Search results for: data driven diagnosis

Authors: Nizar Karim Uribe-Orihuela, Fernando Brambila-Paz, Ivette Caldelas, Rodrigo Montufar-Chaveznava

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In this paper, we present the advances corresponding to the implementation of a strategy to visualize geographical data using a Software Development Kit (SDK) for video games. We use multispectral images from Landsat 7 platform and Laser Imaging Detection and Ranging (LIDAR) data from The National Institute of Geography and Statistics of Mexican (INEGI). We select a place of interest to visualize from Landsat platform and make some processing to the image (rotations, atmospheric correction and enhancement). The resulting image will be our gray scale color-map to fusion with the LIDAR data, which was selected using the same coordinates than in Landsat. The LIDAR data is translated to 8-bit raw data. Both images are fused in a software developed using Unity (an SDK employed for video games). The resulting image is then displayed and can be explored moving around. The idea is the software could be used for students of geology and geophysics at the Engineering School of the National University of Mexico. They will download the software and images corresponding to a geological place of interest to a smartphone and could virtually visit and explore the site with a virtual reality visor such as Google cardboard.

Keywords: virtual reality, interactive technologies, geographical data visualization, video games technologies, educational material

Procedia PDF Downloads 237

Authors: Fortune Mangara

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The world is undergoing the largest wave of urban growth in history due to rapid urbanization. Africa’s fast rate of urbanization is being driven by several factors such as population growth and migration. Urbanization results in development pressure on existing infrastructure, and numerous existing buildings are being destroyed in the process. Many of these buildings are built by environmental heritage resources which are part of the city's heritage and are therefore valuable. Many built environment heritage resources are currently being destroyed due to development pressure, while others are facing the risk of destruction or abandonment. There are different approaches that inform urban development and urban conservation. The modernist and post-modernist dichotomy has played an influencing role on how development or conservation of built environment heritage resources are approached. The fragmented nature of historical urban conservation paradigms and theories are also reflected in the evolution of policy and legislation that guide urban development and conservation of built heritage resources. Urban development and conservation have a long history of being guided by separated policies and legislation. However, recent international and South African policy and legislation had started to acknowledge the importance of integrating urban development and urban conservation. Spatial planning guides urban development and can be used as an integrative tool. With the aforementioned in mind, the main research question that guides this study is: What role does spatial planning play in the coexistence of urban development and urban conservation in a city in transition? The main purpose of this research is to use spatial planning as a tool for integrating urban conservation and urban development with reference to built environmental heritage resources. A qualitative research methodology is going to be employed in which a singular case study will be used as the research design. A qualitative document analysis will be used to collect data. Potchefstroom is going to be used as a case study as it is the oldest town in the North West province therefore is rich in built environmental heritage resources.

Keywords: built environmental heritage resources, document analysis, spatial planning, urban conservation, urban development

Procedia PDF Downloads 124

Authors: Chad Brown

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This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size.

Keywords: sieve extremum estimates, nonparametric estimation, deep learning, neural networks, rectified linear unit, nonstationary processes

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Authors: Y. K. Park, B. K. Kim, J. W. Lee, S. J. Lee

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To assess the risk of the underground structures and surrounding ground, we collect basic data by the engineering method of measurement, exploration and surveys and, derive the risk through proper analysis and each assessment for urban railroad underground structures and surrounding ground including station inflow. Basic data are obtained by the fiber-optic sensors, MEMS sensors, water quantity/quality sensors, tunnel scanner, ground penetrating radar, light weight deflectometer, and are evaluated if they are more than the proper value or not. Based on these data, we analyze the risk level of urban railroad underground structures and surrounding ground. And we develop the risk management system to manage efficiently these data and to support a convenient interface environment at input/output of data.

Keywords: urban railroad, underground structures, ground subsidence, station inflow, risk

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Ng Liang Shen, Hau Yuan Wen

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Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

Procedia PDF Downloads 369

Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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Authors: Noureddine Adjlane, Nizar Haddad

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Deformed Wing Virus (DWV) is considered as the most prevalent virus that dangerous the honeybee health worldwide today. In this study we aimed to evaluate the impact of the virus on honeybees (Apis mellifera intermissa) mortality in Algeria and we conducted the study on samples collected from the central area in the country. We used PCR for the diagnoses of the (DWV) in the diagnosis. The results had shown a high infestation in the sampled colonies and it represented 42% of the total sample. In this study, we found a clear role of both Varroa destructor mite and DWV on hive mortality in the experimented apiary. Further studies need to be conducted in order to give soled recommendations to the beekeepers, decision makers and stockholders of the Algerian beekeeping sector.

Keywords: honey bee, DWV, Varroa destructor, mortality, prevalence, infestation

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Authors: Amani A. Saad, Ahmed A. El-Farag, El-Sayed A. Helali

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Cloud computing is an important and promising field in the recent decade. Cloud computing allows sharing resources, services and information among the people of the whole world. Although the advantages of using clouds are great, but there are many risks in a cloud. The data security is the most important and critical problem of cloud computing. In this research a new security model for cloud computing is proposed for ensuring secure communication system, hiding information from other users and saving the user's times. In this proposed model Blowfish encryption algorithm is used for exchanging information or data, and SHA-2 cryptographic hash algorithm is used for data integrity. For user authentication process a user-name and password is used, the password uses SHA-2 for one way encryption. The proposed system shows an improvement of the processing time of uploading and downloading files on the cloud in secure form.

Keywords: cloud Ccomputing, data security, SAAS, PAAS, IAAS, Blowfish

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Authors: Anil Koklu, Amin Mansoorifar, Ali Beskok

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Dielectric spectroscopy (DS) is a noninvasive, label free technique for a long term real-time measurements of the impedance spectra of biological cells. DS enables characterization of cellular dielectric properties such as membrane capacitance and cytoplasmic conductivity. We have developed a lab-on-a-chip device that uses an electro-activated microwells array for loading, DS measurements, and unloading of biological cells. We utilized from dielectrophoresis (DEP) to capture target cells inside the wells and release them after DS measurement. DEP is a label-free technique that exploits differences among dielectric properties of the particles. In detail, DEP is the motion of polarizable particles suspended in an ionic solution and subjected to a spatially non-uniform external electric field. To the best of our knowledge, this is the first microfluidic chip that combines DEP and DS to analyze biological cells using electro-activated wells. Device performance is tested using two different cell lines of prostate cancer cells (RV122, PC-3). Impedance measurements were conducted at 0.2 V in the 10 kHz to 40 MHz range with 6 s time resolution. An equivalent circuit model was developed to extract the cell membrane capacitance and cell cytoplasmic conductivity from the impedance spectra. We report the time course of the variations in dielectric properties of PC-3 and RV122 cells suspended in low conductivity medium (LCB), which enhances dielectrophoretic and impedance responses, and their response to sudden pH change from a pH of 7.3 to a pH of 5.8. It is shown that microfluidic chip allowed online measurements of dielectric properties of prostate cancer cells and the assessment of the cellular level variations under external stimuli such as different buffer conductivity and pH. Based on these data, we intend to deploy the current device for single cell measurements by fabricating separately addressable N × N electrode platforms. Such a device will allow time-dependent dielectric response measurements for individual cells with the ability of selectively releasing them using negative-DEP and pressure driven flow.

Keywords: microfluidic, microfabrication, lab on a chip, AC electrokinetics, dielectric spectroscopy

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Authors: Wang Zhanquan, Yang Zeping, Gu Chunhua, Zhu Fazhi, Guo Weibin

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Recently, Massive Open Online Courses (MOOCs) are becoming the new trend of education. There are many problems under the environment of Database Principle curriculum teaching process in MOOCs, such as teaching ideas and theories which are out of touch with the reality, how to carry out the technical teaching and interactive practice in the MOOCs environment, thus the methods of database course under the environment of MOOCs are proposed. There are three processes to deal with problem solving in the research, which are problems proposed, problems solved, and inductive analysis. The present research includes the design of teaching contents, teaching methods in classroom, flipped classroom teaching mode under the environment of MOOCs, learning flow method and large practice homework. The database designing ability is systematically improved based on the researching methods.

Keywords: problem solving-driven, MOOCs, teaching art, learning flow;

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Authors: Wang Yichen, Haruka Yamashita

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In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: recurrent neural network, players lineup, basketball data, decision making model

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Authors: Mohamed Adel Ahmed, Warda Youssef Morsy , Hanaa Ali El Feky

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Background: Literature review cited that sleep is absolutely essential for surviving and reclamation of the quality of life. Critically ill patients often have poor sleep quality with prolonged sleep latency, sleep fragmentation, decreased sleep efficiency and frequent arousals. Nurses have a unique role for the early diagnosis of sleep disorders, decreasing stressors levels and providing the necessary environmental regulations to create a therapeutic ambiance. The aim of the study: to assess perceived sleep quality and identify factors affecting sleep quality among adult critically ill patients At El Manial University Hospital. Research Design: A descriptive exploratory design was utilized. Research questions: a) how do adult critically ill patients perceive sleep quality in the Critical Care Department of El Manial University Hospital? b) What are the factors affecting sleep quality among adult critically ill patients at El Manial University Hospital? Setting: selected critical and cardiac care units at El Manial University Hospital. Sample: A samples of convenience consisting of 100 adult male and female patients were included in the study. Tools of data collection: tool 1: Socio-demographic and Medical Data Sheet, tool 2: Modified St Mary's Hospital Sleep Questionnaire tool 3: Factors Affecting Sleep Quality Questionnaire among ICU Patients Results: The current study revealed that 76.0% of the studied sample had lack of sleep disturbance before hospitalization. However, 84 % had sleep disturbances during ICU stay, of these more than two-thirds (67 %) had moderate sleep disturbance. Presence of strange and bad odors, noise, having pain, fear of death and a loud voice produced by the ICU personnel had the most significant negative impact on patients’ sleep in percentage of 52.4, 50, 61.9, 45.2, 52.4, respectively. Conclusion: Sleep disturbances in the ICU are multifactorial, and ICU patients’ perceived degrees of sleep disturbance as a moderate. Recommendations: Based on findings of the present study, the following are recommended to be done by ICU nurses; create a healing ICU environment that should incorporate noise, light and temperature controls; decrease stimuli during night time hours to promote regulation of the circadian rhythm, allow usage of sleeping aids such as relaxing music, eye patches and earplugs into their daily nursing practice; cluster nursing activities and eliminate non-essential treatments during night time hours to allow uninterrupted sleep periods of at least 90 minutes to complete one sleep cycle , and minimize staff conversation, alarm noise and light during the quiet night time hours.

Keywords: sleep quality, critically ill, patients, perception

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Authors: Rajeev Kumar Bedi, Jaswinder Singh, Sunil Kumar Gupta

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The demand for cloud storage is increasing because users want continuous access their data. Cloud Storage revolutionized the way how users access their data. A lot of cloud storage service providers are available as DropBox, G Drive, and providing limited free storage and for extra storage; users have to pay money, which will act as a burden on users. To avoid the issue of limited free storage, the concept of Multi Cloud Storage introduced. In this paper, we will discuss the limitations of existing Multi Cloud Storage systems for mobile devices.

Keywords: cloud storage, data privacy, data security, multi cloud storage, mobile devices

Procedia PDF Downloads 689

Authors: Jin-Wei Liang, Hung-Yi Chen, Lung Lin

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In this paper feedforward controller is designed to eliminate nonlinear hysteresis behaviors of a piezoelectric stack actuator (PSA) driven system. The control design is based on inverse Prandtl-Ishlinskii (P-I) hysteresis model identified using particle swarm optimization (PSO) technique. Based on the identified P-I model, both the inverse P-I hysteresis model and feedforward controller can be determined. Experimental results obtained using the inverse P-I feedforward control are compared with their counterparts using hysteresis estimates obtained from the identified Bouc-Wen model. Effectiveness of the proposed feedforward control scheme is demonstrated. To improve control performance feedback compensation using traditional PID scheme is adopted to integrate with the feedforward controller.

Keywords: the Bouc-Wen hysteresis model, particle swarm optimization, Prandtl-Ishlinskii model, automation engineering

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Authors: Christina Largent, Tazley Hobbs

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Objective: A literature review was performed to examine the existing research on COVID-19 lockdown as it relates to ADHD child/adolescent individuals, media use, and impact on educational performance/learning. It was surmised that with the COVID-19 shut-down and transition to remote learning, a less structured learning environment, increased screen time, in addition to potential difficulty accessing school resources would impair ADHD individuals’ performance and learning. A resulting increase in the number of youths diagnosed and treated for ADHD would be expected. As of yet, there has been little to no published data on the incidence of ADHD as it relates to COVID-19 outside of reports from several nonprofit agencies such as CHADD (Children and Adults with Attention-Deficit/Hyperactivity Disorder ), who reported an increased number of calls to their helpline, The New York based Child Mind Institute, who reported an increased number of appointments to discuss medications, and research released from Athenahealth showing an increase in the number of patients receiving new diagnosis of ADHD and new prescriptions for ADHD medications. Methods: A literature search for articles published between 2020 and 2021 from Pubmed, Google Scholar, PsychInfo, was performed. Search phrases and keywords included “covid, adhd, child, impact, remote learning, media, screen”. Results: Studies primarily utilized parental reports, with very few from the perspective of the ADHD individuals themselves. Most findings thus far show that with the COVID-19 quarantine and transition to online learning, ADHD individuals’ experienced decreased ability to keep focused or adhere to the daily routine, as well as increased inattention-related problems, such as careless mistakes or lack of completion in homework, which in turn translated into overall more difficulty with remote learning. To add further injury, one study showed (just on evaluation of two different sites within the US) that school based services for these individuals decreased with the shift to online-learning. Increased screen time, television, social media, and gaming were noted amongst ADHD individuals. One study further differentiated the degree of digital media, identifying individuals with “problematic “ or “non-problematic” use. ADHD children with problematic digital media use suffered from more severe core symptoms of ADHD, negative emotions, executive function deficits, damage to family environment, pressure from life events, and a lower motivation to learn. Conclusions and Future Considerations: Studies found not only was online learning difficult for ADHD individuals but it, in addition to greater use of digital media, was associated with worsening ADHD symptoms impairing schoolwork, in addition to secondary findings of worsening mood and behavior. Currently, data on the number of new ADHD cases, in addition to data on the prescription and usage of stimulants during COVID-19, has not been well documented or studied; this would be well-warranted out of concern for over diagnosing or over-prescribing our youth. It would also be well-worth studying how reversible or long-lasting these negative impacts may be.

Keywords: COVID-19, remote learning, media use, ADHD, child, adolescent

Procedia PDF Downloads 120

Authors: Tomas Premoli, Sareh Rowlands

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In this study, we investigate the impact of various convolutional neural network (CNN) architectures on the accuracy of diagnosing Alzheimer’s disease (AD) using patient MRI scans. Alzheimer’s disease is a debilitating neurodegenerative disorder that affects millions worldwide. Early, accurate, and non-invasive diagnostic methods are required for providing optimal care and symptom management. Deep learning techniques, particularly CNNs, have shown great promise in enhancing this diagnostic process. We aim to contribute to the ongoing research in this field by comparing the effectiveness of different CNN architectures and providing insights for future studies. Our methodology involved preprocessing MRI data, implementing multiple CNN architectures, and evaluating the performance of each model. We employed intensity normalization, linear registration, and skull stripping for our preprocessing. The selected architectures included VGG, ResNet, and DenseNet models, all implemented using the Keras library. We employed transfer learning and trained models from scratch to compare their effectiveness. Our findings demonstrated significant differences in performance among the tested architectures, with DenseNet201 achieving the highest accuracy of 86.4%. Transfer learning proved to be helpful in improving model performance. We also identified potential areas for future research, such as experimenting with other architectures, optimizing hyperparameters, and employing fine-tuning strategies. By providing a comprehensive analysis of the selected CNN architectures, we offer a solid foundation for future research in Alzheimer’s disease diagnosis using deep learning techniques. Our study highlights the potential of CNNs as a valuable diagnostic tool and emphasizes the importance of ongoing research to develop more accurate and effective models.

Keywords: Alzheimer’s disease, convolutional neural networks, deep learning, medical imaging, MRI

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Authors: Boukari Nassim

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This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals.

Keywords: epilepsy, EEG signals classification, combined odd pair autoregressive coefficients, radial basis function neural network

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

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Authors: Huang Chiung Chiu

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This article is about the nursing experience of assisting an outpatient with premature menopause, osteoporosis and sarcopenia through a multi-discipline care model. The nursing period is from September 22nd, 2020, to December 7th, 2020, collecting data through interviews with the patient, observation, and physical assessment. It was found that the main health problems were insufficient nutrition, less physical need, insomnia, and potentially dangerous falls. As an outpatient nurse, the author observed that in recent years, the age group of women with premature menopause, osteoporosis and sarcopenia had shifted downward. Integrated multi-disciplinary interventions were provided upon the initial diagnosis of osteoporosis and sarcopenia. Under the outpatient care setting, the collaborative team works between the doctors, nutritionists, osteoporosis educators, rehabilitates, physical therapists and other specialized teams were applied to provide individualized, integrated multi-disciplinary care. Through empathy and the establishment of attentive care, companionship and trust, we discussed care plans and treatment guidelines with the case, providing accurate, complete disease information and feedback education to strengthen the patient’s knowledge and motivation for exercise. Nursing guidance regarding the dietary nutrition and adjustment of daily routine was provided to increase the self-care ability, improve the health problems of muscle weakness and insomnia, and prevent falls. For patients with postmenopausal osteoporosis and sarcopenia, it is recommended that the nurses coordinate the multi-discipline integrated care model, adjust patients’ lifestyle and diet, and establish a regular exercise plan so that the cases can be evaluated holistically to improve the quality of care and physical and mental comfort.

Keywords: multi-discipline care model, premature menopause, osteoporosis, sarcopenia, insomnia

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Authors: Wookyong Kwon, Sangmoon Lee

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In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method.

Keywords: model predictive control, sampled-data control, linear parameter varying systems, LPV

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Authors: Catarina Isabel Ramos Pereira, Jorge Machado, Begona Alonso Criado, Maria João Santos

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Introduction: Gait disorders are one of the symptoms that have severe implications on the quality of life in Parkinson's disease (PD). Currently, there is no therapy to reverse or treat this condition. None of the drugs used in conventional medical treatment is entirely efficient, and all have a high incidence of side effects. Acupuncture treatment is believed to improve motor ability, but there is still little scientific evidence in individuals with PD. Aim: The aim of the study is to investigate the acute effect of acupuncture on gait parameters in Parkinson's disease. Methods: This is a randomized and controlled crossover study. The same individual patient was part of both the experimental (real acupuncture) and control group (false acupuncture/sham), and the sequence was randomized. Gait parameters were measured at two different moments, before and after treatment, using four force platforms as well as the collection of 3D markers positions taken by 11 cameras. Images were quantitatively analyzed using Qualisys Track Manager software that let us extract data related to the quality of gait and balance. Seven patients with the diagnosis of Parkinson's disease were included in the study. Results: Statistically significant differences were found in gait speed (p = 0.016), gait cadence (p = 0.006), support base width (p = 0.0001), medio-lateral oscillation (p = 0.017), left-right step length (p = 0.0002), and stride length: right-right (p = 0.0000) and left-left (p = 0.0018), time of left support phase (p = 0.029), right support phase (p = 0.025) and double support phase (p = 0.015), between the initial and final moments for the experimental group. Differences in right-left stride length were found for both groups. Conclusion: Our results show that acupuncture could enhance gait in Parkinson's disease patients. Deep research involving a larger number of volunteers should be accomplished to validate these encouraging findings.

Keywords: acupuncture, traditional Chinese medicine, Parkinson's disease, gait

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Authors: Fernando Duarte

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The work aims to evaluate the use of classical image processing methodologies towards skin tone classification in dermatological images. The skin tone is an important attribute when considering several factor for skin cancer diagnosis. Currently, there is a lack of clear methodologies to classify the skin tone based only on the dermatological image. In this work, a recent released dataset with the label for skin tone was used as reference for the evaluation of classical methodologies for segmentation and adjustment of color space for classification of skin tone in dermatological images. It was noticed that even though the classical methodologies can work fine for segmentation and color adjustment, classifying the skin tone without proper control of the aquisition of the sample images ended being very unreliable.

Keywords: segmentation, classification, color space, skin tone, Fitzpatrick

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Authors: Nasser A. Al-Azri

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The effectiveness of passive cooling techniques is assessed based on bioclimatic charts that require the typical meteorological year (TMY) for a specified location for their development. However, TMYs are not always available; mainly due to the scarcity of records of solar radiation which is an essential component used in developing common TMYs intended for general uses. Since solar radiation is not required in the development of the bioclimatic chart, this work suggests developing TMYs based solely on the relevant parameters. This approach improves the accuracy of the developed TMY since only the relevant parameters are considered and it also makes the development of the TMY more accessible since solar radiation data are not used. The presented paper will also discuss the development of the TMY from the raw data available at the NOAA-NCDC archive of world weather data and the construction of the bioclimatic charts for some randomly selected locations around the world.

Keywords: bioclimatic charts, passive cooling, TMY, weather data

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: D. Nam Kim, D. Jin Kim, Jeonghwan Jeon

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Defense Defensive Modeling and Simulation (M&S) is a system which enables impracticable training for reducing constraints of time, space and financial resources. The necessity of defensive M&S has been increasing not only for education and training but also virtual fight. Soldiers who are using defensive M&S for education and training will obtain empirical knowledge and know-how. However, the obtained knowledge of individual soldiers have not been managed and utilized yet since the nature of military organizations: confidentiality and frequent change of members. Therefore, this study aims to develop a management system for the experience of defensive M&S based on data mining approach. Since individual empirical knowledge gained through using the defensive M&S is both quantitative and qualitative data, data mining approach is appropriate for dealing with individual empirical knowledge. This research is expected to be helpful for soldiers and military policy makers.

Keywords: data mining, defensive m&s, management system, knowledge management

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Authors: Wei Lu

Abstract:

With the development of information technology in China, network consumption is becoming more and more popular. As a special group, college students have a high degree of education and distinct opinions and personalities. In the future, the key groups of network consumption have gradually become the focus groups of network consumption. Studying college students’ online consumption behavior has important theoretical significance and practical value. Based on the Stimulus-Organism-Response (SOR) driving model and the structural equation model, this paper establishes the influencing factors model of College students’ online consumption behavior, evaluates and amends the model by using SPSS and AMOS software, analyses and determines the positive factors of marketing college students’ consumption, and provides an effective basis for guiding and promoting college student consumption.

Keywords: college students, online consumption, stimulate-organism-reaction driving model, structural equation model

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Authors: Hyun-Woo Cho

Abstract:

The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

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Authors: Changhong Guo, Shaomei Fang, Yong He

Abstract:

In this paper, fractional Black-Scholes models for the European option pricing were established based on the fractional G-Brownian motion (fGBm), which generalizes the concepts of the classical Brownian motion, fractional Brownian motion and the G-Brownian motion, and that can be used to be a tool for considering the long range dependence and uncertain volatility for the financial markets simultaneously. A generalized fractional Black-Scholes equation (FBSE) was derived by using the Taylor’s series of fractional order and the theory of absence of arbitrage. Finally, some explicit option pricing formulas for the European call option and put option under the FBSE were also solved, which extended the classical option pricing formulas given by F. Black and M. Scholes.

Keywords: European option pricing, fractional Black-Scholes equations, fractional g-Brownian motion, Taylor's series of fractional order, uncertain volatility

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Authors: Eusebio Odiari, Mark Birkin, Susan Grant-Muller, Nicolas Malleson

Abstract:

Big data typically refers to consumer datasets revealing some detailed heterogeneity in human behavior, which if harnessed appropriately, could potentially revolutionize our understanding of the collective phenomena of the physical world. Inadvertent missing values skew these datasets and compromise the validity of the thesis. Here we discuss a conceptually consistent strategy for identifying other relevant datasets to combine with available big data, to plug the gaps and to create a rich requisite comprehensive dataset for subsequent analysis. Specifically, emphasis is on how these methodologies can for the first time enable the construction of more detailed pictures of passenger demand and drivers of mobility on the railways. These methodologies can predict the influence of changes within the network (like a change in time-table or impact of a new station), explain local phenomena outside the network (like rail-heading) and the other impacts of urban morphology. Our analysis also reveals that our new imputation data model provides for more equitable revenue sharing amongst network operators who manage different parts of the integrated UK railways.

Keywords: big-data, micro-simulation, mobility, ticketing-data, commuters, transport, synthetic, population

Procedia PDF Downloads 223