Search results for: clinical error

Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

Procedia PDF Downloads 538

Authors: Apolinar Picado, Ronald Solís, Rafael Gamero

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The thin-layer drying characteristics of instant coffee solution were investigated in a laboratory tunnel dryer. Drying experiments were carried out at three temperatures (80, 100 and 120 °C) and an air velocity of 1.2 m/s. Drying experimental data obtained are fitted to six (6) thin-layer drying models using the non-linear least squares regression analysis. The acceptability of the thin-layer drying model has been based on a value of the correlation coefficient that should be close to one, and low values for root mean square error (RMSE) and chi-square (x²). According to this evaluation, the most suitable model for describing drying process of thin-layer instant coffee solution is the Page model. Further, the effective moisture diffusivity and the activation energy were computed employing the drying experimental data. The effective moisture diffusivity values varied from 1.6133 × 10⁻⁹ to 1.6224 × 10⁻⁹ m²/s over the temperature range studied and the activation energy was estimated to be 162.62 J/mol.

Keywords: activation energy, diffusivity, instant coffee, thin-layer models

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Authors: Fernando P. Silva, Valter J. S. Leite, Erivelton G. Nepomuceno

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In this paper, a nonlinear controller design for an omnidirectional mobile is presented. The robot controller consists of an inner-loop controller and an outer-loop controller, the first is designed using state feedback (robust allocation) and the second controller is designed based on Robust Multi Inversion (RMI) approach. The objective of RMI controller is rendering the robust inversion of the dynamic, when the model is affected by uncertainties. A model nonlinear MIMO of an omni-directional robot (small-league of Robocup) is used to simulate the RMI approach. The parameters of linear and nonlinear model are varied to cause modelling uncertainties among the model and the real model (real system) generating an error in inner-loop controller signal that must be compensated by RMI controller. The simulation test results show that the RMI is capable of compensating the uncertainties and keep the system stable and controlled under uncertainties.

Keywords: robust multi inversion, omni-directional robot, robocup, nonlinear control

Procedia PDF Downloads 589

Authors: Rashid Azeem

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Introduction: Eosinophilia, which can be categorized as mild, moderate, and severe form on the basis of increasing eosinophil counts, might be responsible for a wide range of cardiac manifestations, varying from a simple myocarditis to a severe state like endomyocardial fibrosis. Eosinophils are involved in the pathogenesis of a variety of cardiovascular disorder like Loffler endocarditis, eosinophilic granulomatosis with polyangitis (EGPH), and hyper eosinophilic (HES). Among them HES carries and incidence rate b/w 48% and 75% and is the main causes of cardiac motility and mobility due to eosinophilia involvement. Aims and objectives: The aim of this study is to determine the frequency of eosinophilia in cardiac patients and to ascertain the evidence of endomyocardial diseases in eosinophilic patients in a cardiology institution Material and Methods: This cross sectional analytical study was conducted in hematology Department of Peshawar institute of Cardiology after approval from hospital ethical and research committee. All 70 patients were subjected to detailed history and clinical examination. Investigation like CBC, Chest X-ray, ECG, Echo, Angiography findings were used to monitor patient’s clinical status. Data is analyzed using SPSS version 25 and MS Excel. Results: Out of 70 patients in our study, a total of 66 patients(94 %) shows evidence of cardiac manifestations. In our study, we have observed a number of abnormal ECG patterns in cardiac patients presenting with eosinophilia, like T wave changes, loss of R wave, sinus bradycardia with LVH strain, and ST wave abnormality. abnormal echocardiographic findings were observed in our patients, like valvular abnormalities (in 45.7%), RWMA abnormalities (in 2.8%), isolated ventricular dysfunction (in 21.4%), and in 10% patients, normal echocardiography. We further noted abnormal coronary angiography findings in cardiac patients with eosinophilia ranging from single vessel to multi vessel occlusions. Conclusions: Eosinophils are involved in the pathogenesis of a variety of cardiovascular disorders which can be detected by various diagnostic means, and the severity of the disease increases with time and with increasing eosinophil count ranging from simple myocarditis to a fatal condition like endomyocardial fibrosis. Thus, increased eosinophilic count as a laboratory parameter in cardiac patients may be a sign of endomyocardial damage which will further help cardiologist to intervene more aggressively then routine approach to a cardiac patient.

Keywords: eosinophilia, endomyocardial fibrosis, cardiac, hypereosinophilic syndrome

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Authors: Li Zhang, Yuehong Su

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Lightpipe as a mature solar light tube technique has been employed worldwide. Accurately assessing the performance of lightpipe and evaluate daylighting available has been a challenging topic. Previous research had used regression model and computational simulation methods to estimate the performance of lightpipe. However, due to the nonlinear nature of solar light transferring in lightpipe, the methods mentioned above express inaccurate and time-costing issues. In the present study, a neural network model as an alternative method is investigated to predict the transmittance of lightpipe. Four types of commercial lightpipe with bended angle 0°, 30°, 45° and 60° are discussed under clear, intermediate and overcast sky conditions respectively. The neural network is generated in MATLAB by using the outcomes of an optical software Photopia simulations as targets for networks training and testing. The coefficient of determination (R²) for each model is higher than 0.98, and the mean square error (MSE) is less than 0.0019, which indicate the neural network strong predictive ability and the use of the neural network method could be an efficient technique for determining the performance of lightpipe.

Keywords: neural network, bended lightpipe, transmittance, Photopia

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Authors: Wayan Widhiada, Cok Indra Partha, Gusti Ngurah Nitya Santhiarsa

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The purpose of this paper is to investigate the sophistication of the use of Proportional Integral and Derivative Control to control the kinematic motion of the mobile robot manipulator. Simulation and experimental methods will be used to investigate the sophistication of PID control to control the mobile robot arm in the collection and placement of several kinds of objects quickly, accurately and correctly. Mathematical modeling will be done by utilizing the integration of Solidworks and MATLAB / Simmechanics software. This method works by converting the physical model file into the xml file. This method is easy, fast and accurate done in modeling and design robotics. The automatic control design of this robot manipulator will be validated in simulations and experimental in control labs as evidence that the mobile robot manipulator gripper control design can achieve the best performance such as the error signal is lower than 5%, small overshoot and get steady signal response as quickly.

Keywords: control analysis, kinematics motion, mobile robot manipulator, performance

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Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

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PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

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Authors: Stefano A. Denes, Riccardo Tieghi, Giovanni Elia

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The buccal fat pad is a well-established tool in oral and maxillofacial surgery and its use has proved of value for the closure of oroantral communications. Oroantral communication may be a common complication after sequestrectomy in "Bisphosphonate-related osteonecrosis of the jaws". We report a clinical case of a 70-year-old female patient in bisphosphonate therapy presented with right maxillary sinusitis and oroantral communication after implants insertion. The buccal fat pad was used to close the defect. The case had an uneventful postoperative healing without dehiscence, infection and necrosis. We postulate that the primary closure of the site with buccal fat pad may ensure a sufficient blood supply and adequate protection for an effective bone-healing response to occur.

Keywords: buccal fat pad, oroantral communication, oral surgery, dehiscence

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Authors: Haniye Dehestani, Yadollah Ordokhani

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In this work, we present an efficient approach for solving variable-order time-fractional partial differential equations, which are based on Legendre and Laguerre polynomials. First, we introduced the pseudo-operational matrices of integer and variable fractional order of integration by use of some properties of Riemann-Liouville fractional integral. Then, applied together with collocation method and Legendre-Laguerre functions for solving variable-order time-fractional partial differential equations. Also, an estimation of the error is presented. At last, we investigate numerical examples which arise in physics to demonstrate the accuracy of the present method. In comparison results obtained by the present method with the exact solution and the other methods reveals that the method is very effective.

Keywords: collocation method, fractional partial differential equations, legendre-laguerre functions, pseudo-operational matrix of integration

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Authors: Belkacem Laimouche

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With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

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Authors: Rupinder Kaler, Faith Ndebele, Nadia Saleem, Hafsa Sheikh

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Background: Workplace related violence and aggression seems to be considered an acceptable occupational hazard for staff in mental health services. There is literature evidence that healthcare workers in mental health settings are at higher risk from aggression from patients. Aggressive behaviours pose a physical and psychological threat to the psychiatric staff and can result in stress, burnout, sickness, and exhaustion. Further evidence informs that health professionals are the most exposed to psychological disorders such as anxiety, depression and post-traumatic stress disorder. Fear that results from working in a dangerous environment and exhaustion can have a damaging impact on patient care and healthcare relationship. Aim: The aim of this study is to investigate the prevalence and impact of aggressive behaviour on staff working at Coventry and Warwickshire Partnership Trust. Methodology: The study methodology included carrying out a manual, anonymised, multi-disciplinary cross-sectional survey questionnaire across all clinical and non-clinical staff at CWPT from both inpatient and community settings. Findings: The unsurprising finding was that of higher prevalence of aggressive behaviours in in-patients in comparison to community staff. Conclusion: There is a high rate of verbal and physical aggression at work and this has a negative impact on the staff emotional and physical well- being. There is also a higher reliance on colleagues for support on an informal basis than formal organisational support systems. Recommendations: A workforce that is well and functioning is the biggest resource for an organisation. Staff safety during working hours is everyone's responsibility and sits with both individual staff members and the organisation. Post-incident organisational support needs to be consolidated, and hands-on, timely support offered to help maintain emotionally well staff on CWPT. The authors recommend development of preventative and practical protocols for aggression with patient and carer involvement. Post-incident organisational support needs to be consolidated, and hands-on, timely support offered to help maintain emotionally well staff on CWPT.

Keywords: safer staff, survey of staff experiences, violence and aggression, mental health

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Authors: Mukesh K. Dwivedi, Rajeshwar N. Srivastava, Amit K. Bhagat, Saloni Raj

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Introduction: Management of Pressure Ulcer (PU) is an ongoing clinical challenge particularly in traumatic paraplegia patients in developing countries where socio economic conditions often dictate treatment modalities. When negative pressure wound therapy (NPWT) was introduced, there were a series of devices (V.A.C., KCI, San Antonio, TX) manufactured. These devices for NPWT are costly and hard to afford by patients in developing countries like India. Considering this limitation, this study was planned to design an RCT to compare NPWT by an indigenized locally constructed NPD and conventional gauze dressing for the treatment of PU. Material and Methods: This RCT (CTRI/2014/09/0050) was conducted in the Department of Orthopaedic Surgery at King George’s Medical University (KGMU), India. Thirty-four (34) subjects of traumatic paraplegia having PU of stage 3 or 4, were enrolled and randomized in two treatment groups (NPWT Group & Conventional dressing group). The outcome measures of this study were surface area and depth of PU, exudates, microorganisms and matrix metalloproteinase-8 (MMP-8) during 0 to 9 weeks follow-ups. Levels of MMP-8 were analyzed in the tissues of PU at week 0, 3, 6 and week 9 by Enzyme Linked Immuno Sorbent Assay (ELISA). Results: Significantly reduced length of PU in NPWT group was observed at week 6 (p=0.04) which further reduced at week 9 (p=0.001) as compared to conventionally treated group. Similarly significant reduction of width and depth of PU was observed in NPWT at week 9 (p<0.05). The exudate became significantly (p=0.001) lower in NPWT group as compared with conventionally treated group from 6th to 9th week. Clearance and conversion of slough into red granulation tissue was significantly higher in NPWT group (p=0.001). At week 9, the wound culture was negative in all the subjects of NPWT group, while it was positive in 10 (41⋅6%) subjects of conventional group. Significantly lower level of MMP-8 was observed in subjects of NPWT group at week 6 (0.006**), and continually more reduction was observed at week 9 (<0.0001**) as compared to the conventional group. Conclusion: NPWT by locally constructed NPD is better wound care procedure for management of PU. Our device gave similar results as commercially available devices. Reduction of level of MMP-8 and increased rate of healing was achieved by negative pressure wound therapy (NPWT) as compared to conventional dressing.

Keywords: NPWT, NPD, MMP8, ELISA

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Authors: Osarobo Omorogieva Ighodaro, Josephus Otejere

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This paper contains the modelling and simulation of GT13E2 combined cycle gas turbine with the aid of the software EBSILON PROFESSIONAL. The design mode was modeled using guaranteed performance data from the power plant, in the off design, temperature variation of ambient air and fogging (spray water at inlet to compressor) was simulated. The fogging was simulated under two different modes; constant fuel consumption and constant turbine exhaust temperature .The model results were validated using actual operating data by applying error percentage analysis. The validation results obtained ranged from -0.0038% to 0% in design condition while the results varied from -0.9202% to 10.24% The model shows that fogging decreases compressor inlet temperature which in turn decreases the power required to drive the compressor hence improving the simple cycle efficiency and hence increasing power generated.

Keywords: inlet fogging, off design, combined cycle, modelling

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Authors: H. Barati, M. Wu, A. Kharicha, A. Ludwig

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A transient model for nozzle clogging has been developed and successfully validated against a laboratory experiment. Key steps of clogging are considered: transport of particles by turbulent flow towards the nozzle wall; interactions between fluid flow and nozzle wall, and the adhesion of the particle on the wall; the growth of the clog layer and its interaction with the flow. The current paper is to investigate the mesh (size and type) sensitivity of the model in both two and three dimensions. It is found that the algorithm for clog growth alone excluding the flow effect is insensitive to the mesh type and size, but the calculation including flow becomes sensitive to the mesh quality. The use of 2D meshes leads to overestimation of the clog growth because the 3D nature of flow in the boundary layer cannot be properly solved by 2D calculation. 3D simulation with tetrahedron mesh can also lead to an error estimation of the clog growth. A mesh-independent result can be achieved with hexahedral mesh, or at least with triangular prism (inflation layer) for near-wall regions.

Keywords: clogging, continuous casting, inclusion, simulation, submerged entry nozzle

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Authors: A. Ejah Umraeni Salam, M. Tola, M. Selintung, F. Maricar

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Clean water is an essential and fundamental human need. Therefore, its supply must be assured by maintaining the quality, quantity and water pressure. However the fact is, on its distribution system, leakage happens and becomes a common world issue. One of the technical causes of the leakage is a leaking pipe. The purpose of the research is how to use the Radial Basis Function Neural (RBFNN) model to detect the location and the magnitude of the pipeline leakage rapidly and efficiently. In this study the RBFNN are trained and tested on data from EPANET hydraulic modeling system. Method of Radial Basis Function Neural Network is proved capable to detect location and magnitude of pipeline leakage with of the accuracy of the prediction results based on the value of RMSE (Root Meant Square Error), comparison prediction and actual measurement approaches 0.000049 for the whole pipeline system.

Keywords: radial basis function neural network, leakage pipeline, EPANET, RMSE

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Authors: Shubham Jaiswal

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During modeling of transport phenomena in porous media, many nonlinear partial differential equations (NPDEs) encountered which greatly described the convection, diffusion and reaction process. To solve such types of nonlinear problems, a reliable and efficient technique is needed. In this article, the numerical solution of NPDEs encountered in porous media is derived. Here Jacobi collocation method is used to solve the considered problems which convert the NPDEs in systems of nonlinear algebraic equations that can be solved using Newton-Raphson method. The numerical results of some illustrative examples are reported to show the efficiency and high accuracy of the proposed approach. The comparison of the numerical results with the existing analytical results already reported in the literature and the error analysis for each example exhibited through graphs and tables confirms the exponential convergence rate of the proposed method.

Keywords: nonlinear porous media equation, shifted Jacobi polynomials, operational matrix, spectral collocation method

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Authors: Marco Avila Lopez, Hasnae Ait-Douchi, Silvia De Los Santos, Badr Eddine Lebrouhi, Pamela Ramírez Vidal

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In the context of the energy transition, fuel cell technology has emerged as a solution for harnessing hydrogen energy and mitigating greenhouse gas emissions. An in-depth study was conducted on a PEMFC-type fuel cell, with an initiation of an analysis of its operational principles and constituent components. Subsequently, the modelling of the fuel cell was undertaken using the Python programming language, encompassing both steady-state and transient regimes. In the case of the steady-state regime, the physical and electrochemical phenomena occurring within the fuel cell were modelled, with the assumption of uniform temperature throughout all cell compartments. Parametric identification was carried out, resulting in a remarkable mean error of only 1.62% when the model results were compared to experimental data documented in the literature. The dynamic model that was developed enabled the scrutiny of the fuel cell's response in terms of temperature and voltage under varying current conditions.

Keywords: fuel cell, modelling, dynamic, thermal model, PEMFC

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Authors: Shilpa Sondkar, Maithili Patil, Atharva Potnis

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The current health care landscape desires efficiency and patient satisfaction for optimal performance. Medical appointment booking apps have increased the overall efficiency of clinics, hospitals, and e-health marketplaces while simplifying processes. These apps allow patients to connect with doctors online. Not only are mobile doctor appointment apps a reliable and efficient solution, but they are also the future of clinical progression and a distinct new stage in the patient-doctor relationship. Compared to the usual queuing method, the web-based appointment system could significantly increase patients' satisfaction with registration and reduce total waiting time effectively.

Keywords: appointment, patient, scheduling, design and development, Figma

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Authors: Marin Kanarev, Delyan Stoyanov, Ivanna Popova, Nadezhda Petrova

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Thanks to increased survival rates in patients bearing oncological malignancies due to recent developments in anti-cancer therapies and diagnostic techniques, observation of clinical cases of metachronous cancers is more common and can provide more in-depth knowledge of their development and, as a result, help clinicians apply suitable therapy. This unusual case of three metachronous tumors presented the opportunity to follow their occurrence, progression, and treatment thoroughly. A 77-year-old male presented with carcinoma ventriculi of the pylorus region, which was surgically removed via upper subtotal stomach resection, a lateral antecolical gastro-enteroanastomosis, and a subsequent Braun anastomosis. An EOX chemotherapy regimen followed. A CT scan four months later showed no indication of recurrence or dissemination. The same scan, performed as a part of the follow-up plan two years later, showed an indication of neoplastic growth in the urinary bladder. After the patient had been directed to a urologist, the suspicion was confirmed, and the growth was histologically diagnosed as a carcinoma of the urinary bladder. An immunohistochemistry test showed an expression of PDL1 of less than 5%, which resulted in treatment with GemCis chemotherapy regimen that led to full remission. Two years and seven months after the first surgery, a CT scan showed again that the two carcinomas were gone. However, four months later, elevated tumor markers prompted a PET/CT scan, which showed data indicative of recurring neoplastic growth in the region of the stomach cardia. It was diagnosed as an adenocarcinoma infiltrating the esophagus. Preoperative chemotherapy with the ECF regimen was completed in four courses, and a CT scan showed no progression of the disease. In less than a month after therapy, the patient underwent laparotomy, debridement, gastrectomy, and a subsequent mechanical terminal-lateral esophago-jejunoanasthomosis. It was verified that the tumor originated from metastasis from the carcinoma ventriculi, which was located in the pylorus. In conclusion, this case report highlights the importance of patient follow-up and studying recurring neoplastic growth. Despite the absence of symptoms, clinicians should maintain a high level of suspicion when evaluating the patient data and choosing the most suitable therapy.

Keywords: carcinoma, follow-up, metachronous, neoplastic growth, recurrence

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Authors: N. Ploysook, R. Rugsaj, C. Suvanjumrat

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The characteristic requirement for producing rectangular shape bottles was a uniform thickness of the plastic bottle wall. Die shaping was a good technique which controlled the wall thickness of bottles. An advance technology which was the finite element method (FEM) for blowing parison to be a rectangular shape bottle was conducted to reduce waste plastic from a trial and error method of a die shaping and parison control method. The artificial intelligent (AI) comprised of artificial neural network and genetic algorithm was selected to optimize the die gap shape from the FEM results. The application of AI technique could optimize the suitable die gap shape for the parison blow molding which did not depend on the parison control method to produce rectangular bottles with the uniform wall. Particularly, this application can be used with cheap blow molding machines without a parison controller therefore it will reduce cost of production in the bottle blow molding process.

Keywords: AI, bottle, die shaping, FEM

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Authors: Rana I. Abdulghani, Amera I. Melhum

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With the abnormal growth of the usage of computers over networks and under the consideration or agreement of most of the computer security experts who said that the goal of building a secure system is never achieved effectively, all these points led to the design of the intrusion detection systems(IDS). This research adopts a comparison between two techniques for network intrusion detection, The first one used the (Particles Swarm Optimization) that fall within the field (Swarm Intelligence). In this Act, the algorithm Enhanced for the purpose of obtaining the minimum error rate by amending the cluster centers when better fitness function is found through the training stages. Results show that this modification gives more efficient exploration of the original algorithm. The second algorithm used a (Back propagation NN) algorithm. Finally a comparison between the results of two methods used were based on (NSL_KDD) data sets for the construction and evaluation of intrusion detection systems. This research is only interested in clustering the two categories (Normal and Abnormal) for the given connection records. Practices experiments result in intrude detection rate (99.183818%) for EPSO and intrude detection rate (69.446416%) for BP neural network.

Keywords: IDS, SI, BP, NSL_KDD, PSO

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Authors: Annie Gedion, Johan Severinus Tati, Jacinta Caroline Peter

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Syntax error analysis studies have a significant role in English language teaching especially in the second language. This study investigates the syntax errors in written composition by 50 multilingual ESL learners in Politeknik Kota Kinabalu Sabah, Malaysia. The subjects speak their own dialect, Malay as their second language and English as their third or foreign language. Data were collected from the written discourse in the form of descriptive essays. The subjects were asked to write in the classroom within 45 minutes. 15 categories of errors were classified into a set of syntactic categories and were analysed based on the five steps of the syntactic analysis procedure. The findings of the study showed that the mother tongue interference, as well as lack of vocabulary and grammar knowledge, were the major sources of syntax errors in the learners’ written composition. Learners should be exposed to the differentiation of Malay and English grammar to avoid interference and effective learning of second language writing.

Keywords: errors analysis, syntactic analysis, English as a second language, ESL writing

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Authors: Deepak Jugran, Rajesh Gill

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Purpose: To study the demographic, sociological, gender and clinical profile of the children presented for childhood cataract at a public sector tertiary eye care centre in India. Methodology: The design of the study is retrospective, and hospital-based data is available with the Central Registration Department of the PGIMER, Chandigarh. The majority of the childhood cataract cases are being reported in this hospital, yet not each and every case of childhood cataract approaches PGI, Chandigarh. Nevertheless, this study is going to be pioneering research in India, covering five-year data of the childhood cataract patients who visited the Advanced Eye Centre, PGIMER, Chandigarh, from 1.1.2015 to 31.12.2019. The SPSS version 23 was used for all statistical calculations. Results: A Total of 354 children were presented for childhood cataract from 1.1.2015 to 31.12.2019. Out of 354 children, 248 (70%) were male, and 106 (30%) were female. In-spite of 2 flagship programmes, namely the National Programme for Control of Blindness (NPCB) and Aayushman Bharat (PM JAY) for eradication of cataract, no children received any financial assistance from these two programmes. A whopping 99% of these children belong to the poor families. In most of these families, the mothers were house-wives and did not employ anywhere. These interim results will soon be conveyed to the Govt. of India so that a suitable mechanism can be evolved to address this pertinent issue. Further, the disproportionate ratio of male and female children in this study is an area of concern as we don’t know whether the prevalence of childhood cataract is lower in female children or they are not being presented on time in the hospital by the families. Conclusion: The World Health Organization (WHO) has categorized Childhood blindness resulting from cataract as a priority area and urged all member countries to develop institutionalized mechanisms for its early detection, diagnosis and management. The childhood cataract is an emerging and major cause of preventable and avoidable childhood blindness, especially in low and middle-income countries. In the formative years, the children require a sound physical, mental and emotional state, and in the absence of either one of them, it can severely dent their future growth. The recent estimate suggests that India could suffer an economic loss of US$12 billion (Rs. 88,000 Crores) due to blindness, and almost 35% of cases of blindness are preventable and avoidable if detected at an early age. Besides reporting these results to the policy makers, synchronized efforts are needed for early detection and management of avoidable causes of childhood blindness such as childhood cataract.

Keywords: childhood blindness, cataract, Who, Npcb

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Authors: Justin Reverdi, Sixin Zhang, Serge Gratton, Said Aoues, Thomas Pellegrini

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In Vapor Cycle Systems, the flow sensor plays a key role in different monitoring and control purposes. However, physical sensors can be expensive, inaccurate, heavy, cumbersome, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor based on other standard sensors is a good alternative. In this paper, a data-driven model using a Convolutional Neural Network is proposed to estimate the flow of the compressor. To fit the model to our dataset, we tested different loss functions. We show in our application that a Dynamic Time Warping based loss function called DILATE leads to better dynamical performance than the vanilla mean squared error (MSE) loss function. DILATE allows choosing a trade-off between static and dynamic performance.

Keywords: deep learning, dynamic time warping, vapor cycle system, virtual sensor

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Authors: Umit Duru

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The Soil and Water Assessment Tool (SWAT) was tested for prediction of water balance and sediment yield in the Ankara gauged basin, Turkey. The overall objective of this study was to evaluate the performance and applicability of the SWAT in this region of Turkey. Thirteen years of monthly stream flow, and suspended sediment, data were used for calibration and validation. This research assessed model performance based on differences between observed and predicted suspended sediment yield during calibration (1987-1996) and validation (1982-1984) periods. Statistical comparisons of suspended sediment produced values for NSE (Nash Sutcliffe efficiency), RE (relative error), and R² (coefficient of determination), of 0.81, -1.55, and 0.93, respectively, during the calibration period, and NSE, RE (%), and R² of 0.77, -2.61, and 0.87, respectively, during the validation period. Based on the analyses, SWAT satisfactorily simulated observed hydrology and sediment yields and can be used as a tool in decision making for water resources planning and management in the basin.

Keywords: calibration, GIS, sediment yield, SWAT, validation

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Authors: Nawaf Al Khashram, Graham Arnold, Weijie Wang

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Background—Gait classifying allows clinicians to differentiate gait patterns into clinically important categories that help in clinical decision making. Reliable comparison of gait data between normal and patients requires knowledge of the gait parameters of normal children's specific age group. However, there is still a lack of the gait database for normal children of different ages. Objectives—The aim of this study is to investigate the kinematics of the lower limb joints during gait for normal children in different age groups. Methods—Fifty-three normal children (34 boys, 19 girls) were recruited in this study. All the children were aged between 5 to 16 years old. Age groups were defined as three types: young child aged (5-7), child (8-11), and adolescent (12-16). When a participant agreed to take part in the project, their parents signed a consent form. Vicon® motion capture system was used to collect gait data. Participants were asked to walk at their comfortable speed along a 10-meter walkway. Each participant walked up to 20 trials. Three good trials were analyzed using the Vicon Plug-in-Gait model to obtain parameters of the gait, e.g., walking speed, cadence, stride length, and joint parameters, e.g. joint angle, force, moments, etc. Moreover, each gait cycle was divided into 8 phases. The range of motion (ROM) angle of pelvis, hip, knee, and ankle joints in three planes of both limbs were calculated using an in-house program. Results—The temporal-spatial variables of three age groups of normal children were compared between each other; it was found that there was a significant difference (p < 0.05) between the groups. The step length and walking speed were gradually increasing from young child to adolescent, while cadence was gradually decreasing from young child to adolescent group. The mean and standard deviation (SD) of the step length of young child, child and adolescent groups were 0.502 ± 0.067 m, 0.566 ± 0.061 m and 0.672 ± 0.053 m, respectively. The mean and SD of the cadence of the young child, child and adolescent groups were 140.11±15.79 step/min, 129±11.84 step/min, and a 115.96±6.47 step/min, respectively. Moreover, it was observed that there were significant differences in kinematic parameters, either whole gait cycle or each phase. For example, RoM of knee angle in the sagittal plane in whole cycle of young child group is (65.03±0.52 deg) larger than child group (63.47±0.47 deg). Conclusion—Our result showed that there are significant differences between each age group in the gait phases and thus children walking performance changes with ages. Therefore, it is important for the clinician to consider age group when analyzing the patients with lower limb disorders before any clinical treatment.

Keywords: age group, gait analysis, kinematics, normal children

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Authors: Nahla Al-Bayyari, Rae’d Hailat

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Vitamin D deficiency and overweight are common in women suffering from polycystic ovary syndrome (PCOS). Weight gain in PCOS is an important factor for the development of menstrual dysfunction and signs of hyperandrogenism and alopecia. Features of PCOS such as oligomenorrhea can be predicted by anthropometric measurements as body mass index (BMI). Therefore, the aim of this trial was to study the effect of 50,000 IU/week of vitamin D₃ supplementation on the body composition and on the anthropometric measurements of overweight women with PCOS and to examine the impact of this effect on ovaries ultrasonography and menstrual cycle regularity. The study design was a prospective randomized, double-blinded placebo-controlled clinical trial conducted on 60 overweight Jordanian women aged (18-49) years with PCOS and vitamin D deficiency. The study participants were divided into two groups; vitamin D group (n = 30) who were assigned to receive 50,000 IU/week of vitamin D₃ and placebo group (n = 30) who were assigned to receive placebo tablets orally for 90 days. The anthropometric measurements and body composition were measured at baseline and after treatment for the PCOS and vitamin D deficient women. Also, assessment of the participants’ picture of ovaries by ultrasound and menstrual cycle regulatory were performed before and after treatment. Results showed that there were no significant (p > 0.05) differences between the placebo and vitamin D group basal 25(OH)D levels, body composition and anthropometric parameters. After treatment, vitamin D group serum levels of 25(OH)D increased (12.5 ± 0.61 to 50.2 ± 2.04 ng/mL, (p < 0.001), and decreased (50.2 ± 2.04 to 48.2 ± 2.03 ng/mL, p < 0.001) after 14 days of vitamin D₃ treatment cessation. There were no significant changes in the placebo group. In the vitamin D group, there were significant (p < 0.001) decreases in body weight, BMI, waist, and hip circumferences and fat mass. In addition, there were significant increases (p < 0.05) in fat free mass and total body water. These improvements in both anthropometric and body composition as well as in 25(OH)D concentrations, resulted in significant improvements in the picture of PCOS women ovaries ultrasonography and in menstrual cycle regularity, where nearly most of them (93%) had regular cycles after vitamin D₃ supplementation. In the placebo group, there were only significant decreases (p < 0.05) in waist and hip circumferences. It can be concluded that vitamin D supplementation improving serum 25(OH)D levels and PCOS prognosis by reducing body weight of overweight PCOS women and regulating their menstrual cycle.

Keywords: anthropometric, overweight, polycystic ovary syndrome, vitamin D₃

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Authors: The Danh Phan

Abstract:

House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

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Authors: Elizaveta S. Prokofyeva, Svetlana V. Maltseva, Roman D. Zaitsev

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Rapidly evolving modern data analysis technologies in healthcare play a large role in understanding the operation of the system and its characteristics. Nowadays, one of the key tasks in urban healthcare is to optimize the resource allocation. Thus, the application of data analysis in medical institutions to solve optimization problems determines the significance of this study. The purpose of this research was to establish the dependence between the indicators of the effectiveness of the medical institution and its resources. Hospital discharges by diagnosis; hospital days of in-patients and in-patient average length of stay were selected as the performance indicators and the demand of the medical facility. The hospital beds by type of care, medical technology (magnetic resonance tomography, gamma cameras, angiographic complexes and lithotripters) and physicians characterized the resource provision of medical institutions for the developed models. The data source for the research was an open database of the statistical service Eurostat. The choice of the source is due to the fact that the databases contain complete and open information necessary for research tasks in the field of public health. In addition, the statistical database has a user-friendly interface that allows you to quickly build analytical reports. The study provides information on 28 European for the period from 2007 to 2016. For all countries included in the study, with the most accurate and complete data for the period under review, predictive models were developed based on historical panel data. An attempt to improve the quality and the interpretation of the models was made by cluster analysis of the investigated set of countries. The main idea was to assess the similarity of the joint behavior of the variables throughout the time period under consideration to identify groups of similar countries and to construct the separate regression models for them. Therefore, the original time series were used as the objects of clustering. The hierarchical agglomerate algorithm k-medoids was used. The sampled objects were used as the centers of the clusters obtained, since determining the centroid when working with time series involves additional difficulties. The number of clusters used the silhouette coefficient. After the cluster analysis it was possible to significantly improve the predictive power of the models: for example, in the one of the clusters, MAPE error was only 0,82%, which makes it possible to conclude that this forecast is highly reliable in the short term. The obtained predicted values of the developed models have a relatively low level of error and can be used to make decisions on the resource provision of the hospital by medical personnel. The research displays the strong dependencies between the demand for the medical services and the modern medical equipment variable, which highlights the importance of the technological component for the successful development of the medical facility. Currently, data analysis has a huge potential, which allows to significantly improving health services. Medical institutions that are the first to introduce these technologies will certainly have a competitive advantage.

Keywords: data analysis, demand modeling, healthcare, medical facilities

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Authors: Muluegziabher Semagne Mekonnen

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This study was intended to show the irrigation water requirements and evaluation of canal hydraulics steady state conditions to improve on scheme performance of the Meki-Ziway irrigation project. The methodology used was the CROPWAT 8.0 model to estimate the irrigation water requirements of five major crops irrigated in the study area. The results showed that for the whole existing and potential irrigation development area of 2000 ha and 2599 ha, crop water requirements were 3,339,200 and 4,339,090.4 m³, respectively. Hydraulic simulation models are fundamental tools for understanding the hydraulic flow characteristics of irrigation systems. Hydraulic simulation models are fundamental tools for understanding the hydraulic flow characteristics of irrigation systems. In this study Hydraulic Analysis of Irrigation Canals Using HEC-RAS Model was conducted in Meki-Ziway Irrigation Scheme. The HEC-RAS model was tested in terms of error estimation and used to determine canal capacity potential.

Keywords: HEC-RAS, irrigation, hydraulic. canal reach, capacity

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