Search results for: artificial intelligence (AI)

Authors: Lana Dalawr Jalal

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This paper addresses the problem of offline path planning for Unmanned Aerial Vehicles (UAVs) in complex three-dimensional environment with obstacles, which is modelled by 3D Cartesian grid system. Path planning for UAVs require the computational intelligence methods to move aerial vehicles along the flight path effectively to target while avoiding obstacles. In this paper Modified Particle Swarm Optimization (MPSO) algorithm is applied to generate the optimal collision free 3D flight path for UAV. The simulations results clearly demonstrate effectiveness of the proposed algorithm in guiding UAV to the final destination by providing optimal feasible path quickly and effectively.

Keywords: obstacle avoidance, particle swarm optimization, three-dimensional path planning unmanned aerial vehicles

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Authors: Navdeep Singh Randhawa, Shally Sharma

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Wireless Sensor Network is an autonomous technology emanating in the current scenario at a fast pace. This technology faces a number of defiance’s and energy management is one of them, which has a huge impact on the network lifetime. To sustain energy the different types of routing protocols have been flourished. The classical routing protocols are no more compatible to perform in complicated environments. Hence, in the field of routing the intelligent algorithms based on nature systems is a turning point in Wireless Sensor Network. These nature-based algorithms are quite efficient to handle the challenges of the WSN as they are capable of achieving local and global best optimization solutions for the complex environments. So, the main attention of this paper is to develop a routing algorithm based on some swarm intelligent technique to enhance the performance of Wireless Sensor Network.

Keywords: wireless sensor network, routing, swarm intelligence, MPRSO

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Authors: S. Tokuomi, K. Mori, Y. Tsuruzono

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A concrete-wall-climbing testing robot, has been developed. This robot adheres and climbs concrete walls using two sets of suction cups, as well as being able to rotate by the use of the alternating motion of the suction cups. The maximum climbing speed is about 60 cm/min. Each suction cup has a pressure sensor, which monitors the adhesion of each suction cup. The impact acoustic method is used in testing concrete walls. This robot has an impact acoustic device and four microphones for the acquisition of the impact sound. The effectiveness of the impact acoustic system was tested by applying it to an inspection of specimens with artificial circular void defects. A circular void defect with a diameter of 200 mm at a depth of 50 mm was able to be detected. The weight and the dimensions of the robot are about 17 kg and 1.0 m by 1.3 m, respectively. The upper limit of testing is about 10 m above the ground due to the length of the power cable.

Keywords: concrete wall, nondestructive testing, climbing robot, impact acoustic method

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Authors: Kolade Olawande Owoeye, George R. S. Weir

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The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification.

Keywords: extremist, web pages, classification, semantics, posit

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Authors: Meriem Mossaddek, El Mehdi Laadissi, El Mehdi Loualid, Chouaib Ennawaoui, Sohaib Bouzaid, Abdelowahed Hajjaji

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An electrochemical system is a subset of mechatronic systems that includes a wide variety of batteries and nickel-cadmium, lead-acid batteries, and lithium-ion. Those structures have several non-linear behaviors and uncertainties in their running range. This paper studies an effective technique for modeling Lithium-Ion (Li-Ion) batteries using a Nonlinear Auto-Regressive model with exogenous input (NARX). The Artificial Neural Network (ANN) is trained to employ the data collected from the battery testing process. The proposed model is implemented on a Li-Ion battery cell. Simulation of this model in MATLAB shows good accuracy of the proposed model.

Keywords: lithium-ion battery, neural network, energy storage, battery model, nonlinear models

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Authors: Michał Rogala, Jakub Gajewski

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As the automotive industry develops, passive safety is becoming an increasingly important aspect when designing motor vehicles. A commonly used solution is energy absorption by thin-walled construction. One such structure is a closed thin-walled profile fixed to the vehicle stringers. The article presents numerical tests of conical thin-walled profiles filled with aluminum foam. The columns were loaded axially with constant energy. On the basis of the results obtained, efficiency indicators were calculated. The efficiency of the foam filling was evaluated. Artificial neural networks were used for data analysis. The application of regression analysis was used as a tool to study the relationship between the quantities characteristic of the dynamic crush.

Keywords: aluminium foam, crashworthiness, neural networks, thin-walled structure

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Authors: Manon Frédout, Laëtitia Bucari, Mathias Aloui, Gaëtan Duhamel, Olivier Rovellotti, Javier Blanco

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Ecosystems must be resilient to ensure cleaner air, better water and soil quality, and thus healthier citizens. Technology can be an excellent tool to support urban ecosystem restoration projects, especially when based on Open Source and promoting Open Data. This is the goal of the ecoTeka application: one single digital tool for tree management which allows decision-makers to improve their urban forestry practices, enabling more responsible urban planning and climate change adaptation. EcoTeka provides city councils with three main functionalities tackling three of their challenges: easier biodiversity inventories, better green space management, and more efficient planning. To answer the cities’ need for reliable tree inventories, the application has been first built with open data coming from the websites OpenStreetMap and OpenTrees, but it will also include very soon the possibility of creating new data. To achieve this, a multi-source algorithm will be elaborated, based on existing artificial intelligence Deep Forest, integrating open-source satellite images, 3D representations from LiDAR, and street views from Mapillary. This data processing will permit identifying individual trees' position, height, crown diameter, and taxonomic genus. To support urban forestry management, ecoTeka offers a dashboard for monitoring the city’s tree inventory and trigger alerts to inform about upcoming due interventions. This tool was co-constructed with the green space departments of the French cities of Alès, Marseille, and Rouen. The third functionality of the application is a decision-making tool for urban planning, promoting biodiversity and landscape connectivity metrics to drive ecosystem restoration roadmap. Based on landscape graph theory, we are currently experimenting with new methodological approaches to scale down regional ecological connectivity principles to local biodiversity conservation and urban planning policies. This methodological framework will couple graph theoretic approach and biological data, mainly biodiversity occurrences (presence/absence) data available on both international (e.g., GBIF), national (e.g., Système d’Information Nature et Paysage) and local (e.g., Atlas de la Biodiversté Communale) biodiversity data sharing platforms in order to help reasoning new decisions for ecological networks conservation and restoration in urban areas. An experiment on this subject is currently ongoing with Montpellier Mediterranee Metropole. These projects and studies have shown that only 26% of tree inventory data is currently geo-localized in France - the rest is still being done on paper or Excel sheets. It seems that technology is not yet used enough to enrich the knowledge city councils have about biodiversity in their city and that existing biodiversity open data (e.g., occurrences, telemetry, or genetic data), species distribution models, landscape graph connectivity metrics are still underexploited to make rational decisions for landscape and urban planning projects. This is the goal of ecoTeka: to support easier inventories of urban biodiversity and better management of urban spaces through rational planning and decisions relying on open databases. Future studies and projects will focus on the development of tools for reducing the artificialization of soils, selecting plant species adapted to climate change, and highlighting the need for ecosystem and biodiversity services in cities.

Keywords: digital software, ecological design of urban landscapes, sustainable urban development, urban ecological corridor, urban forestry, urban planning

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Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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Authors: Pierre Tize Mha, Mohammad Jahazi, Amèvi Togne, Olivier Pantalé

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Large-size forged blocks made of medium carbon high-strength steels are extensively used in the automotive industry as dies for the production of bumpers and dashboards through the plastic injection process. The manufacturing process of the large blocks starts with ingot casting, followed by open die forging and a quench and temper heat treatment process to achieve the desired mechanical properties and numerical simulation is widely used nowadays to predict these properties before the experiment. But the temperature gradient inside the specimen remains challenging in the sense that the temperature before loading inside the material is not the same, but during the simulation, constant temperature is used to simulate the experiment because it is assumed that temperature is homogenized after some holding time. Therefore to be close to the experiment, real distribution of the temperature through the specimen is needed before the mechanical loading. Thus, We present here a robust algorithm that allows the calculation of the temperature gradient within the specimen, thus representing a real temperature distribution within the specimen before deformation. Indeed, most numerical simulations consider a uniform temperature gradient which is not really the case because the surface and core temperatures of the specimen are not identical. Another feature that influences the mechanical properties of the specimen is recrystallization which strongly depends on the deformation conditions and the type of deformation like Upsetting, Cogging...etc. Indeed, Upsetting and Cogging are the stages where the greatest deformations are observed, and a lot of microstructural phenomena can be observed, like recrystallization, which requires in-depth characterization. Complete dynamic recrystallization plays an important role in the final grain size during the process and therefore helps to increase the mechanical properties of the final product. Thus, the identification of the conditions for the initiation of dynamic recrystallization is still relevant. Also, the temperature distribution within the sample and strain rate influence the recrystallization initiation. So the development of a technique allowing to predict the initiation of this recrystallization remains challenging. In this perspective, we propose here, in addition to the algorithm allowing to get the temperature distribution before the loading stage, an analytical model leading to determine the initiation of this recrystallization. These two techniques are implemented into the Abaqus finite element software via the UAMP and VUHARD subroutines for comparison with a simulation where an isothermal temperature is imposed. The Artificial Neural Network (ANN) model to describe the plastic behavior of the material is also implemented via the VUHARD subroutine. From the simulation, the temperature distribution inside the material and recrystallization initiation is properly predicted and compared to the literature models.

Keywords: dynamic recrystallization, finite element modeling, artificial neural network, numerical implementation

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Authors: Cedric Leong, Parth Desai, Parth Patel

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The purpose of this project was to develop a neural network based on qualitative team data to predict alliance scores to determine winners of matches in the FIRST Robotics Competition (FRC). The game for the competition changes every year with different objectives and game objects, however the idea was to create a prediction system which can be reused year by year using some of the statistics that are constant through different games, making our system adaptable to future games as well. Aerial Assist is the FRC game for 2014, and is played in alliances of 3 teams going against one another, namely the Red and Blue alliances. This application takes any 6 teams paired into 2 alliances of 3 teams and generates the prediction for the final score between them.

Keywords: artifical neural network, prediction system, qualitative team data, FIRST Robotics Competition (FRC)

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Authors: Precious Ehiomogue, Ifechukwude Israel Ahuchaogu, Isiguzo Edwin Ahaneku

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Vermicompost is the product of the decomposition process using various species of worms, to create a mixture of decomposing vegetable or food waste, bedding materials, and vemicast. This process is called vermicomposting, while the rearing of worms for this purpose is called vermiculture. Several works have verified the adsorption of toxic metals using vermicompost but the application is still scarce for the retention of organic compounds. This research brings to knowledge the effectiveness of earthworm waste (vermicompost) for the remediation of crude oil contaminated soils. The remediation methods adopted in this study were two soil washing methods namely, batch and column process which represent laboratory and in-situ remediation. Characterization of the vermicompost and crude oil contaminated soil were performed before and after the soil washing using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD) and Atomic adsorption spectrometry (AAS). The optimization of washing parameters, using response surface methodology (RSM) based on Box-Behnken Design was performed on the response from the laboratory experimental results. This study also investigated the application of machine learning models [Artificial neural network (ANN), Adaptive neuro fuzzy inference system (ANFIS). ANN and ANFIS were evaluated using the coefficient of determination (R²) and mean square error (MSE)]. Removal efficiency obtained from the Box-Behnken design experiment ranged from 29% to 98.9% for batch process remediation. Optimization of the experimental factors carried out using numerical optimization techniques by applying desirability function method of the response surface methodology (RSM) produce the highest removal efficiency of 98.9% at absorbent dosage of 34.53 grams, adsorbate concentration of 69.11 (g/ml), contact time of 25.96 (min), and pH value of 7.71, respectively. Removal efficiency obtained from the multilevel general factorial design experiment ranged from 56% to 92% for column process remediation. The coefficient of determination (R²) for ANN was (0.9974) and (0.9852) for batch and column process, respectively, showing the agreement between experimental and predicted results. For batch and column precess, respectively, the coefficient of determination (R²) for RSM was (0.9712) and (0.9614), which also demonstrates agreement between experimental and projected findings. For the batch and column processes, the ANFIS coefficient of determination was (0.7115) and (0.9978), respectively. It can be concluded that machine learning models can predict the removal of crude oil from polluted soil using vermicompost. Therefore, it is recommended to use machines learning models to predict the removal of crude oil from contaminated soil using vermicompost.

Keywords: ANFIS, ANN, crude-oil, contaminated soil, remediation and vermicompost

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Authors: Chivon Choeung, Heng Tang, Panha Soth, Vichet Huy

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This paper presents a robust control strategy for a single-phase DC-AC inverter with an output LC-filter. An all-pass filter is utilized to create an artificial β-signal so that the proposed controller can be simply used in dq-synchronous frame. The proposed robust controller utilizes a state feedback control with integral action in the dq-synchronous frame. A linear matrix inequality-based optimization scheme is used to determine stabilizing gains of the controllers to maximize the convergence rate to steady state in the presence of uncertainties. The uncertainties of the system are described as the potential variation range of the inductance and resistance in the LC-filter.

Keywords: single-phase inverter, linear matrix inequality, robust control, all-pass filter

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Authors: S. S. Salehi, A. Shamloo

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Poor ability of cartilage tissue when experiencing a damage leads scientists to use tissue engineering as a reliable and effective method for regenerating or replacing damaged tissues. An artificial tissue should have some features such as biocompatibility, biodegradation and, enough mechanical properties like the original tissue. In this work, a composite hydrogel is prepared by using natural and synthetic materials that has high porosity. Mechanical properties of different combinations of polymers such as modulus of elasticity were tested, and a hydrogel with good mechanical properties was selected. Bone marrow derived mesenchymal stem cells were also seeded into the pores of the sponge, and the results showed the adhesion and proliferation of cells within the hydrogel after one month. In comparison with previous works, this study offers a new and efficient procedure for the fabrication of cartilage like tissue and further cartilage repair.

Keywords: cartilage tissue engineering, hydrogel, mechanical strength, mesenchymal stem cell

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Authors: A. Kabiri-Samani, M. J. Seddighi

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There is no doubt that a key factor in the manifestation of architecture is the interaction of human and nature. Explaining the type of relationship defined by “the architect” between architecture and nature opens a window towards understanding the theoretical conceptions of the architect as the creator of “architecture”. Now, if these theoretical foundations are put under scrutiny from the viewpoint of Islam, and an architect considers the relationship of human and nature within the context of Islam, he would let nature to manifest itself in architecture. The reasons for such a relationship is explicable in terms of the degree and nature of knowledge of the architect about nature; while the way it comes to existence is explained by defining the force of nature – ruling the entire nature – and its acts. It is by the scientific command of the architect and his mastery in the hermetic force of nature that the material bodies of buildings evolve from artificial to natural. Additionally, the presence of nature creates hermetic architectural spaces for the spiritual development of humans while serving for living at different levels.

Keywords: nature, Islam, cognition, science, presence, elixir

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: deregulated energy market, forecasting, machine learning, system marginal price

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Authors: L. Parisi

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Kinematic data wisely correlate vector quantities in space to scalar parameters in time to assess the degree of symmetry between the intact limb and the amputated limb with respect to a normal model derived from the gait of control group participants. Furthermore, these particular data allow a doctor to preliminarily evaluate the usefulness of a certain rehabilitation therapy. Kinetic curves allow the analysis of ground reaction forces (GRFs) to assess the appropriateness of human motion. Electromyography (EMG) allows the analysis of the fundamental lower limb force contributions to quantify the level of gait asymmetry. However, the use of this technological tool is expensive and requires patient’s hospitalization. This research work suggests overcoming the above limitations by applying artificial neural networks.

Keywords: kinetics, kinematics, cyclograms, neural networks, transtibial amputation

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Authors: Dariush Jafari, Seyed Ali Jafari

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The recommended limit for cadmium concentration in potable water is less than 0.005 mg/L. A continuous biosorption process using indigenous red seaweed, Gracilaria corticata, was performed to remove cadmium from the potable water. The process was conducted under fixed conditions and the breakthrough curves were achieved for three consecutive sorption-desorption cycles. A modeling based on Artificial Neural Network (ANN) was employed to fit the experimental breakthrough data. In addition, a simplified semi empirical model, Thomas, was employed for this purpose. It was found that ANN well described the experimental data (R2>0.99) while the Thomas prediction were a bit less successful with R2>0.97. The adjusted design parameters using the nonlinear form of Thomas model was in a good agreement with the experimentally obtained ones. The results approve the capability of ANN to predict the cadmium concentration in potable water.

Keywords: ANN, biosorption, cadmium, packed-bed, potable water

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: L. Mouzai, M. Bouhadef

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Rills begin to be generated once overland flow shear capacity overcomes the soil surface resistance. This resistance depends on soil texture, the arrangement of soil particles and on chemical and physical properties. The rill density could affect soil erosion, especially when the distance between the rills (interrill) contributes to the variation of the rill characteristics, and consequently on sediment concentration. To investigate this point, agricultural sandy soil, a soil tray of 0.2x1x3m³ and a piece of hardwood rectangular in shape to build up rills were the base of this work. The results have shown that small lines have been developed between the rills and the flow acceleration increased in comparison to the flow on the flat surface (interrill). Sediment concentration increased with increasing rill number (density).

Keywords: artificial rainfall, experiments, rills, soil erosion, transport capacity

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Authors: Kok-Chong Yong

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Poly(butadiene-co-acrylonitrile)-polyaniline Dodecylbenzenesulfonate [NBR-PAni.DBSA] blends with useful electrical conductivity (up to 0.1 S/cm) were prepared and their corrosion inhibiting behaviours for carbon steel were successfully assessed for the first time. The level of compatibility between NBR and PAni.DBSA was enhanced through the introduction of 1.0 wt % hydroquinone. As found from both total immersion and electrochemical corrosion tests, NBR-PAni.DBSA blends with 10.0-30.0 wt% of PAni.DBSA content exhibited the best corrosion inhibiting behaviour for carbon steel, either in acid or artificial brine environment. On the other hand, blends consisting of very low and very high PAni.DBSA contents (i.e. ≤ 5.0 wt % and ≥ 40.0 wt %) showed significantly poorer corrosion inhibiting behaviour for carbon steel.

Keywords: conductive rubber, nitrile rubber, polyaniline, carbon steel, corrosion inhibition

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Authors: N. Szczepanska, B. Kudlak, G. Yotova, S. Tsakovski, J. Namiesnik

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In the scientific literature related to the widely understood issue of packaging materials designed to have contact with food (food contact materials), there is much information on raw materials used for their production, as well as their physiochemical properties, types, and parameters. However, not much attention is given to the issues concerning migration of toxic substances from packaging and its actual influence on the health of the final consumer, even though health protection and food safety are the priority tasks. The goal of this study was to estimate the impact of particular foodstuff packaging type, food production, and storage conditions on the degree of leaching of potentially toxic compounds and endocrine disruptors to foodstuffs using the acute toxicity test Microtox and XenoScreen YES YAS assay. The selected foodstuff packaging materials were metal cans used for fish storage and tetrapak. Five stimulants respectful to specific kinds of food were chosen in order to assess global migration: distilled water for aqueous foods with a pH above 4.5; acetic acid at 3% in distilled water for acidic aqueous food with pH below 4.5; ethanol at 5% for any food that may contain alcohol; dimethyl sulfoxide (DMSO) and artificial saliva were used in regard to the possibility of using it as an simulation medium. For each packaging three independent variables (temperature and contact time) factorial design simulant was performed. Xenobiotics migration from epoxy resins was studied at three different temperatures (25°C, 65°C, and 121°C) and extraction time of 12h, 48h and 2 weeks. Such experimental design leads to 9 experiments for each food simulant as conditions for each experiment are obtained by combination of temperature and contact time levels. Each experiment was run in triplicate for acute toxicity and in duplicate for estrogen disruption potential determination. Multi-factor analysis of variation (MANOVA) was used to evaluate the effects of the three main factors solvent, temperature (temperature regime for cup), contact time and their interactions on the respected dependent variable (acute toxicity or estrogen disruption potential). From all stimulants studied the most toxic were can and tetrapak lining acetic acid extracts that are indication for significant migration of toxic compounds. This migration increased with increase of contact time and temperature and justified the hypothesis that food products with low pH values cause significant damage internal resin filling. Can lining extracts of all simulation medias excluding distilled water and artificial saliva proved to contain androgen agonists even at 25°C and extraction time of 12h. For tetrapak extracts significant endocrine potential for acetic acid, DMSO and saliva were detected.

Keywords: food packaging, extraction, migration, toxicity, biotest

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Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang

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For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.

Keywords: high-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network

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Authors: Aouiche Abdelaziz, Soudani Mouhamed Salah, Aouiche El Moundhe

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The present paper addresses the utilization of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs) for the identification and control of dynamical systems with some degree of uncertainty. Because ANNs and FISs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers. In this work, we show how ANNs and FISs can be put in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with ANNs and FISs to model non-linear systems. Four systems were used to test the identification and control of the structures proposed. The results show the ANNs and FISs (Back Propagation Algorithm) used were efficient in modeling and controlling the non-linear plants.

Keywords: non-linear systems, fuzzy set Models, neural network, control law

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Authors: Robert E. Kenney, Efrain Antunez, Samuel Nodal, Ameer Malik, Richard B. Aguilar

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Physician burnout has gained much attention during the COVID pandemic. After-hours workload, HCC coding, HEDIS metrics, and clinical documentation negatively impact career satisfaction. These and other influences have increased the rate of physicians leaving the workforce. In addition, roughly 1% of the entire physician workforce will be retiring earlier than expected based on pre-pandemic trends. The two Medical Specialties with the highest rates of burnout are Family Medicine and Primary Care. With a predicted shortage of primary care physicians looming, the need to address physician burnout is crucial. Commonly reported issues leading to clinician burnout are clerical documentation requirements, increased time working on Electronic Health Records (EHR) after hours, and a decrease in work-life balance. Clinicians experiencing burnout with physical and emotional exhaustion are at an increased likelihood of providing lower quality and less efficient patient care. This may include a lack of suitable clinical documentation, medication reconciliation, clinical assessment, and treatment plans. While the annual baseline turnover rates of physicians hover around 6-7%, the COVID pandemic profoundly disrupted the delivery of healthcare. A report found that 43% of physicians switched jobs during the initial two years of the COVID pandemic (2020 and 2021), tripling the expected average annual rate to 21.5 %/yr. During this same time, an average of 4% and 1.5% of physicians retired or left the workforce for a non-clinical career, respectively. The report notes that 35.2% made career changes for a better work-life balance and another 35% reported the reason as being unhappy with their administration’s response to the pandemic. A physician-led primary care-focused health organization, Cano Health (CH), based out of Florida, sought to preemptively address this problem by implementing several supportive measures. Working with >120 clinics and >280 PCPs from Miami to Tampa and Orlando, managing nearly 120,000 Medicare Advantage lives, CH implemented a number of changes to assist with the clinician’s workload. Supportive services such as after hour and home visits by APRNs, in-clinic care managers, and patient educators were implemented. In 2021, assistive Artificial Intelligence Software (AIS) was integrated into the EHR platform. This AIS converts free text within PDF files into a usable (copy-paste) format facilitating documentation. The software also systematically and chronologically organizes clinical data, including labs, medical records, consultations, diagnostic images, medications, etc., into an easy-to-use organ system or chronic disease state format. This reduced the excess time and documentation burden required to meet payor and CMS guidelines. A clinician Documentation Support team was employed to improve the billing/coding performance. The effects of these newly designed workflow interventions were measured via analysis of clinician turnover from CH’s hiring and termination reporting software. CH’s annualized average clinician turnover rate in 2020 and 2021 were 17.7% and 12.6%, respectively. This represents a 30% relative reduction in turnover rate compared to the reported national average of 21.5%. Retirement rates during both years were 0.1%, demonstrating a relative reduction of >95% compared to the national average (4%). This model successfully promoted the retention of clinicians in a Value-Based Care setting.

Keywords: clinician burnout, COVID-19, value-based care, burnout, clinician retirement

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Authors: Hocine Terrab , Abdelhafid Bayadi, Adel Kara, Ayman El-Hag

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Insulator flashover under polluted conditions has been a serious threat on the reliability of power systems. It is known that the flashover process is mainly affected by the environmental conditions such as; the pollution level and humidity. Those are the essential parameters influencing the wetting process. This paper presents an investigation of the characteristics of leakage current (LC) developed on the surface of porcelain insulator at contaminated conditions under AC voltage. The study is done in an artificial fog chamber and the LC is characterized for different stages; dry, wetted and presence of discharge activities. Time-frequency and spectral analysis are adopted to calculate the evolution of LC characteristics with various stages prior to flashover occurrence. The preliminary results could be used in analysing the LC to develop more effective diagnosis of early signs of dry band arcing as an indication for insulation washing.

Keywords: flashover, harmonic components, leakage current, phase angle, statistical analysis

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Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu

Abstract:

A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.

Keywords: Indian automotive sector, stock market decisions, equity portfolio analysis, decision tree classifiers, statistical data analysis

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Authors: Y. Kourd, D. Lefebvre, N. Guersi

Abstract:

This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty behaviors Models (NNFM's). NNFM's are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it becomes possible to take the appropriate decision regarding the actual process behavior by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.

Keywords: neural networks, decision trees, diagnosis, behaviors

Procedia PDF Downloads 505

Authors: Rabia Bashir, Sajjad Akbar

Abstract:

There is an inevitable demand of web mining due to rapid increase of huge information on the Internet, but the striking variety of web structures has made required content retrieval a difficult task. To counter this issue, Web Content Mining (WCM) emerges as a potential candidate which extracts and integrates suitable resources of data to users. In past few years, research has been done on several extraction techniques for WCM i.e. agent-based, template-based, assumption-based, statistic-based, wrapper-based and machine learning. However, it is still unclear that either these approaches are efficiently tackling the significant challenges of WCM or not. To answer this question, this paper identifies these challenges such as language independency, structure flexibility, performance, automation, dynamicity, redundancy handling, intelligence, relevant content retrieval, and privacy. Further, mapping of these challenges is done with existing extraction mechanisms which helps to adopt the most suitable WCM approach, given some conditions and characteristics at hand.

Keywords: content mining challenges, web content mining, web content extraction approaches, web information retrieval

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Authors: Mohammed Saad Alanazi

Abstract:

Satisfaction of travelers with services provided in the airports is a sign of competitiveness and the corporate image of the airport. This study conducted a systematic literature review of recent studies published after 2017 regarding the factors that positively influence travelers’ satisfaction and encourage them to report positive reviews online. This study found variations among the studies found. They used several research methodologies, and datasets and focused on different airports, yet, they commonly categorized airport services into seven categories that should receive high intention because their qualities were found increasing review rate and positivity. It was found that studies targeting travelers’ satisfaction and intention of revisiting tended to use primary sources of data (survey); meanwhile, studies concerned positivity and negativity of comments towards airport services often used online reviews provided by travelers.

Keywords: business Intelligence, airport service quality, passenger satisfaction, thorough analysis

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Authors: Jun-Yong Lee, Hyoung-Gook Kim

Abstract:

In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.

Keywords: automatic equalization, genre classification, music segment detection, spatial audio processing

Procedia PDF Downloads 429