Search results for: time prediction algorithms

Authors: Anusuya Ghosh, Vishnu Narayanan

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The approximation of convex set by semidefinite representable set plays an important role in semidefinite programming, especially in modern convex optimization. To optimize a linear function over a convex set is a hard problem. But optimizing the linear function over the semidefinite representable set which approximates the convex set is easy to solve as there exists numerous efficient algorithms to solve semidefinite programming problems. So, our approximation technique is significant in optimization. We develop a technique to approximate any closed convex set, say K by compactly semidefinite representable set. Further we prove that there exists a sequence of compactly semidefinite representable sets which give tighter approximation of the closed convex set, K gradually. We discuss about the convergence of the sequence of compactly semidefinite representable sets to closed convex set K. The recession cone of K and the recession cone of the compactly semidefinite representable set are equal. So, we say that the sequence of compactly semidefinite representable sets converge strongly to the closed convex set. Thus, this approximation technique is very useful development in semidefinite programming.

Keywords: semidefinite programming, semidefinite representable set, compactly semidefinite representable set, approximation

Procedia PDF Downloads 367

Authors: Emad Alenany, M. Adel El-Baz

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In this paper, the flow of different classes of patients into a hospital is modelled and analyzed by using the queueing network analyzer (QNA) algorithm and discrete event simulation. Input data for QNA are the rate and variability parameters of the arrival and service times in addition to the number of servers in each facility. Patient flows mostly match real flow for a hospital in Egypt. Based on the analysis of the waiting times, two approaches are suggested for improving performance: Separating patients into service groups, and adopting different service policies for sequencing patients through hospital units. The separation of a specific group of patients, with higher performance target, to be served separately from the rest of patients requiring lower performance target, requires the same capacity while improves performance for the selected group of patients with higher target. Besides, it is shown that adopting the shortest processing time and shortest remaining processing time service policies among other tested policies would results in, respectively, 11.47% and 13.75% reduction in average waiting time relative to first come first served policy.

Keywords: queueing network, discrete-event simulation, health applications, SPT

Procedia PDF Downloads 174

Authors: S. O. Oyamakin, A. U. Chukwu,

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We proposed a Hyperbolic Gompertz Growth Model (HGGM), which was developed by introducing a stabilizing parameter called θ using hyperbolic sine function into the classical gompertz growth equation. The resulting integral solution obtained deterministically was reprogrammed into a statistical model and used in modeling the height and diameter of Pines (Pinus caribaea). Its ability in model prediction was compared with the classical gompertz growth model, an approach which mimicked the natural variability of height/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using goodness of fit tests and model selection criteria. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the compliance of the error term to normality assumptions while using testing the independence of the error term using the runs test. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic gompertz growth models better than the source model (classical gompertz growth model) while the results of R2, Adj. R2, MSE, and AIC confirmed the predictive power of the Hyperbolic Monomolecular growth models over its source model.

Keywords: height, Dbh, forest, Pinus caribaea, hyperbolic, gompertz

Procedia PDF Downloads 427

Authors: Alaa I. Arafa, Hemdan O. A. Said. Marwa A. M. Ali

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This paper investigates and evaluates experimentally the structural behavior of high strength concrete (HSC) beams under fire and compares it with that of Normal strength concrete (NSC) beams. The main investigated parameters are: concrete compressive strength (300 or 600 kg/cm²); the concrete cover thickness (3 or 5 cm); the degree of temperature (room temperature or 600 ^oC); the type of cooling (air or water); and the fire exposure time (3 or 5 hours). Test results showed that the concrete compressive strength decreases significantly as the exposure time to fire increases.

Keywords: experimental, fire, high strength concrete beams, monotonic loading

Procedia PDF Downloads 381

Authors: Rimmy Yadav, Avtar Singh

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—Ant Colony Optimization (ACO) is a promising modern approach to the unused combinatorial optimization. Here ACO is applied to finding the shortest during communication link failure. In this paper, the performances of the prim’s and ACO algorithm are made. By comparing the time complexity and program execution time as set of parameters, we demonstrate the pleasant performance of ACO in finding excellent solution to finding shortest path during communication link failure.

Keywords: ant colony optimization, link failure, prim’s algorithm, shortest path

Procedia PDF Downloads 381

Authors: Mira Trebar

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Logistics processes of perishable food in the supply chain include the distribution activities and the real time temperature monitoring to fulfil the cold chain requirements. The paper presents the use of RFID (Radio Frequency Identification) technology as an identification tool of receiving and shipping activities in the cold store. At the same time, the use of RFID data loggers with temperature sensors is presented to observe and store the temperatures for the purpose of analyzing the processes and having the history data available for traceability purposes and efficient recall management.

Keywords: logistics, warehouse, RFID device, cold chain

Procedia PDF Downloads 613

Authors: Feng Li, Qian Peng

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The mass start speed skating (MSSS) is a new event for the 2018 PyeongChang Winter Olympics and will be an official race for the 2022 Beijing Winter Olympics. Considering that the event rankings were based on points gained on laps, it is worthwhile to investigate the pacing behavior on each lap that directly influences the ranking of the race. The aim of this study was to detect the pacing behavior and performance on MSSS regarding skaters’ level (SL), competition stage (semi-final/final) (CS) and gender (G). All the men's and women's races in the World Cup and World Championships were analyzed in the 2018-2019 and 2019-2020 seasons. As a result, a total of 601 skaters from 36 games were observed. ANOVA for repeated measures was applied to compare the pacing behavior on each lap, and the three-way ANOVA for repeated measures was used to identify the influence of SL, CS, and G on pacing behavior and total time spent. In general, the results showed that the pacing behavior from fast to slow were cluster 1—laps 4, 8, 12, 15, 16, cluster 2—laps 5, 9, 13, 14, cluster 3—laps 3, 6, 7, 10, 11, and cluster 4—laps 1 and 2 (p=0.000). For CS, the total time spent in the final was less than the semi-final (p=0.000). For SL, top-level skaters spent less total time than the middle-level and low-level (p≤0.002), while there was no significant difference between the middle-level and low-level (p=0.214). For G, the men’s skaters spent less total time than women on all laps (p≤0.048). This study could help to coach staff better understand the pacing behavior regarding SL, CS, and G, further providing references concerning promoting the pacing strategy and decision making before and during the race.

Keywords: performance analysis, pacing strategy, winning strategy, winter Olympics

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Authors: Abel S. Huaynacho, Yoni D. Huaynacho

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The improvement of procedure test ASTM D1556, plays an important role in the developing of testing in field to obtain a higher quality of data QA/QC. The traditional process takes a considerable amount of time for only one test. Even making various testing are tasks repeating and it takes a long time to obtain better results. Moreover, if the adequate tools the help these testing are not properly managed, the improvement in the development for various testing could be stooped. This paper presents an optimized process for various testing ASTM D1556 which uses an initial standard process to another one the uses a simpler and improved management tools.

Keywords: cone sand test, density bulk, ASTM D1556, QA/QC

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Authors: Osamede Asowata

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The current resurgence of interest in the use of renewable energy is driven by the need to reduce the high environmental impact of fossil-based energy. The aim of this paper is to evaluate the effect of a stationary PV panel on the charging rate of deep-cycle valve regulated lead-acid (DCVRLA) batteries. Stationary PV panels are set to a fixed tilt and orientation angle, which plays a major role in dictating the output power of a PV panel and subsequently on the charging time of a DCVRLA battery. In a basic PV system, an energy storage device that stores the power from the PV panel is necessary due to the fluctuating nature of the PV voltage caused by climatic conditions. The charging and discharging times of a DCVRLA battery were determined for a twelve month period from January through December 2012. Preliminary results, which include regression analysis (R2), conversion-time per week and work-time per day, indicate that a 36 degrees tilt angle produces a good charging rate for a latitude of 26 degrees south throughout the year.

Keywords: tilt and orientation angles, solar chargers, PV panels, storage devices, direct solar radiation.

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Authors: Beeto W. C. Leung, Nicole C. Y. Lee

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It was well known that college students experienced a high level of stress and anxiety. College athletes, a special group of college students, may even encounter a higher level of pressure and distress due to their dual endeavors in academic and athletic settings. Due to the high demands and costs of mental health services, easily accessible, web-based self-help interventions are getting more popular. The aim of the present experimental study was to examine the potential intervention effect of a mindfulness-based self-help mobile App, called 'Smiling Mind', on mindfulness and psychological well-being. Forty-six college athletes, recruited from athletic teams of two local universities in Hong Kong, were randomly assigned to the Mindfulness App Group (MAG) and the Control Group (CG). All participants were administered the Mindful Attention Awareness Scale, Geriatric Depression Scale, and Perceived Stress Scale-10 before the study (Time 1, T1) and after the 4-week intervention (Time 2, T2). MAG was requested to use the app and follow the instructions every day for at least 5 days per week. Participants were also asked to record their daily app usage time. Results showed that, for MAG, from T1 to T2, mindfulness has been increased from 3.25 to 3.92; depressive symptoms and stress has been significantly decreased from 8.6 to 5.1 and 24.8 to 13.5 respectively while for the CG, mindfulness has been decreased slightly from 3.29 to 3.13; depressive symptoms and stress has been slightly increased from 7.1 to 7.3 and 24.1 to 27.1 respectively. Three mixed-design ANOVAs with time (T1, T2) as the within-subjects factor and intervention group (MAG, CG) as the between-subjects factor revealed a main effect of time on mindfulness, F(1, 41) = 10.28, p < 0.01, depressive symptoms, F(1, 41) = 6.55, p < 0.02 and stress, F(1, 41) = 16.96, p < 0.001 respectively. Both predicted interaction between time and intervention group on mindfulness, F(1, 41) = 26.6, p < 0.001, ηp 2 =0.39, depressive symptoms, F(1, 41) = 8.00, p < 0.01, ηp 2 =0.16 and Stress F(1, 41) = 49.3, p < 0.001, ηp 2 =0.55 were significant meaning that participants using the Mindfulness Mobile App in the intervention did experienced a significant increase in mindfulness and significant decrease in depressive symptoms and perceived level of stress after the 4-week intervention when compared with the control group. The present study provided encouraging empirical support for using Smiling Mind, a self-help mobile app, to promote mindfulness and mental health in a cost-effective way. Further studies should examine the potential use of Smiling Mind in different samples, including children and adolescence, as well as, investigate the lasting effects of using the app on other psychosocial outcomes such as emotional regulations.

Keywords: college athletes, experimental study, mindfulness mobile apps, psychological well-being

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Authors: Meriem Harmel, Houari Khachai

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We have investigated the structural, electronic and optical properties of a compound perovskite CsSrF3 using the full-potential linearized augmented plane wave (FP-LAPW) method within density functional theory (DFT). In this approach, both the local density approximation (LDA) and the generalized gradient approximation (GGA) were used for exchange-correlation potential calculation. The ground state properties such as lattice parameter, bulk modulus and its pressure derivative were calculated and the results are compared whit experimental and theoretical data. Electronic and bonding properties are discussed from the calculations of band structure, density of states and electron charge density, where the fundamental energy gap is direct under ambient conditions. The contribution of the different bands was analyzed from the total and partial density of states curves. The optical properties (namely: the real and the imaginary parts of the dielectric function ε(ω), the refractive index n(ω) and the extinction coefficient k(ω)) were calculated for radiation up to 35.0 eV. This is the first quantitative theoretical prediction of the optical properties for the investigated compound and still awaits experimental confirmations.

Keywords: DFT, fluoroperovskite, electronic structure, optical properties

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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

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Authors: Anthony Usoro

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In this paper, two classes of bilinear time series model are obtained under certain conditions from the general bilinear autoregressive moving average model. Bilinear Autoregressive (BAR) and Bilinear Moving Average (BMA) Models have been identified. From the general bilinear model, BAR and BMA models have been proved to exist for q = Q = 0, => j = 0, and p = P = 0, => i = 0 respectively. These models are found useful in modelling most of the economic and financial data.

Keywords: autoregressive model, bilinear autoregressive model, bilinear moving average model, moving average model

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Authors: Chien-Jen Liu, Shu Ching Yang

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Using the technology acceptance model (TAM), this study examined the external variables of technological complexity (TC) to acquire a better understanding of the factors that influence the acceptance of computer application courses by learners at Active Aging Universities. After the learners in this study had completed a 27-hour Facebook course, 44 learners responded to a modified TAM survey. Data were collected to examine the path relationships among the variables that influence the acceptance of Facebook-mediated community learning. The partial least squares (PLS) method was used to test the measurement and the structural model. The study results demonstrated that attitudes toward Facebook use directly influence behavioral intentions (BI) with respect to Facebook use, evincing a high prediction rate of 58.3%. In addition to the perceived usefulness (PU) and perceived ease of use (PEOU) measures that are proposed in the TAM, other external variables, such as TC, also indirectly influence BI. These four variables can explain 88% of the variance in BI and demonstrate a high level of predictive ability. Finally, limitations of this investigation and implications for further research are discussed.

Keywords: technology acceptance model (TAM), technological complexity, partial least squares (PLS), perceived usefulness

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Authors: S. R. Nikhashemi, Laily Haj Paim, Ali Khatibi

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Structural Equation Modeling (SEM) was used to test a hypothesized model explaining Malaysian hypermarket customers’ perceptions of brand trust (BT), customer perceived value (CPV) and perceived service quality (PSQ) on building their brand loyalty (CBL) and generating positive word-of-mouth communication (WOM). Self-administered questionnaires were used to collect data from 374 Malaysian hypermarket customers from Mydin, Tesco, Aeon Big and Giant in Kuala Lumpur, a metropolitan city of Malaysia. The data strongly supported the model exhibiting that BT, CPV and PSQ are prerequisite factors in building customer brand loyalty, while PSQ has the strongest effect on prediction of customer brand loyalty compared to other factors. Besides, the present study suggests the effect of the aforementioned factors via customer brand loyalty strongly contributes to generate positive word of mouth communication.

Keywords: brand trust, perceived value, Perceived Service Quality, Brand loyalty, positive word of mouth communication

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Authors: Chayanoot Sangwichien, Taweesak Reungpeerakul, Kyaw Thu

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Oil palm mills in Southern Thailand produced a large amount of biomass solid wastes. Lignocellulose biomass is the main source for production of biofuel which can be combined or used as an alternative to fossil fuels. Biomass composed of three main constituents of cellulose, hemicellulose, and lignin. Thermochemical conversion process applied to produce biofuel from biomass. Pyrolysis of biomass is the best way to thermochemical conversion of biomass into pyrolytic products (bio-oil, gas, and char). Operating parameters play an important role to optimize the product yields from fast pyrolysis of biomass. This present work concerns with the modeling of reaction kinetics parameters for fast pyrolysis of empty fruit bunch in the fluidized bed reactor. A global kinetic model used to predict the product yields from fast pyrolysis of empty fruit bunch. The reaction temperature and vapor residence time parameters are mainly affected by product yields of EFB pyrolysis. The reaction temperature and vapor residence time parameters effects on empty fruit bunch pyrolysis are considered at the reaction temperature in the range of 450-500˚C and at a vapor residence time of 2 s, respectively. The optimum simulated bio-oil yield of 53 wt.% obtained at the reaction temperature and vapor residence time of 450˚C and 2 s, 500˚C and 1 s, respectively. The simulated data are in good agreement with the reported experimental data. These simulated data can be applied to the performance of experiment work for the fast pyrolysis of biomass.

Keywords: kinetics, empty fruit bunch, fast pyrolysis, modeling

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Authors: Sun Li-Ping, Zhu Jian-Xun, Liu Sheng-Nan

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In order to study the performance of dynamic positioning system during S-lay operations, dynamic positioning system is simulated with the hull-stinger-pipe coupling effect. The roller of stinger is simulated by the generalized elastic contact theory. The stinger is composed of Morrison members. Force on pipe is calculated by lumped mass method. Time domain of fully coupled barge model is analyzed combining with PID controller, Kalman filter and allocation of thrust using Sequential Quadratic Programming method. It is also analyzed that the effect of hull wave frequency motion on pipe-stinger coupling force and dynamic positioning system. Besides, it is studied that how S-lay operations affect the dynamic positioning accuracy. The simulation results are proved to be available by checking pipe stress with API criterion. The effect of heave and yaw motion cannot be ignored on hull-stinger-pipe coupling force and dynamic positioning system. It is important to decrease the barge’s pitch motion and lay pipe in head sea in order to improve safety of the S-lay installation and dynamic positioning.

Keywords: S-lay operation, dynamic positioning, coupling motion, time domain, allocation of thrust

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Authors: Daniel Olshansky, Ramiro Rodrıguez Colmeiro

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Relay Mining presents a scalable solution employing probabilistic mechanisms and crypto-economic incentives to estimate RPC volume usage, facilitating decentralized multitenant rate limiting. Network traffic from individual applications can be concurrently serviced by multiple RPC service providers, with costs, rewards, and rate limiting governed by a native cryptocurrency on a distributed ledger. Building upon established research in token bucket algorithms and distributed rate-limiting penalty models, our approach harnesses a feedback loop control mechanism to adjust the difficulty of mining relay rewards, dynamically scaling with network usage growth. By leveraging crypto-economic incentives, we reduce coordination overhead costs and introduce a mechanism for providing RPC services that are both geopolitically and geographically distributed.

Keywords: remote procedure call, crypto-economic, commit-reveal, decentralization, scalability, blockchain, rate limiting, token bucket

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Authors: P. Javid, A. Aeenmehr, J. Taghavifar

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In this paper, supply air pressure of HVAC system has been modeled with second-order transfer function plus dead-time. In HVAC system, the desired input has step changes, and the output of proposed control system should be able to follow the input reference, so the idea of using model based predictive control is proceeded and designed in this paper. The closed loop control system is implemented in MATLAB software and the simulation results are provided. The simulation results show that the model based predictive control is able to control the plant properly.

Keywords: air conditioning system, GPC, dead time, air supply control

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel

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Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.

Keywords: classification, data mining, spam filtering, naive bayes, decision tree

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Authors: Jalil Boudjadar

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Dynamic Voltage and Frequency Scaling (DVFS) multicore platforms are promising execution platforms that enable high computational performance, less energy consumption and flexibility in scheduling the system processes. However, the resulting interleaving and memory interference together with per-core frequency tuning make real-time guarantees hard to be delivered. Besides, energy consumption represents a strong constraint for the deployment of such systems on energy-limited settings. Identifying the system configurations that would achieve a high performance and consume less energy while guaranteeing the system schedulability is a complex task in the design of modern embedded systems. This work studies the trade-off between energy consumption, cores utilization and memory bottleneck and their impact on the schedulability of DVFS multicore time-critical systems with a hierarchy of shared memories. We build a model-based framework using Parametrized Timed Automata of UPPAAL to analyze the mutual impact of performance, energy consumption and schedulability of DVFS multicore systems, and demonstrate the trade-off on an actual case study.

Keywords: time-critical systems, multicore systems, schedulability analysis, energy consumption, performance analysis

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Authors: Devina Aprilia Gunawan, Tasya Aspiranti, Inugrah Ratia Pratiwi

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This research aims to classify the mining sector companies based on Altman’s Z-score model, and providing an analysis based on the Altman’s Z-score model’s financial ratios to provide a picture about the financial condition in mining sector companies in Indonesia and their viability in the future, and to find out the partial and simultaneous impact of each of the financial ratio variables in the Altman’s Z-score model, namely (WC/TA), (RE/TA), (EBIT/TA), (MVE/TL), and (S/TA), toward the financial condition represented by the Z-score itself. Among 38 mining sector companies listed in Indonesia Stock Exchange (IDX), 28 companies are selected as research sample according to the purposive sampling criteria.The results of this research showed that during 3 years research period at 2010-2012, the amount of the companies that was predicted to be healthy in each year was less than half of the total sample companies and not even reach up to 50%. The multiple regression analysis result showed that all of the research hypotheses are accepted, which means that (WC/TA), (RE/TA), (EBIT/TA), (MVE/TL), and (S/TA), both partially and simultaneously had an impact towards company’s financial condition.

Keywords: Altman’s Z-score model, financial condition, mining companies, Indonesia

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: opinion mining, opinion summarization, sentiment analysis, text mining

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Authors: Lawrence A. Farinola

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Four point implicit schemes for the approximation of the first and pure second order derivatives for the solution of the Dirichlet problem for one dimensional Schrodinger equation with respect to the time variable t were constructed. Also, special four-point implicit difference boundary value problems are proposed for the first and pure second derivatives of the solution with respect to the spatial variable x. The Grid method is also applied to the mixed second derivative of the solution of the Linear Schrodinger time-dependent equation. It is assumed that the initial function belongs to the Holder space C⁸⁺ᵃ, 0 < α < 1, the Schrodinger wave function given in the Schrodinger equation is from the Holder space Cₓ,ₜ⁶⁺ᵃ, ³⁺ᵃ/², the boundary functions are from C⁴⁺ᵃ, and between the initial and the boundary functions the conjugation conditions of orders q = 0,1,2,3,4 are satisfied. It is proven that the solution of the proposed difference schemes converges uniformly on the grids of the order O(h²+ k) where h is the step size in x and k is the step size in time. Numerical experiments are illustrated to support the analysis made.

Keywords: approximation of derivatives, finite difference method, Schrödinger equation, uniform error

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Authors: Hiroki Yoshikai, Shin’ichi Arakawa, Tetsuya Takine, Masayuki Murata

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API (Application Programming Interface) economy is expected to create new value by converting corporate services such as information processing and data provision into APIs and using these APIs to connect services. Understanding the dynamics of a market of API economy under the strategies of participants is crucial to fully maximize the values of the API economy. To capture the behavior of a market in which the number of participants changes over time, we present a time-evolving market model for a platform in which API providers who provide APIs to service providers participate in addition to service providers and consumers. Then, we use the market model to clarify the role API providers play in expanding market participants and forming ecosystems. The results show that the platform with API providers increased the number of market participants by 67% and decreased the cost to develop services by 25% compared to the platform without API providers. Furthermore, during the expansion phase of the market, it is found that the profits of participants are mostly the same when 70% of the revenue from consumers is distributed to service providers and API providers. It is also found that when the market is mature, the profits of the service provider and API provider will decrease significantly due to their competition, and the profit of the platform increases.

Keywords: API economy, ecosystem, platform, API providers

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Authors: Neeta Kumari, Gopal Pathak

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Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

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Authors: Syed Jawad Hussain Shahzad, Muhammad Zakaria, Mobeen Ur Rehman, Saniya Khaild

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This study examines the co-movement between the Pakistan, Indian, S&P 500 and Nikkei 225 stock markets using weekly data from 1998 to 2013. The time-frequency relationship between the selected stock markets is conducted by using measures of continuous wavelet power spectrum, cross-wavelet transform and cross (squared) wavelet coherency. The empirical evidence suggests strong dependence between Pakistan and Indian stock markets. The co-movement of Pakistani index with U.S and Japanese, the developed markets, varies over time and frequency where the long-run relationship is dominant. The results of cross wavelet and wavelet coherence analysis indicate moderate covariance and correlation between stock indexes and the markets are in phase (i.e. cyclical in nature) over varying durations. Pakistan stock market was lagging during the entire period in relation to Indian stock market, corresponding to the 8~32 and then 64~256 weeks scale. Similar findings are evident for S&P 500 and Nikkei 225 indexes, however, the relationship occurs during the later period of study. All three wavelet indicators suggest strong evidence of higher co-movement during 2008-09 global financial crises. The empirical analysis reveals a strong evidence that the portfolio diversification benefits vary across frequencies and time. This analysis is unique and have several practical implications for regional and international investors while assigning the optimal weightage of different assets in portfolio formulation.

Keywords: co-movement, Pakistan stock exchange, S&P 500, Nikkei 225, wavelet analysis

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Authors: Rashmi Tamhankar, Madhav Bapat

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In many projects, we have to use human judgment for determining the duration of the activities which may vary from person to person. Hence, there is vagueness about the time duration for activities in network planning. Fuzzy sets can handle such vague or imprecise concepts and has an application to such network. The vague activity times can be represented by triangular fuzzy numbers. In this paper, a general network with fuzzy activity times is considered and conditions for the critical path are obtained also we compute total float time of each activity. Several numerical examples are discussed.

Keywords: PERT, CPM, triangular fuzzy numbers, fuzzy activity times

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Authors: Kuniaki Kudo, Raviraj Solanki, Kaushal Patel, Yash Virani

Abstract:

This research mainly concerns optimizing UI test automation for large-scale web applications. The test target application is the HHAexchange homecare management WEB application that seamlessly connects providers, state Medicaid programs, managed care organizations (MCOs), and caregivers through one platform with large-scale functionalities. This study focuses on user interface automation testing for the WEB application. The quality assurance team must execute many manual users interface test cases in the development process to confirm no regression bugs. The team automated 346 test cases; the UI automation test execution time was over 17 hours. The business requirement was reducing the execution time to release high-quality products quickly, and the quality assurance automation team modernized the test automation framework to optimize the execution time. The base of the WEB UI automation test environment is Selenium, and the test code is written in Python. Adopting a compilation language to write test code leads to an inefficient flow when introducing scalability into a traditional test automation environment. In order to efficiently introduce scalability into Test Automation, a scripting language was adopted. The scalability implementation is mainly implemented with AWS's serverless technology, an elastic container service. The definition of scalability here is the ability to automatically set up computers to test automation and increase or decrease the number of computers running those tests. This means the scalable mechanism can help test cases run parallelly. Then test execution time is dramatically decreased. Also, introducing scalable test automation is for more than just reducing test execution time. There is a possibility that some challenging bugs are detected by introducing scalable test automation, such as race conditions, Etc. since test cases can be executed at same timing. If API and Unit tests are implemented, the test strategies can be adopted more efficiently for this scalability testing. However, in WEB applications, as a practical matter, API and Unit testing cannot cover 100% functional testing since they do not reach front-end codes. This study applied a scalable UI automation testing strategy to the large-scale homecare management system. It confirmed the optimization of the test case execution time and the detection of a challenging bug. This study first describes the detailed architecture of the scalable test automation environment, then describes the actual performance reduction time and an example of challenging issue detection.

Keywords: aws, elastic container service, scalability, serverless, ui automation test

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