Search results for: time consuming

Authors: Himanshu Sharma, Karthik Kumar, Harish Kumar

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The major problem in crop cultivation is the occurrence of multiple crop diseases. During the growth stage, timely identification of crop diseases is paramount to ensure the high yield of crops, lower production costs, and minimize pesticide usage. In most cases, crop diseases produce observable characteristics and symptoms. The Surveyors usually diagnose crop diseases when they walk through the fields. However, surveyor inspections tend to be biased and error-prone due to the nature of the monotonous task and the subjectivity of individuals. In addition, visual inspection of each leaf or plant is costly, time-consuming, and labour-intensive. Furthermore, the plant pathologists and experts who can often identify the disease within the plant according to their symptoms in early stages are not readily available in remote regions. Therefore, this study specifically addressed early detection of leaf scald, red rot, and eyespot types of diseases within sugarcane plants. The study proposes a computer vision-based approach using a convolutional neural network (CNN) for automatic identification of crop diseases. To facilitate this, firstly, images of sugarcane diseases were taken from google without modifying the scene, background, or controlling the illumination to build the training dataset. Then, the testing dataset was developed based on the real-time collected images from the sugarcane field from India. Then, the image dataset is pre-processed for feature extraction and selection. Finally, the CNN-based Visual Geometry Group (VGG) model was deployed on the training and testing dataset to classify the images into diseased and healthy sugarcane plants and measure the model's performance using various parameters, i.e., accuracy, sensitivity, specificity, and F1-score. The promising result of the proposed model lays the groundwork for the automatic early detection of sugarcane disease. The proposed research directly sustains an increase in crop yield.

Keywords: automatic classification, computer vision, convolutional neural network, image processing, sugarcane disease, visual geometry group

Procedia PDF Downloads 97

Authors: M. Khouil, N. Saber, M. Mestari

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In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the collision between a point and a polyhedron and then the collision between two convex polyhedra. The aim of this research is to determine through the AMAXNET network a mini maximum point in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: collision identification, fixed time, convex polyhedra, neural network, AMAXNET

Procedia PDF Downloads 400

Authors: Siti Sofo Ismail, Siti Noraina Wahida Mohd Alwi, Mohamad Hafiz Ameran, Masrudin M. Yusoff

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Locating clandestine cadaver is crucially important in forensic investigations. However, it requires a lot of man power, costly and time consuming. Therefore, the development of a new method to locate the clandestine graves is urgently needed as the cases involve burial of cadaver in different types of soils under tropical climates are still not well explored. This study focused on the burial in mangrove and oil palm plantation soils, comparing the fatty acid distributions in different soil acidities. A stimulated burial experiment was conducted using domestic pig (Sus scrofa) to substitute human tissues. Approximately 20g of pig fatty flesh was allowed to decompose in mangrove and oil palm plantation soils, mimicking burial in a shallow grave. The associated soils were collected at different designated sampling points, corresponding different decomposition stages. Modified Bligh-Dyer Extraction method was applied to extract the soil free fatty acids. Then, the obtained free fatty acids were analyzed with gas chromatography-flame ionization (GC-FID). A similar fatty acid distribution was observed for both mangrove and oil palm plantations soils. Palmitic acid (C₁₆) was the most abundance of free fatty acid, followed by stearic acid (C₁₈). However, the concentration of palmitic acid (C₁₆) higher in oil palm plantation compare to mangrove soils. Conclusion, the decomposition rate of cadaver can be affected by different type of soils.

Keywords: clandestine grave, burial, soils, free fatty acid

Procedia PDF Downloads 375

Authors: U. Chattaraj, K. Dhusiya, M. Raviteja

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Perception reaction time of drivers is an outcome of human thought process, which is vague and approximate in nature and also varies from driver to driver. So, in this study a fuzzy logic based model for prediction of the same has been presented, which seems suitable. The control factors, like, age, experience, intensity of driving of the driver, speed of the vehicle and distance of stimulus have been considered as premise variables in the model, in which the perception reaction time is the consequence variable. Results show that the model is able to explain the impacts of the control factors on perception reaction time properly.

Keywords: driver, fuzzy logic, perception reaction time, premise variable

Procedia PDF Downloads 277

Authors: T. Pedersen, A. Lindfeldt

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The minimum dwell time is an important part of railway timetable planning. Due to its stochastic behaviour, the minimum dwell time should be considered to create resilient timetables. While there has been significant focus on how to determine and estimate dwell times, to our knowledge, little research has been carried out regarding temporal and running direction variations of these. In this paper, we examine how the minimum dwell time varies depending on temporal factors such as the time of day, day of the week and time of the year. We also examine how it is affected by running direction and station type. The minimum dwell time is estimated by means of track occupation data. A method is proposed to ensure that only minimum dwell times and not planned dwell times are acquired from the track occupation data. The results show that on an aggregated level, the average minimum dwell times in both running directions at a station are similar. However, when temporal factors are considered, there are significant variations. The minimum dwell time varies throughout the day with peak hours having the longest dwell times. It is also found that the minimum dwell times are influenced by weekday, and in particular, weekends are found to have lower minimum dwell times than most other days. The findings show that there is a potential to significantly improve timetable planning by taking minimum dwell time variations into account.

Keywords: minimum dwell time, operations quality, timetable planning, track occupation data

Procedia PDF Downloads 180

Authors: Chih-Chiang Hua, Yi-Hsiung Fang, Yuan-Jhen Siao

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A bridgeless boost (BLB) power factor correction (PFC) rectifier with hold-up time extension circuit is proposed in this paper. A full bridge rectifier is widely used in the front end of the ac/dc converter. Since the shortcomings of the full bridge rectifier, the bridgeless rectifier is developed. A BLB rectifier topology is utilized with the hold-up time extension circuit. Unlike the traditional hold-up time extension circuit, the proposed extension scheme uses fewer active switches to achieve a longer hold-up time. Simulation results are presented to verify the converter performance.

Keywords: bridgeless boost (BLB), boost converter, power factor correction (PFC), hold-up time

Procedia PDF Downloads 394

Authors: Saeed Sayyad Hagh Shomar

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Modeling users’ decisions on departure time choice is the main motivation for this research. In particular, it examines the impact of social-demographic features, household, job characteristics and trip qualities on individuals’ departure time choice. Departure time alternatives are presented as adjacent discrete time periods. The choice between these alternatives is done using a discrete choice model. Since a great deal of early morning trips and traffic congestion at that time of the day comprise work trips, the focus of this study is on the work trip over the entire day. Therefore, this study by using the users’ stated preference in questionnaire models users’ departure time choice affected by congestion pricing schemes in high traffic suburban entrance roads of Tehran. The results demonstrate efficient social-demographic impact on work trips’ departure time. These findings have substantial outcomes for the analysis of transportation planning. Particularly, the analysis shows that ignoring the effects of these variables could result in erroneous information and consequently decisions in the field of transportation planning and air quality would fail and cause financial resources loss.

Keywords: congestion pricing, departure time, modeling, travel timing, time of the day, transportation planning

Procedia PDF Downloads 278

Authors: E. K. Mendez, N. A. Salazar, C. E. Orrego

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There is undoubted proof that increasing the intake of fruit lessens the risk of hypertension, coronary heart disease, stroke, and probable evidence that lowers the risk of cancer. Proper fruit drying is an excellent alternative to make their shelf-life longer, commercialization easier, and ready-to-eat healthy products or ingredients. The conventional way of drying is by hot air forced convection. However, this process step often requires a very long residence time; furthermore, it is highly energy consuming and detrimental to the product quality. Nowadays, power ultrasound (US) technic has been considered as an emerging and promising technology for industrial food processing. Most of published works dealing with drying food assisted by US have studied the effect of ultrasonic pre-treatment prior to air-drying on food and the airborne US conditions during dehydration. In this work a new approach was tested taking in to account drying time and two quality parameters of mango slices dehydrated by convection assisted by 20 KHz power US applied directly using a holed plate as product support and sound transmitting surface. During the drying of mango (Mangifera indica L.) slices (ca. 6.5 g, 0.006 m height and 0.040 m diameter), their weight was recorded every hour until final moisture content (10.0±1.0 % wet basis) was reached. After previous tests, optimization of three drying parameters - frequencies (2, 5 and 8 minutes each half-hour), air temperature (50-55-60⁰C) and power (45-70-95W)- was attempted by using a Box–Behnken design under the response surface methodology for the optimal drying time, color parameters and rehydration rate of dried samples. Assays involved 17 experiments, including a quintuplicate of the central point. Dried samples with and without US application were packed in individual high barrier plastic bags under vacuum, and then stored in the dark at 8⁰C until their analysis. All drying assays and sample analysis were performed in triplicate. US drying experimental data were fitted with nine models, among which the Verna model resulted in the best fit with R2 > 0.9999 and reduced χ2 ≤ 0.000001. Significant reductions in drying time were observed for the assays that used lower frequency and high US power. At 55⁰C, 95 watts and 2 min/30 min of sonication, 10% moisture content was reached in 211 min, as compared with 320 min for the same test without the use of US (blank). Rehydration rates (RR), defined as the ratio of rehydrated sample weight to that of dry sample and measured, was also larger than those of blanks and, in general, the higher the US power, the greater the RR. The direct contact and intermittent US treatment of mango slices used in this work improve drying rates and dried fruit rehydration ability. This technique can thus be used to reduce energy processing costs and the greenhouse gas emissions of fruit dehydration.

Keywords: ultrasonic assisted drying, fruit drying, mango slices, contact ultrasonic drying

Procedia PDF Downloads 325

Authors: Sogand Barghi

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The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

Procedia PDF Downloads 47

Authors: Kh. Alaa, Sh. Sarah, J. Khowlah, S. Liyakathunsia

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Education has become very important nowadays and with the rapidly increasing number of student, taking the attendance manually is getting very difficult and time wasting. In order to solve this problem, an automated solution is required. An effective automated system can be implemented to manage student attendance in different ways. This research will discuss a unique class attendance system which integrates both Face Recognition and RFID technique. This system focuses on reducing the time spent on submitting of the lecture and the wastage of time on submitting and getting approval for the absence excuse and sick leaves. As a result, the suggested solution will enhance not only the time, also it will also be helpful in eliminating fake attendance.

Keywords: attendance system, face recognition, RFID, process model, cost, time

Procedia PDF Downloads 339

Authors: Harri Jalonen

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Social media has changed the ways we communicate, collaborate and connect with each other. It has also influenced our habits of consuming sports. Social media has allowed direct interaction between sponsoring companies, athletes/players and fans. Drawing on the service dominant logic of value co-creation, the conceptual paper identifies three operant resources which are beneficial for value co-creation: i) social identity and sense of community, ii) congruence and brand personality, and iii) participatory culture and fan activation. The paper contributes to the theoretical discussion on how social can be media used for value co-creation purposes in the sports industry.

Keywords: sports, value co-creation, social media, service

Procedia PDF Downloads 257

Authors: Lata Kiran Dey, Rajendra Kumar, Biren Karmakar

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Web Real Time Communications is a collection of standards, protocols, which provides real-time communications capabilities between web browsers and devices. This paper outlines the design and further implementation of web real-time communications on secure web applications having audio and video call capabilities. This proposed application may put up a system that will be able to work over both desktops as well as the mobile browser. Though, WebRTC also gives a set of JavaScript standard RTC APIs, which primarily works over the real-time communication framework. This helps to build a suitable communication application, which enables the audio, video, and message transfer in between the today’s modern browsers having WebRTC support.

Keywords: WebRTC, SIP, RTC, JavaScript, SRTP, secure web sockets, browser

Procedia PDF Downloads 122

Authors: Sania K. Rao, Feza T. Azmi

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The purpose of the present study is to explore the relationship between time management behaviour and job satisfaction of academicians of higher education institutes in India. The analyses of this study were carried out with AMOS (version 20.0); and Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) were conducted. The factor analysis and findings show that perceived control of time serves as the partial mediating factor to have a significant and positive influence on job satisfaction. Further, at the end, a number of suggestions to improve one’s time management behaviour were provided.

Keywords: time management behaviour, job satisfaction, higher education, India, mediation analysis

Procedia PDF Downloads 369

Authors: Mohamed Hassan Abdullahi

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Decomposition is used as a synthesis tool in several physical systems. It can also be used for tearing and restructuring, which is large-scale system analysis. On the other hand, the commutativity of series-connected systems has fascinated the interest of researchers, and its advantages have been emphasized in the literature. The presentation looks into the necessary conditions for decomposing any third-order discrete-time linear time-varying system into a commutative pair of first- and second-order systems. Additional requirements are derived in the case of nonzero initial conditions. MATLAB simulations are used to verify the findings. The work is unique and is being published for the first time. It is critical from the standpoints of synthesis and/or design. Because many design techniques in engineering systems rely on tearing and reconstruction, this is the process of putting together simple components to create a finished product. Furthermore, it is demonstrated that regarding sensitivity to initial conditions, some combinations may be better than others. The results of this work can be extended for the decomposition of fourth-order discrete-time linear time-varying systems into lower-order commutative pairs, as two second-order commutative subsystems or one first-order and one third-order commutative subsystems.

Keywords: commutativity, decomposition, discrete time-varying systems, systems

Procedia PDF Downloads 87

Authors: Jing Zhang, Jie Chen

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In this paper, we deal with the optimal I/O point location in an automated parking system. In this system, the S/R machine (storage and retrieve machine) travels independently in vertical and horizontal directions. Based on the characteristics of the parking system and the basic principle of AS/RS system (Automated Storage and Retrieval System), we obtain the continuous model in units of time. For the single command cycle using the randomized storage policy, we calculate the probability density function for the system travel time and thus we develop the travel time model. And we confirm that the travel time model shows a good performance by comparing with discrete case. Finally in this part, we establish the optimal model by minimizing the expected travel time model and it is shown that the optimal location of the I/O point is located at the middle of the left-hand above corner.

Keywords: parking system, optimal location, response time, S/R machine

Procedia PDF Downloads 391

Authors: Thampi Rawther, Jack Cecire, Andrew Sutherland

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Introduction: CT facilitates the diagnosis of potentially life-threatening injuries and facilitates early management. There is evidence that reduced CT acquisition time reduces mortality and length of hospital stay. Currently, there are variable recommendations for ideal timing. Indeed, the NHS standard contract for a major trauma service and STAG both recommend immediate access to CT within a maximum time of 60min and appropriate reporting within 60min of the scan. At Coffs Harbour Health Campus (CHHC), a CT radiographer is on site between 8am-11pm. Aim: To investigate the average time to CT at CHHC and assess for any significant relationship between time to CT and injury severity score (ISS) or time of triage. Method: All major trauma calls between Jan 2021-Oct 2021 were audited (N=87). Patients were excluded if they went from ED to the theatre. Time to CT is defined as the time between triage to the timestamp on the first CT image. Median and interquartile range was used as a measure of central tendency as the data was not normally distributed, and Chi-square test was used to determine association. Results: The median time to CT is 51.5min (IQR 40-74). We found no relationship between time to CT and ISS (P=0.18) and time of triage to time to CT (P=0.35). We compared this to other centres such as John Hunter Hospital and Gold Coast Hospital. We found that the median CT acquisition times were 76min (IQR 52-115) and 43min, respectively. Conclusion: This shows an avenue for improvement given 35% of CT’s were >30min. Furthermore, being proactive and aware of time to CT as an important factor to trauma management can be another avenue for improvement. Based on this, we will re-audit in 12-24months to assess if any improvement has been made.

Keywords: imaging, rural surgery, trauma surgery, improvement

Procedia PDF Downloads 90

Authors: Kamaljit Pati, Manas Kumar Mohanty, Sanjib Sadhu

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A heuristic has been designed to generate a random simple monotone polygon from a given set of ‘n’ points lying on a 2-Dimensional plane. Our heuristic generates a random monotone polygon in O(n) time after O(nℓogn) preprocessing time which is improved over the previous work where a random monotone polygon is produced in the same O(n) time but the preprocessing time is O(k) for n < k < n2. However, our heuristic does not generate all possible random polygons with uniform probability. The space complexity of our proposed heuristic is O(n).

Keywords: sorting, monotone polygon, visibility, chain

Procedia PDF Downloads 406

Authors: Intan Maisarah Abd Rahim, Herlina Abdul Rahim, Rashidah Ghazali

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Diabetes is a medical condition that can lead to various diseases such as stroke, heart disease, blindness and obesity. In clinical practice, the concern of the diabetic patients towards the blood glucose examination is rather alarming as some of the individual describing it as something painful with pinprick and pinch. As for some patient with high level of glucose level, pricking the fingers multiple times a day with the conventional glucose meter for close monitoring can be tiresome, time consuming and painful. With these concerns, several non-invasive techniques were used by researchers in measuring the glucose level in blood, including ultrasonic sensor implementation, multisensory systems, absorbance of transmittance, bio-impedance, voltage intensity, and thermography. This paper is discussing the application of the near-infrared (NIR) spectroscopy as a non-invasive method in measuring the glucose level and the implementation of the linear system identification model in predicting the output data for the NIR measurement. In this study, the wavelengths considered are at the 1450 nm and 1950 nm. Both of these wavelengths showed the most reliable information on the glucose presence in blood. Then, the linear Autoregressive Moving Average Exogenous model (ARMAX) model with both un-regularized and regularized methods was implemented in predicting the output result for the NIR measurement in order to investigate the practicality of the linear system in this study. However, the result showed only 50.11% accuracy obtained from the system which is far from the satisfying results that should be obtained.

Keywords: diabetes, glucose level, linear, near-infrared, non-invasive, prediction system

Procedia PDF Downloads 136

Authors: Salem Alsanusi, Loubna Bentaher

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Response Surface Methods (RSM) provide statistically validated predictive models that can then be manipulated for finding optimal process configurations. Variation transmitted to responses from poorly controlled process factors can be accounted for by the mathematical technique of propagation of error (POE), which facilitates ‘finding the flats’ on the surfaces generated by RSM. The dual response approach to RSM captures the standard deviation of the output as well as the average. It accounts for unknown sources of variation. Dual response plus propagation of error (POE) provides a more useful model of overall response variation. In our case, we implemented this technique in predicting compressive strength of concrete of 28 days in age. Since 28 days is quite time consuming, while it is important to ensure the quality control process. This paper investigates the potential of using design of experiments (DOE-RSM) to predict the compressive strength of concrete at 28th day. Data used for this study was carried out from experiment schemes at university of Benghazi, civil engineering department. A total of 114 sets of data were implemented. ACI mix design method was utilized for the mix design. No admixtures were used, only the main concrete mix constituents such as cement, coarse-aggregate, fine aggregate and water were utilized in all mixes. Different mix proportions of the ingredients and different water cement ratio were used. The proposed mathematical models are capable of predicting the required concrete compressive strength of concrete from early ages.

Keywords: mix proportioning, response surface methodology, compressive strength, optimal design

Procedia PDF Downloads 246

Authors: Airy Huang, Fuji Foo, Aries Prasetya Wibowo

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In recent years, the focus of Natural Language Processing (NLP) development has been gradually shifting from the semantics-based approach to deep learning one, which performs faster with fewer resources. Although it performs well in many applications, the deep learning approach, due to the lack of semantics understanding, has difficulties in noticing and expressing a novel business case with a pre-defined scope. In order to meet the requirements of specific robotic services, deep learning approach is very labor-intensive and time consuming. It is very difficult to improve the capabilities of conversational AI in a short time, and it is even more difficult to self-learn from experiences to deliver the same service in a better way. In this paper, we present an adaptive conversational AI algorithm that combines both semantic knowledge and deep learning to address this issue by learning new business cases through conversations. After self-learning from experience, the robot adapts to the business cases originally out of scope. The idea is to build new or extended robotic services in a systematic and fast-training manner with self-configured programs and constructed dialog flows. For every cycle in which a chat bot (conversational AI) delivers a given set of business cases, it is trapped to self-measure its performance and rethink every unknown dialog flows to improve the service by retraining with those new business cases. If the training process reaches a bottleneck and incurs some difficulties, human personnel will be informed of further instructions. He or she may retrain the chat bot with newly configured programs, or new dialog flows for new services. One approach employs semantics analysis to learn the dialogues for new business cases and then establish the necessary ontology for the new service. With the newly learned programs, it completes the understanding of the reaction behavior and finally uses dialog flows to connect all the understanding results and programs, achieving the goal of self-learning process. We have developed a chat bot service mounted on a kiosk, with a camera for facial recognition and a directional microphone array for voice capture. The chat bot serves as a concierge with polite conversation for visitors. As a proof of concept. We have demonstrated to complete 90% of reception services with limited self-learning capability.

Keywords: conversational AI, chatbot, dialog management, semantic analysis

Procedia PDF Downloads 117

Authors: H. Namazi, H. T. N. Kuan

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Fick's first law relates the diffusive flux to the concentration field, by postulating that the flux goes from regions of high concentration to regions of low concentration, with a magnitude that is proportional to the concentration gradient (spatial derivative). It is clear that the diffusion of flux cannot be instantaneous and should be some time delay in this propagation. But Fick’s first law doesn’t consider this delay which results in some errors especially when there is a considerable time delay in the process. In this paper, we introduce a time delay to Fick’s first law. By this modification, we consider that the diffusion of flux cannot be instantaneous. In order to verify this claim an application sample in fluid diffusion is discussed and the results of modified Fick’s first law, Fick’s first law and the experimental results are compared. The results of this comparison stand for the accuracy of the modified model. The modified model can be used in any application where the time delay has considerable value and neglecting its effect reflects in undesirable results.

Keywords: Fick's first law, flux, diffusion, time delay, modified Fick’s first law

Procedia PDF Downloads 380

Authors: Mahawish Masud, Muhammad Iqbal, M. U. Khan, Farooque Azam

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Manual writing of test cases from functional requirements is a time-consuming task. Such test cases are not only difficult to write but are also challenging to maintain. Test cases can be drawn from the functional requirements that are expressed in natural language. However, manual test case generation is inefficient and subject to errors.  In this paper, we have presented a systematic procedure that could automatically derive test cases from user stories. The user stories are specified in a restricted natural language using a well-defined template.  We have also presented a detailed methodology for writing our test ready user stories. Our tool “Test-o-Matic” automatically generates the test cases by processing the restricted user stories. The generated test cases are executed by using open source Selenium IDE.  We evaluate our approach on a case study, which is an open source web based application. Effectiveness of our approach is evaluated by seeding faults in the open source case study using known mutation operators.  Results show that the test case generation from restricted user stories is a viable approach for automated testing of web applications.

Keywords: automated testing, natural language, restricted user story modeling, software engineering, software testing, test case specification, transformation and automation, user story, web application testing

Procedia PDF Downloads 365

Authors: Arielle Ferdinand

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A plant-based diet focuses on foods from plant sources, limiting or altogether omitting animal products. Some of the most common chronic illnesses seen in primary care are hypertension, hyperlipidemia, and diabetes type II. These common chronic illnesses can often be debilitating, costly, time-consuming, and, when left untreated, can lead to an early death. Treatment and maintenance of care are also labor intensive for the patient. They are often required to have at least four blood pressure checks yearly and a hemoglobin A1C checked quarterly. Though preventative interventions and prevention education should be included in patient visits in the primary care setting, education about dietary interventions, such as a plant-based diet, also yields positive outcomes for patients who already have hypertension, hyperlipidemia, and diabetes mellitus type 2. Evidence will show that incorporating a plant-based diet results in decreased blood pressure, as well as decreased levels of LDL-C, improved post-prandial glucose levels, and a reduction in HbA1C. It is cost-effective for the patient by generally lower grocery costs, and it can either reduce or prevent the need to pay for more office visits and pharmacotherapy. Incorporating this method of dietary changes is an easy intervention during a primary care office visit that would greatly benefit the patient in many ways.

Keywords: plant-based, nutrition, diabetes, hyperlipidemia

Procedia PDF Downloads 68

Authors: Q. Toufeeq, R. Kauser, U. R. Jamil, N. Sohaib

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Foundation design of a structure needs soil investigation to avoid failures due to settlements. This soil investigation is expensive and time-consuming. Developments of new residential societies involve huge leveling of large sites that is accompanied by heavy land filling. Poor practices of land fill for deep depths cause differential settlements and consolidations of underneath soil that sometimes result in the collapse of structures. The extent of filling remains unknown to the individual developer unless soil investigation is carried out. Soil investigation cannot be performed on each available site due to involved costs. However, fair estimate of bearing capacity can be made if such tests are already done in the surrounding areas. The geotechnical guidance maps can provide a fair assessment of soil properties. Previously, GIS-based approaches have been used to develop maps using extrapolation and interpolations techniques for bearing capacities, underground recharge, soil classification, geological hazards, landslide hazards, socio-economic, and soil liquefaction mapping. Standard penetration test (SPT) data of surrounding sites were already available. Google Earth is used for digitization of collected data. Few points were considered for data calibration and validation. Resultant Geographic information system (GIS)-based guidance maps are helpful to anticipate the bearing capacity in the real estate industry.

Keywords: bearing capacity, soil classification, geographical information system, inverse distance weighted, radial basis function

Procedia PDF Downloads 114

Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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The assessment of disease levels in crop fields is an important and time-consuming task that generally relies on expert knowledge of trained individuals. Image classification in agriculture problems historically has been based on classical machine learning strategies that make use of hand-engineered features in the top of a classification algorithm. This approach tends to not produce results with high accuracy and generalization to the classes classified by the system when the nature of the elements has a significant variability. The advent of deep convolutional neural networks has revolutionized the field of machine learning, especially in computer vision tasks. These networks have great resourcefulness of learning and have been applied successfully to image classification and object detection tasks in the last years. The objective of this work was to propose a new method based on deep learning convolutional neural networks towards the task of disease level monitoring. Common RGB images of winter wheat were obtained during a growing season. Five categories of disease levels presence were produced, in collaboration with agronomists, for the algorithm classification. Disease level tasks performed by experts provided ground truth data for the disease score of the same winter wheat plots were RGB images were acquired. The system had an overall accuracy of 84% on the discrimination of the disease level classes.

Keywords: crop disease assessment, deep learning, precision agriculture, residual neural networks

Procedia PDF Downloads 304

Authors: Zhenjie Liu

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This paper investigates the existence and uniqueness of solutions for singular higher order boundary value problems on time scales by using mixed monotone method. The theorems obtained are very general. For the different time scale, the problem may be the corresponding continuous or discrete boundary value problem.

Keywords: mixed monotone operator, boundary value problem, time scale, green's function, positive solution, singularity

Procedia PDF Downloads 235

Authors: Yang Jian-Hong, Zhang Ren-Cheng, Huang Li

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Arc fault is one of the main inducements of electric fires. Arc Fault Detection Device (AFDD) can detect arc fault effectively. Arc fault detections and unhooking standards are the keys to AFDD practical application. First, an arc fault continuous production system was developed, which could count the arc half wave number. Then, Combining with the UL1699 standard, ignition probability curve of cotton and unhooking time of various currents intensity were obtained by experiments. The combustion degree of arc fault could be expressed effectively by arc area. Experiments proved that electric fires would be misjudged or missed only using arc half wave number as AFDD unhooking basis. At last, Practical tests were carried out on the self-developed AFDD system. The result showed that actual AFDD unhooking time was the sum of arc half wave cycling number, Arc wave identification time and unhooking mechanical operation time And the first two shared shorter time. Unhooking time standard depended on the shortest mechanical operation time.

Keywords: arc fault detection device, arc area, arc half wave, unhooking time, arc fault

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Authors: Ghofrane Rehaiem, Hamza Gharsellaoui, Samir Benahmed

Abstract:

In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-time scheduling of embedded systems under resources constraints designed for real-time applications running. The objective of this work is to implement a neural networks based approach for real-time scheduling of embedded systems in order to handle real-time constraints in execution scenarios. In our proposed approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption. In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage. Indeed, we present in this paper a hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm. Experimental results illustrate the efficiency of our original proposed approach.

Keywords: optimization, neural networks, real-time scheduling, low-power consumption

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Authors: Habib Alehossein, M. S. K. Fernando

Abstract:

Soil density depends on the volume of the voids and the proportion of the water and air in the voids. However, there is a limit to the contraction of the voids at any given compaction energy, whereby additional water is used to reduce the void volume further by lubricating the particles' frictional contacts. Hence, at an optimum moisture content and specific compaction energy, the density of unsaturated soil can be maximized where the void volume is minimum. However, when considering a full compaction curve and permutations and variations of all these components (soil, air, water, and energy), laboratory soil compaction tests can become expensive, time-consuming, and exhausting. Therefore, analytical methods constructed on a few test data can be developed and used to reduce such unnecessary efforts significantly. Concentrating on the compaction testing results, this study discusses the analytical modelling method developed for some fine-grained and coarse-grained soils of Queensland. Soil properties and characteristics, such as full functional compaction curves under various compaction energy conditions, were studied and developed for a few soil types. Using MATLAB, several generic analytical codes were created for this study, covering all possible compaction parameters and results as they occur in a soil mechanics lab. These MATLAB codes produce a family of curves to determine the relationships between the density, moisture content, void ratio, saturation, and compaction energy.

Keywords: analytical, MATLAB, modelling, compaction curve, void ratio, saturation, moisture content

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Authors: Alica Höpken, Hergen Pargmann

Abstract:

The resource-efficient planning of the complex production planning processes in the meat industry and the reduction of food waste is a permanent challenge. The complexity of the production planning process occurs in every part of the supply chain, from agriculture to the end consumer. It arises from long and uncertain planning phases. Uncertainties such as stochastic yields, fluctuations in demand, and resource variability are part of this process. In the meat industry, waste mainly relates to incorrect storage, technical causes in production, or overproduction. The high amount of food waste along the complex supply chain in the meat industry could not be reduced by simple solutions until now. Therefore, resource-efficient production planning by conventional methods is currently only partially feasible. The realization of intelligent, automated production planning is basically possible through the application of machine learning algorithms, such as those of reinforcement learning. By applying the adapted design thinking method, machine learning methods (especially reinforcement learning algorithms) are used for the complex production planning process in the meat industry. This method represents a concretization to the application area. A resource-efficient production planning process is made available by adapting the design thinking method. In addition, the complex processes can be planned efficiently by using this method, since this standardized approach offers new possibilities in order to challenge the complexity and the high time consumption. It represents a tool to support the efficient production planning in the meat industry. This paper shows an elegant adaption of the design thinking method to apply the reinforcement learning method for a resource-efficient production planning process in the meat industry. Following, the steps that are necessary to introduce machine learning algorithms into the production planning of the food industry are determined. This is achieved based on a case study which is part of the research project ”REIF - Resource Efficient, Economic and Intelligent Food Chain” supported by the German Federal Ministry for Economic Affairs and Climate Action of Germany and the German Aerospace Center. Through this structured approach, significantly better planning results are achieved, which would be too complex or very time consuming using conventional methods.

Keywords: change management, design thinking method, machine learning, meat industry, reinforcement learning, resource-efficient production planning

Procedia PDF Downloads 108