Search results for: forecast accuracy unemployment rate

Authors: Lianzhong Zhang, Chao Huang

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Sea-land segmentation is a basic step in many tasks such as sea surface monitoring and ship detection. The existing sea-land segmentation algorithms have poor segmentation accuracy, and the parameter adjustments are cumbersome and difficult to meet actual needs. Also, the current sea-land segmentation adopts traditional deep learning models that use Convolutional Neural Networks (CNN). At present, the transformer architecture has achieved great success in the field of natural images, but its application in the field of radar images is less studied. Therefore, this paper proposes a sea-land segmentation method based on the transformer architecture to strengthen edge supervision. It uses a self-attention mechanism with a gating strategy to better learn relative position bias. Meanwhile, an additional edge supervision branch is introduced. The decoder stage allows the feature information of the two branches to interact, thereby improving the edge precision of the sea-land segmentation. Based on the Gaofen-3 satellite image dataset, the experimental results show that the method proposed in this paper can effectively improve the accuracy of sea-land segmentation, especially the accuracy of sea-land edges. The mean IoU (Intersection over Union), edge precision, overall precision, and F1 scores respectively reach 96.36%, 84.54%, 99.74%, and 98.05%, which are superior to those of the mainstream segmentation models and have high practical application values.

Keywords: SAR, sea-land segmentation, deep learning, transformer

Procedia PDF Downloads 179

Authors: Priyadarsini Samal, Rajesh Singla

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Many mobile games come with the benefits of entertainment by introducing stress to the human brain. In recognizing this mental stress, the brain-computer interface (BCI) plays an important role. It has various neuroimaging approaches which help in analyzing the brain signals. Electroencephalogram (EEG) is the most commonly used method among them as it is non-invasive, portable, and economical. Here, this paper investigates the pattern in brain signals when introduced with mental stress. Two healthy volunteers played a game whose aim was to search hidden words from the grid, and the levels were chosen randomly. The EEG signals during gameplay were recorded to investigate the impacts of stress with the changing levels from easy to medium to hard. A total of 16 features of EEG were analyzed for this experiment which includes power band features with relative powers, event-related desynchronization, along statistical features. Support vector machine was used as the classifier, which resulted in an accuracy of 93.9% for three-level stress analysis; for two levels, the accuracy of 92% and 98% are achieved. In addition to that, another game that was similar in nature was played by the volunteers. A suitable regression model was designed for prediction where the feature sets of the first and second game were used for testing and training purposes, respectively, and an accuracy of 73% was found.

Keywords: brain computer interface, electroencephalogram, regression model, stress, word search

Procedia PDF Downloads 185

Authors: Muhammad Noor Ul Ane, Dong-Soon Kim, Myron P. Zalucki

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Models can be useful to help understand population dynamics of insects under diverse environmental conditions and in developing strategies to manage pest species better. Adult longevity and fecundity of Helicoverpa armigera (Hübner) were evaluated against a wide range of constant temperatures (15, 20, 25, 30, 35 and 37.5ᵒC). The modified Sharpe and DeMichele model described adult aging rate and was used to estimate adult physiological age. Maximum fecundity of H. armigera was 973 egg/female at 25ᵒC decreasing to 72 eggs/female at 37.5ᵒC. The relationship between adult fecundity and temperature was well described by an extreme value function. Age-specific cumulative oviposition rate and age-specific survival rate were well described by a two-parameter Weibull function and sigmoid function, respectively. An oviposition model was developed using three temperature-dependent components: total fecundity, age-specific oviposition rate, and age-specific survival rate. The oviposition model was validated against independent field data and described the field occurrence pattern of egg population of H. armigera very well. Our model should be a useful component for population modeling of H. armigera and can be independently used for the timing of sprays in management programs of this key pest species.

Keywords: cotton bollworm, life table, temperature-dependent adult development, temperature-dependent fecundity

Procedia PDF Downloads 151

Authors: Dalila Oulhaci, Mohammed Zahaf

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The watering technique is essential to maintain a moist perimeter around the roots of the crop. This is the case with topical watering, where the soil around the root system can be kept permanently moist between the two extremes of water content. Moreover, one of the oldest methods used since Roman times throughout North Africa and the Near East was based on the repeated pouring of water into porous earthen vessels buried in the ground. In this context, these two techniques have been combined by replacing the earthen vase with plastic bottles filled with sand which release water through their perforated walls into the surrounding soil. The objective of this work is to first determine the purifying power of the activated sludge treatment plant of Toggourt and then that of the bottled Sawdust filter. For the station, the BOD purification rate was (96.5%), the COD purification rate was (87%) and suspended solids (90%). For the bottle, the BOD removal rate was (35%), and COD removal rate was (12.58%). This work falls within the framework of water saving, sustainable development and environmental protection, and also within the framework of agriculture.

Keywords: wasterwater, sawdust, purification, irrigation, touggourt (Algeria)

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Authors: S. S. Patil, Sachidanand Kini

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Classification of land use patterns is compelling in complexity and variability of remote sensing imageries data. An imperative research in remote sensing application exploited to mine some of the significant spatially variable factors as land cover and land use from satellite images for remote arid areas in Karnataka State, India. The diverse classification techniques, unsupervised and supervised consisting of maximum likelihood, Mahalanobis distance, and minimum distance are applied in Bellary District in Karnataka State, India for the classification of the raw satellite images. The accuracy evaluations of results are compared visually with the standard maps with ground-truths. We initiated with the maximum likelihood technique that gave the finest results and both minimum distance and Mahalanobis distance methods over valued agriculture land areas. In meanness of mislaid few irrelevant features due to the low resolution of the satellite images, high-quality accord between parameters extracted automatically from the developed maps and field observations was found.

Keywords: Mahalanobis distance, minimum distance, supervised, unsupervised, user classification accuracy, producer's classification accuracy, maximum likelihood, kappa coefficient

Procedia PDF Downloads 182

Authors: Gil Bae, Seung Uk Choi, Jae Eun Lee, Joon Hwa Rho

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Using proprietary engagement partner identity information for the Big 4 audit firms in Korea over the 2001-2011 period, we find that expert engagement partners obtain significantly higher total compensation than do non-expert partners. Importantly, we also find that expert partners increase the number of audit hours compared to their non-expert counterparts. The hourly billing rate, calculated as total fees divided by total audit hours, of expert partners is not higher than that of non-expert partners, indicating that there is no expert partner premium reflected in the hourly rate. This finding suggests that the increase in total audit fees is attributable mainly to the increase in the quantity of audit hours that expert partners work, not from the higher fee per hour. The results are not attributable to auditor selection bias.

Keywords: industry expert partners, expert premiums, audit hours, hourly charge rate

Procedia PDF Downloads 301

Authors: Taiyo Matsumura, Kuninori Takahashi, Takashi Ono

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The purpose of this research is to improve the convenience of waiting for trains at level crossings and stations and to prevent accidents resulting from forcible entry into level crossings, by providing level crossing users and passengers with information that tells them when the next train will pass through or arrive. For this paper, we proposed methods for estimating operation by means of an average value method, variable response smoothing method, and exponential smoothing method, on the basis of open data, which has low accuracy, but for which performance schedules are distributed in real time. We then examined the accuracy of the estimations. The results showed that the application of an exponential smoothing method is valid.

Keywords: exponential smoothing method, open data, operation estimation, train schedule

Procedia PDF Downloads 387

Authors: Khalid Elbadawi Elshafea, Mahmoud Hassan Onsa

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This paper presents the results of the experiments to measure the impact of pasteurization cows dung on important parameter of anaerobic digestion (retention time) and measure the effect in daily production rate of biogas, were used local materials in these experiments, two experiments were carried out in two bio-digesters (1 and 2) (18.0 L), volume of the mixture 16.0-litre and the mass of dry matter in the mixture 4.0 Kg of cow dung. Pasteurization process has been conducted on the mixture into the digester 2, and put two digesters under room temperature. Digester (1) produced 268.5 liter of methane in period of 49 days with daily methane production rate 1.37L/Kg/day, and digester (2) produced 302.7-liter of methane in period of 26 days with daily methane production rate 2.91 L/Kg/day. This study concluded that the use of system pasteurization cows dung speed up hydrolysis in anaerobic process, because heat to certain temperature in certain time lead to speed up chemical reactions (transfer Protein to Amino acids, Carbohydrate to Sugars and Fat to Long chain fatty acids), this lead to reduce the retention time an therefore increase the daily methane production rate with 212%.

Keywords: methane, cow dung, daily production, pasteurization, increase

Procedia PDF Downloads 308

Authors: Alexander P. Smirnov, Sergey A. Matveev, Dmitry A. Zheltkov, Eugene E. Tyrtyshnikov

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We propose a new computational technique for multidimensional (multicomponent) Smoluchowski coagulation equation. Using low-rank approximations in Tensor Train format of both the solution and the coagulation kernel, we accelerate the classical finite-difference Runge-Kutta scheme keeping its level of accuracy. The complexity of the taken finite-difference scheme is reduced from O(N^2d) to O(d^2 N log N ), where N is the number of grid nodes and d is a dimensionality of the problem. The efficiency and the accuracy of the new method are demonstrated on concrete problem with known analytical solution.

Keywords: tensor train decomposition, multicomponent Smoluchowski equation, runge-kutta scheme, convolution

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Authors: Leon C. Prieto, Simone T. A. Phipps

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There is a need to promote social entrepreneurship in order to solve some of the complex problems facing various countries in Africa (poverty, unemployment, crime, HIV, etc.). For example, one possible consequence of the HIV/AIDS crisis in Zimbabwe and elsewhere is a deterioration in the educational opportunities for orphans and other vulnerable children. Given that high returns are associated with education, the loss of education for a large segment of the population would likely worsen the already dire economic consequences of the HIV/AIDS crisis. Using a systems approach, this paper argues that social entrepreneurship can be used as a vehicle to promote national human resource development, which will assist in the alleviation of societal ills on the national level as well as throughout Africa.

Keywords: human resource development, pan-african, social entrepreneurship, social enterprise

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Authors: Zheng Zhang, Xin Chen, Guoqing Ding

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Electronic apex locators (EAL) has been widely used clinically for measuring root canal working length with high accuracy, which is crucial for successful endodontic treatment. In order to maintain high accuracy in different measurement environments, this study presented a system for root canal length measurement based on multifrequency impedance method. This measuring system can generate a sweep current with frequencies from 100 Hz to 1 MHz through a direct digital synthesizer. Multiple impedance ratios with different combinations of frequencies were obtained and transmitted by an analog-to-digital converter and several of them with representatives will be selected after data process. The system analyzed the functional relationship between these impedance ratios and the distance between the file and the apex with statistics by measuring plenty of teeth. The position of the apical foramen can be determined by the statistical model using these impedance ratios. The experimental results revealed that the accuracy of the system based on multifrequency impedance ratios method to determine the position of the apical foramen was higher than the dual-frequency impedance ratio method. Besides that, for more complex measurement environments, the performance of the system was more stable.

Keywords: root canal length, apex locator, multifrequency impedance, sweep frequency

Procedia PDF Downloads 154

Authors: Cristina Vatamanu, Doina Cosovan, Dragos Gavrilut, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through semi-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: ensembles, false positives, feature selection, one side class algorithm

Procedia PDF Downloads 291

Authors: Mojtaba Noghabaei, Khashayar Asadi, Kevin Han

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Simultaneous Localization and Mapping (SLAM) technology has recently attracted the attention of construction companies for real-time performance monitoring. To effectively use SLAM for construction performance monitoring, SLAM results should be registered to a Building Information Models (BIM). Registring SLAM and BIM can provide essential insights for construction managers to identify construction deficiencies in real-time and ultimately reduce rework. Also, registering SLAM to BIM in real-time can boost the accuracy of SLAM since SLAM can use features from both images and 3d models. However, registering SLAM with the BIM in real-time is a challenge. In this study, a novel SLAM platform named Model-Based SLAM (MB-SLAM) is proposed, which not only provides automated registration of SLAM and BIM but also improves the localization accuracy of the SLAM system in real-time. This framework improves the accuracy of SLAM by aligning perspective features such as depth, vanishing points, and vanishing lines from the BIM to the SLAM system. This framework extracts depth features from a monocular camera’s image and improves the localization accuracy of the SLAM system through a real-time iterative process. Initially, SLAM can be used to calculate a rough camera pose for each keyframe. In the next step, each SLAM video sequence keyframe is registered to the BIM in real-time by aligning the keyframe’s perspective with the equivalent BIM view. The alignment method is based on perspective detection that estimates vanishing lines and points by detecting straight edges on images. This process will generate the associated BIM views from the keyframes' views. The calculated poses are later improved during a real-time gradient descent-based iteration method. Two case studies were presented to validate MB-SLAM. The validation process demonstrated promising results and accurately registered SLAM to BIM and significantly improved the SLAM’s localization accuracy. Besides, MB-SLAM achieved real-time performance in both indoor and outdoor environments. The proposed method can fully automate past studies and generate as-built models that are aligned with BIM. The main contribution of this study is a SLAM framework for both research and commercial usage, which aims to monitor construction progress and performance in a unified framework. Through this platform, users can improve the accuracy of the SLAM by providing a rough 3D model of the environment. MB-SLAM further boosts the application to practical usage of the SLAM.

Keywords: perspective alignment, progress monitoring, slam, stereo matching.

Procedia PDF Downloads 222

Authors: T. V. K. Gupta, J. Ramkumar, Puneet Tandon, N. S. Vyas

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Abrasive Water Jet Machining (AWJM) is an unconventional machining process well known for machining hard to cut materials. The primary research focus on the process was for through cutting and a very limited literature is available on pocket milling using AWJM. The present work is an attempt to use this process for milling applications considering a set of various process parameters. Four different input parameters, which were considered by researchers for part separation, are selected for the above application i.e. abrasive size, flow rate, standoff distance, and traverse speed. Pockets of definite size are machined to investigate surface roughness, material removal rate, and pocket depth. Based on the data available through experiments on SS304 material, it is observed that higher traverse speeds gives a better finish because of reduction in the particle energy density and lower depth is also observed. Increase in the standoff distance and abrasive flow rate reduces the rate of material removal as the jet loses its focus and occurrence of collisions within the particles. ANOVA for individual output parameter has been studied to know the significant process parameters.

Keywords: abrasive flow rate, surface finish, abrasive size, standoff distance, traverse speed

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Authors: Dharamashi Rabari, Anand Patel

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n-Butanol can be regarded as a potential biofuel. Being resistive to corrosion and having high calorific value, butanol is a very attractive energy source as opposed to ethanol. By fermentation process called ABE (acetone, butanol, ethanol), bio-butanol can be produced. ABE carried out mostly by bacteria Clostridium acetobutylicum. The major drawback of the process is the butanol concentration higher than 10 g/L, delays the growth of microbes resulting in a low yield. It indicates the simultaneous separation of butanol from the fermentation broth. Two hydrophobic Ionic Liquids (ILs) 1-butyl-1-methylpiperidinium bis (trifluoromethylsulfonyl)imide [bmPIP][Tf₂N] and 1-hexyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide [hmim][Tf₂N] were chosen. The binary interaction parameters for both ternary systems i.e. [bmPIP][Tf₂N] + water + n-butanol and [hmim][Tf₂N] + water +n-butanol were taken from the literature that was generated by NRTL model. Particle swarm optimization (PSO) with the isothermal sum rate (ISR) method was used to optimize the cost of liquid-liquid extractor. For [hmim][Tf₂N] + water +n-butanol system, PSO shows 84% success rate with the number of stages equal to eight and solvent flow rate equal to 461 kmol/hr. The number of stages was three with 269.95 kmol/hr solvent flow rate for [bmPIP][Tf₂N] + water + n-butanol system. Moreover, both ILs were very efficient as the loss of ILs in raffinate phase was negligible.

Keywords: particle swarm optimization, isothermal sum rate method, success rate, extraction

Procedia PDF Downloads 121

Authors: Ugwu O. C., Mamah R. O., Awudu W. S.

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This work is aimed at enhancing signal reception on a mobile radio network and minimizing outage probability in a mobile radio network using adaptive beamforming antenna arrays. In this research work, an empirical real-time drive measurement was done in a cellular network of Globalcom Nigeria Limited located at Ikeja, the headquarters of Lagos State, Nigeria, with reference base station number KJA 004. The empirical measurement includes Received Signal Strength and Bit Error Rate which were recorded for exact prediction of the signal strength of the network as at the time of carrying out this research work. The Received Signal Strength and Bit Error Rate were measured with a spectrum monitoring Van with the help of a Ray Tracer at an interval of 100 meters up to 700 meters from the transmitting base station. The distance and angular location measurements from the reference network were done with the help Global Positioning System (GPS). The other equipment used were transmitting equipment measurements software (Temsoftware), Laptops and log files, which showed received signal strength with distance from the base station. Results obtained were about 11% from the real-time experiment, which showed that mobile radio networks are prone to signal failure and can be minimized using an Adaptive Beamforming Antenna Array in terms of a significant reduction in Bit Error Rate, which implies improved performance of the mobile radio network. In addition, this work did not only include experiments done through empirical measurement but also enhanced mathematical models that were developed and implemented as a reference model for accurate prediction. The proposed signal models were based on the analysis of continuous time and discrete space, and some other assumptions. These developed (proposed) enhanced models were validated using MATLAB (version 7.6.3.35) program and compared with the conventional antenna for accuracy. These outage models were used to manage the blocked call experience in the mobile radio network. 20% improvement was obtained when the adaptive beamforming antenna arrays were implemented on the wireless mobile radio network.

Keywords: beamforming algorithm, adaptive beamforming, simulink, reception

Procedia PDF Downloads 40

Authors: Maria L. G. Lourenço, Keylla H. N. P. Pereira, Viviane Y. Hibaru, Fabiana F. Souza, João C. P. Ferreira, Simone B. Chiacchio, Luiz H. A. Machado

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The neonatal mortality rates in dogs are considered high, varying between 5.7 and 21.2% around the world, and the causes of the deaths are often unknown. Data regarding canine neonatal mortality are scarce in Brazil. This study aims at describing the neonatal mortality rates in dogs, as well as the main causes of death. The study included 152 litters and 669 neonates admitted to the São Paulo State University (UNESP) Veterinary Hospital, Botucatu, São Paulo, Brazil between January 2018 and September 2019. The overall mortality rate was 16.7% (112/669), with 40% (61/152) of the litters presenting at least one case of stillbirth or neonatal mortality. The rate of stillbirths was 7.7% (51/669), while the neonatal mortality rate was 9% (61/669). The early mortality rate (0 to 2 days) was 13.7% (92/669), accounting for 82.1% (92/112) of all deaths. The late mortality rate (3 to 30 days) was 2.7% (18/669), accounting for 16% (18/112) of all deaths. Infection was the causa mortis in 51.8% (58/112) of the newborns, of which 30.3% (34/112) were caused by bacterial sepsis, and 21.4% (24/112) were caused by other bacterial, viral or parasite infections. Other causes of death included congenital malformations (15.2%, 17/112), of which 5.3% (6/112) happened through euthanasia due to malformations incompatible with life; asphyxia/hypoxia by dystocia (9.8%, 11/112); wasting syndrome in debilitated newborns (6.2%, 7/112); aspiration pneumonia (3.6%, 4/112); agalactia (2.7%, 3/112); trauma (1.8%, 2/112); administration of contraceptives to the mother (1.8%, 2/112) and unknown causes (7.1%, 8/112). The neonatal mortality rate was considered high, but they may be even higher in locations without adequate care for the mothers and neonates. Therefore, prenatal examinations and early neonatal care are of utmost importance for the survival of these patients.

Keywords: neonate dogs, puppies, mortality rate, neonatal death

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Authors: Ashutosh Mishra, Nikhil Goyal

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A 3D facial emotion recognition model based on deep learning is proposed in this paper. Two convolution layers and a pooling layer are employed in the deep learning architecture. After the convolution process, the pooling is finished. The probabilities for various classes of human faces are calculated using the sigmoid activation function. To verify the efficiency of deep learning-based systems, a set of faces. The Kaggle dataset is used to verify the accuracy of a deep learning-based face recognition model. The model's accuracy is about 65 percent, which is lower than that of other facial expression recognition techniques. Despite significant gains in representation precision due to the nonlinearity of profound image representations.

Keywords: facial recognition, computational intelligence, convolutional neural network, depth map

Procedia PDF Downloads 229

Authors: Abdunaser Abduelmula, Maria Luisa M. Bastos, José A. Gonçalves

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The modeling of the earth's surface and evaluation of urban environment, with 3D models, is an important research topic. New stereo capabilities of high-resolution optical satellites images, such as the tri-stereo mode of Pleiades, combined with new image matching algorithms, are now available and can be applied in urban area analysis. In addition, photogrammetry software packages gained new, more efficient matching algorithms, such as SGM, as well as improved filters to deal with shadow areas, can achieve denser and more precise results. This paper describes a comparison between 3D data extracted from tri-stereo and dual stereo satellite images, combined with pixel based matching and Wallis filter. The aim was to improve the accuracy of 3D models especially in urban areas, in order to assess if satellite images are appropriate for a rapid evaluation of urban environments. The results showed that 3D models achieved by Pleiades tri-stereo outperformed, both in terms of accuracy and detail, the result obtained from a Geo-eye pair. The assessment was made with reference digital surface models derived from high-resolution aerial photography. This could mean that tri-stereo images can be successfully used for the proposed urban change analyses.

Keywords: 3D models, environment, matching, pleiades

Procedia PDF Downloads 328

Authors: Basit Aftab, Zhichao Wang, Feng Zhongke

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Land Use and Land Cover Change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the spatiotemporal dynamics of land use and land cover (LULC) across a three-decade period (1992–2022) in a district area. The goal is to support sustainable land management and urban planning by utilizing the combination of remote sensing, GIS data, and observations from Landsat satellites 5 and 8 to provide precise predictions of the trajectory of urban sprawl. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the Random Forest method with Multilayer Perceptron (MLP) and Markov Chain analysis. To predict the dynamics of LULC change for the year 2035, a hybrid technique based on multilayer Perceptron and Markov Chain Model Analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. The study also discovered that between 1998 and 2023, the built-up area increased by 468 km² as a result of the replacement of natural resources. It is estimated that 25.04% of the study area's urbanization will be increased by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. It provides valuable insights for policymakers, land managers, and researchers to support sustainable land use planning, conservation efforts, and climate change mitigation strategies.

Keywords: land use land cover, Markov chain model, multi-layer perceptron, random forest, sustainable land, remote sensing.

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Authors: Ren-Jieh Kuo, Chun-Shou Huang

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The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance.

Keywords: flavor forecasting, artificial neural networks, Support Vector Regression, China

Procedia PDF Downloads 492

Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: András Rövid, Takeshi Hashimoto

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The paper focuses on the problem of the point correspondence matching in stereo images. The proposed matching algorithm is based on the combination of simpler methods such as normalized sum of squared differences (NSSD) and a more complex phase correlation based approach, by considering the noise and other factors, as well. The speed of NSSD and the preciseness of the phase correlation together yield an efficient approach to find the best candidate point with sub-pixel accuracy in stereo image pairs. The task of the NSSD in this case is to approach the candidate pixel roughly. Afterwards the location of the candidate is refined by an enhanced phase correlation based method which in contrast to the NSSD has to run only once for each selected pixel.

Keywords: stereo matching, sub-pixel accuracy, phase correlation, SVD, NSSD

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Authors: Chi Zhang, Jun Jiang

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Constitutive equations are very important in finite element (FE) modeling, and the accuracy of the material constants in the equations have significant effects on the accuracy of the FE models. A wide range of constitutive equations are available; however, fitting the material constants in the constitutive equations could be complex and time-consuming due to the strong non-linearity and relationship between the constants. This work will focus on the development of a set of unified MATLAB programs for fitting the material constants in the constitutive equations efficiently. Users will only need to supply experimental data in the required format and run the program without modifying functions or precisely guessing the initial values, or finding the parameters in previous works and will be able to fit the material constants efficiently.

Keywords: constitutive equations, FE modelling, MATLAB program, non-linear curve fitting

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Authors: India Sanders, Jeffrey Sherman

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Colorism has been a persistent and ingrained issue in the history of the United States, with far-reaching consequences that continue to affect various aspects of daily life, institutional policies, public spaces, economic structures, and social norms. This complex problem has had a particularly profound impact on the African-American community, shaping how they are perceived and treated within society at large. The prevalence of negative stereotypes surrounding African Americans can lead to severe repercussions such as discrimination and mental health disparities. The effects of such biases can also materialize in diverse forms, impacting the well-being and livelihoods of individuals within this community. Current research has examined how people from different racial groups perceive different skin tones of Black people, looking at the cognitive processes that manifest through categorization and stereotypes. Additionally, studies observed consequences related to colorism and how it directly affects those with darker versus lighter skin tones. However, not much research has been conducted on the influence of stereotypes associated with various skin tones. In the present study, it is hypothesized that participants in Group A will rate positive stereotypes associated with lighter skin tones significantly higher than positive stereotypes associated with darker skin tones. It is also hypothesized that participants in Group B will rate negative stereotypes associated with darker skin tones significantly higher than negative stereotypes associated with lighter skin tones. For this study, a quantitative study on stereotypes of skin tone representation within the African-American community will be conducted. Participants will rate the accuracy of various visual representations within mass media of African Americans with light skin tones and dark skin tones using a Likert scale. Participants will also be provided a questionnaire further examining the perception of stereotypes and how this affects their interactions with African Americans with lighter versus darker skin tones. The purpose of this study is to investigate the impact of skin tone portrayals on African Americans, including associated stereotypes and societal perceptions. It is expected that participants will more likely associate negative stereotypes with African Americans who have darker skin tones, as this is a common and reinforced viewpoint in the cultural and social system.

Keywords: colorism, discrimination, racism, stereotype

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Authors: Amr Hamdy

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Background: Postpartum haemorrhage (PPH) is the leading cause of maternal mortality worldwide. Many methods have been developed to decrease its rate. The aim of this study was to evaluate the applicability of a new non-pharmacologic maneuver in decreasing its rate. Methods: This case series study was conducted in one centre in Cairo, Egypt, from January-2010 to June-2013. 400 pregnant–women aged 18 years or more and candidate for normal labour; were enrolled to this study. High-risk subjects for PPH were excluded. After placental delivery, the new maneuver was done by sustained traction of the anterior and posterior lips of the cervix by two ovum forceps for duration of 90 seconds. The amount of blood loss was estimated by standardized visual estimation after removal of the forceps. All subjects were followed up for 6 hours. Results: The rate of PPH, defined as more than 500 ml, was 8 cases (2%) with 95% CI (0.63-3.37%). The rate of PPH was not affected by parity, gestational age, episiotomy or the presence of tears. PPH is more in cases with anemia (p 0.032). It occurred in all cases with uterine atony (p <0.001). The range of estimated blood loss was 550-600ml in cases with PPH and 150-450ml in cases without PPH. Severe PPH more than 1000 ml, did not occur. Conclusion: This pilot study introduced a novel maneuver that can be helpful in decreasing the rate of PPH and reducing the amount of post partum blood loss.Despite the low rate of PPH showed in this study, the need for conducting a randomized controlled study is at its highest level before further inclusion of such manoeuvre to be a part of the current medical practice and before considering it as an evident tool to decrease the burden of PPH.

Keywords: maternal mortality, new manoeuvre, post-partum haemorrhage, uterine atony

Procedia PDF Downloads 194

Authors: Syed Ali Taqi, Muhammad Ismail

Abstract:

A comparative study of estimators of population means in two phase sampling in the presence of non-response when Unknown population means of the auxiliary variable(s) and incomplete information of study variable y as well as of auxiliary variable(s) is made. Three real data sets of University students, hospital and unemployment are used for comparison of all the available techniques in two phase sampling in the presence of non-response with the newly generalized ratio estimators.

Keywords: two-phase sampling, ratio estimator, product estimator, generalized estimators

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

Abstract:

Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Gwi Hyun Lee, Mun Soo Na

Abstract:

Solar energy has many advantages of infinite and clean source, and also it can be used for reduction of greenhouse gases and environment pollution. Concentrated solar system is a very useful to achieve reasonably high thermal efficiency. Different types of solar concentrating systems have been developed such as parabolic trough and parabolic dish. Conical solar concentrator is one of the most reliable and promising renewable energy systems for higher temperature applications. The objectives of this study were to investigate the influence of flow rate affecting the thermal efficiency of a conical solar collector, which has a double tube absorber placed at focal axis for collecting solar radiation. A conical solar concentrator consists of a conical reflector, which reflects direct solar radiation into an absorber. A double tube absorber was placed at the center of focal axis for collecting the solar radiation reflected from a conical reflector. A dual tracking system consists of a linear actuator and slew drive with driving cycle of 6 seconds. Water was used as circulating fluid, which flows from inlet to outlet of an absorber for collecting solar radiation. Three identical conical solar concentrator systems were installed side by side at the same place for the accurate performance analysis under the same environmental conditions. Performance evaluations were carried out with different volumetric flow rate of 2, 4 and 6 L/min to find the influence of flow rate affecting on thermal efficiency. The results indicated that average thermal efficiency was 73.24%, 81.96%, and 79.78% for each flow rate of 2 L/min, 4 L/min, and 6 L/min. It shows that the flow rate of circulating water has a significant effect on the thermal efficiency of the conical solar concentrator. It is concluded that an optimum flow rate of conical solar concentrator is 6 L/min.

Keywords: conical solar concentrator, performance evaluation, solar energy, solar energy system

Procedia PDF Downloads 277

Authors: Olivia A. Kjorlien, Maryam Asghari, Farshid Alizadeh-Shabdiz

Abstract:

The goal of this paper is to investigate the impact of mosaic augmentation on the performance of object detection solutions. To carry out the study, YOLOv4 and YOLOv4-Tiny models have been selected, which are popular, advanced object detection models. These models are also representatives of two classes of complex and simple models. The study also has been carried out on two categories of objects, simple and complex. For this study, YOLOv4 and YOLOv4 Tiny are trained with and without mosaic augmentation for two sets of objects. While mosaic augmentation improves the performance of simple object detection, it deteriorates the performance of complex object detection, specifically having the largest negative impact on the false positive rate in a complex object detection case.

Keywords: accuracy, false positives, mosaic augmentation, object detection, YOLOV4, YOLOV4-Tiny

Procedia PDF Downloads 125