Search results for: random stimulation

Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: Bharatendra Rai

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Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.

Keywords: degradation signal, drill-bit breakage, random forest, multinomial logistic regression

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Authors: Ardiansyah I. Ryan, Ashsholih K. R., Fathurrohman G. R., Kurniadi M. R., Huda P. A

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The burden of brain-related disease is very high. There are a lot of brain-related diseases with limited treatment result and thus raise the burden more. The Parkinson Disease (PD), Mental Health Problem, or Paralysis of extremities treatments had risen concern, as the patients for those diseases usually had a low quality of life and low chance to recover fully. There are also many other brain or related neural diseases with the similar condition, mainly the treatments for those conditions are still limited as our understanding of the brain function is insufficient. Brain Implant Technology had given hope to help in treating this condition. In this paper, we examine the current update of the brain implant technology. Neurotechnology is growing very rapidly worldwide. The United States Food and Drug Administration (FDA) has approved the use of Deep Brain Stimulation (DBS) as a brain implant in humans. As for neural implant both the cochlear implant and retinal implant are approved by FDA too. All of them had shown a promising result. DBS worked by stimulating a specific region in the brain with electricity. This device is planted surgically into a very specific region of the brain. This device consists of 3 main parts: Lead (thin wire inserted into the brain), neurostimulator (pacemaker-like device, planted surgically in the chest) and an external controller (to turn on/off the device by patient/programmer). FDA had approved DBS for the treatment of PD, Pain Management, Epilepsy and Obsessive Compulsive Disorder (OCD). The target treatment of DBS in PD is to reduce the tremor and dystonia symptoms. DBS has been showing the promising result in animal and limited human trial for other conditions such as Alzheimer, Mental Health Problem (Major Depression, Tourette Syndrome), etc. Every surgery has risks of complications, although in DBS the chance is very low. DBS itself had a very satisfying result as long as the subject criteria to be implanted this device based on indication and strictly selection. Other than DBS, there are several brain implant devices that still under development. It was included (not limited to) implant to treat paralysis (In Spinal Cord Injury/Amyotrophic Lateral Sclerosis), enhance brain memory, reduce obesity, treat mental health problem and treat epilepsy. The potential of neurotechnology is unlimited. When brain function and brain implant were fully developed, it may be one of the major breakthroughs in human history like when human find ‘fire’ for the first time. Support from every sector for further research is very needed to develop and unveil the true potential of this technology.

Keywords: brain implant, deep brain stimulation (DBS), deep brain stimulation, Parkinson

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new COVID cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new COVID cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest

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Authors: Nursel Koyuncu, Nihal Ata Tutkun

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Frailty model is a survival model that takes into account the unobserved heterogeneity for exploring the relationship between the survival of an individual and several covariates. In the recent years, proposed survival models become more complex and this feature causes convergence problems especially in large data sets. Therefore selection of sample from these big data sets is very important for estimation of parameters. In sampling literature, some authors have defined new sampling schemes to predict the parameters correctly. For this aim, we try to see the effect of sampling design in semi-parametric frailty model. We conducted a simulation study in R programme to estimate the parameters of semi-parametric frailty model for different sample sizes, censoring rates under classical simple random sampling and ranked set sampling schemes. In the simulation study, we used data set recording 17260 male Civil Servants aged 40–64 years with complete 10-year follow-up as population. Time to death from coronary heart disease is treated as a survival-time and age, systolic blood pressure are used as covariates. We select the 1000 samples from population using different sampling schemes and estimate the parameters. From the simulation study, we concluded that ranked set sampling design performs better than simple random sampling for each scenario.

Keywords: frailty model, ranked set sampling, efficiency, simple random sampling

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Authors: A. ElZawam, D. McLean, A. Tibary

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In alpaca, age at puberty is variable and the factors regulating the pattern of puberty and sexual maturation are a subject of controversy. Plasma testosterone level is often used as an indicator of sexual maturity. Our hypothesis is that hCG treatment will cause an increase in testosterone level that is correlated with animal age. The specific aim was to investigate the testicular tissue response to a single hCG injection by monitoring the serum testosterone concentration. Eighty four (n=84) males ranging in age from 6 to 60 months were used. Alpacas were grouped based on their ages into 15 groups. Each group had three to five male animals. Blood samples were collected from the jugular vein before treatment with hCG and 2 hours after intravenous administration of 3000 IU of hCG (Chorulon®). The serum was harvested and stored at -20ºC until the analysis. The effect of age on basal testosterone level and response to hCG treatment was evaluated by Analysis of Variance. As a result, basal serum testosterone concentrations were very low (<0.1ng/ml) until 9 months of age. Although basal serum testosterone concentrations increased steadily with age there was a significant variation amongst males within the same age group. Administration of 3000 IU of hCG, resulted in an average increase of 50% (P<0.05) in serum testosterone concentration after 2 hours. The percentage increase in serum testosterone in response to hCG stimulation varied from 51 to 81%. There was no correlation between the degree of response and age. However, the response to hCG injection presented two modes of increase depending on the age of animals. The first mode occurred at ages 9 to 14 months and the second mode was observed between 22 and 36 months. In conclusion, our results suggest that testicular growth and sensitivity to LH stimulation may be bimodal in the male alpaca with a rapid increase in growth and sensitivity between 9 and 14 months of age and a second phase of increased responsiveness after 21 months of ages.

Keywords: alpaca, testosterone, hCG, animal science

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Authors: Roya Yumul, Ofelia Loani Elvir-Lazo, Sevan Komshian, Ruby Wang, Jun Tang

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BACKGROUND: An objective means for monitoring the anti-nociceptive effects of perioperative medications has long been desired as a way to provide anesthesiologists information regarding a patient’s level of antinociception and preclude any untoward autonomic responses and reflexive muscular movements from painful stimuli intraoperatively. To this end, electroencephalogram (EEG) based tools including BIS and qCON were designed to provide information about the depth of sedation while qNOX was produced to inform on the degree of antinociception. The goal of this study was to compare the reliability of qCON/qNOX to BIS as specific indicators of response to nociceptive stimulation. METHODS: Sixty-two patients undergoing general anesthesia with LMA were included in this study. Institutional Review Board (IRB) approval was obtained, and informed consent was acquired prior to patient enrollment. Inclusion criteria included American Society of Anesthesiologists (ASA) class I-III, 18 to 80 years of age, and either gender. Exclusion criteria included the inability to consent. Withdrawal criteria included conversion to the endotracheal tube and EEG malfunction. BIS and qCON/qNOX electrodes were simultaneously placed on all patients prior to induction of anesthesia and were monitored throughout the case, along with other perioperative data, including patient response to noxious stimuli. All intraoperative decisions were made by the primary anesthesiologist without influence from qCON/qNOX. Student’s t-distribution, prediction probability (PK), and ANOVA were used to statistically compare the relative ability to detect nociceptive stimuli for each index. Twenty patients were included for the preliminary analysis. RESULTS: A comparison of overall intraoperative BIS, qCON and qNOX indices demonstrated no significant difference between the three measures (N=62, p> 0.05). Meanwhile, index values for qNOX (62±18) were significantly higher than those for BIS (46±14) and qCON (54±19) immediately preceding patient responses to nociceptive stimulation in a preliminary analysis (N=20, * p= 0.0408). Notably, certain hemodynamic measurements demonstrated a significant increase in response to painful stimuli (MAP increased from 74 ±13 mm Hg at baseline to 84 ± 18 mm Hg during noxious stimuli [p= 0.032] and HR from 76 ± 12 BPM at baseline to 80 ± 13 BPM during noxious stimuli [p=0.078] respectively). CONCLUSION: In this observational study, BIS and qCON/qNOX provided comparable information on patients’ level of sedation throughout the course of an anesthetic. Meanwhile, increases in qNOX values demonstrated a superior correlation to an imminent response to stimulation relative to all other indices

Keywords: antinociception, BIS, general anesthesia, LMA, qCON/qNOX

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Authors: Halil Ibrahim Demir, Tarık Cakar, Ibrahim Cil, Muharrem Dugenci, Caner Erden

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Conventionally, process planning, scheduling, and due-date assignment functions are performed separately and sequentially. The interdependence of these functions requires integration. Although integrated process planning and scheduling, and scheduling with due date assignment problems are popular research topics, only a few works address the integration of these three functions. This work focuses on the integration of process planning, WMS scheduling, and WPPW due date assignment. Another novelty of this work is the use of a weighted due date assignment. In the literature, due dates are generally assigned without considering the importance of customers. However, in this study, more important customers get closer due dates. Typically, only tardiness is punished, but the JIT philosophy punishes both earliness and tardiness. In this study, all weighted earliness, tardiness, and due date related costs are penalized. As no customer desires distant due dates, such distant due dates should be penalized. In this study, various levels of integration of these three functions are tested and genetic search and random search are compared both with each other and with ordinary solutions. Higher integration levels are superior, while search is always useful. Genetic searches outperformed random searches.

Keywords: process planning, weighted scheduling, weighted due-date assignment, genetic algorithm, random search

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Authors: Azita Ramezani, Atousa Ramezani

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In the ever-escalating landscape of windows malware the necessity for pioneering defense strategies turns into undeniable this study introduces an avant-garde approach fusing the capabilities of clustering random forests and support vector machines SVM to combat the intricate web of cyber threats our fusion model triumphs with a staggering accuracy of 98.67 and an equally formidable f1 score of 98.68 a testament to its effectiveness in the realm of windows malware defense by deciphering the intricate patterns within malicious code our model not only raises the bar for detection precision but also redefines the paradigm of cybersecurity preparedness this breakthrough underscores the potential embedded in the fusion of diverse analytical methodologies and signals a paradigm shift in fortifying against the relentless evolution of windows malicious threats as we traverse through the dynamic cybersecurity terrain this research serves as a beacon illuminating the path toward a resilient future where innovative fusion models stand at the forefront of cyber threat defense.

Keywords: fusion models, cyber threat defense, windows malware, clustering, random forests, support vector machines (SVM), accuracy, f1-score, cybersecurity, malicious code detection

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Authors: Andrei Pomana, Graham Brewer

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Autism is a lifelong developmental disorder that affects verbal and non-verbal communication, behaviour and sensory processing. As a result, people on the autism spectrum have a difficult time when confronted with environments that have high levels of sensory stimulation. This is often compounded by the inability to properly communicate their wants and needs to caregivers. The capacity for people with autism to integrate depends on their ability to at least tolerate highly stimulating public environments for short periods of time. The overall challenges that people on the spectrum and their caregivers face need to be established in order to properly create and assess methods to mitigate the effects of high stimulus public spaces. The paper aims to identify the challenges that people on the autism spectrum and their caregivers face in typical public environments. Nine experienced autism therapists have participated in a semi-structured interview regarding the challenges that people with autism and their caregivers face in public environments. The qualitative data shows that the unpredictability of events and the high sensory stimulation present in public environments, especially auditory, are the two biggest contributors to the difficulties that people on the spectrum face. If the stimuli are not removed in a short period of time, uncontrollable behaviours or 'meltdowns' can occur, which leave the person incapacitated and unable to respond to any outside input. Possible solutions to increase integration in public spaces for people with autism revolve around removing unwanted sensory stimulus, creating personalized barriers for certain stimuli, equipping people with autism with better tools to communicate their needs or to orient themselves to a safe location and providing a predictable pattern of events that would prepare individuals for tasks ahead of time.

Keywords: autism, built environment, meltdown, public environment, sensory processing disorders

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Authors: Jaehong Yu, Seoung Bum Kim

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Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors.

Keywords: clustering analysis, multiple-k ensemble, random subspace-based feature evaluation, unsupervised feature ranking

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Authors: Joanna Maj

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Body machine interfaces (BMIs) afford users a non-invasive way coordinate movement. Vibrotactile stimulation has been incorporated into BMIs to allow feedback in real-time and guide movement control to benefit patients with cognitive deficits, such as stroke survivors. To advance research in this area, we examined vibrational discrimination thresholds at four body locations to determine suitable application sites for future multi-channel BMIs using vibration cues to guide movement planning and control. Twelve healthy adults had a pair of small vibrators (tactors) affixed to the skin at each location: forearm, shoulders, torso, and knee. A "standard" stimulus (186 Hz; 750 ms) and "probe" stimuli (11 levels ranging from 100 Hz to 235 Hz; 750 ms) were delivered. Probe and test stimulus pairs could occur sequentially or simultaneously (timing). Participants verbally indicated which stimulus felt more intense. Stimulus order was counterbalanced across tactors and body locations. Probabilities that probe stimuli felt more intense than the standard stimulus were computed and fit with a cumulative Gaussian function; the discrimination threshold was defined as one standard deviation of the underlying distribution. Threshold magnitudes depended on stimulus timing and location. Discrimination thresholds were better for stimuli applied sequentially vs. simultaneously at the torso as well as the knee. Thresholds were small (better) and relatively insensitive to timing differences for vibrations applied at the shoulder. BMI applications requiring multiple channels of simultaneous vibrotactile stimulation should therefore consider the shoulder as a deployment site for a vibrotactile BMI interface.

Keywords: electromyography, electromyogram, neuromuscular disorders, biomedical instrumentation, controls engineering

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Authors: Nguyen Chan Hung, Mai Ngoc Anh, Nguyen Xuan Ha, Tran Xuan Duc, Dang Bao Lam, Nguyen Hoang Viet

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This paper presents a design and implementation of a navigation controller for a humanoid mobile robot platform to operate in indoor office environments. In order to fulfil the requirement of recognizing and approaching human to provide service while avoiding random obstacles, a behavior-based fuzzy logic controller was designed to simultaneously coordinate multiple behaviors. Experiments in real office environment showed that the fuzzy controller deals well with complex scenarios without colliding with random objects and human.

Keywords: behavior control, fuzzy logic, humanoid robot, mobile robot

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Authors: Samir Benzegane, Said Sadoudi, Mustapha Djeddou

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In this paper, we present a software development of video streaming encryption for Device-to-Device (D2D) communications by using Hyperchaos-based Random Number Generator (HRNG) implemented in C#. The software implements and uses the proposed HRNG to generate key stream for encrypting and decrypting real-time video data. The used HRNG consists of Hyperchaos Lorenz system which produces four signal outputs taken as encryption keys. The generated keys are characterized by high quality randomness which is confirmed by passing standard NIST statistical tests. Security analysis of the proposed encryption scheme confirms its robustness against different attacks.

Keywords: hyperchaos Lorenz system, hyperchaos-based random number generator, D2D communications, C#

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Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.

Keywords: logistic regression, decisions tree, random forest, VAR model

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Authors: Ahmed Guerine, Ali El Hafidi, Bruno Martin, Philippe Leclaire

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In this paper, we propose a method for the dynamic response of multi-storey structures with uncertain-but-bounded parameters. The effectiveness of the proposed method is demonstrated by a numerical example of three-storey structures. This equation is integrated numerically using Newmark’s method. The numerical results are obtained by the proposed method. The simulation accounting the interval analysis method results are compared with a probabilistic approach results. The interval analysis method provides a mean curve that is between an upper and lower bound obtained from the probabilistic approach.

Keywords: multi-storey structure, dynamic response, interval analysis method, random parameters

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Authors: Wajeeha Shahid

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The study was designed to assess the impact of transformational and transactional leadership styles on work motivation and organizational commitment among faculty members of universities of Punjab. 713 faculty members were selected as sample through convenient random sampling technique. Three self-constructed questionnaires namely Leadership Styles Questionnaire (LSQ), Work Motivation Questionnaire (WMQ) and Organizational Commitment Questionnaire (OCMQ) were used as research instruments. Major objectives of the study included assessing the effect and impact of transformational and transactional leadership styles on work motivation and organizational commitment. Theoretical frame work of the study included Idealized Influence, Inspirational Motivation, Intellectual Stimulation, Individualized Consideration, Contingent Rewards and Management by Exception as independent variables and Extrinsic motivation, Intrinsic motivation, Affective commitment, Continuance commitment and Normative commitment as dependent variables. SPSS Version 21 was used to analyze and tabulate data. Cronbach's Alpha reliability, Pearson Correlation and Multiple regression analysis were applied as statistical treatments for the analysis. Results revealed that Idealized Influence correlated significantly with intrinsic motivation and Affective commitment whereas Contingent rewards had a strong positive correlation with extrinsic motivation and affective commitment. Multiple regression models revealed a variance of 85% for transformational leadership style over work motivation and organizational commitment. Whereas transactional style as a predictor manifested a variance of 79% for work motivation and 76% for organizational commitment. It was suggested that changing organizational cultures are demanding more from their leadership. All organizations need to consider transformational leadership style as an important part of their equipment in leveraging both soft and hard organizational targets.

Keywords: leadership styles, work motivation, organizational commitment, faculty member

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Adijat Olubukola Olateju

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Women empowerment is an essential developmental tool in every economy especially in less developed countries; as it helps to enhance women's socio-economic well-being. Some empirical evidence has shown that microcredit has been an effective tool in enhancing women empowerment, especially in developing countries. This paper therefore, investigates the impact of microcredit programme on women empowerment in Lagos State, Nigeria. The study used Cowries Microfinance Bank (CMB) as a case study bank, and a total of 359 women entrepreneurs were selected by simple random sampling technique from the list of Cowries Microfinance Bank. Selection bias which could arise from non-random selection of participants or non-random placement of programme, was adjusted for by dividing the data into participant women entrepreneurs and non-participant women entrepreneurs. The data were analyzed with a Propensity Score Matching (PSM) technique. The result of the Average Treatment Effect on the Treated (ATT) obtained from the PSM indicates that the credit programme has a significant effect on the empowerment of women in the study area. It is therefore, recommended that microfinance banks should be encouraged to give loan to women and for more impact of the loan to be felt by the beneficiaries the loan programme should be complemented with other programmes such as training, grant, and periodic monitoring of programme should be encouraged.

Keywords: empowerment, microcredit, socio-economic wellbeing, development

Procedia PDF Downloads 269

Authors: Samira Cherid, Samir Bentata, Feyza Zahira Meghoufel, Yamina Sefir, Sabria Terkhi, Fatima Bendahma, Bouabdellah Bouadjemi, Ali Zitouni

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Using the transfer-matrix technique and the Kronig Penney model, we numerically and analytically investigate the effect of short-range correlated disorder in random dimer model (RDM) on transmission properties of light in one dimension photonic crystals made of three different materials. Such systems consist of two different structures randomly distributed along the growth direction, with the additional constraint that one kind of these layers appears in pairs. It is shown that the one-dimensional random dimer photonic crystals support two types of extended modes. By shifting of the dimer resonance toward the host fundamental stationary resonance state, we demonstrate the existence of the ballistic response in these systems.

Keywords: photonic crystals, disorder, correlation, transmission

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Authors: Marc Sader, Michiel Stock, Bernard De Baets

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Considering a large amount of adsorption data of adsorbate gases on adsorbent materials in literature, it is interesting to predict such adsorption data without experimentation. A quantitative structure-activity relationship (QSAR) is developed to correlate molecular characteristics of gases and existing knowledge of materials with their respective adsorption properties. The application of Random Forest, a machine learning method, on a set of adsorption isotherms at a wide range of partial pressures and concentrations is studied. The predicted adsorption isotherms are fitted to several adsorption equations to estimate the adsorption properties. To impute the adsorption properties of desired gases on desired materials, leave-one-out cross-validation is employed. Extensive experimental results for a range of settings are reported.

Keywords: adsorption, predictive modeling, QSAR, random forest

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Authors: Thedy Yogasara, Fredy Agus

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In designing a product, it is currently crucial to focus not only on the product’s usability based on performance measures, but also on user experience (UX) that includes pragmatic and hedonic aspects of product use. These aspects play a significant role in fulfillment of user needs, both functionally and psychologically. Pragmatic quality refers to as product’s perceived ability to support the fulfillment of behavioral goals. It is closely linked to functionality and usability of the product. In contrast, hedonic quality is product’s perceived ability to support the fulfillment of psychological needs. Hedonic quality relates to the pleasure of ownership and use of the product, including stimulation for personal development and communication of user’s identity to others through the product. This study evaluates the pragmatic and hedonic aspects of gaming mice G600 and Razer Krait using AttrakDiff tool to create an improved design that is able to generate positive UX. AttrakDiff is a method that measures pragmatic and hedonic scores of a product with a scale between -3 to +3 through four attributes (i.e. Pragmatic Quality, Hedonic Quality-Identification, Hedonic Quality-Stimulation, and Attractiveness), represented by 28 pairs of opposite words. Based on data gathered from 15 participants, it is identified that gaming mouse G600 needs to be redesigned because of its low grades (pragmatic score: -0.838, hedonic score: 1, attractiveness score: 0.771). The redesign process focuses on the attributes with poor scores and takes into account improvement suggestions collected from interview with the participants. The redesigned mouse G600 is evaluated using the previous method. The result shows higher scores in pragmatic quality (1.929), hedonic quality (1.703), and attractiveness (1.667), indicating that the redesigned mouse is more capable of creating pleasurable experience of product use.

Keywords: AttrakDiff, hedonic aspect, pragmatic aspect, product design, user experience

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Authors: Halil Ibrahim Demir, Abdullah Hulusi Kokcam, Fuat Simsir, Özer Uygun

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Traditionally, three important manufacturing functions which are process planning, scheduling and due-date assignment are performed sequentially and separately. Although there are numerous works on the integration of process planning and scheduling and plenty of works focusing on scheduling with due date assignment, there are only a few works on integrated process planning, scheduling and due-date assignment. Although due-dates are determined without taking into account of weights of the customers in the literature, here weighted due-date assignment is employed to get better performance. Jobs are scheduled according to weighted earliest due date dispatching rule and due dates are determined according to some popular due date assignment methods by taking into account of the weights of each job. Simulated Annealing, Evolutionary Strategies, Random Search, hybrid of Random Search and Simulated Annealing, and hybrid of Random Search and Evolutionary Strategies, are applied as solution techniques. Three important manufacturing functions are integrated step-by-step and higher integration levels are found better. Search meta-heuristics are found to be very useful while improving performance measure.

Keywords: process planning, weighted scheduling, weighted due-date assignment, simulated annealing, evolutionary strategies, hybrid searches

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Authors: Li Zhou, Yanmei Fang

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With current technology, images and videos can be obtained more easily than ever. It is so easy to manipulate these digital multimedia information when obtained, and that the content or source of the image and video could be easily tampered. In this paper, we propose to identify the image and video frame by the texture-based approach, e.g. Markov Transition Probability (MTP), which is in space domain, DCT domain and DWT domain, respectively. In the experiment, image and video frame database is constructed, and is used to train and test the classifier Support Vector Machine (SVM). Experiment results show that the texture-based approach has good performance. In order to verify the experiment result, and testify the universality and robustness of algorithm, we build a random testing dataset, the random testing result is in keeping with above experiment.

Keywords: multimedia forensics, video frame, LBP, MTP, SVM

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Authors: Mahmoud I. Syam

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Stratified double median ranked set sampling (SDMRSS) method is suggested for estimating the population mean. The SDMRSS is compared with the simple random sampling (SRS), stratified simple random sampling (SSRS), and stratified ranked set sampling (SRSS). It is shown that SDMRSS estimator is an unbiased of the population mean and more efficient than SRS, SSRS, and SRSS. Also, by SDMRSS, we can increase the efficiency of mean estimator for specific value of the sample size. SDMRSS is applied on real life examples, and the results of the example agreed the theoretical results.

Keywords: efficiency, double ranked set sampling, median ranked set sampling, ranked set sampling, stratified

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Authors: Christopher Latella, Ashlee M. Hendy, Dan Vander Westhuizen, Wei-Peng Teo

Abstract:

Introduction: Neural adaptations following resistance training interventions have been widely investigated, however the evidence regarding the mechanisms of early adaptation are less clear. Understanding neural responses from an acute resistance training session is pivotal in the prescription of frequency, intensity and volume in applied strength and conditioning practice. Therefore the primary aim of this study was to investigate the time course of neurophysiological mechanisms post training against current super compensation theory, and secondly, to examine whether these responses reflect neural adaptations observed with resistance training interventions. Methods: Participants (N=14) completed a randomised, counterbalanced crossover study comparing; control, strength and hypertrophy conditions. The strength condition involved 3 x 5RM leg extensions with 3min recovery, while the hypertrophy condition involved 3 x 12 RM with 60s recovery. Transcranial magnetic stimulation (TMS) and peripheral nerve stimulation were used to measure excitability of the central and peripheral neural pathways, and maximal voluntary contraction (MVC) to quantify strength changes. Measures were taken pre, immediately post, 10, 20 and 30 mins and 1, 2, 6, 24, 48, 72 and 96 hrs following training. Results: Significant decreases were observed at post, 10, 20, 30 min, 1 and 2 hrs for both training groups compared to control group for force, (p <.05), maximal compound wave; (p < .005), silent period; (p < .05). A significant increase in corticospinal excitability; (p < .005) was observed for both groups. Corticospinal excitability between strength and hypertrophy groups was near significance, with a large effect (η2= .202). All measures returned to baseline within 6 hrs post training. Discussion: Neurophysiological mechanisms appear to be significantly altered in the period 2 hrs post training, returning to homeostasis by 6 hrs. The evidence suggests that the time course of neural recovery post resistance training occurs 18-40 hours shorter than previous super compensation models. Strength and hypertrophy protocols showed similar response profiles with current findings suggesting greater post training corticospinal drive from hypertrophy training, despite previous evidence that strength training requires greater neural input. The increase in corticospinal drive and decrease inl inhibition appear to be a compensatory mechanism for decreases in peripheral nerve excitability and maximal voluntary force output. The changes in corticospinal excitability and inhibition are akin to adaptive processes observed with training interventions of 4 wks or longer. It appears that the 2 hr recovery period post training is the most influential for priming further neural adaptations with resistance training. Secondly, the frequency of prescribed resistance sessions can be scheduled closer than previous super compensation theory for optimal strength gains.

Keywords: neural responses, resistance training, super compensation, transcranial magnetic stimulation

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Authors: M. Alkhalidi, S. Neelamani, Z. Al-Zaqah

Abstract:

This paper investigates the interaction of single and twin offshore rubble mound breakwaters with regular and random water waves through physical modeling to assess their reflection, transmission and energy dissipation characteristics. Various combinations of wave heights and wave periods were utilized in a series of experiments, along with three different water depths. The single and twin permeable breakwater models were both constructed with one layer of rubbles. Both models had the same total volume; however, the single breakwater was of trapezoidal type while the twin breakwaters were of triangular type. Physical modeling experiments were carried out in the wave flume of the coastal engineering laboratory of Kuwait Institute for Scientific Research (KISR). Measurements of the six wave probes which were fixed in the two-dimensional wave flume were collected and used to determine the generated incident wave heights, as well as the reflected and transmitted wave heights resulting from the wave-breakwater interaction. The possible factors affecting the wave attenuation efficiency of the breakwater models are the relative water depth (d/L), wave steepness (H/L), relative wave height ((h-d)/Hi), relative height of the breakwater (h/d), and relative clear spacing between the twin breakwaters (S/h). The results indicated that the single and double breakwaters show different responds to the change in their relative height as well as the relative wave height which demonstrates that the effect of the relative water depth on wave reflection, transmission, and energy dissipation is highly influenced by the change in the relative breakwater height, the relative wave height and the relative breakwater spacing. In general, within the range of the relative water depth tested in this study, and under both regular and random waves, it is found that the single breakwater allows for lower wave transmission and shows higher energy dissipation effect than both of the tested twin breakwaters, and hence has the best overall performance.

Keywords: random waves, regular waves, relative water depth, relative wave height, single breakwater, twin breakwater, wave steepness

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Authors: Tetyana Baydyk, Ernst Kussul, Sandra Bonilla Meza

Abstract:

There are many articles that attempt to establish the role of different facial fragments in face recognition. Various approaches are used to estimate this role. Frequently, authors calculate the entropy corresponding to the fragment. This approach can only give approximate estimation. In this paper, we propose to use a more direct measure of the importance of different fragments for face recognition. We propose to select a recognition method and a face database and experimentally investigate the recognition rate using different fragments of faces. We present two such experiments in the paper. We selected the PCNC neural classifier as a method for face recognition and parts of the LFW (Labeled Faces in the Wild) face database as training and testing sets. The recognition rate of the best experiment is comparable with the recognition rate obtained using the whole face.

Keywords: face recognition, labeled faces in the wild (LFW) database, random local descriptor (RLD), random features

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Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, molecular descriptors, machine learning, random forest

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Authors: Shahram Jamali, Samira Hamed

Abstract:

One of the open issues in Random Early Detection (RED) algorithm is how to set its parameters to reach high performance for the dynamic conditions of the network. Although original RED uses fixed values for its parameters, this paper follows a model-based approach to upgrade performance of the RED algorithm. It models the routers queue behavior by using the Markov model and uses this model to predict future conditions of the queue. This prediction helps the proposed algorithm to make some tunings over RED's parameters and provide efficiency and better performance. Widespread packet level simulations confirm that the proposed algorithm, called Markov-RED, outperforms RED and FARED in terms of queue stability, bottleneck utilization and dropped packets count.

Keywords: active queue management, RED, Markov model, random early detection algorithm

Procedia PDF Downloads 512