination.
“It Is A Carrot-Stick Model”: A Qualitative Study of Rural-Serving Clinician and Rural-Residing Veteran Perceptions of Requirements to Quit Smoking prior to Elective Surgery
Authors: Golden Sara E. , Young Allison , Sun Christina J. , Mayeda Marissa Song , Katz David A. , Vander Weg Mark W. , Gundle Kenneth R. , Bailey Steffani R. , Ossip Deborah
Published: November 2023

Introduction. Some medical centers and surgeons require patients to stop smoking cigarettes prior to elective orthopaedic surgeries in an effort to decrease surgical complications. Given higher rates of smoking among rural individuals, rural patients may be disproportionately impacted by these requirements. We assessed the perceptions and experiences of rural-residing Veterans and clinicians related to this requirement. Methods. We conducted qualitative semistructured one-on-one interviews of 26 rural-residing veterans, 10 VA orthopaedic surgery staff (from two Veterans Integrated Services Networks), 24 PCPs who serve rural veterans (14 VA; 10 non-VA), and 4 VA pharmacists. Using the knowledge, attitudes, and behavior framework, we performed conventional content analysis. Results. We found three primary themes across respondents: (1) knowledge of and the evidence base for the requirement varied widely; (2) strong personal attitudes toward the requirement; and (3) implementation and possible implications of this requirement. All surgery staff reported knowledge of requirements at their institution. VA PCPs reported knowledge of requirements but typically could not recall specifics. Most patients were unaware. The majority of respondents felt this requirement could increase motivation to quit smoking. Some PCPs felt a more thorough explanation of smoking-related complications would result in increased quit attempts. About half of all patients reported belief that the requirement was reasonable regardless of initial awareness. Respondents expressed little concern that the requirement might increase rural-urban disparities. Most PCPs and patients felt that there should be exceptions for allowing surgery, while surgical staff disagreed. Discussion. Most respondents thought elective surgery was a good motivator to quit smoking; but patients, PCPs, and surgical staff differed on whether there should be exceptions to the requirement that patients quit preoperatively. Future efforts to augment perioperative smoking cessation may benefit from improving coordination across services and educating patients more about the benefits of quitting.
Exploring Appropriate Strategies for Vaccination against Classical Swine Fever under a Dynamic Change in Antibody Titer in Sows after Starting Vaccination in a Japanese Farm Setting
Authors: Ukita Makoto , Kuwata Keisuke , Tanaka Eiji , Matsuyama Ryota , Isoda Norikazu , Sakoda Yoshihiro , Yamamoto Takehisa , Makita Kohei
Published: November 2023

After 26 years of absence in Japan, a classical swine fever (CSF) outbreak occurred at a domestic pig farm in 2018. Vaccination against the CSF virus with a live attenuated vaccine at pig farms was restarted in October 2019, which was 13 years after the 2006 ban on vaccination. An individual-based simulation model for CSF antibody dynamics was developed to determine an effective CSF vaccination strategy for pig populations. In creating a simulated pig herd, the optimal vaccination age of piglets and the effect of vaccinating piglets twice were evaluated. Additionally, the herd immunity was monitored every 6 months for 4 years after the start of vaccination, and the effects of intensive sow replacement policies were assessed. The simulation results indicated that the vaccination age should be delayed relative to the age used before the 2006 ban on vaccination and shifted earlier, from 8 weeks to 6 weeks, as time elapses. The simulations indicated a tradeoff in protection between the weaning period (i.e., maternally derived antibodies) and the fattening period (i.e., by vaccine-induced antibodies). Mixing sows with high and low antibody titers, particularly sows that received the first vaccination and those born after the start of vaccination, resulted in a high variation in antibody titer among pigs on the farm. This study also clarified the positive effect of intensive sow replacement strategies on shortening the period in which sows show diverse titers. Differences in sow replacement rates among farms and/or the time lag in starting vaccination in different prefectures result in heterogeneity in herd immunity in Japan; thus, herd immunity status should be examined at every farm using this simulation model.
Comparison of Chest Computed Tomography (CT) Imaging Patterns and Severity among COVID-19 Patients during the First and Fourth Waves in Addis Ababa, Ethiopia
Authors: Baharu Lensa Million , Alwan Amir , Dellie Seife Teferi , Legesse Tesfaye Kebede , Abore Kibruyisfaw Weldeab
Published: November 2023

Background. Studies done globally had shown that chest imaging patterns of Corona virus disease 2019 (COVID-19) infection varied depending on the strains of the virus and the waves of the pandemic. There is no published literature done in Ethiopia to examine whether there is any difference in chest computed tomography (CT) findings of COVID-19 patients during the first and fourth waves. Thus, this study tries to fill the gap of knowledge in that regard. Objective. To describe and compare chest CT scan imaging pattern and assess the predictors of chest CT severity of the first and fourth wave of COVID-19 infection. Methods. An institution-based cross-sectional study was conducted on 200 polymerase chain reaction test confirmed COVID-19 patients who underwent chest CT scan imaging in two diagnostic centers in Addis Ababa city. Pioneer and Wudassie diagnostic centers were selected due to the high case load and availability of well-experienced cardiothoracic radiologists. Data were collected from July 1 to August 3, 2022, using a structured Google form sheet questionnaire. Binary logistic regression was performed, and statistical significance was assessed at a level of significance α = 0.05. Results. Comparatively higher proportion of patients from the first wave had positive chest CT finding than fourth wave (99% vs. 69%). Bilateral lung involvement and lower lobe predilection were seen for both waves of COVID-19. Ground glass opacity and consolidation were the most common CT features for both waves. Delayed chest CT features such as traction bronchiectasis were primarily seen among first-wave patients. Mean global CT severity score was higher for the first-wave patients (13.18 vs. 8.31), and the mean difference is statistically significant (

p

 < 0.001). Duration of symptoms was a statistically significant predictor of CT severity during the first wave of COVID-19, and patients that presented later than 14 days had 4.12 times higher odds of being in the severe CT score category than those that presented less than 7 days (AOR = 4.12,

p

 = 0.011). There was no statistically significant predictor of CT severity for the fourth wave in this study. Conclusion. Chest CT positivity was comparatively higher for first wave patients. Common features included bilateral involvement, lower lobe involvement, ground glass opacity, and consolidation. Mean chest CT severity was comparatively higher for the first wave than the fourth wave, and the duration of symptoms was a statistically significant predictor of the CT severity for first wave.
Trends and Progress on Antibiotic-Resistant Mycobacterium tuberculosis and Genes in relation to Human Immunodeficiency Virus
Authors: Mbewana Ntshanka N. G. , Msagati T. A. M.
Published: November 2023

Human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) and tuberculosis (TB) are among the infectious diseases that cause high rates of mortality worldwide. The epidemiology of antibiotic resistance in correlation to people that live with TB and HIV has not been thoroughly investigated particularly in South Africa. Numerous cases of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) have been announced immensely worldwide. The spread and control of the MDR-TB pandemic due to unsuccessful treatment is one of the most serious public issues of concern, and this challenge is of international interest. Despite all measures that have been executed to overcome the challenge of MDR-TB in recent decades, the global MDR-TB trends have kept on accelerating with more and more people becoming victims. This is attributed to the abuse, misuse, and overuse of different antibacterial agents in human medicine, animal farms, and agricultural activities which serve as a wellspring for the evolution of antimicrobial resistance within the population. Over and above, the impetuous evolution, mutation, and the transfer of resistant genes via horizontal gene transfer are well-known contributive factors towards the antimicrobial resistance problem. Among the public health concerns in the world currently is the ever-increasing problem of antibiotic resistance which outpaces the progress of newly developed antimicrobials. The propagation of antimicrobial resistance (AMR) is even more amplified in areas where the pressure of antimicrobial resistant pathogens is elevated, and hence the population with ubiquitous HIV and AIDS is considered the hotspot. This review therefore aims to give in-depth coverage on the trends and the progress on the development of TB and HIV-resistant strains, highlight strategies to solve the problem, and accentuate the repercussions of the COVID-19 epidemic on the AMR.
Impact of Personal Protective Equipment on Cardiopulmonary Resuscitation and Rescuer Safety
Authors: Cheng Cheng-Hsin , Cheng Ya-Yun , Yuan Mei-Kang , Juang Yow-Jer , Zeng Xuan-Yu , Chen Chung-Yu , Foo Ning-Ping
Published: November 2023

Background. High-quality cardiopulmonary resuscitation (CPR) is a key element in the rescue of cardiac arrest patients but is difficult to achieve in circumstances involving aerosol transmission, such as the COVID-19 pandemic. Methods. This prospective randomized crossover trial included 30 experienced health care providers to evaluate the impact of personal protective equipment (PPE) on CPR quality and rescuer safety. Participants were asked to perform continuous CPR for 5 minutes on a manikin with three types of PPE: level D-PPE, level C-PPE, and PAPR. The primary outcome was effective chest compression per minute. Secondary outcomes were the fit factor by PortaCount, vital signs and fatigue scores before and after CPR, and perceptions related to wearing PPE. Repeated-measures ANOVA was used, and a two-tailed test value of 0.05 was considered statistically significant. Results. The rates of effective chest compressions for 5 minutes with level D-PPE, level C-PPE, and PAPRs were 82.0 ± 0.2%, 78.4 ± 0.2%, and 78.0 ± 0.2%, respectively (

p
=
0.584

). The fit-factor test values of level C-PPE and PAPRs were 182.9 ± 39.9 vs. 198.9 ± 9.2 (

p
<
0.001

). The differences in vital signs before and after CPR were not significantly different among the groups. In addition, the fatigue and total perception scores of wearing PPE were significantly higher for level C-PPE than PAPRs: 3.8 ± 1.6 vs. 3.0 ± 1.6 (

p
<
0.001

) and 27.9 ± 5.4 vs. 26.0 ± 5.3 (

p
<
0.001

), respectively. Conclusion. PAPRs are recommended when performing CPR in situations where aerosol transmission is suspected. When PAPRs are in short supply, individual fit-tested N95 masks are an alternative. This trial is registered with NCT04802109.
Segmentation of High‐Resolution Remote Sensing Images Using the Gabor Texture Feature‐Based Mean Shift Method
Authors: Wang Ligang , Liu Dan , Kong Weijiang , Mao Liang , Liu Qiaoyang
Published: November 2023

High‐resolution remote sensing images (HRRSIs) play an important role in the construction and development of society with their rich and detailed information. In the process of remote sensing image segmentation, the conventional method of mean shift usually involves spatial features and spectral features to preserve edge information and reduce the effect of noise. However, this traditional method often misses some important information in HRRSIs processing and reduces the segmentation accuracy. Thus, we proposed an improved mean shift image segmentation method based on Gabor texture features to make full use of the delicate information in HRRSIs. Specifically, the multiscale and multidirectional Gabor filters were employed in this study to extract pixel‐to‐pixel features from the QuickBird and GeoEye‐1 images, and the local variance was used to determine the optimal texture bandwidth. Two parameters, global segmentation (GS) quality index and error rate (ER), were adopted to evaluate the segmentation quality of these two HRRSIs. Results showed that the proposed approach in this study outperforms the traditional mean shift method on both remote sensing datasets. Compared with the traditional method, the improved mean shift approach corresponded to higher GS values and smaller ER values. This study confirms that the improved mean shift approach based on Gabor filtering has a potential for HRRSIs segmentation.
Relevancia de la Información Financiera de las Entidades Públicas en México, a partir de la adopción de las IFRS
Authors: Aguilera Aguilera José Rafael , Alcaraz Vera Jorge Víctor
Published: November 2023

Este artículo muestra el impacto que puede tener en el mejoramiento de la información financiera de las empresas públicas mexicanas el cambio de normatividad contable. Es decir, describe como podría ser el comportamiento de la relevancia de la información financiera de las empresas que cotizan en la Bolsa Mexicana de Valores (BMV), al emigrar de normas contables mexicanas, llamadas Normas de Información Financiera (NIF’s) a las Normas Internacionales de Información Financiera (NIIF).
Utilización de Stock Options: Evaluación de su Implementación en Empresas del Retail que Cotizan en la Bolsa de Valores y Propuesta de Mitigación de Riesgos para Accionistas Minoritarios
Authors: Báez Valdés Alejandro , Cárdenas Araya Juan
Published: November 2023

El presente artículo proporciona una perspectiva analítica de la gestión de riesgos que involucra a los accionistas minoritarios que poseen participación en empresas de la industria del retail, con presencia en bolsa de valores de Chile y que han implementado planes de Stock Options. Se cotejaron las posturas teóricas con casos prácticos, identificando las ventajas y desventajas de este tipo de incentivos, para luego, determinar y valorizar los riesgos inherentes. La investigación incluye un estudio empírico, de acuerdo a una serie de factores determinados que permiten entregar una propuesta y emitir las respectivas conclusiones.
Effectiveness of Mepolizumab in Patients with Severe Eosinophilic Asthma with/without Nasal Polyposis: A Real-Life Study
Authors: Bravo-Gutiérrez Francisco Javier , Miralles-López Juan Carlos , Valverde-Molina José , Alemany Francés María Loreto , Andújar-Espinosa Rubén , Castilla-Martínez Manuel , Avilés-Inglés María Jesús , Mora-González Ana , Pajarón-Fernández Manuel José , Cabrejos-Perotti Sheila , Meseguer-Arce José , Flores Martín Isabel , Pérez-Fernández Virginia ,   RE-ASGRAMUR Group
Published: November 2023

Introduction: Asthma is one of the most common chronic diseases and affects around 334 million people worldwide. The estimated prevalence of severe asthma is 3–10% of the asthmatic population. Mepolizumab has demonstrated efficacy in reducing exacerbations, oral corticosteroid use, and improving quality of life, asthma control, and lung function in patients with severe eosinophilic asthma (SEA). Our study aimed to check the response to mepolizumab in a series of severe asthma patients regarding exacerbations, oral corticosteroid use, asthma control, quality of life, and lung function and to compare the response between patients with and without nasal polyps. Method: This is a retrospective, multicenter study of RE-ASGRAMUR (Register of Severe Asthma of the Region of Murcia) performed in eight hospitals of the Region of Murcia (Spain) under routine clinical practice conditions. We included patients diagnosed with SEA who completed at least 1 year of treatment with mepolizumab. We analyzed clinical characteristics, drug tolerance, and effectiveness: exacerbations, ACT, miniAQLQ, forced expiratory volume in 1 s (FEV1), and use of oral corticosteroids. We also compared the results between patients with and without nasal polyps. Results: The median of exacerbations before treatment was 3 and decreased to 0 after treatment (mean decrease of 77.4%). The median diary oral prednisone intake was 15 mg before treatment and 5 mg after treatment (mean 56% reduction). We have obtained a significant improvement in other variables: ED visits and hospitalizations, asthma control (ACT), quality of life (miniAQLQ), and lung function (FEV1). Thirty-four out of 70 patients (48.57%) fulfilled the criteria of super-responder, and 17 out of 70 (24.29%) had a complete response. More patients in the group with nasal polyps fulfilled the criteria of super-responder and complete response to mepolizumab. Conclusions: Mepolizumab is a safe and effective treatment for SEA patients, improving exacerbations, oral corticosteroid intake, asthma control, quality of life, and lung function. In patients with associated nasal polyposis, there is a statistically significant higher proportion of super-responders and complete responders.
Automatic detection of broiler’s feeding and aggressive behavior using you only look once algorithm
Authors: Wahjuni Sri , Wulandari Wulandari , Eknanda Rafael Tektano Grandiawan , Susanto Iman Rahayu Hidayati , Akbar Auriza Rahmad
Published: November 2023

The high market demand for broiler chickens requires that chicken farmers improve their production performance. Production cost and poultry welfare are important competitiveness aspects in the poultry industry. To optimize these aspects, chicken behavior such as feeding and aggression needs to be observed continuously. However, this is not practically done entirely by humans. Implementation of precision live stock farming with deep learning can provide continuous, real-time and automated decisions. In this study, the you only look once version 4 (YOLOv4) architecture is used to detect feeding and aggressive chicken behavior. The data used includes 1,045 feeding bounding boxes and 753 aggressive bounding boxes. The model training is performed using the k-fold cross validation method. The best mean average precision (mAP) values obtained were 99.98% for eating behavior and 99.4% for aggressive behavior.
Applying Bayesian networks in making intelligent applications for static and dynamic unbalance diagnosis
Authors: Romahadi Dedik , Fitri Muhamad , Feriyanto Dafit , Hidayat Imam , Imran Muhammad
Published: November 2023

One of the problems often encountered in vibration analysis is unbalanced or imbalanced, namely the occurrence of a shift in the center of mass from the center of rotation to cause high vibrations. Unbalance itself is divided into two, namely static and dynamic unbalance. Identification of the right type of unbalance must be done because each type of unbalance requires different handling. Therefore, this study aims to design a system to identify the type of unbalance based on the required parameters. The system design determines the input and then builds an algorithm by combining vibration analysis methods and Bayesian networks (BN). Systems and applications are built using MATLAB. After the application is finished, testing is carried out using vibration measurement data obtained from a demo machine that has previously been conditioned for damage. The BN method has been successfully applied to the unbalance diagnosis system. When there is evidence of large amplitude in 1X the frequency spectrum and the value of the static phase range, the percentage of static unbalance from 26.8% increases to 75%. The system can predict all testing data quickly and precisely for the six experiments.
Using deep neural networks in classifying electromyography signals for hand gestures
Authors: Al-Khazzar Ahmed M. , Altaweel Zainab , Hussain Jabbar S.
Published: November 2023

Electromyography (EMG) signals are used for various applications, especially in smart prostheses. Recognizing various gestures (hand movements) in EMG systems introduces challenges. These challenges include the noise effect on EMG signals and the difficulty in identifying the exact movement from the collected EMG data amongst others. In this paper, three neural network models are trained using an open EMG dataset to classify and recognize seven different gestures based on the collected EMG data. The three implemented models are: a four-layer deep neural network (DNN), an eight-layer DNN, and a five-layer convolutional neural network (CNN). In addition, five optimizers are tested for each model, namely Adam, Adamax, Nadam, Adagrad, and AdaDelta. It has been found that four layers achieve respectable recognition accuracy of 95% in the proposed model. 
A study on attention-based deep learning architecture model for image captioning
Authors: Fudholi Dhomas Hatta , Al-Faruq Umar Abdul Aziz , Nayoan Royan Abida N. , Zahra Annisa
Published: November 2023

Image captioning has been widely studied due to its ability in a visual scene understanding. Automatic visual scene understanding is useful for remote monitoring system and visually impaired people. Attention-based models, including transformer, are the current state-of-the-art architectures used in developing image captioning model. This study examines the works in the development of image captioning model, especially models that are developed based on attention mechanism. The architecture, the dataset, and the evaluation metrics analysis are done to the collected works. A general flow of image captioning model development is also presented. The literature search process carried out on Google Scholar. There are 36 literatures used in this study, including a specific image captioning development in Indonesian. It is done to take one point of view of image captioning development in a low resource language. Studies using transformer model generally achieves higher evaluation metric scores. In our finding, the highest evaluation scores on the consensus-based image description evaluation (CIDEr) c5 and c40 metrics are 138.5 and 140.5 respectively. This study gives a baseline on future development of image captioning model and brings the general concept of the image captioning development process including a picture of the development in low resource language.
An automated speech recognition and feature selection approach based on improved Northern Goshawk optimization
Authors: Suryakumar Santosh Kumar , Hiremath Bharathi S. , Mohankumar Nageswara Guptha
Published: November 2023

Automatic speech recognition (ASR) approach is dependent on optimal speech feature extraction, which attempts to get a parametric depiction of an input speech signal. Feature extraction (FE) strategy combined with a feature selection (FS) approach should capture the most important features of the signal while discarding the rest. FS is a crucial process that can affect the pattern classification and recognition system's performance. In this research, we introduce a hybrid supervised learning using metaheuristic technique for optimum FE and FS termed Northern Goshawk optimization (NGO) and opposition-based learning (OBL). Pre-processing, feature extraction and selection, and recognition are the three steps of the proposed technique. The pre-processing is done first to lessen the amount of noise. In the FE stage, we extract features. The OBL-NGO method is used to pick the best collection of extracted characteristics. Finally, these optimised features are utilised to train the k-nearest neighbour (KNN) classifier, and the matching text is shown as the output based on these optimised characteristics of the provided input audio signal. The system's performance is outstanding, and the suggested OBL-NGO is best suited for ASR, according to the testing data.
Detecting cyberbullying text using the approaches with machine learning models for the low-resource Bengali language
Authors: Hoque Md. Nesarul , Seddiqui Md. Hanif
Published: November 2023

The rising usage of social media sites and the advances in communication technologies have led to a considerable increase in cyberbullying events. Here, people are intimidated, harassed, and humiliated via digital messaging. To identify cyberbullying texts, several research have been undertaken in English and other languages with abundant resources, but relatively few studies have been conducted in low-resource languages like Bengali. This research focuses on Bengali text to find cyberbullying material by experimenting with pre-processing, feature selection, and three types of machine learning (ML) models: classical ML, deep learning (DL), and transformer learning. In classical ML, four models, support vector machine (SVM), multinomial Naive Bayes (MNB), random forest (RF), and logistic regression (LR) are used. In DL, three models, long short term memory (LSTM), Bidirectional LSTM, and convolutional neural network with bidirectional LSTM (CNN-BiLSTM) are employed. As the transformerbased pre-trained model, bidirectional encoder representations from transformers (BERT) is utilized. Using our proposed pre-processing tasks, the MNB-based approach achieves the best accuracy of 78.816% among the other classical ML models, the LSTM-based approach gains the highest result of 77.804% accuracy among the DL models, and the BERT-based approach outperforms both with 80.165% accuracy.
Model for autism disorder detection using deep learning
Authors: Sharma Anshu , Tanwar Poonam
Published: November 2023

Autism spectrum disorder (ASD) is a neurodegenerative illness that impacts individuals' social abilities. The majority of available approaches rely on structural and resting-state functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. To detect ASD with a limited dataset, the bulk of known technologies involve Machine Learning, pattern recognition, and other techniques, leading to high accuracy but moderate generality. To address this constraint and improve the efficacy of the automated autism diagnosis model, an ASD detection model based on deep learning (DL) is provided in this work. The classification challenge is solved using a convolutional neural network classifier. The suggested model beats state-of-the-art methodologies in terms of accuracy, according to simulation findings. The proposed approach investigates how anatomical and functional connectivity indicators can be used to determine whether or not a person is autistic. The proposed method delivers state-of-the-art results, with the classification of Autistic patients achieving 93.41% accuracy and the localization of the classified data regressed to 0.29 mean absolute error (MAE). 
Partial half fine-tuning for object detection with unmanned aerial vehicles
Authors: Pebrianto Wahyu , Mudjirahardjo Panca , Pramono Sholeh Hadi
Published: November 2023

Deep learning has shown outstanding performance in object detection tasks with unmanned aerial vehicles (UAVs), which involve the fine-tuning technique to improve performance by transferring features from pre-trained models to specific tasks. However, despite the immense popularity of fine-tuning, no works focused on to study of the precise fine-tuning effects of object detection tasks with UAVs. In this research, we conduct an experimental analysis of each existing fine-tuning strategy to answer which is the best procedure for transferring features with fine-tuning techniques. We also proposed a partial half fine-tuning strategy which we divided into two techniques: first half fine-tuning (First half F-T) and final half fine-tuning (Final half F-T). We use the VisDrone dataset for the training and validation process. Here we show that the partial half fine-tuning: Final half F-T can outperform other fine-tuning techniques and are also better than one of the state-of-the-art methods by a difference of 19.7% from the best results of previous studies.
Classifying electrocardiograph waveforms using trained deep learning neural network based on wavelet representation
Authors: Jawad Noor Yahya , Merza Ahmed Mohammed , Sim Hussein Tami
Published: November 2023

Due to the rise in cardiac patients, an automated system that can identify different heart disorders has been created to lighten and distribute the duty of physicians. This research uses three different electrocardiograph (ECG) signals as indicators of a person's cardiac problems: Normal sinus rhythm (NSR), arrhythmia (ARR), and congestive heart failure (CHF). The continuous wavelet transform (CWT) provides the mechanism for classifying the 190 individual cases of ECG data into a 2-dimensional time-frequency representation. In this paper, the modified GoogLeNet is used for ECG data classification. Using a transfer learning approach and adjustments to parts of the output layers, ECG classification was conducted and the effectiveness of convolutional neural network (CNN) designs was tested. By comparing the results that the optimized neural network and GoogLeNet both had classification accuracy about of 80% and 100%, respectively. The GoogLeNet provide the best result in term of accuracy and training time.
A novel approach to optimizing customer profiles in relation to business metrics
Authors: Elveny Marischa , Nasution Mahyuddin K. M. , Zarlis Muhammad , Efendi Syahril , Syah Rahmad B. Y.
Published: November 2023

Business is very closely related to customers. Each user owns the data, and the data is used to identify cross-selling opportunities for each customer. For example, the type of product or service purchased, the frequency of purchases, geographic location, and so on. By doing so, you can gain the ability to manage and analyze customer data, allowing you to create new opportunities in industries that were previously difficult to enter. The purpose of optimizing user profiles is to determine minimum or maximum business value and improve efficiency by determining user needs. In this study, multivariate adaptive regression spline (MARS) is a statistical model used to explain the relationship between the response variable and the predictor variable. Robust is used to find variable relationships to make predictions. To improve classification performance, the model is validated using a confusion matrix. The results show an accuracy value of 84.5%, with better time management (period management) reflected in the number of hours spent by merchants as well as discounts during that time period, which has a significant impact on any business. In addition, the distance between customers and merchants is also important, as customers prefer merchants who are closer to them to save time and transportation costs.
Towards a Docker-based architecture for open multi-agent systems
Authors: Lima Gustavo Lameirão de , Aguiar Marilton Sanchotene de
Published: November 2023

In open multi-agent systems (OMAS), heterogeneous agents in different environments or models can migrate from one system to another, taking their attributes and knowledge and increasing developing complexity compared to conventional multi-agent systems (MAS). Furthermore, the complexity of opening may be due to the uncertainties and dynamic behavior that the change of agents entails, needing to formulate techniques to analyze this complexity and understand the system’s global behavior. We used Docker to approach these problems and make the architecture flexible to handle distinct types of programming languages and frameworks of agents. This paper presents a Docker-based architecture to aid OMAS development, acting on agent migration between different models running in heterogeneous hardware and software scenarios. We present a simulation scenario with NetLogo’s Open Sugarscape 2 Constant Growback and JaCaMo’s Gold Miners to verify the proposal’s feasibility.
Optimizer algorithms and convolutional neural networks for text classification
Authors: Qorich Mohammed , Ouazzani Rajae El
Published: November 2023

Lately, deep learning has improved the algorithms and the architectures of several natural language processing (NLP) tasks. In spite of that, the performance of any deep learning model is widely impacted by the used optimizer algorithm; which allows updating the model parameters, finding the optimal weights, and minimizing the value of the loss function. Thus, this paper proposes a new convolutional neural network (CNN) architecture for text classification (TC) and sentiment analysis and uses it with various optimizer algorithms in the literature. Actually, in NLP, and particularly for sentiment classification concerns, the need for more empirical experiments increases the probability of selecting the pertinent optimizer. Hence, we have evaluated various optimizers on three types of text review datasets: small, medium, and large. Thereby, we examined the optimizers regarding the data amount and we have implemented our CNN model on three different sentiment analysis datasets so as to binary label text reviews. The experimental results illustrate that the adaptive optimization algorithms Adam and root mean square propagation (RMSprop) have surpassed the other optimizers. Moreover, our best CNN model which employed the RMSprop optimizer has achieved 90.48% accuracy and surpassed the state-of-the-art CNN models for binary sentiment classification problems. 
Pneumonia prediction on chest x-ray images using deep learning approach
Authors: Puspita Rani , Rahayu Cindy
Published: November 2023

Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest x-ray images. In deep learning, computers can automatically identify useful features for the model, directly from the raw data, bypassing the difficult step of manual information refinement. The main part of the deep learning method is the focus on automatically learning data representations. Visual geometry group 16 (VGG16) and DenseNet121 are methods in deep learning. The data used is a chest x-ray of pneumonia. Data is divided into training, testing, and validation. The best method for this research case is VGG16 with 93% accuracy training and 90% accuracy testing. In this study, DenseNet121 obtained accuracy below VGG16, with 92% accuracy in training and 88% for accuracy testing. Parameters have a significant influence on the accuracy of each model, and with the parameters that have been used, the VGG16 is a method that has high accuracy and can be used to predict chest x-ray images aimed at checking pneumonia in patients. 
Parallel multivariate deep learning models for time-series prediction: A comparative analysis in Asian stock markets
Authors: Widiputra Harya , Juwono Edhi
Published: November 2023

This study investigates deep learning models for financial data prediction and examines whether the architecture of a deep learning model and time-series data properties affect prediction accuracy. Comparing the performance of convolutional neural network (CNN), long short-term memory (LSTM), Stacked-LSTM, CNN-LSTM, and convolutional LSTM (ConvLSTM) when used as a prediction approach to a collection of financial time-series data is the main methodology of this study. In this instance, only those deep learning architectures that can predict multivariate time-series data sets in parallel are considered. This research uses the daily movements of 4 (four) Asian stock market indices from 1 January 2020 to 31 December 2020. Using data from the early phase of the spread of the Covid-19 pandemic that has created worldwide economic turmoil is intended to validate the performance of the analyzed deep learning models. Experiment results and analytical findings indicate that there is no superior deep learning model that consistently makes the most accurate predictions for all states' financial data. In addition, a single deep learning model tends to provide more accurate predictions for more stable time-series data, but the hybrid model is preferred for more chaotic time-series data.
Early detection of tomato leaf diseases based on deep learning techniques
Authors: Najim Mohammed Hussein , Abdulateef Salwa Khalid , Alasadi Abbas Hanon
Published: November 2023

Tomato leaf diseases are a big issue for producers, and finding a single method to combat them is tough. Deep learning techniques, notably convolutional neural networks (CNNs), show promise in recognizing early indicators of illness, which can help producers avoid costly concerns in the future. In this study, we present a CNN-based model for the early identification of tomato leaf diseases to preserve output and boost yield. We used a dataset from the plantvillage database with 11,000 photos from 10 distinct disease categories to train our model. Our CNN was trained on this dataset, and the suggested model obtained an astounding 96% accuracy rate. This shows that our method has the potential to be efficient in detecting tomato leaf diseases early on, therefore assisting producers in managing and reducing disease outbreaks and, as a result, resulting in higher crop yields.
Deep learning-based classification of cattle behavior using accelerometer sensors
Authors: El Moutaouakil Khalid , Falih Noureddine
Published: November 2023

The increasing demand for food has led to the adoption of precision livestock, which relies on information and communication technology to promote the best practices in meat production. By automating various aspects of the industry, precision livestock allows for increased productivity, more effective management strategies, and decision-making. The paper proposes a methodology that uses deep learning techniques to automatically classify cattle behavior using accelerometer sensors embedded in collars. The work aims to enhance the efficiency and productivity of the industry by improving the classification of cattle behaviors, which is essential for farmers and barn managers to make informed decisions. We tested three different classification techniques to classify rumination, movement, resting, feeding, salting and other cattle behaviors and we achieved promising results that can contribute to a better understanding and management of cattle behavior in the livestock industry.
Handwritten digit recognition using quantum convolution neural network
Authors: Daniel Ravuri , Prasad Bode , Pasam Prudhvi Kiran , Sudarsa Dorababu , Sudhakar Ambarapu , Rajanna Bodapati Venkata
Published: November 2023

The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.
Performance analysis of optimization algorithms for convolutional neural network-based handwritten digit recognition
Authors: Albayati Abdulhakeem Qusay , Altaie Sarmad A. Jameel , Al-Obaydy Wasseem N. Ibrahem , Alkhalid Farah Flayyeh
Published: November 2023

Handwritten digit recognition has been widely researched by the recognition society during the last decades. Deep convolutional neural networks (CNN) have been exploited to propose efficient handwritten digit recognition approaches. However, the CNN model may need an optimization algorithm to achieve satisfactory performance. In this work, a performance evaluation of seven optimization methods applied in a straightforward CNN architecture is presented. The inspected algorithms are stochastic gradient descent (SGD), adaptive gradient (AdaGrad), adaptive delta (AdaDelta), adaptive moment estimation (ADAM), maximum adaptive moment estimation (AdaMax), nesterov-accelerated adaptive moment estimation (Nadam), and root mean square propagation (RMSprop). Experiments have been carried out on two standard digit datasets, namely Modified National Institute of Standards and Technology (MNIST) and Extended MNIST (EMNIST). The results have shown the superior performance of RMSprop and Adam algorithms over the peer methods, respectively.
Internet of things in public healthcare organizations: the mediating role of attitude
Authors: Noor Bashar Dheyaa , Katheeth Zainab Dalaf , Noor Ammar Dheyaa
Published: November 2023

Internet of things (IoT) is a promising technology to face the challenges of COVID19 and enhance the capacities of public hospitals. However, few of the literature examined the behavioural intention (BI) of patients to use the IoT wearable health device (IoTWHD). This paper aims to examine the factor that affect the BI toward using the IoTWHD. The study proposes that variables of Technology acceptance model (TAM3) along with unified theory of acceptance and use of technology (UTAUT) can explain the BI. The population is the patients of public hospitals. Convivence sampling was deployed to collect the data using a questionnaire. 161 respondents participated in this study. The finding of Smart Partial Least Square showed that subjective norms (SN) affected the perceived usefulness (PU). Perceived enjoyment (PE) affected the perceived ease of use (PEOU). Further, PU, PEOU and perceived security (PS) affected the BI to use IoTWHD. Attitude mediated the effect of PU and PEOU on BI. More positive word of mouth are needed to enhance the perception of patients about BI to use IoTWHD in public health organizations.