Search results for: accuracy and consistency

Authors: M.R Asghari Bejestani, Gh. R. Mohammad Khani, V.R. Nafisi

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Imagined speech recognition is one of the most interesting approaches to BCI development and a lot of works have been done in this area. Many different experiments have been designed and hundreds of combinations of feature extraction methods and classifiers have been examined. Reported classification accuracies range from the chance level to more than 90%. Based on non-stationary nature of brain signals, we have introduced 3 classification modes according to time difference in inter and intra-class samples. The modes can explain the diversity of reported results and predict the range of expected classification accuracies from the brain signal accusation procedure. In this paper, a few samples are illustrated by inspecting results of some previous works.

Keywords: brain computer interface, silent talk, imagined speech, classification, signal processing

Procedia PDF Downloads 142

Authors: Hardeep

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The aim of this research work to improve the solubility and bioavailability of Simvastatin using a self nanoemulsifying drug delivery system (SNEDDS). Self emulsifying property of various oils including essential oils was evaluated with suitable surfactants and co-surfactants. Validation of a method for accuracy, repeatability, Interday and intraday precision, ruggedness, and robustness were within acceptable limits. The liquid SNEDDS was prepared and optimized using a ternary phase diagram, thermodynamic, centrifugation and cloud point studies. The globule size of optimized formulations was less than 200 nm which could be an acceptable nanoemulsion size range. The mean droplet size, drug loading, PDI and zeta potential were found to be 141.0 nm, 92.22%, 0.23 and -10.13 mV and 153.5nm, 93.89 % ,0.41 and -11.7 mV and 164.26 nm, 95.26% , 0.41 and -10.66mV respectively.

Keywords: simvastatin, self nanoemulsifying drug delivery system, solubility, bioavailability

Procedia PDF Downloads 189

Authors: A. J. Regan, F. J. Lidgey, M. Betteridge, P. Georgiou, C. Toumazou, K. Hayatleh, J. R. Dibble

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Our goal is development of an algorithm capable of predicting the directional trend of the Standard and Poor’s 500 index (S&P 500). Extensive research has been published attempting to predict different financial markets using historical data testing on an in-sample and trend basis, with many authors employing excessively complex mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, we moved to an out-of-sample strategy based on linear regression analysis of an extensive set of financial data correlated with historical closing prices of the S&P 500. We are pleased to report a directional trend accuracy of greater than 55% for tomorrow (t+1) in predicting the S&P 500.

Keywords: financial algorithm, GPU, S&P 500, stock market prediction

Procedia PDF Downloads 342

Authors: Gregory W. Smith, Paul J. Riccomini

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The goal of this empirical inquiry is to explore the effect of noise reducing headphones on the short-term memory recall of college students. Immediately following the presentation (via PowerPoint) of 12 unrelated and randomly selected one- and two-syllable words, students were asked to recall as many words as possible. Using a linear model with conditions marked with binary indicators, we examined the frequency and accuracy of words that were recalled. The findings indicate that for some students, a reduction of noise has a significant positive impact on their ability to recall information. As classrooms become more aurally distracting due to the implementation of cooperative learning activities, these findings highlight the need for a quiet learning environment for some learners.

Keywords: auditory distraction, education, instruction, noise, working memory

Procedia PDF Downloads 325

Authors: Fethi Bouksila, Magnus Persson, Ronny Berndtsson, Akissa Bahri

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Under salinity conditions, we evaluate the performance of Hilhost (2000) model to predict pore electrical conductivity ECp from dielectric permittivity and bulk electrical conductivity (ECa) using Time and Frequency Domain Reflectometry sensors (TDR, FDR). Using FDR_WET sensor, RMSE of ECp was 4.15 dS m-1. By replacing the standard soil parameter (K0) in Hilhost model by K0-ECa relationship, the RMSE of ECp decreased to 0.68 dS m-1. WET sensor could give similar accuracy to estimate ECp than TDR if calibrated values of K0 were used instead of standard values in Hilhost model.

Keywords: hilhost model, soil salinity, time domain reflectometry, frequency domain reflectometry, dielectric methods

Procedia PDF Downloads 128

Authors: Tianle Yang

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As a common mental disorder, depression is a leading cause of various diseases worldwide. Early detection and treatment of depression can dramatically promote remission and prevent relapse. However, conventional ways of depression diagnosis require considerable human effort and cause economic burden, while still being prone to misdiagnosis. On the other hand, recent studies report that physical characteristics are major contributors to the diagnosis of depression, which inspires us to mine the internal relationship by neural networks instead of relying on clinical experiences. In this paper, neural networks are constructed to predict depression from physical characteristics. Two initialization methods are examined - Xaiver and Kaiming initialization. Experimental results show that a 3-layers neural network with Kaiming initialization achieves 83% accuracy.

Keywords: depression, neural network, Xavier initialization, Kaiming initialization

Procedia PDF Downloads 120

Authors: Hyekyung Woo, Gwihyun Kim

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mHealth, which encompasses mobile health technologies and interventions, is rapidly evolving in various medical specialties, and its impact is evident in oncology. This review describes current trends in research addressing the integration of mHealth into the management of breast cancer by examining evaluations of mHealth and its contributions across the cancer care continuum. Mobile technologies are perceived as effective in prevention and as feasible for managing breast cancer, but the diagnostic accuracy of these tools remains in doubt. Not all phases of breast cancer treatment involve mHealth, and not all have been addressed by research. These drawbacks in the application of mHealth to breast cancer management call for intensified research to strengthen its role in breast cancer care.

Keywords: mobile application, breast cancer, content analysis, mHealth

Procedia PDF Downloads 302

Authors: Mansoureh Naderipour, Susan Bastani, Mohammad Fazel Zarandi

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Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach.

Keywords: social network, link prediction, granular computing, type-2 fuzzy sets

Procedia PDF Downloads 316

Authors: Johan Loeckx, Joeri Bultheel

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Recently, the application of deep learning to music has gained popularity. Its true potential, however, has been largely unexplored. In this paper, a new idea for representing the dynamic behavior of music is proposed. A ”forward” conditional RBM takes into account not only preceding but also future samples during training. Though this may sound controversial at first sight, it will be shown that it makes sense from a musical and neuro-cognitive perspective. The model is applied to reconstruct music based upon the first notes and to improvise in the musical style of a composer. Different to expectations, reconstruction accuracy with respect to a regular CRBM with the same order, was not significantly improved. More research is needed to test the performance on unseen data.

Keywords: deep learning, restricted boltzmann machine, music generation, conditional restricted boltzmann machine (CRBM)

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Authors: Krasimir I. Ivanov, Penka S. Zapryanova, Stefan V. Krustev, Violina R. Angelova

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Three commonly used digestion methods (dry ashing, acid digestion, and microwave digestion) in different variants were compared for digestion of tobacco leaves. Three main macroelements (K, Ca and Mg) were analysed using AAS Spectrometer Spectra АА 220, Varian, Australia. The accuracy and precision of the measurements were evaluated by using Polish reference material CTR-VTL-2 (Virginia tobacco leaves). To elucidate the problems with elemental recovery X-Ray and SEM–EDS analysis of all residues after digestion were performed. The X-ray investigation showed a formation of KClO₄ when HClO4 was used as a part of the acids mixture. The use of HF at Ca and Mg determination led to the formation of CaF₂ and MgF₂. The results were confirmed by energy dispersive X-ray microanalysis. SPSS program for Windows was used for statistical data processing.

Keywords: digestion methods, plant tissue, determination of macroelements, K, Ca, Mg

Procedia PDF Downloads 305

Authors: Elsayeda M. Elgaml, Dina M. Ibrahim, Elsayed A. Sallam

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Association rule mining is one of the most important fields of data mining and knowledge discovery. In this paper, we propose an efficient multiple support frequent pattern growth algorithm which we called “MSFP-growth” that enhancing the FP-growth algorithm by making infrequent child node pruning step with multiple minimum support using maximum constrains. The algorithm is implemented, and it is compared with other common algorithms: Apriori-multiple minimum supports using maximum constraints and FP-growth. The experimental results show that the rule mining from the proposed algorithm are interesting and our algorithm achieved better performance than other algorithms without scarifying the accuracy.

Keywords: association rules, FP-growth, multiple minimum supports, Weka tool

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Authors: Rafal Sochaczewski, Ksenia Siadkowska, Tytus Tulwin

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The paper describes the modeling of a fuel injector for common rail systems. A one-dimensional model of a solenoid-valve-controlled injector with Valve Closes Orifice (VCO) spray was modelled in the AVL Hydsim. This model shows the dynamic phenomena that occur in the injector. The accuracy of the calibration, based on a regulation of the parameters of the control valve and the nozzle needle lift, was verified by comparing the numerical results of injector flow rate. Our model is capable of a precise simulation of injector operating parameters in relation to injection time and fuel pressure in a fuel rail. As a result, there were made characteristics of the injector flow rate and backflow.

Keywords: common rail, diesel engine, fuel injector, modeling

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Authors: Firas Gerges, Frank Y. Shih

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Malignant melanoma, known simply as melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient's death. When detected early, melanoma is curable. In this paper, we propose a deep learning model (convolutional neural networks) in order to automatically classify skin lesion images as malignant or benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.

Keywords: deep learning, skin cancer, image processing, melanoma

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Authors: Tai Mei Kin, Omar Abdull Kareem

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In light of globalization, educational reform has become a top priority for many countries. However, the task of leading change effectively requires a multidimensional set of competencies. Over the past two decades, technical competencies of principal change leadership have been extensively analysed and discussed. Comparatively, little research has been conducted in Malaysian education context on non-technical competencies or popularly known as emotional intelligence, which is equally crucial for the success of change. This article provides a validation of the Principal Change Leadership Non-Technical Competencies (PCLnTC) Scale, a tool that practitioners can easily use to assess school principals’ level of change leadership non-technical competencies that facilitate change and maximize change effectiveness. The overall coherence of the PCLnTC model was constructed by incorporating three theories: a)the change leadership theory whereby leading change is the fundamental role of a leader; b)competency theory in which leadership can be taught and learned; and c)the concept of emotional intelligence whereby it can be developed, fostered and taught. An exploratory factor analysis (EFA) was used to determine the underlying factor structure of PCLnTC model. Before conducting EFA, five important pilot test approaches were conducted to ensure the validity and reliability of the instrument: a)reviewed by academic colleagues; b)verification and comments from panel; c)evaluation on questionnaire format, syntax, design, and completion time; d)evaluation of item clarity; and e)assessment of internal consistency reliability. A total of 335 teachers from 12 High Performing Secondary School in Malaysia completed the survey. The PCLnTCS with six points Liker-type scale were subjected to Principal Components Analysis. The analysis yielded a three-factor solution namely, a)Interpersonal Sensitivity; b)Flexibility; and c)Motivation, explaining a total 74.326 per cent of the variance. Based on the results, implications for instrument revisions are discussed and specifications for future confirmatory factor analysis are delineated.

Keywords: exploratory factor analysis, principal change leadership non-technical competencies (PCLnTC), interpersonal sensitivity, flexibility, motivation

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Authors: Claude Tadonki

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We address the issues related to the computation of the covariance matrix. This matrix is likely to be ill conditioned following its canonical expression, thus consequently raises serious numerical issues. The underlying linear system, which therefore should be solved by means of iterative approaches, becomes computationally challenging. A huge number of iterations is expected in order to reach an acceptable level of convergence, necessary to meet the required accuracy of the computation. In addition, this linear system needs to be solved at each iteration following the general form of the covariance matrix. Putting all together, its comes that we need to compute as fast as possible the associated matrix-vector product. This is our purpose in the work, where we consider and discuss skillful formulations of the problem, then propose a parallel implementation of the matrix-vector product involved. Numerical and performance oriented discussions are provided based on experimental evaluations.

Keywords: covariance-matrix, multicore, numerical computing, parallel computing

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Authors: Michelle Rippy

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Medicolegal death investigation historically is a field that does not receive much research attention or advancement, as all of the subjects are deceased. Public health threats, drug epidemics and contagious diseases are typically recognized in decedents first, with thorough and accurate death investigations able to assist in epidemiology research and prevention programs. One vital component of medicolegal death investigation is determining the decedent’s time of death. An accurate time of death can assist in corroborating alibies, determining sequence of death in multiple casualty circumstances and provide vital facts in civil situations. Popular television portrays an unrealistic forensic ability to provide the exact time of death to the minute for someone found deceased with no witnesses present. The actuality of unattended decedent time of death determination can generally only be narrowed to a 4-6 hour window. In the mid- to late-20th century, liver temperatures were an invasive action taken by death investigators to determine the decedent’s core temperature. The core temperature was programmed into an equation to determine an approximate time of death. Due to many inconsistencies with the placement of the thermometer and other variables, the accuracy of the liver temperatures was dispelled and this once common place action lost scientific support. Currently, medicolegal death investigators utilize three major after death or post-mortem changes at a death scene. Many factors are considered in the subjective determination as to the time of death, including the cooling of the decedent, stiffness of the muscles, release of blood internally, clothing, ambient temperature, disease and recent exercise. Current research is utilizing non-invasive hospital grade tympanic thermometers to measure the temperature in the each of the decedent’s ears. This tool can be used at the scene and in conjunction with scene indicators may provide a more accurate time of death. The research is significant and important to investigations and can provide an area of accuracy to a historically inaccurate area, considerably improving criminal and civil death investigations. The goal of the research is to provide a scientific basis to unwitnessed deaths, instead of the art that the determination currently is. The research is currently in progress with expected termination in December 2018. There are currently 15 completed case studies with vital information including the ambient temperature, decedent height/weight/sex/age, layers of clothing, found position, if medical intervention occurred and if the death was witnessed. This data will be analyzed with the multiple variables studied and available for presentation in January 2019.

Keywords: algor mortis, forensic pathology, investigations, medicolegal, time of death, tympanic

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Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

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Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

Procedia PDF Downloads 197

Authors: Akram Farhadi, Mahshid Froughan, Farahnaz Mohammadi, Maryam Rassouli, Maryam Noroozian, Leila Sadeghmoghaddam

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Background: The caregivers’ assessment of their own caregiving is considered the most important concept in exploring their experiences and has a major role in care outcomes. The rising number of people with dementia and their need for care makes family caregiving really important matter to consider and evaluate. Objectives: This study was conducted with the aim to naturalize and validate the Persian version of the Revised Caregiving Appraisal Scale (RCAS) in family caregivers of older adults with dementia. Patients and Method: In this cross-sectional methodological study, the Revised Caregiving Appraisal Scale (RCAS) was translated using International Quality of Life Assessment (IQOLA) protocol, and then a panel of experts examined its face and content validities. To ensure construct validity, the translated Revised Caregiving Appraisal Scale (RCAS) was completed by 236 family caregivers, and factor construct of the scale was assessed with 5 initial factors using confirmatory factor analysis. Internal consistency was found using Cronbach's alpha, and test-retest using intraclass correlation coefficient. Confirmatory factor analysis was performed in LISREL-8.8 software in Windows®. Results: Participating caregivers' mean age was 53.5±13.13 years. Content and face validities of the scale were confirmed according to the views expressed by family caregivers and panel of experts. The confirmatory factor analysis (CFA) results showed appropriate values for all fitness indices (RMSEA=0.046, df/X2=2.428, CFI=0.98, AGFI=0.84, GFI=0.9), and the 5-factor model was confirmed with 27 items. Overall Cronbach's alpha was reported 0.894, and test retest showed overall ICC=0.94. Conclusion: The Persian version of RCAS is a valid and reliable tool for family caregivers' assessment of their caregiving of older adults with dementia, and can be useful in assessing family caregiving interventions.

Keywords: psychometric, family caregivers, reliability and validity, elderly, dementia, self-appraisal

Procedia PDF Downloads 224

Authors: G. Nitesh

Abstract:

The shear strength of soils is a crucial parameter instability analysis. Therefore, it is important to determine reliable values for the accuracy of stability analysis. Direct shear tests are mostly performed to determine the shear strength of cohesionless soils. The major limitation of the direct shear test is that the failure takes place through the pre-defined failure plane but the failure is not along pre-defined plane and is along the weakest plane in actual shearing mechanism that goes on in the field. This leads to overestimating the strength parameter; hence, a new apparatus called simple shear is developed and used in this study to determine the shear strength parameter that simulates the field conditions.

Keywords: direct shear, simple shear, angle of shear resistance, cohesionless soils

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Authors: Nawaporn Thongnut

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The objective was to study the process and how to prepare the accounting of the Thai temples and to study the performance and quality in the accounting preparation of the temples in accordance with the regulation. The population was the accountants and individuals involved in the accounting preparation of 17 temples in the suburban Bangkok. The measurement used in this study was questionnaire. The statistics used in the analysis are the descriptive statistic. The data was presented in the form of percentage tables to describe the data on the demographic characteristics. The study found that temple wardens were responsible for the accounting and reporting of the temples. Abbots were to check the accuracy of the accounts in the monasteries. Mostly, there was no account auditing of the monasteries from the outside. The practice when receiving income for most of the monasteries had been keeping financial document in an orderly manner.

Keywords: corporate social responsibility, creating shared value, management accountant’s roles, stock exchange of Thailand

Procedia PDF Downloads 223

Authors: Senol Dogan, Gunay Karli

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Identification of relevant publications based on a Medline query is time-consuming and error-prone. An all based process has the potential to solve this problem without any manual work. To the best of our knowledge, our study is the first to investigate the ability of machine learning to identify relevant articles accurately. 5 different machine learning algorithms were tested using 23 predictors based on several metadata fields attached to publications. We find that the Boosted model is the best-performing algorithm and its overall accuracy is 96%. In addition, specificity and sensitivity of the algorithm is 97 and 93%, respectively. As a result of the work, we understood that we can apply the same procedure to understand cancer gene expression big data.

Keywords: prediction of publications, machine learning, algorithms, bariatric surgery, comparison of algorithms, boosted, tree, logistic regression, ANN model

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Authors: Félix David Suárez Bonilla

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A sentiment analysis service to detect polarity (positive, neural, and negative), based on transfer learning, was built using a Spanish version of BERT and applied to tweets written in Spanish. The dataset that was used consisted of 11975 reviews, which were extracted from Google Play using the google-play-scrapper package. The BETO trained model used: the AdamW optimizer, a batch size of 16, a learning rate of 2x10⁻⁵ and 10 epochs. The system was tested using tweets of three presidential pre-candidates from Costa Rica. The system was finally validated using human labeled examples, achieving an accuracy of 83.3%.

Keywords: NLP, transfer learning, BERT, sentiment analysis, social media, opinion mining

Procedia PDF Downloads 164

Authors: Mkhinini Maher, Knani Jilani

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We present in this work the performances of a mobile omnidirectional robot through evaluating its management of the redundancy of actuation. Thus we come to the predictive control implemented. The distribution of the wringer on the robot actions, through the inverse pseudo of Moore-Penrose, corresponds to a -geometric- distribution of efforts. We will show that the load on vehicle wheels would not be equi-distributed in terms of wheels configuration and of robot movement. Thus, the threshold of sliding is not the same for the three wheels of the vehicle. We suggest exploiting the redundancy of actuation to reduce the risk of wheels sliding and to ameliorate, thereby, its accuracy of displacement. This kind of approach was the subject of study for the legged robots.

Keywords: mobile robot, actuation, redundancy, omnidirectional, inverse pseudo moore-penrose, reductive control

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Authors: Li Ziming, He Cunfu, Liu Zenghua

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With aim for delamination type defects detection in manufacturing process of seamless pipe,this paper studies the interaction of the circumferential lamb wave with delamination in aluminum pipe.The delamination is located in the middle of pipe wall.A numerical study is carried out,the circumferential lamb wave used here is CL0 mode,which is generated with a finite element method code.Wave structures from the simulation are compared with theoretical results to verify the model’s accuracy.Delamination along the circumferential direction is established by demerging nodes of the same coordinates.When CL0 mode is incident at the entrance and exit of a delamination,it generates new mode-CL1,undergoes multiple reverberation and mode conversions between the two ends of the delamination. Signals of different receptions are obtained to provide insight in using CL0 mode for locating the delamination.

Keywords: circumferential lamb wave, delamination, FEM, seamless pipe

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Authors: Mekha Mathew, Varun P Gopi

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Wireless Capsule Endoscopy (WCE) has become a great device in Gastrointestinal (GI) tract diagnosis, which can examine the entire GI tract, especially the small intestine without invasiveness and sedation. Bleeding in the digestive tract is a symptom of a disease rather than a disease itself. Hence the detection of bleeding is important in diagnosing many diseases. In this paper we proposes a novel method for distinguishing bleeding regions from normal regions based on Contourlet transform and Local Binary Pattern (LBP). Experiments show that this method provides a high accuracy rate of 96.38% in CIE XYZ colour space for k-Nearest Neighbour (k-NN) classifier.

Keywords: Wireless Capsule Endoscopy, local binary pattern, k-NN classifier, contourlet transform

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Authors: Aya Mousa, Mahmoud Alsaheb

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Question Answering System (QAS) aims to provide the most suitable answer to the user's question in any natural language. In the recent future, it will be a future version of web search. Much research has already been done on answering Arabic factoid questions and achieved good accuracy. In contrast, the progress in research on Arabic non-factoid question answering is still immature. In this survey, we summarize, discuss, and compare the existing Arab non-factoid question-answering systems to identify the limitations and the achievements that were accomplished. Furthermore, we investigate the challenges in developing non-factoid Arabic QAS and the possible future improvements. The survey is written to help the researchers to understand the field of Arabic non-factoid QAS and to motivate them to utilize different approaches to develop and enhance the Non-factoid Arabic QAS

Keywords: Arabic question answering system, non-factoid question answering, Arabic NLP, question answering

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Authors: Sushil Kumar, M. Dakshina Murty

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Thermal network modelling has proven to be important tool for thermal analysis of electrical machine. This article investigates numerical thermal network model and experimental performance of a large-scale AC motor. Experimental temperatures were measured using RTD in the stator which have been compared with the numerical data. Thermal network modelling fairly predicts the temperature of various components inside the large-scale AC motor. Results of stator winding temperature is compared with experimental results which are in close agreement with accuracy of 6-10%. This method of predicting hot spots within AC motors can be readily used by the motor designers for estimating the thermal hot spots of the machine.

Keywords: AC motor, thermal network, heat transfer, modelling

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Authors: Lan Luo, Jingjing Huang, Huafang Li

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Introduction: Distressing low sexual desire is common in China, while the lack of reliable and valid instruments to evaluate symptoms of hypoactive sexual desire disorder (HSDD) impedes related research and clinical services. Aim: This study aimed to validate the reliability and validity of the Female Sexual Function Index (FSFI) and the Female Sexual Distress Scale-Desire/Arousal/Orgasm (FSDS-DAO) in Chinese female HSDD patients. Methods: We administered FSFI and FSDS-DAO in a convenient sample of Chinese adult women. Participants were diagnosed by a psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR). Results: We had a valid analysis sample of 279 Chinese women, of which 107 were HSDD patients. The Cronbach's α of FSFI and FSDS-DAO were 0.947 and 0.956, respectively, and the intraclass correlation coefficients of which were 0.86 and 0.89, respectively (the interval was 13-15 days). The correlation coefficient between the Revised Adult Attachment Scale (RAAS) and FSFI (or FSDS-DAO) did not exceed 0.4; the area under the receiver operating characteristic (ROC) curve was 0. 83 when combined FSFI-d (the desire domain of FSFI) and FSDS-DAO to diagnose HSDD, which was significantly different from that of using these scales individually. FSFI-d of less than 2.7 (1.2-6) and FSDS-DAO of no less than 15 (0-60) (Sensitivity 65%, Specificity 83%), or FSFI-d of no more than 3.0 (1.2-6) and FSDS-DAO of no less than 14 (0-60) (Sensitivity 74%, Specificity 77%) can be used as cutoff scores in clinical research or outpatient screening. Clinical implications: FSFI (including FSFI-d) and FSDS-DAO are suitable for the screening and evaluation of Chinese female HSDD patients of childbearing age. Strengths and limitations: Strengths include a thorough validation of FSFI and FSDS-DAO and the exploration of the cutoff score combing FSFI-d and FSDS-DAO. Limitations include a small convenience sample and the requirement of being sexually active for HSDD patients. Conclusion: FSFI (including FSFI-d) and FSDS-DAO have good internal consistency, test-retest reliability, construct validity, and criterion validity in Chinese female HSDD patients of childbearing age.

Keywords: sexual desire, sexual distress, hypoactive sexual desire disorder, scale

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Authors: R. Srinivas, K. V. Ramana

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This paper presents a novel method called VESTAL to label vertebrae and inter vertebral discs. Each vertebra has certain statistical features properties. To label vertebrae and discs, a new equation to model the path of spinal cord is derived using statistical properties of the spinal canal. VESTAL uses this equation for labeling vertebrae and discs. For each vertebrae and inter vertebral discs both posterior, interior width, height are measured. The calculated values are compared with real values which are measured using venires calipers and the comparison produced 95% efficiency and accurate results. The VESTAL is applied on 50 patients 350 MR images and obtained 100% accuracy in labeling.

Keywords: spine, vertebrae, inter vertebral disc, labeling, statistics, texture, disc

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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