Search results for: trained athletes

Authors: Toan Vo

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Electronic attacks, illegal drones, interference, and jamming are no longer capabilities reserved for a state-sponsored, near-peer adversary. The proliferation of jammers on auction websites has lowered the price of entry for electronics hobbyists and nefarious actors. To enable local authorities and enforcement bodies to keep up with these challenges, this paper proposes a training as a service model to quickly and economically train and equip police departments and local law enforcement agencies. Using the U.S Department of Defense’s investment in Electronic Warfare as a guideline, a large number of personnel can be trained on effective spectrum monitoring techniques using commercial equipment readily available on the market. Finally, this paper will examine the economic benefits to the test and measurement industry if the TaaS model is applied.

Keywords: training, electronic warfare, economics, law enforcement

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Authors: Alireza Lavaei, Alireza Lohrasbi, Mohammadali M. Shahlaei

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To reduce the overall time of structural optimization for earthquake loads two strategies are adopted. In the first strategy, a neural system consisting self-organizing map and radial basis function neural networks, is utilized to predict the time history responses. In this case, the input space is classified by employing a self-organizing map neural network. Then a distinct RBF neural network is trained in each class. In the second strategy, an improved genetic algorithm is employed to find the optimum design. A 72-bar space truss is designed for optimal weight using exact and approximate analysis for the El Centro (S-E 1940) earthquake loading. The numerical results demonstrate the computational advantages and effectiveness of the proposed method.

Keywords: optimization, genetic algorithm, neural networks, self-organizing map

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Authors: Qi Wang

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Accurate pronunciation is the first condition of communication. Compared with the alphabetic languages, Chinese is more difficult for the foreigners to study as a second language, due to the tonal language with the meaningful characters as the written system, especially speaking. This research first presents the statistics of the typical errors of the pronunciations, based on the data of our two- year program of graduate students, which shown 90% of their speaking with strong foreign accents and no obvious change of the pitches, even if they could speak Chinese fluently. Second part, analyzed the caused reasons in the learning and teaching processes. Third part, this result of this research, based the theory of Chinese prosodic words, shown that the earlier the students get trained on prosodics at the beginning and suprasegmentals at intermediate and advanced levels, the better effects for them to communicate in Chinese as a second language.

Keywords: second language, prosodic word, foot, suprasegmental

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Authors: Oluwaponmile David Alao

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Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

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Authors: Muhammad Zikril Hakim Md. Azizi, Norhafizan Ahmad, Raja Ariffin Raja Ghazilla

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Postural stability, attention level of the archer and particularly the vibrations of the bow itself plays a prominent role in determining the athletes performance. Many techniques and systems had been developing to monitor the parameters of the archers during training. In Malaysia, archery coaches tend to use non-scientific ways that they are familiar with, to evaluate archer performance. An approach that provides more affordable yet accurate systems to the masses and relatively easy system deployment procedure need to be proposed. Hence, this project will address to fulfil the needs. Three area of the archer parameter were included for data monitoring sensors. Attention level can be measured using EEG sensor, centre of mass linked to the postural stability can be measured by foot pressure sensor, and the bow vibrations in three axis will be relayed by the vibrations sensors placed directly on the bow using wireless sensors. Arduino based microcontroller used to relay all the data back to the interfacing systems. Interface systems will be using Python language and C++ framework for user interface and hardware interfacing systems. All sensor data can be observed in real time using the in-house applications, and each sessions can be saved to common files so that coach and the team can have a further discussion and comparisons.

Keywords: archery, graphical user interface, microcontroller, wireless sensor, monitoring system

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Authors: Kaili Wang, Botao Zhang, Enming Zhang

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Background: Low back pain (LBP) is a common problem affecting athletes' training and competition. Although the association between a limited hip range of motion and prevalence of low back pain has been studied extensively, it has not been studied in table tennis. Aim: The main purposes of this study in table tennis players were (1) to investigate if there is a difference in hip internal rotation (HIR) and external rotation (HER) range of motion (ROM) between players with LBP and players without LBP and (2) to analyze the association between HIR and HER ROM and LBP. Methods: Forty-six table tennis players from the Chinese table tennis team were evaluated for passive maximum HIR and HER ROM. LBP was retrospectively recorded for the last 12 months before the date of ROM assessment by a physical therapist. The data were analyzed the difference in HIR and HER ROM between players with LBP and players without LBP by Mann-Whitney U test, and the association between the difference in HIR and HER ROM and LBP was analyzed via a binary logistic regression. Results: The 54% of players had developed LBP during the retrospective study period. Significant difference between LBP group and the asymptomatic group for HIR ROM (z=4.007, p<0.001) was observed. Difference between LBP group and asymptomatic group for HER ROM (z=1.117, p=0.264) was not significant. Players who had HIR ROM deficit had an increased risk of LBP compared with players without HIR ROM deficit (OR=5.344, 95%CI: 1.006-28.395, P=0.049). Conclusion: HIR ROM of a table tennis player with LBP was less than a table tennis player without LBP. Compared with player whose HIR ROM was normal, player who had HIR ROM deficit appeared to have a higher risk for LBP.

Keywords: assessment, injury prevention, low back pain, table tennis players

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Authors: John Kosmeh

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Introduction – For years, senior health care and skilled nursing facilities have been plagued with the dilemma of not having the necessary tools and equipment to adequately care for senior residents in their communities. This has led to high transport rates to emergency departments and high 30-day readmission rates, costing billions of unnecessary dollars each year, as well as quality assurance issues. Our Senior care telemedicine program is designed to solve this issue. Methods – We conducted a 1-year pilot program using our technology coupled with our 24/7 telemedicine program with skilled nursing facilities in different parts of the United States. We then compared transports rates and 30-day readmission rates to previous years before the use of our program, as well as transport rates of other communities of similar size not using our program. This data was able to give us a clear and concise look at the success rate of reducing unnecessary transport and readmissions as well as cost savings. Results – A 94% reduction nationally of unnecessary out-of-facility transports, and to date, complete elimination of 30-day readmissions. Our virtual platform allowed us to instruct facility staff on the utilization of our tools and system as well as deliver treatment by our ER-trained providers. Delay waiting for PCP callbacks was eliminated. We were able to obtain lung, heart, and abdominal ultrasound imaging, 12 lead EKG, blood labs, auscultate lung and heart sounds, and collect other diagnostic tests at the bedside within minutes, providing immediate care and allowing us to treat residents within the SNF. Are virtual capabilities allowed for loved ones, family members, and others who had medical power of attorney to virtually connect with us at the time of visit, to speak directly with the medical provider, providing increased confidence in the decision to treat the resident in-house. The decline in transports and readmissions will greatly reduce governmental cost burdens, as well as fines imposed on SNF for high 30-day readmissions, reduce the cost of Medicare A readmissions, and significantly impact the number of patients visiting overcrowded ERs. Discussion – By utilizing our program, SNF can effectively reduce the number of unnecessary transports of residents, as well as create significant savings from loss of day rates, transportation costs, and high CMS fines. The cost saving is in the thousands monthly, but more importantly, these facilities can create a higher quality of life and medical care for residents by providing definitive care instantly with ER-trained personnel.

Keywords: senior care, long term care, telemedicine, technology, senior care communities

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Authors: Lin Cheng, Zijiang Yang

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Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Keywords: program synthesis, flow chart, specification, graph recognition, CNN

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Authors: Ahmed Fawzy, Khaled Ayad, Gh. M. Koura, W. Reda

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The knee joint complex is one of the most commonly injured areas of the body in athletes. Excessive frontal plane knee excursion is considered a risk factor for multiple knee pathologies such as anterior cruciate ligament and patellofemoral joint injuries, however, little is known about the biomechanical factors that contribute to this loading pattern. Objectives: The purpose of this study was to investigate if there is a relationship between hip abductors isometric strength and the value of FPPA during single leg landing tasks in normal male subjects. Methods: One hundred (male) subjects free from lower extremity injuries for at least six months ago participated in this study. Their mean age was (23.25 ± 2.88) years, mean weight was (74.76 ± 13.54) (Kg), mean height was (174.23 ± 6.56) (Cm). The knee frontal plane projection angle was measured by digital video camera using single leg landing task. Hip abductors isometric strength were assessed by portable hand-held dynamometer. Muscle strength had been normalized to the body weight to obtain more accurate measurements. Results: The results demonstrated that there was no significant relationship between hip abductors isometric strength and the value of FPPA during single leg landing tasks in normal male subjects. Conclusion: It can be concluded that there is no relationship between hip abductors isometric strength and the value of FPPA during functional activities in normal male subjects.

Keywords: 2-dimensional motion analysis, hip strength, kinematics, knee injuries

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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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

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Authors: Punnoose A. K.

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This paper presents an approach for identifying noisy speech recording using a multi-layer perception (MLP) trained to predict phonemes from acoustic features. Characteristics of the MLP posteriors are explored for clean speech and noisy speech at the frame level. Appropriate density functions are used to fit the softmax probability of the clean and noisy speech. A function that takes into account the ratio of the softmax probability density of noisy speech to clean speech is formulated. These phoneme independent scoring is weighted using a phoneme-specific weightage to make the scoring more robust. Simple thresholding is used to identify the noisy speech recording from the clean speech recordings. The approach is benchmarked on standard databases, with a focus on precision.

Keywords: noisy speech identification, speech pre-processing, noise robustness, feature engineering

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Fereshteh Zarei, Mahdi Kohandel

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The aim of this study was investigate and compare plyometric and vibration exercises on muscle explosive power in non-athlete female students. For this purpose, 45 female students from non-athletes selected target then divided in to the three groups, two experimental and one control groups. From all groups were getting pre-tested. Experimental A did whole-body vibration exercises involved standing on one of machine vibration with frequency 30 Hz, amplitude 10 mm and in 5 different postures. Training for each position was 40 seconds with 60 seconds rest between it, and each season 5 seconds was added to duration of each body condition, until time up to 2 minutes for each postures. Exercises were done three times a week for 2 month. Experimental group B did plyometric exercises that include jumping, such as horizontal, vertical, and skipping .They included 10 times repeat for 5 set in each season. Intensity with increasing repetitions and sets were added. At this time, asked from control group that keep a daily activity and avoided strength training, explosive power and. after do exercises by groups we measured factors again. One-way analysis of variance and paired t statistical methods were used to analyze the data. There was significant difference in the amount of explosive power between the control and vibration groups (p=0/048) there was significant difference between the control and plyometric groups (019/0 = p). But between vibration and plyometric groups didn't observe significant difference in the amount of explosive power.

Keywords: vibration, plyometric, exercises, explosive power, non-athlete

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Ahmed Emad Ahmed

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This paper aims to address the concept of human resources information systems (HRIS) and how to link it to new technologies such as artificial intelligence (AI) to be implemented in two human resources processes. A literature view has been collected to cover the main points related to HRIS, AI, and BC. A study case has been presented by generating a random HRIS to apply some AI operations to it. Then, an algorithm was applied to the database to complete some human resources processes, including training and performance appraisal, using a pre-trained AI model. After that, outputs and results have been presented and discussed briefly. Finally, a conclusion has been introduced to show the ability of new technologies such as AI and ML to be applied to the human resources management processes.

Keywords: human resources new technologies, HR artificial intelligence, HRIS AI models, construction AI HRIS

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Authors: Kadidiatou Toure

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Mali has started experimentation of teaching the national languages at school through the convergent pedagogy in 1987. Then, it is in 1994 that it will become widespread with eleven of the thirteen former national languages used at primary school. The aim was to improve the Malian educational system because the use of French as the only medium of instruction was considered a contributing factor to the significant number of student dropouts and the high rate of repetition. The Convergent pedagogy highlights the knowledge acquired by children at home, their vision of the world and especially the knowledge they have of their mother tongue. That pedagogy requires the use of a specific medium only during classroom practices and teachers have been trained in this sense. The specific medium depends on the learning content, which sometimes is French, other times, it is the national language. Research has shown that bilingual learners do not only use the required medium in their learning activities, but they code switch. It is part of their learning processes. Currently, many scholars agree on the importance of CS in bilingual classes, and teachers have been told about the necessity of integrating it into their classroom practices. One of the challenges of the Malian bilingual education curriculum is the question of ‘effective languages management’. Theoretically, depending on the classrooms, an average have been established for each of the involved language. Following that, teachers make use of CS differently, sometimes, it favors the learners, other times, it contributes to the development of some linguistic weaknesses. The present research tries to fill that gap through a tentative model of didactization of CS, which simply means the practical management of the languages involved in the bilingual classrooms. It is to know how to use CS for effective learning. Moreover, the didactization of CS tends to sensitize the teachers about the functional role of CS so that they may overcome their own weaknesses. The overall goal of this research is to make code switching a real tool for bilingual education. The specific objectives are: to identify the types of CS used during classroom activities to present the functional role of CS for the teachers as well as the pupils. to develop a tentative model of code-switching, which will help the teachers in transitional classes of bilingual schools to recognize the appropriate moment for making use of code switching in their classrooms. The methodology adopted is a qualitative one. The study is based on recorded videos of teachers of 3rd year of primary school during their classroom activities and interviews with the teachers in order to confirm the functional role of CS in bilingual classes. The theoretical framework adopted is the typology of CS proposed by Poplack (1980) to identify the types of CS used. The study reveals that teachers need to be trained on the types of CS and the different functions they assume and on the consequences of inappropriate use of language alternation.

Keywords: bilingual curriculum, code switching, didactization, national languages

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Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

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Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

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Authors: Sarah Abdourraziq, Rachid El Bachtiri

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The solar power source for pumping water is one of the most promising areas in photovoltaic applications. The implementation of these systems allows to protect the environment and reduce the CO₂ gas emission compared to systems trained by diesel generators. This paper presents a comparative study between the photovoltaic pumping system driven by DC motor, and AC motor to define the optimum design of this application. The studied system consists of PV array, DC-DC Boost Converter, inverter, motor-pump set and storage tank. The comparison was carried out to define the characteristics and the performance of each system. Each subsystem is modeled in order to simulate the whole system in MATLAB/ Simulink. The results show the efficiency of the proposed technique.

Keywords: photovoltaic water pumping system, DC motor-pump, AC motor-pump, DC-DC boost converter

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Authors: Mark Ashton Newman, Richard Blagrove, Jonathan Folland

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The force-velocity profile of an individual has been shown to influence success in ballistic movements, independent of the individuals' maximal power output; therefore, effective and accurate evaluation of an individual’s F-V characteristics and not solely maximal power output is important. The relatively narrow range of loads typically utilised during force-velocity profiling protocols due to the difficulty in obtaining force data at high velocities may bring into question the accuracy of the F-V slope along with predictions pertaining to the maximum force that the system can produce at a velocity of null (F₀) and the theoretical maximum velocity against no load (V₀). As such, the reliability of the slope of the force-velocity profile, as well as V₀, has been shown to be relatively poor in comparison to F₀ and maximal power, and it has been recommended to assess velocity at loads closer to both F₀ and V₀. The aim of the present study was to assess the relative and absolute reliability of an instrumented novel leg press machine which enables the assessment of force and velocity data at loads equivalent to ≤ 10% of one repetition maximum (1RM) through to 1RM during a ballistic leg press movement. The reliability of maximal and mean force, velocity, and power, as well as the respective force-velocity and power-velocity relationships and the linearity of the force-velocity relationship, were evaluated. Sixteen male strength-trained individuals (23.6 ± 4.1 years; 177.1 ± 7.0 cm; 80.0 ± 10.8 kg) attended four sessions; during the initial visit, participants were familiarised with the leg press, modified to include a mounted force plate (Type SP3949, Force Logic, Berkshire, UK) and a Micro-Epsilon WDS-2500-P96 linear positional transducer (LPT) (Micro-Epsilon, Merseyside, UK). Peak isometric force (IsoMax) and a dynamic 1RM, both from a starting position of 81% leg length, were recorded for the dominant leg. Visits two to four saw the participants carry out the leg press movement at loads equivalent to ≤ 10%, 30%, 50%, 70%, and 90% 1RM. IsoMax was recorded during each testing visit prior to dynamic F-V profiling repetitions. The novel leg press machine used in the present study appears to be a reliable tool for measuring F and V-related variables across a range of loads, including velocities closer to V₀ when compared to some of the findings within the published literature. Both linear and polynomial models demonstrated good to excellent levels of reliability for SFV and F₀ respectively, with reliability for V₀ being good using a linear model but poor using a 2nd order polynomial model. As such, a polynomial regression model may be most appropriate when using a similar unilateral leg press setup to predict maximal force production capabilities due to only a 5% difference between F₀ and obtained IsoMax values with a linear model being best suited to predict V₀.

Keywords: force-velocity, leg-press, power-velocity, profiling, reliability

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Hussam Elias, Ninovic Perez, Holger Hirsch

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The completion of the EMC regulations worldwide is growing steadily as the usage of electronics in our daily lives is increasing more than ever. In this paper, we introduce a novel method to perform the final phase of Electromagnetic compatibility (EMC) measurement and to reduce the required test time according to the norm EN 55032 by using a developed tool and the conventional neural network(CNN). The neural network was trained using real EMC measurements, which were performed in the Semi Anechoic Chamber (SAC) by CETECOM GmbH in Essen, Germany. To implement our proposed method, we wrote software to perform the radiated electromagnetic interference (EMI) measurements and use the CNN to predict and determine the position of the turntable that meets the maximum radiation value.

Keywords: conventional neural network, electromagnetic compatibility measurement, mean absolute error, position error

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Authors: Szymon Rutkowski, Tomasz Korbak

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We present a reinforcement-learning-based method of training neural machine translation models without parallel corpora. The standard encoder-decoder approach to machine translation suffers from two problems we aim to address. First, it needs parallel corpora, which are scarce, especially for low-resource languages. Second, it lacks psychological plausibility of learning procedure: learning a foreign language is about learning to communicate useful information, not merely learning to transduce from one language’s 'encoding' to another. We instead pose the problem of learning to translate as learning a policy in a communication game between two agents: the translator and the classifier. The classifier is trained beforehand on a natural language inference task (determining the entailment relation between a premise and a hypothesis) in the target language. The translator produces a sequence of actions that correspond to generating translations of both the hypothesis and premise, which are then passed to the classifier. The translator is rewarded for classifier’s performance on determining entailment between sentences translated by the translator to disciple’s native language. Translator’s performance thus reflects its ability to communicate useful information to the classifier. In effect, we train a machine translation model without the need for parallel corpora altogether. While similar reinforcement learning formulations for zero-shot translation were proposed before, there is a number of improvements we introduce. While prior research aimed at grounding the translation task in the physical world by evaluating agents on an image captioning task, we found that using a linguistic task is more sample-efficient. Natural language inference (also known as recognizing textual entailment) captures semantic properties of sentence pairs that are poorly correlated with semantic similarity, thus enforcing basic understanding of the role played by compositionality. It has been shown that models trained recognizing textual entailment produce high-quality general-purpose sentence embeddings transferrable to other tasks. We use stanford natural language inference (SNLI) dataset as well as its analogous datasets for French (XNLI) and Polish (CDSCorpus). Textual entailment corpora can be obtained relatively easily for any language, which makes our approach more extensible to low-resource languages than traditional approaches based on parallel corpora. We evaluated a number of reinforcement learning algorithms (including policy gradients and actor-critic) to solve the problem of translator’s policy optimization and found that our attempts yield some promising improvements over previous approaches to reinforcement-learning based zero-shot machine translation.

Keywords: agent-based language learning, low-resource translation, natural language inference, neural machine translation, reinforcement learning

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Gabriel I. Oke, Faremi O. Ayodeji

Abstract:

Background: Access to youth Friendly Health Corners is vital for ensuring sexual reproductive health and total well being of young Adults since human sexuality has been widely misunderstood. Meanwhile, behavior of young people towards it remains at variance with the alarm. This study attempt to access the impact of youth corners also called Adolescent Friendly Health Corners on manifestation of human sexual behavior among Nigerian adolescent and young adults. Description: Hundred young adults and adolescents of both sex between the Age range of 12-25years were randomly selected from 5 secondary schools and 3 prominent universities in Southwestern Nigeria and focal group discussions (FGD) were conducted among them. Fifty secondary and primary health facilities were visited between February and June 2017 to conduct interviews for health workers and to ascertain the presence or absence of youth corners. Results: 95% of the health facilities visited lack Youth Corners section neither are they willing to make provision for it due to lack of workmanship and sponsorship. However, 5% with Youth corners does not have well-trained Counselors or a Health Educator but health professionals from nursing profession. 90% of the respondents of which 16-17 years of Age is the mean age had their first sexual exposure with no use of protection even before been introduced to what Sexuality is all about. Virtually, none of the respondents had ever visited a Youth Corner before or heard the term before. 86% have heard about the term STI before of which 60% are using protection, 10% care less about any information attached to the term STI, 4% have not heard of the term STI before even when translated to their local dialect. 20% are abstaining as at the time the study was conducted and they attribute their sexual decision to religion and parental influence. Of the age group 20-25, 45% claimed they have had symptoms of one STI or the other and 40% claimed they have been tested positive for an STI before of which 12% have positive HIV status. Promiscuous behaviors were found among them before they reach the age 16years with pornography ranking the highest, followed by masturbation. Respondents blame this on peer pressure, the lack of Youth Friendly Centers in their locality and lack of proper Sexual Orientation on time. About half of the respondents make use of contraceptives while others have varying views. We found out that inability to access Youth Friendly Centers amongst the respondents might be one of the singular reasons of their early experimentation of their sex life and lack of healthy sexual lifestyle. (95% CI, P=0.922) Conclusion: The study reveals that a connection between youth Friendly Centers and Prevention of Sexually Transmitted Diseases, therefore more sustainable Friendly Youth Corners with well-trained educators are needed in various Health facilities to checkmate the numerous risks of Young People along the path of adulthood.

Keywords: adolescents, sexually transmitted infections, reproductive health, youth corners

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Authors: Njeim Pressila, Hajj-Boutros Guy, Antony D. Karelis

Abstract:

Background: Acute resistance exercise is associated with an elevated inflammation response, which could lead to delayed onset muscle soreness (DOMS). There is evidence that suggests that ingesting foods that have anti-inflammation properties may help reduce DOMS. A vegan diet has also been shown to be an anti-inflammatory diet which could, in turn, decrease DOMS. Objective and hypothesis: The purpose of the present study will be to compare markers of DOMS between vegans and omnivores after acute resistance exercise in young females. We hypothesize that vegans will have a better recovery of DOMS markers after a resistance exercise session compared to omnivores. Methods: Population: We will recruit30 vegans and 30 omnivores to participate in this study. Allvolunteers will follow either a vegan or an omnivore diet for at least 2 years. Anthropometric measurements, body composition, musclestrength (leg and chest press), markers of DOMS (swelling, pain, and stiffness), and dietary factors, as well as a wellness and anxiety questionnaire will be measured. All participants will also perform an acute resistance exercise session in order to induce DOMS. Pertinence: This project will give us a better understanding on the recovery process of vegans after a resistance training session and, as such, provide useful information to health professionals and athletes/coaches (kinesiologists and nutritionists)

Keywords: vgeans, omnivores, delayed onset muscle soreness, pain, stifness

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Authors: Trevor R. Higgins

Abstract:

To speed/enhance recovery from sport, cold water immersion (CWI) and contrast water therapy (CWT) have become common practice within the high-level team sport. Initially, research into CWI and CWT protocols and recovery was sparse; athletes relied solely upon an anecdotal support. However, an increase into recovery research has occurred. A number of reviews have subsequently been conducted to clarify scientific evidence. However, as the nature of physiological stress and training status of participants will impact on results, an opportunity existed to narrow the focus to a more exacting review evaluating hydrotherapy for recovery in a team sport. A Boolean logic [AND] keyword search of databases was conducted: SPORTDiscus; AMED; CINAHL; MEDLINE. Data was extracted and the standardized mean differences were calculated with 95% CI. The analysis of pooled data was conducted using a random-effect model, with Heterogeneity assessed using I2. 23 peer reviewed papers (n=606) met the criteria. Meta-analyses results indicated CWI was likely beneficial for recovery at 24h (Countermovement Jump (CMJ): p= 0.05, CI -0.004 to 0.578; All-out sprint: p=0.02, -0.056 to 0.801; DOMS: p=0.08, CI -0.092 to 1.936) and at 72h (accumulated sprinting: p=0.07, CI -0.062 to 1.209; DOMS: p=0.09, CI -0.121 to 1.555) following team sport. Whereas CWT was likely beneficial for recovery at 1h (CMJ: p= 0.07, CI -0.004 to 0.863) and at 48h (fatigue: p=0.04, CI 0.013 to 0.942) following team sport. Athlete’s perceptions of muscle soreness and fatigue are enhanced with CWI and/or CWT, however even though CWI and CWT were beneficial in attenuating decrements in neuromuscular performance 24 hours following team sport, indications are those benefits were no longer Sydney evident 48 hours following team sport.

Keywords: cold water immersion, contrast water therapy, recovery, team sport

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

Abstract:

In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

Abstract:

Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline that ensures that data mirrors real-world settings by incorporating Gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification, and explainability in a single pipeline called DeClEx.

Keywords: machine learning, healthcare, classification, explainability

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Authors: Marina Tsetseli, Eleni Zetou, Maria Michalopoulou, Nikos Vernadakis

Abstract:

Game performance, besides the accuracy and the quality skills execution, depends heavily on where the athletes will focus their attention while performing a skill. The purpose of the present study was to examine and compare the effect of internal and external focus of attention instructions on the decision making in tennis at players 8-9 years old (M=8.4, SD=0.49). The participants (N=40) were divided into two groups and followed an intervention training program that lasted 4 weeks; first group (N=20) under internal focus of attention instructions and the second group (N=20) under external focus of attention instructions. Three measurements took place (pre-test, post-test, and retention test) in which the participants were video recorded while playing matches in real scoring conditions. GPAI (Game Performance Assessment Instrument) was used to evaluate decision making in three game situations; service, return of the service, baseline game. ANOVA repeated measures (2 groups x 3 measurements) revealed a significant interaction between groups and measurements. Specifically, the data analysis showed superiority of the group that was instructed to focus externally. The high scores of the external attention group were maintained at the same level at the third measurement as well, which indicates that the impact was concerning not only performance but also learning. Thus, cues that lead to an external focus of attention enhance the decision-making skill and therefore the game performance of the young tennis players.

Keywords: decision making, evaluation, focus of attention, game performance, tennis

Procedia PDF Downloads 350