Search results for: neural activity
7443 Approaches to Promote Healthy Recreation Activities for Elderly Tourists at Bang Nam Phueng Floating Market, Prapradeang District, Samutprakarn Province
Authors: Sasitorn Chetanont
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The objectives of this study are to find out the approaches to promote healthy recreation activities for elderly tourists and develop Bang Nam Phueng Floating Market to be a health tourism attraction. The research methodology was to analyze internal and external situations according to MP-MF and the MC-STEPS principles. As for the results of this study the researcher found that the healthy recreational activities for elderly tourists could be divided in 7 groups; travelling Bang Nam Phueng Floating Market activity, homestay relaxation, arts center platform activity, healthy massage activity, paying homage to a Buddha image activity, herbal joss-stick home activity, making local desserts and food activity.Keywords: elderly tourists, recreation activities, Bang Nam Phueng Floating Market, health tourism
Procedia PDF Downloads 4117442 The Application of a Neural Network in the Reworking of Accu-Chek to Wrist Bands to Monitor Blood Glucose in the Human Body
Authors: J. K Adedeji, O. H Olowomofe, C. O Alo, S.T Ijatuyi
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The issue of high blood sugar level, the effects of which might end up as diabetes mellitus, is now becoming a rampant cardiovascular disorder in our community. In recent times, a lack of awareness among most people makes this disease a silent killer. The situation calls for urgency, hence the need to design a device that serves as a monitoring tool such as a wrist watch to give an alert of the danger a head of time to those living with high blood glucose, as well as to introduce a mechanism for checks and balances. The neural network architecture assumed 8-15-10 configuration with eight neurons at the input stage including a bias, 15 neurons at the hidden layer at the processing stage, and 10 neurons at the output stage indicating likely symptoms cases. The inputs are formed using the exclusive OR (XOR), with the expectation of getting an XOR output as the threshold value for diabetic symptom cases. The neural algorithm is coded in Java language with 1000 epoch runs to bring the errors into the barest minimum. The internal circuitry of the device comprises the compatible hardware requirement that matches the nature of each of the input neurons. The light emitting diodes (LED) of red, green, and yellow colors are used as the output for the neural network to show pattern recognition for severe cases, pre-hypertensive cases and normal without the traces of diabetes mellitus. The research concluded that neural network is an efficient Accu-Chek design tool for the proper monitoring of high glucose levels than the conventional methods of carrying out blood test.Keywords: Accu-Check, diabetes, neural network, pattern recognition
Procedia PDF Downloads 1367441 Multimodal Direct Neural Network Positron Emission Tomography Reconstruction
Authors: William Whiteley, Jens Gregor
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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography
Procedia PDF Downloads 1017440 Physical Activity Participation Levels as Correlates of Cardiorespiratory Fitness of Senior High School Teachers in the Dormaa East District
Authors: Isaac Yeboah
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Physical activity (PA) is considered the best preventative tool for maintaining physical health and extending lifespan because it offers a range of benefits. However, for people to benefit from physical activity, it must be done at a level that can enhance fitness, health, and overall well-being. Therefore, this study aimed to determine the link between physical activity participation levels and cardiorespiratory fitness (CRF). The study employed the descriptive correlational design. The census sampling technique included all 190 teaching staff from the two senior high schools in the Dormaa East District. An adapted International Physical Activity Questionnaire (IPAQ) and standardized physical fitness tests were used for data collection. Descriptive statistics of frequency counts and percentages were used to answer the research questions, while inferential statistics of Pearson Product Moment Correlation and the independent sample t-test were used to test hypotheses one and two, respectively. A moderately negative (r = -.156) relationship existed between physical activity participation levels and CRF. Female teachers demonstrated a statistically significant value (p = .045) than males. The study concluded that the reported physical activity levels of the teachers did not reflect their CRF. The implication is that either the participants overestimated their physical activity participation levels or the intensity of the activities was not high enough to affect physiological changes. For the teachers' CRF to improve, it was suggested, among other things, that regular physical activity sessions of the appropriate mixes of FITT be organized.Keywords: physical activity, participation levels, cardiorespiratory fitness, sedentary lifestyle, teachers
Procedia PDF Downloads 577439 Determination of the Botanical Origin of Honey by the Artificial Neural Network Processing of PARAFAC Scores of Fluorescence Data
Authors: Lea Lenhardt, Ivana Zeković, Tatjana Dramićanin, Miroslav D. Dramićanin
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Fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC) and artificial neural networks (ANN) were used for characterization and classification of honey. Excitation emission spectra were obtained for 95 honey samples of different botanical origin (acacia, sunflower, linden, meadow, and fake honey) by recording emission from 270 to 640 nm with excitation in the range of 240-500 nm. Fluorescence spectra were described with a six-component PARAFAC model, and PARAFAC scores were further processed with two types of ANN’s (feed-forward network and self-organizing maps) to obtain algorithms for classification of honey on the basis of their botanical origin. Both ANN’s detected fake honey samples with 100% sensitivity and specificity.Keywords: honey, fluorescence, PARAFAC, artificial neural networks
Procedia PDF Downloads 9437438 Performance Analysis of Artificial Neural Network with Decision Tree in Prediction of Diabetes Mellitus
Authors: J. K. Alhassan, B. Attah, S. Misra
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Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. medical dataset is a vital ingredient used in predicting patients health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. The evaluations was done using weka software and found out that DTA performed better than ANN. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. The Root Mean Squared Error (RMSE) of MLP is 0.3913,that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.Keywords: artificial neural network, classification, decision tree algorithms, diabetes mellitus
Procedia PDF Downloads 4027437 In-Vitro Stability of Aspergillus terreus Phytases in Relation to Different Physico-Chemical Factors
Authors: Qaiser Akram, Ahsan Naeem, Hafiz Muhammad Rizwan, Waqas Ahmad, Rubeena Yasmeen
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Aspergillus has good secretory potential for phytases. Morphologically and microscopically identified Aspergillus terreus (A. terreus) (n=20) were screened for phytase production and non-toxicity. Phytases produced by non-toxigenic A. terreus under optimum conditions were quantified. Phytases of highest producer A. terreus were evaluated for stability after exposure to temperature (35, 55, 75 and 95ºC) and pH (2, 4, 6 and 8). Effect of metal ions (Fe⁺³, Ba⁺², Ca⁺², Cu⁺², Mg⁺², Mn⁺², K⁺¹ and Na⁺¹) was assessed on phytase activity. Log reduction in phytase activity was calculated. The highest activity units of phytase produced by A. terreus were 271.49 ± 8.14 phytase unit / mL (FTU/ mL). The lowest reduction in phytase activity was 50.20 ± 7.36 (18.5%) and 68.22 ± 10.3 FTU/mL (25.13%) at 35ºC and pH 6, respectively for 15 minutes. The highest reduction 259 ± 0.84 (95.5%) and 211.99 ± 4.39 FTU/mL (78.1%) was recorded at 95ºC for 60 minutes and pH 2.0 for 45 minutes exposure, respectively. All metal ions negatively affected phytase activity. Phytase activity was inhibited minimum (45.32 ± 28.54 FTU/mL, 16.69%) by K⁺¹(1 mM) and maximum (231.48 ± 3.68 FTU/mL, 80.8%) by Cu⁺² (10 mM). It was concluded that A. terreus phytase stability and activity was dependent on physio-chemical factors.Keywords: stability, phytase, aspergillus terreus, physio-chemical factors and metal ions
Procedia PDF Downloads 2757436 Estimation of Pressure Loss Coefficients in Combining Flows Using Artificial Neural Networks
Authors: Shahzad Yousaf, Imran Shafi
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This paper presents a new method for calculation of pressure loss coefficients by use of the artificial neural network (ANN) in tee junctions. Geometry and flow parameters are feed into ANN as the inputs for purpose of training the network. Efficacy of the network is demonstrated by comparison of the experimental and ANN based calculated data of pressure loss coefficients for combining flows in a tee junction. Reynolds numbers ranging from 200 to 14000 and discharge ratios varying from minimum to maximum flow for calculation of pressure loss coefficients have been used. Pressure loss coefficients calculated using ANN are compared to the models from literature used in junction flows. The results achieved after the application of ANN agrees reasonably to the experimental values.Keywords: artificial neural networks, combining flow, pressure loss coefficients, solar collector tee junctions
Procedia PDF Downloads 3817435 Exploiting Kinetic and Kinematic Data to Plot Cyclograms for Managing the Rehabilitation Process of BKAs by Applying Neural Networks
Authors: L. Parisi
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Kinematic data wisely correlate vector quantities in space to scalar parameters in time to assess the degree of symmetry between the intact limb and the amputated limb with respect to a normal model derived from the gait of control group participants. Furthermore, these particular data allow a doctor to preliminarily evaluate the usefulness of a certain rehabilitation therapy. Kinetic curves allow the analysis of ground reaction forces (GRFs) to assess the appropriateness of human motion. Electromyography (EMG) allows the analysis of the fundamental lower limb force contributions to quantify the level of gait asymmetry. However, the use of this technological tool is expensive and requires patient’s hospitalization. This research work suggests overcoming the above limitations by applying artificial neural networks.Keywords: kinetics, kinematics, cyclograms, neural networks, transtibial amputation
Procedia PDF Downloads 4347434 Artificial Neural Network-Based Bridge Weigh-In-Motion Technique Considering Environmental Conditions
Authors: Changgil Lee, Junkyeong Kim, Jihwan Park, Seunghee Park
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In this study, bridge weigh-in-motion (BWIM) system was simulated under various environmental conditions such as temperature, humidity, wind and so on to improve the performance of the BWIM system. The environmental conditions can make difficult to analyze measured data and hence those factors should be compensated. Various conditions were considered as input parameters for ANN (Artificial Neural Network). The number of hidden layers for ANN was decided so that nonlinearity could be sufficiently reflected in the BWIM results. The weight of vehicles and axle weight were more accurately estimated by applying ANN approach. Additionally, the type of bridge which was a target structure was considered as an input parameter for the ANN.Keywords: bridge weigh-in-motion (BWIM) system, environmental conditions, artificial neural network, type of bridges
Procedia PDF Downloads 4297433 Neural Network Analysis Applied to Risk Prediction of Early Neonatal Death
Authors: Amanda R. R. Oliveira, Caio F. F. C. Cunha, Juan C. L. Junior, Amorim H. P. Junior
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Children deaths are traumatic events that most often can be prevented. The technology of prevention and intervention in cases of infant deaths is available at low cost and with solid evidence and favorable results, however, with low access cover. Weight is one of the main factors related to death in the neonatal period, so the newborns of low birth weight are a population at high risk of death in the neonatal period, especially early neonatal period. This paper describes the development of a model based in neural network analysis to predict the mortality risk rating in the early neonatal period for newborns of low birth weight to identify the individuals of this population with increased risk of death. The neural network applied was trained with a set of newborns data obtained from Brazilian health system. The resulting network presented great success rate in identifying newborns with high chances of death, which demonstrates the potential for using this tool in an integrated manner to the health system, in order to direct specific actions for improving prognosis of newborns.Keywords: low birth weight, neonatal death risk, neural network, newborn
Procedia PDF Downloads 4417432 Aerobic Bioprocess Control Using Artificial Intelligence Techniques
Authors: M. Caramihai, Irina Severin
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This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.Keywords: bioprocess, intelligent control, neural nets, fuzzy structure, hybrid techniques
Procedia PDF Downloads 4067431 Comparative Connectionism: Study of the Biological Constraints of Learning Through the Manipulation of Various Architectures in a Neural Network Model under the Biological Principle of the Correlation Between Structure and Function
Authors: Giselle Maggie-Fer Castañeda Lozano
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The main objective of this research was to explore the role of neural network architectures in simulating behavioral phenomena as a potential explanation for selective associations, specifically related to biological constraints on learning. Biological constraints on learning refer to the limitations observed in conditioning procedures, where learning is expected to occur. The study involved simulations of five different experiments exploring various phenomena and sources of biological constraints in learning. These simulations included the interaction between response and reinforcer, stimulus and reinforcer, specificity of stimulus-reinforcer associations, species differences, neuroanatomical constraints, and learning in uncontrolled conditions. The overall results demonstrated that by manipulating neural network architectures, conditions can be created to model and explain diverse biological constraints frequently reported in comparative psychology literature as learning typicities. Additionally, the simulations offer predictive content worthy of experimental testing in the pursuit of new discoveries regarding the specificity of learning. The implications and limitations of these findings are discussed. Finally, it is suggested that this research could inaugurate a line of inquiry involving the use of neural networks to study biological factors in behavior, fostering the development of more ethical and precise research practices.Keywords: comparative psychology, connectionism, conditioning, experimental analysis of behavior, neural networks
Procedia PDF Downloads 577430 Automating 2D CAD to 3D Model Generation Process: Wall pop-ups
Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski
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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection
Procedia PDF Downloads 1387429 The Catalytic Activity of CU2O Microparticles
Authors: Kanda Wongwailikhit
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Copper (I) oxide microparticles with the morphology of cubic and hollow sphere were synthesized with the assistance of a surfactant as the shape controller. Both particles were then subjected to a study of the catalytic activity and the results of shape effects of catalysts on rate of catalytic reaction was observed. The decolorizing reaction of crystal violet and sodium hydroxide was chosen and the decrease of reactant with respect to time was measured using a spectrophotometer. The result revealed that morphology of the crystal had no effect on the catalytic activity for the crystal violet reaction but contributed to total surface area predominantly.Keywords: copper (I) oxide, catalytic activity, crystal violet
Procedia PDF Downloads 4907428 Thick Data Analytics for Learning Cataract Severity: A Triplet Loss Siamese Neural Network Model
Authors: Jinan Fiaidhi, Sabah Mohammed
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Diagnosing cataract severity is an important factor in deciding to undertake surgery. It is usually conducted by an ophthalmologist or through taking a variety of fundus photography that needs to be examined by the ophthalmologist. This paper carries out an investigation using a Siamese neural net that can be trained with small anchor samples to score cataract severity. The model used in this paper is based on a triplet loss function that takes the ophthalmologist best experience in rating positive and negative anchors to a specific cataract scaling system. This approach that takes the heuristics of the ophthalmologist is generally called the thick data approach, which is a kind of machine learning approach that learn from a few shots. Clinical Relevance: The lens of the eye is mostly made up of water and proteins. A cataract occurs when these proteins at the eye lens start to clump together and block lights causing impair vision. This research aims at employing thick data machine learning techniques to rate the severity of the cataract using Siamese neural network.Keywords: thick data analytics, siamese neural network, triplet-loss model, few shot learning
Procedia PDF Downloads 1017427 Performance Analysis of Artificial Neural Network Based Land Cover Classification
Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul
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Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.Keywords: landcover classification, artificial neural network, remote sensing, SPOT 5
Procedia PDF Downloads 5307426 On the Network Packet Loss Tolerance of SVM Based Activity Recognition
Authors: Gamze Uslu, Sebnem Baydere, Alper K. Demir
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In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.Keywords: activity recognition, support vector machines, acceleration sensor, wireless sensor networks, packet loss
Procedia PDF Downloads 4687425 Heart-Rate Resistance Electrocardiogram Identification Based on Slope-Oriented Neural Networks
Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang
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For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.Keywords: high-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network
Procedia PDF Downloads 4207424 Stereotypical Motor Movement Recognition Using Microsoft Kinect with Artificial Neural Network
Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane
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Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.Keywords: ASD, artificial neural network, kinect, stereotypical motor movements
Procedia PDF Downloads 3007423 Efficient Neural and Fuzzy Models for the Identification of Dynamical Systems
Authors: Aouiche Abdelaziz, Soudani Mouhamed Salah, Aouiche El Moundhe
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The present paper addresses the utilization of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs) for the identification and control of dynamical systems with some degree of uncertainty. Because ANNs and FISs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers. In this work, we show how ANNs and FISs can be put in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with ANNs and FISs to model non-linear systems. Four systems were used to test the identification and control of the structures proposed. The results show the ANNs and FISs (Back Propagation Algorithm) used were efficient in modeling and controlling the non-linear plants.Keywords: non-linear systems, fuzzy set Models, neural network, control law
Procedia PDF Downloads 2017422 Features of Testing of the Neuronetwork Converter Biometrics-Code with Correlation Communications between Bits of the Output Code
Authors: B. S. Akhmetov, A. I. Ivanov, T. S. Kartbayev, A. Y. Malygin, K. Mukapil, S. D. Tolybayev
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The article examines the testing of the neural network converter of biometrics code. Determined the main reasons that prevented the use adopted in the works of foreign researchers classical a Binomial Law when describing distribution of measures of Hamming "Alien" codes-responses.Keywords: biometrics, testing, neural network, converter of biometrics-code, Hamming's measure
Procedia PDF Downloads 11307421 Intelligent Rheumatoid Arthritis Identification System Based Image Processing and Neural Classifier
Authors: Abdulkader Helwan
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Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation
Procedia PDF Downloads 5247420 Design, Synthesis and In-Vitro Antibacterial and Antifungal Activities of Some Novel Spiro[Azetidine-2, 3’-Indole]-2, 4(1’H)-Dione
Authors: Ravi J. Shah
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The present study deals with the synthesis of novel spiro[azetidine-2, 3’-indole]-2’, 4(1’H)-dione derivative from the reactions of 3-(phenylimino)-1,3-dihydro-2H-indol-2-one derivatives with chloracetyl chloride in presence of triethyl amine (TEA). All the compounds were characterized using IR, 1H NMR, MS and elemental analysis. They were screened for their antibacterial and antifungal activities. Results revealed that, compounds (7a), (7b), (7c), (7d) and (7e) showed very good activity with MIC value of 6.25-12.5 μg/ml against three evaluated bacterial strains and the remaining compounds showed good to moderate activity comparable to standard drugs as antibacterial agents. Compounds (7c) and (7h) displayed equipotent antifungal activity in comparison to standard drugs. Structure-activity relationship study of the compounds showed that the presence of electron withdrawing group substitution at 5’ and 7’ positions of indoline ring and on ortho or para position of phenyl ring increases both antibacterial and antifungal activity of the compound. Henceforth, our findings will have a good impact on chemists and biochemists for further investigations in search of bromine containing spiro fused antimicrobial agents.Keywords: antibacterial activity, antifungal activity, 2-Azetidinone, indoline
Procedia PDF Downloads 4817419 One-Step Time Series Predictions with Recurrent Neural Networks
Authors: Vaidehi Iyer, Konstantin Borozdin
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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning
Procedia PDF Downloads 2217418 A Systematic Review on Measuring the Physical Activity Level and Pattern in Persons with Chronic Fatigue Syndrome
Authors: Kuni Vergauwen, Ivan P. J. Huijnen, Astrid Depuydt, Jasmine Van Regenmortel, Mira Meeus
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A lower activity level and imbalanced activity pattern are frequently observed in persons with chronic fatigue syndrome (CFS) / myalgic encephalomyelitis (ME) due to debilitating fatigue and post-exertional malaise (PEM). Identification of measurement instruments to evaluate the activity level and pattern is therefore important. The objective is to identify measurement instruments suited to evaluate the activity level and/or pattern in patients with CFS/ME and review their psychometric properties. A systematic literature search was performed in the electronic databases PubMed and Web of Science until 12 October 2016. Articles including relevant measurement instruments were identified and included for further analysis. The psychometric properties of relevant measurement instruments were extracted from the included articles and rated based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist. The review was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 49 articles and 15 unique measurement instruments were found, but only three instruments were evaluated in patients with CFS/ME: the Chronic Fatigue Syndrome-Activity Questionnaire (CFS-AQ), Activity Pattern Interview (API) and International Physical Activity Questionnaire-Short Form (IPAQ-SF), three self-report instruments measuring the physical activity level. The IPAQ-SF, CFS-AQ and API are all equally capable of evaluating the physical activity level, but none of the three measurement instruments are optimal to use. No studies about the psychometric properties of activity monitors in patients with CFS/ME were found, although they are often used as the gold standard to measure the physical activity pattern. More research is needed to evaluate the psychometric properties of existing instruments, including the use of activity monitors.Keywords: chronic fatigue syndrome, data collection, physical activity, psychometrics
Procedia PDF Downloads 2217417 Keyframe Extraction Using Face Quality Assessment and Convolution Neural Network
Authors: Rahma Abed, Sahbi Bahroun, Ezzeddine Zagrouba
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Due to the huge amount of data in videos, extracting the relevant frames became a necessity and an essential step prior to performing face recognition. In this context, we propose a method for extracting keyframes from videos based on face quality and deep learning for a face recognition task. This method has two steps. We start by generating face quality scores for each face image based on the use of three face feature extractors, including Gabor, LBP, and HOG. The second step consists in training a Deep Convolutional Neural Network in a supervised manner in order to select the frames that have the best face quality. The obtained results show the effectiveness of the proposed method compared to the methods of the state of the art.Keywords: keyframe extraction, face quality assessment, face in video recognition, convolution neural network
Procedia PDF Downloads 2197416 In Vitro Assessment of Anti-microbial Properties of Murraya Koenigii Extract
Authors: Kinza Khan, Dad Muhmmad, Asif Saleem, Nadia Mukhtar, Tahir Yaqub
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Ethomedicines are more commonly used in underdeveloped and developing countries. These medicines are sometimes more potent in controlling microbial infections than conventional medicines. Medicinal plants have been common practice to cure many diseases for centuries. Murraya koenigii is one of these plants and is commonly used in South Asian countries as a flavoring agent in food. To evaluate its anti-microbial activity, six different bacterial strains (Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Salmonella typhi, Bacillus cereus and Klebsiella pneumonia were used. N-hexane extract of Murraya koenigii leaves shows maximum activity against Bacillus cereus. Acetone extract of Murraya koenigii shoots showed more efficient activity against Pseudomonas aeruginosa Dichloromethane extracts showed maximum activity against Bacillus cereus. Ethanol extract exhibited maximum activity against Pseudomonas aeruginosa and Klebsiella pneumoniae. The methanol extract of Murraya koenigii shoots displayed maximum antibacterial activity against Bacillus cereus. Antifungal activity Ethanol extract was more effective against Candida albicans.Keywords: ethnomedicines, bacteria, fungi, murraya koenigii, antimicrobial activity
Procedia PDF Downloads 837415 Physical Activity Participation Levels as Correlates of Cardiorespiratory Fitness of Senior High School Teachers in the Dormaa East District
Authors: Isaac Yeboah, Emmanuel Osei Sarpong
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Physical activity (PA) is considered the best preventative tool for maintaining physical health and extending lifespan because it offers a range of benefits. However, for people to benefit from physical activity, it must be done at a level that can enhance fitness, health, and overall well-being. Therefore, this study aimed to determine the link between physical activity participation levels and cardiorespiratory fitness (CRF). The study employed the descriptive correlational design. The census sampling technique included all 190-teaching staff from the two senior high schools in the Dormaa East District. An adapted International Physical Activity Questionnaire (IPAQ) and standardized physical fitness tests were used for data collection. Descriptive statistics of frequency counts and percentages were used to answer the research questions while inferential statistics of Pearson Product Moment Correlation and the independent sample t-test were used to test hypotheses one and two respectively. A moderately negative (r = -.156) relationship existed between physical activity participation levels and CRF. Female teachers demonstrated a statistically significant value (p = .045) than males. The study concluded that the reported physical activity levels of the teachers did not reflect their CRF. The implication is that either the participants overestimated their physical activity participation levels, or the intensity of the activities was not high enough to affect physiological changes. For the teachers' CRF to improve, it was suggested, among other things, that regular physical activity sessions of the appropriate mixes of FITT be organized.Keywords: physical activity, participation levels, cardiorespiratory fitness, sedentary lifestyle, teachers
Procedia PDF Downloads 637414 Anti-Microbial Activity of Senna garrettiana Extract
Authors: Pun Jankrajangjaeng
Abstract:
Senna garrettiana is a climatic tropical plant in Southeast Asia. Senna garrettiana (Craib) is used as a medicinal plant in Thailand, in which the experiment reported that the plant contains triterpenoids, ligans, phenolics, and fungal metabolites. Thus, it is also reported that the plant possesses interesting biological activity such as antioxidant activity. Therefore, Senna garrettiana is selected to examine the antimicrobial activity. The purpose of this study is to examine the antimicrobial activity of Senna garrettiana (crab) extract against Gram-positive Staphylococcus aureus and Gram-negative Salmonella typhi, and the fungus Candida albicans. This study performed the agar disk-diffusion method and broth microdilution by using five concentrations of plant extract to determine the minimum inhibitory concentration (MIC) of S. garrettiana extract. The result showed that S. garrettiana extract gave the maximum zone inhibition of 11.7 mm, 13.7 mm, and 14.0 mm against S. aureus, S. typhi, and C. albicans, respectively. The MIC value of S. garrettiana against S. aureus was 125 µg/mL while the MIC in S. typhi and C. albicans greater than 2000 µg/mL. To conclude, S. garrettiana extract showed higher sensitivity of antibacterial activity against gram-positive bacteria than gram-negative bacteria. In addition, the plant extracts also possessed antifungal activity. Therefore, further investigation to confirm the mechanism of action of antimicrobial activity in S. garrettiana extract should be performed to identify the target of the antimicrobial action.Keywords: antimicrobial activity, Candida albicans, Salmonella typhi, Senna garrettiana, Staphylococcus aureus
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