Search results for: Artificial intelligence (AI)
1258 AI-Assisted Business Chinese Writing: Comparing the Textual Performances Between Independent Writing and Collaborative Writing
Authors: Stephanie Liu Lu
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With the proliferation of artificial intelligence tools in the field of education, it is crucial to explore their impact on language learning outcomes. This paper examines the use of AI tools, such as ChatGPT, in practical writing within business Chinese teaching to investigate how AI can enhance practical writing skills and teaching effectiveness. The study involved third and fourth-year university students majoring in accounting and finance from a university in Hong Kong within the context of a business correspondence writing class. Students were randomly assigned to a control group, who completed business letter writing independently, and an experimental group, who completed the writing with the assistance of AI. In the latter, the AI-assisted business letters were initially drafted by the students issuing commands and interacting with the AI tool, followed by the students' revisions of the draft. The paper assesses the performance of both groups in terms of grammatical expression, communicative effect, and situational awareness. Additionally, the study collected dialogue texts from interactions between students and the AI tool to explore factors that affect text generation and the potential impact of AI on enhancing students' communicative and identity awareness. By collecting and comparing textual performances, it was found that students assisted by AI showed better situational awareness, as well as more skilled organization and grammar. However, the research also revealed that AI-generated articles frequently lacked a proper balance of identity and writing purpose due to limitations in students' communicative awareness and expression during the instruction and interaction process. Furthermore, the revision of drafts also tested the students' linguistic foundation, logical thinking abilities, and practical workplace experience. Therefore, integrating AI tools and related teaching into the curriculum is key to the future of business Chinese teaching.Keywords: AI-assistance, business Chinese, textual analysis, language education
Procedia PDF Downloads 471257 Electrochemical Behaviour of 2014 and 2024 Al-Cu-Mg Alloys of Various Tempers
Authors: K. S. Ghosh, Sagnik Bose, Kapil Tripati
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Potentiodynamic polarization studies carried out on AA2024 and AA2014 Al-Cu-Mg alloys of various tempers in 3.5 wt. % NaCl and in 3.5 wt. % NaCl + 1.0 % H2O2 solution characteristic E-i curves. Corrosion potential (Ecorr) value has shifted towards more negative potential with the increase of artificial aging time. The Ecorr value for the alloy tempers has also shifted anodically in presence of H2O2 in 3.5 % NaCl solution. Further, passivity phenomenon has been observed in all the alloy tempers when tested in 3.5 wt. % NaCl solution at pH 12. Stress corrosion cracking (SCC) behaviour of friction stir weld (FSW) joint of AA2014 alloy has been studied bu slow strain rate test (SSRT) in 3.5 wt. % NaCl solution. Optical micrographs of the corroded surfaces of polarised samples showed general corrosion, extensive pitting and intergranular corrosion as well. Further, potentiodynamic cyclic polarization curves displayed wide hysteresis loop indicating that the alloy tempers are susceptible to pit growth damage. Attempts have been made to explain the variation of observed electrochemical and SCC behaviour of the alloy tempers and the electrolyte conditions with the help of microstructural features.Keywords: AA 2014 and AA 2024 Al-C-Mg alloy, artificial ageing, potentiodynamic polarization, TEM micrographs, stress corrosion cracking (SCC)
Procedia PDF Downloads 3201256 The Effect of Artificial Intelligence on Electric Machines and Welding
Authors: Mina Malak Zakaria Henin
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The finite detail evaluation of magnetic fields in electromagnetic devices shows that the machine cores revel in extraordinary flux patterns consisting of alternating and rotating fields. The rotating fields are generated in different configurations variety, among circular and elliptical, with distinctive ratios between the fundamental and minor axes of the flux locus. Experimental measurements on electrical metal uncovered one-of-a-kind flux patterns that divulge distinctive magnetic losses in the samples below the test. Therefore, electric machines require unique interest throughout the core loss calculation technique to bear in mind the flux styles. In this look, a circular rotational unmarried sheet tester is employed to measure the middle losses in the electric-powered metallic pattern of M36G29. The sample becomes exposed to alternating fields, circular areas, and elliptical fields with axis ratios of zero.2, zero. Four, 0.6 and 0.8. The measured statistics changed into applied on 6-4 switched reluctance motors at 3 distinctive frequencies of interest to the industry 60 Hz, 400 Hz, and 1 kHz. The effects reveal an excessive margin of error, which can arise at some point in the loss calculations if the flux pattern difficulty is overlooked. The mistake in exceptional components of the gadget associated with considering the flux styles may be around 50%, 10%, and a couple of at 60Hz, 400Hz, and 1 kHz, respectively. The future paintings will focus on the optimization of gadget geometrical shape, which has a primary effect on the flux sample on the way to decrease the magnetic losses in system cores.Keywords: converters, electric machines, MEA (more electric aircraft), PES (power electronics systems) synchronous machine, vector control Multi-machine/ Multi-inverter, matrix inverter, Railway tractionalternating core losses, finite element analysis, rotational core losses
Procedia PDF Downloads 61255 Information Visualization Methods Applied to Nanostructured Biosensors
Authors: Osvaldo N. Oliveira Jr.
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The control of molecular architecture inherent in some experimental methods to produce nanostructured films has had great impact on devices of various types, including sensors and biosensors. The self-assembly monolayers (SAMs) and the electrostatic layer-by-layer (LbL) techniques, for example, are now routinely used to produce tailored architectures for biosensing where biomolecules are immobilized with long-lasting preserved activity. Enzymes, antigens, antibodies, peptides and many other molecules serve as the molecular recognition elements for detecting an equally wide variety of analytes. The principles of detection are also varied, including electrochemical methods, fluorescence spectroscopy and impedance spectroscopy. In this presentation an overview will be provided of biosensors made with nanostructured films to detect antibodies associated with tropical diseases and HIV, in addition to detection of analytes of medical interest such as cholesterol and triglycerides. Because large amounts of data are generated in the biosensing experiments, use has been made of computational and statistical methods to optimize performance. Multidimensional projection techniques such as Sammon´s mapping have been shown more efficient than traditional multivariate statistical analysis in identifying small concentrations of anti-HIV antibodies and for distinguishing between blood serum samples of animals infected with two tropical diseases, namely Chagas´ disease and Leishmaniasis. Optimization of biosensing may include a combination of another information visualization method, the Parallel Coordinate technique, with artificial intelligence methods in order to identify the most suitable frequencies for reaching higher sensitivity using impedance spectroscopy. Also discussed will be the possible convergence of technologies, through which machine learning and other computational methods may be used to treat data from biosensors within an expert system for clinical diagnosis.Keywords: clinical diagnosis, information visualization, nanostructured films, layer-by-layer technique
Procedia PDF Downloads 3261254 Methaheuristic Bat Algorithm in Training of Feed-Forward Neural Network for Stock Price Prediction
Authors: Marjan Golmaryami, Marzieh Behzadi
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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange
Procedia PDF Downloads 5361253 Upcoming Fight Simulation with Smart Shadow
Authors: Ramiz Kuliev, Fuad Kuliev-Smirnov
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The 'Shadow Sparring' training exercise is widely used in the training of boxers and martial artists. The main disadvantage of the usual shadow sparring is that the trainer cannot fully control such training and evaluate its results. During the competition, the athlete, preparing for the upcoming fight, imagines the Shadow (upcoming opponent) in accordance with his own imagination. A ‘Smart-Shadow Sparring’ (SSS) is an innovative version of the ‘Shadow Sparring’. During SSS, the fighter will see the Shadow (virtual opponent that moves, defends, and punches) and understand when he misses the punches from the Shadow. The task of a real athlete is to spar with a virtual one, move around, punch in the direction of unprotected areas of the Shadow and dodge his punches. Moves and punches of Shadow are set up before each training. The system will give the coach full information about virtual sparring: (i) how many and what type of punches has the fighter landed, (ii) accuracy of these punches, (iii) how many and what type of virtual punches (punches of Smart-Shadow) has the fighter missed, etc. SSS will be recorded as animated fighting of two fighters and will help the coach to analyze past training. SSS can be configured to fit the physical and technical characteristics of the next real opponent (size, techniques, speed, missed and landed punches, etc.). This will allow to simulate and rehearse the upcoming fight and improve readiness for the next opponent. For amateur fighters, SSS will be reconfigured several times during a tournament, when the real opponent becomes known. SSS can be used in three versions: (1) Digital Shadow: the athlete will see a Shadow on a monitor (2) VR-Shadow: the athlete will see a Shadow in a VR-glasses (3) Smart Shadow: a Shadow will be controlled by artificial intelligence. These technologies are based on the ‘semi-real simulation’ method. The technology allows coaches to train athletes remotely. Simulation of different opponents will help the athletes better prepare for competition. Repeat rehearsals of the upcoming fight will help improve results. SSS can improve results in Boxing, Taekwondo, Karate, and Fencing. 41 sets of medals will be awarded in these sports at the 2020 Olympic Games.Keywords: boxing, combat sports, fight simulation, shadow sparring
Procedia PDF Downloads 1201252 A Method of Representing Knowledge of Toolkits in a Pervasive Toolroom Maintenance System
Authors: A. Mohamed Mydeen, Pallapa Venkataram
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The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.Keywords: knowledge representation, pervasive computing, agent technology, ECA rules
Procedia PDF Downloads 3231251 Maturity Classification of Oil Palm Fresh Fruit Bunches Using Thermal Imaging Technique
Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris
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Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging
Procedia PDF Downloads 3501250 Analytical Modelling of Surface Roughness during Compacted Graphite Iron Milling Using Ceramic Inserts
Authors: Ş. Karabulut, A. Güllü, A. Güldaş, R. Gürbüz
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This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.Keywords: CGI, milling, surface roughness, ANN, regression, modeling, analysis
Procedia PDF Downloads 4411249 Simulation-Based Optimization of a Non-Uniform Piezoelectric Energy Harvester with Stack Boundary
Authors: Alireza Keshmiri, Shahriar Bagheri, Nan Wu
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This research presents an analytical model for the development of an energy harvester with piezoelectric rings stacked at the boundary of the structure based on the Adomian decomposition method. The model is applied to geometrically non-uniform beams to derive the steady-state dynamic response of the structure subjected to base motion excitation and efficiently harvest the subsequent vibrational energy. The in-plane polarization of the piezoelectric rings is employed to enhance the electrical power output. A parametric study for the proposed energy harvester with various design parameters is done to prepare the dataset required for optimization. Finally, simulation-based optimization technique helps to find the optimum structural design with maximum efficiency. To solve the optimization problem, an artificial neural network is first trained to replace the simulation model, and then, a genetic algorithm is employed to find the optimized design variables. Higher geometrical non-uniformity and length of the beam lowers the structure natural frequency and generates a larger power output.Keywords: piezoelectricity, energy harvesting, simulation-based optimization, artificial neural network, genetic algorithm
Procedia PDF Downloads 1141248 Effects of Artificial Nectar Feeders on Bird Distribution and Erica Visitation Rate in the Cape Fynbos
Authors: Monique Du Plessis, Anina Coetzee, Colleen L. Seymour, Claire N. Spottiswoode
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Artificial nectar feeders are used to attract nectarivorous birds to gardens and are increasing in popularity. The costs and benefits of these feeders remain controversial, however. Nectar feeders may have positive effects by attracting nectarivorous birds towards suburbia, facilitating their urban adaptation, and supplementing bird diets when floral resources are scarce. However, this may come at the cost of luring them away from the plants they pollinate in neighboring indigenous vegetation. This study investigated the effect of nectar feeders on an African pollinator-plant mutualism. Given that birds are important pollinators to many fynbos plant species, this study was conducted in gardens and natural vegetation along the urban edge of the Cape Peninsula. Feeding experiments were carried out to compare relative bird abundance and local distribution patterns for nectarivorous birds (i.e., sunbirds and sugarbirds) between feeder and control treatments. Resultant changes in their visitation rates to Erica flowers in the natural vegetation were tested by inspection of their anther ring status. Nectar feeders attracted higher densities of nectarivores to gardens relative to natural vegetation and decreased their densities in the neighboring fynbos, even when floral abundance in the neighboring vegetation was high. The consequent changes to their distribution patterns and foraging behavior decreased their visitation to at least Erica plukenetii flowers (but not to Erica abietina). This study provides evidence that nectar feeders may have positive effects for birds themselves by reducing their urban sensitivity but also highlights the unintended negative effects feeders may have on the surrounding fynbos ecosystem. Given that nectar feeders appear to compete with the flowers of Erica plukenetii, and perhaps those of other Erica species, artificial feeding may inadvertently threaten bird-plant pollination networks.Keywords: avian nectarivores, bird feeders, bird pollination, indirect effects in human-wildlife interactions, sugar water feeders, supplementary feeding
Procedia PDF Downloads 1461247 Optimum Dimensions of Hydraulic Structures Foundation and Protections Using Coupled Genetic Algorithm with Artificial Neural Network Model
Authors: Dheyaa W. Abbood, Rafa H. AL-Suhaili, May S. Saleh
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A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs length sand their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy.The optimization carried out subjected to constraints that ensure a safe structure against the uplift pressure force and sufficient protection length at the downstream side of the structure to overcome an excessive exit gradient. The Geo-studios oft ware, was used to analyze 1200 different cases. For each case the length of protection and volume of structure required to satisfy the safety factors mentioned previously were estimated. An ANN model was developed and verified using these cases input-output sets as its data base. A MatLAB code was written to perform a genetic algorithm optimization modeling coupled with this ANN model using a formulated optimization model. A sensitivity analysis was done for selecting the cross-over probability, the mutation probability and level ,the number of population, the position of the crossover and the weights distribution for all the terms of the objective function. Results indicate that the most factor that affects the optimum solution is the number of population required. The minimum value that gives stable global optimum solution of this parameters is (30000) while other variables have little effect on the optimum solution.Keywords: inclined cutoff, optimization, genetic algorithm, artificial neural networks, geo-studio, uplift pressure, exit gradient, factor of safety
Procedia PDF Downloads 3151246 Improving Chest X-Ray Disease Detection with Enhanced Data Augmentation Using Novel Approach of Diverse Conditional Wasserstein Generative Adversarial Networks
Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang
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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.Keywords: CNN, classification, deep learning, GAN, Resnet50
Procedia PDF Downloads 711245 Load Forecasting Using Neural Network Integrated with Economic Dispatch Problem
Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq
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High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch
Procedia PDF Downloads 1781244 The Implications of Technological Advancements on the Constitutional Principles of Contract Law
Authors: Laura Çami (Vorpsi), Xhon Skënderi
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In today's rapidly evolving technological landscape, the traditional principles of contract law are facing significant challenges. The emergence of new technologies, such as electronic signatures, smart contracts, and online dispute resolution mechanisms, is transforming the way contracts are formed, interpreted, and enforced. This paper examines the implications of these technological advancements on the constitutional principles of contract law. One of the fundamental principles of contract law is freedom of contract, which ensures that parties have the autonomy to negotiate and enter into contracts as they see fit. However, the use of technology in the contracting process has the potential to disrupt this principle. For example, online platforms and marketplaces often offer standard-form contracts, which may not reflect the specific needs or interests of individual parties. This raises questions about the equality of bargaining power between parties and the extent to which parties are truly free to negotiate the terms of their contracts. Another important principle of contract law is the requirement of consideration, which requires that each party receives something of value in exchange for their promise. The use of digital assets, such as cryptocurrencies, has created new challenges in determining what constitutes valuable consideration in a contract. Due to the ambiguity in this area, disagreements about the legality and enforceability of such contracts may arise. Furthermore, the use of technology in dispute resolution mechanisms, such as online arbitration and mediation, may raise concerns about due process and access to justice. The use of algorithms and artificial intelligence to determine the outcome of disputes may also raise questions about the impartiality and fairness of the process. Finally, it should be noted that there are many different and complex effects of technical improvements on the fundamental constitutional foundations of contract law. As technology continues to evolve, it will be important for policymakers and legal practitioners to consider the potential impacts on contract law and to ensure that the principles of fairness, equality, and access to justice are preserved in the contracting process.Keywords: technological advancements, constitutional principles, contract law, smart contracts, online dispute resolution, freedom of contract
Procedia PDF Downloads 1201243 Deep Routing Strategy: Deep Learning based Intelligent Routing in Software Defined Internet of Things.
Authors: Zabeehullah, Fahim Arif, Yawar Abbas
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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.Keywords: SDN, IoT, DL, ML, DRS
Procedia PDF Downloads 1041242 Multi Biomertric Personal Identification System Based On Hybird Intellegence Method
Authors: Laheeb M. Ibrahim, Ibrahim A. Salih
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Biometrics is a technology that has been widely used in many official and commercial identification applications. The increased concerns in security during recent years (especially during the last decades) have essentially resulted in more attention being given to biometric-based verification techniques. Here, a novel fusion approach of palmprint, dental traits has been suggested. These traits which are authentication techniques have been employed in a range of biometric applications that can identify any postmortem PM person and antemortem AM. Besides improving the accuracy, the fusion of biometrics has several advantages such as increasing, deterring spoofing activities and reducing enrolment failure. In this paper, a first unimodel biometric system has been made by using (palmprint and dental) traits, for each one classification applying an artificial neural network and a hybrid technique that combines swarm intelligence and neural network together, then attempt has been made to combine palmprint and dental biometrics. Principally, the fusion of palmprint and dental biometrics and their potential application has been explored as biometric identifiers. To address this issue, investigations have been carried out about the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. Also the results of the multimodal approach have been compared with each one of these two traits authentication approaches. This paper studies the features and decision fusion levels in multimodal biometrics. To determine the accuracy of GAR to parallel system decision-fusion including (AND, OR, Majority fating) has been used. The backpropagation method has been used for classification and has come out with result (92%, 99%, 97%) respectively for GAR, while the GAR) for this algorithm using hybrid technique for classification (95%, 99%, 98%) respectively. To determine the accuracy of the multibiometric system for feature level fusion has been used, while the same preceding methods have been used for classification. The results have been (98%, 99%) respectively while to determine the GAR of feature level different methods have been used and have come out with (98%).Keywords: back propagation neural network BP ANN, multibiometric system, parallel system decision-fusion, practical swarm intelligent PSO
Procedia PDF Downloads 5291241 Sustainable Landscape Strategies For The 21st Century Suburb
Authors: William Batson, Yunsik Song, Abel Simie
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Recent trends in suburban design and planning have centered on economic efficiency in construction and completion. In doing so, developers, builders, and architects have bypassed free and reliable sustainable solutions to minimize the carbon footprint and improve the environment. Often, suburban areas are designed without landscape features, sidewalks, parks, adequate lighting, or walking space. Much of the design concern involves minimizing construction costs and streamlining streets and utilities. A new development in creating retention ponds to mitigate flooding and slow runoff is one step in the positive direction. However, "if you build them (suburbs), they (fauna) will come." The inevitable flora and fauna that soon propagate and take refuge within these artificial retention ponds create an additional dilemma. Architects, planners, and developers know the requirements and current strategies to provide residents and wildlife with a viable and sustainable environment. This includes habitat for hibernating animals and facilitating opportunities, especially for cold-blooded mammals. Many species that migrate to these artificial ponds struggle to survive, especially during flooding and when the water table drains below the artificial rim, preventing aquatic mammals from climbing on land. This flooding often results from large areas of impervious asphalt and concrete. These impervious surfaces retain and dispense large amounts of rainwater and contaminants that carry industrial pollutants, oil, plastics, animal waste, and fertilizers into storm drains and then deposited in these retention ponds. This paper will identify and show how simple and logical solutions are used to create a sustainable suburb and reduce the carbon footprint using landscape architectural strategies and cost-free design solutions. We will also demonstrate simple changes in the present suburban design model to provide a viable and sustainable suburb for the 21st century.Keywords: sustainavilty, suburban, flora, fauna, carbon footprint
Procedia PDF Downloads 551240 General Mood and Emotional Regulation as Predictors of Bullying Behaviors among Adolescent Males: Basis for a Proposed Bullying Intervention Program
Authors: Angelyn Del Mundo
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Bullying cases are a proliferating issue that schools need to address. This calls for a challenge in providing effective measures to reduce bullying. The study aimed to determine which among the socio-emotional aspects of adolescent males could predict bullying. The respondents of the study were the grades 10 and 11 level and the selection of the respondents was based on the names listed by the teachers and guidance counselors through the Student Nomination Questionnaire. The Bullying Survey Questionnaire Checklist was answered by the respondents to be able to identify their most observed bullying behavior. On the other hand, the level of their mental ability was measured through the use of Otis-Lennon School Ability Test, while their socio-emotional aspects was is classified into 2 contexts: emotional intelligence and personality traits which were determined with the use of Bar-On Emotional Quotient Inventory: Youth Version (BarOn EQ-i:YV) and the Five-Factor Personality Inventory-Children (FFPI-C). Results indicated that majority of the respondents have average level of mental ability and socio-emotional aspects. However, many students have low to markedly low level interpersonal scale. Furthermore, general mood and emotional regulation were found as predictors of bullying behaviors. These findings became the basis for a proposed bullying intervention program.Keywords: bullying, emotional intelligence, mental ability, personality traits
Procedia PDF Downloads 2731239 E-Learning Platform for School Kids
Authors: Gihan Thilakarathna, Fernando Ishara, Rathnayake Yasith, Bandara A. M. R. Y.
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E-learning is a crucial component of intelligent education. Even in the midst of a pandemic, E-learning is becoming increasingly important in the educational system. Several e-learning programs are accessible for students. Here, we decided to create an e-learning framework for children. We've found a few issues that teachers are having with their online classes. When there are numerous students in an online classroom, how does a teacher recognize a student's focus on academics and below-the-surface behaviors? Some kids are not paying attention in class, and others are napping. The teacher is unable to keep track of each and every student. Key challenge in e-learning is online exams. Because students can cheat easily during online exams. Hence there is need of exam proctoring is occurred. In here we propose an automated online exam cheating detection method using a web camera. The purpose of this project is to present an E-learning platform for math education and include games for kids as an alternative teaching method for math students. The game will be accessible via a web browser. The imagery in the game is drawn in a cartoonish style. This will help students learn math through games. Everything in this day and age is moving towards automation. However, automatic answer evaluation is only available for MCQ-based questions. As a result, the checker has a difficult time evaluating the theory solution. The current system requires more manpower and takes a long time to evaluate responses. It's also possible to mark two identical responses differently and receive two different grades. As a result, this application employs machine learning techniques to provide an automatic evaluation of subjective responses based on the keyword provided to the computer as student input, resulting in a fair distribution of marks. In addition, it will save time and manpower. We used deep learning, machine learning, image processing and natural language technologies to develop these research components.Keywords: math, education games, e-learning platform, artificial intelligence
Procedia PDF Downloads 1451238 Improving Grade Control Turnaround Times with In-Pit Hyperspectral Assaying
Authors: Gary Pattemore, Michael Edgar, Andrew Job, Marina Auad, Kathryn Job
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As critical commodities become more scarce, significant time and resources have been used to better understand complicated ore bodies and extract their full potential. These challenging ore bodies provide several pain points for geologists and engineers to overcome, poor handling of these issues flows downs stream to the processing plant affecting throughput rates and recovery. Many open cut mines utilise blast hole drilling to extract additional information to feed back into the modelling process. This method requires samples to be collected during or after blast hole drilling. Samples are then sent for assay with turnaround times varying from 1 to 12 days. This method is time consuming, costly, requires human exposure on the bench and collects elemental data only. To address this challenge, research has been undertaken to utilise hyperspectral imaging across a broad spectrum to scan samples, collars or take down hole measurements for minerals and moisture content and grade abundances. Automation of this process using unmanned vehicles and on-board processing reduces human in pit exposure to ensure ongoing safety. On-board processing allows data to be integrated into modelling workflows with immediacy. The preliminary results demonstrate numerous direct and indirect benefits from this new technology, including rapid and accurate grade estimates, moisture content and mineralogy. These benefits allow for faster geo modelling updates, better informed mine scheduling and improved downstream blending and processing practices. The paper presents recommendations for implementation of the technology in open cut mining environments.Keywords: grade control, hyperspectral scanning, artificial intelligence, autonomous mining, machine learning
Procedia PDF Downloads 1011237 Scour Depth Prediction around Bridge Piers Using Neuro-Fuzzy and Neural Network Approaches
Authors: H. Bonakdari, I. Ebtehaj
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The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.Keywords: adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), bridge pier, scour depth, nonlinear regression (NLR)
Procedia PDF Downloads 2091236 Obstacle Avoidance Using Image-Based Visual Servoing Based on Deep Reinforcement Learning
Authors: Tong He, Long Chen, Irag Mantegh, Wen-Fang Xie
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This paper proposes an image-based obstacle avoidance and tracking target identification strategy in GPS-degraded or GPS-denied environment for an Unmanned Aerial Vehicle (UAV). The traditional force algorithm for obstacle avoidance could produce local minima area, in which UAV cannot get away obstacle effectively. In order to eliminate it, an artificial potential approach based on harmonic potential is proposed to guide the UAV to avoid the obstacle by using the vision system. And image-based visual servoing scheme (IBVS) has been adopted to implement the proposed obstacle avoidance approach. In IBVS, the pixel accuracy is a key factor to realize the obstacle avoidance. In this paper, the deep reinforcement learning framework has been applied by reducing pixel errors through constant interaction between the environment and the agent. In addition, the combination of OpenTLD and Tensorflow based on neural network is used to identify the type of tracking target. Numerical simulation in Matlab and ROS GAZEBO show the satisfactory result in target identification and obstacle avoidance.Keywords: image-based visual servoing, obstacle avoidance, tracking target identification, deep reinforcement learning, artificial potential approach, neural network
Procedia PDF Downloads 1341235 Fuzzy Neuro Approach for Integrated Water Management System
Authors: Stuti Modi, Aditi Kambli
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This paper addresses the need for intelligent water management and distribution system in smart cities to ensure optimal consumption and distribution of water for drinking and sanitation purposes. Water being a limited resource in cities require an effective system for collection, storage and distribution. In this paper, applications of two mostly widely used particular types of data-driven models, namely artificial neural networks (ANN) and fuzzy logic-based models, to modelling in the water resources management field are considered. The objective of this paper is to review the principles of various types and architectures of neural network and fuzzy adaptive systems and their applications to integrated water resources management. Final goal of the review is to expose and formulate progressive direction of their applicability and further research of the AI-related and data-driven techniques application and to demonstrate applicability of the neural networks, fuzzy systems and other machine learning techniques in the practical issues of the regional water management. Apart from this the paper will deal with water storage, using ANN to find optimum reservoir level and predicting peak daily demands.Keywords: artificial neural networks, fuzzy systems, peak daily demand prediction, water management and distribution
Procedia PDF Downloads 1731234 Detection of Alzheimer's Protein on Nano Designed Polymer Surfaces in Water and Artificial Saliva
Authors: Sevde Altuntas, Fatih Buyukserin
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Alzheimer’s disease is responsible for irreversible neural damage of brain parts. One of the disease markers is Amyloid-β 1-42 protein that accumulates in the brain in the form plaques. The basic problem for detection of the protein is the low amount of protein that cannot be detected properly in body liquids such as blood, saliva or urine. To solve this problem, tests like ELISA or PCR are proposed which are expensive, require specialized personnel and can contain complex protocols. Therefore, Surface-enhanced Raman Spectroscopy (SERS) a good candidate for detection of Amyloid-β 1-42 protein. Because the spectroscopic technique can potentially allow even single molecule detection from liquid and solid surfaces. Besides SERS signal can be improved by using nanopattern surface and also is specific to molecules. In this context, our study proposes to fabricate diagnostic test models that utilize Au-coated nanopatterned polycarbonate (PC) surfaces modified with Thioflavin - T to detect low concentrations of Amyloid-β 1-42 protein in water and artificial saliva medium by the enhancement of protein SERS signal. The nanopatterned PC surface that was used to enhance SERS signal was fabricated by using Anodic Alumina Membranes (AAM) as a template. It is possible to produce AAMs with different column structures and varying thicknesses depending on voltage and anodization time. After fabrication process, the pore diameter of AAMs can be arranged with dilute acid solution treatment. In this study, two different columns structures were prepared. After a surface modification to decrease their surface energy, AAMs were treated with PC solution. Following the solvent evaporation, nanopatterned PC films with tunable pillared structures were peeled off from the membrane surface. The PC film was then modified with Au and Thioflavin-T for the detection of Amyloid-β 1-42 protein. The protein detection studies were conducted first in water via this biosensor platform. Same measurements were conducted in artificial saliva to detect the presence of Amyloid Amyloid-β 1-42 protein. SEM, SERS and contact angle measurements were carried out for the characterization of different surfaces and further demonstration of the protein attachment. SERS enhancement factor calculations were also completed via experimental results. As a result, our research group fabricated diagnostic test models that utilize Au-coated nanopatterned polycarbonate (PC) surfaces modified with Thioflavin-T to detect low concentrations of Alzheimer’s Amiloid – β protein in water and artificial saliva medium. This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) Grant No: 214Z167.Keywords: alzheimer, anodic aluminum oxide, nanotopography, surface enhanced Raman spectroscopy
Procedia PDF Downloads 2831233 Solar Radiation Time Series Prediction
Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs
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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting
Procedia PDF Downloads 3771232 Machine Learning Predictive Models for Hydroponic Systems: A Case Study Nutrient Film Technique and Deep Flow Technique
Authors: Kritiyaporn Kunsook
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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting
Procedia PDF Downloads 3571231 Grid and Market Integration of Large Scale Wind Farms using Advanced Predictive Data Mining Techniques
Authors: Umit Cali
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The integration of intermittent energy sources like wind farms into the electricity grid has become an important challenge for the utilization and control of electric power systems, because of the fluctuating behaviour of wind power generation. Wind power predictions improve the economic and technical integration of large amounts of wind energy into the existing electricity grid. Trading, balancing, grid operation, controllability and safety issues increase the importance of predicting power output from wind power operators. Therefore, wind power forecasting systems have to be integrated into the monitoring and control systems of the transmission system operator (TSO) and wind farm operators/traders. The wind forecasts are relatively precise for the time period of only a few hours, and, therefore, relevant with regard to Spot and Intraday markets. In this work predictive data mining techniques are applied to identify a statistical and neural network model or set of models that can be used to predict wind power output of large onshore and offshore wind farms. These advanced data analytic methods helps us to amalgamate the information in very large meteorological, oceanographic and SCADA data sets into useful information and manageable systems. Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility customers. An accurate forecast allows grid operators to schedule economically efficient generation to meet the demand of electrical customers. This study is also dedicated to an in-depth consideration of issues such as the comparison of day ahead and the short-term wind power forecasting results, determination of the accuracy of the wind power prediction and the evaluation of the energy economic and technical benefits of wind power forecasting.Keywords: renewable energy sources, wind power, forecasting, data mining, big data, artificial intelligence, energy economics, power trading, power grids
Procedia PDF Downloads 5081230 Concept for Determining the Focus of Technology Monitoring Activities
Authors: Guenther Schuh, Christina Koenig, Nico Schoen, Markus Wellensiek
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Identification and selection of appropriate product and manufacturing technologies are key factors for competitiveness and market success of technology-based companies. Therefore many companies perform technology intelligence (TI) activities to ensure the identification of evolving technologies at the right time. Technology monitoring is one of the three base activities of TI, besides scanning and scouting. As the technological progress is accelerating, more and more technologies are being developed. Against the background of limited resources it is therefore necessary to focus TI activities. In this paper, we propose a concept for defining appropriate search fields for technology monitoring. This limitation of search space leads to more concentrated monitoring activities. The concept will be introduced and demonstrated through an anonymized case study conducted within an industry project at the Fraunhofer Institute for Production Technology. The described concept provides a customized monitoring approach, which is suitable for use in technology-oriented companies especially those that have not yet defined an explicit technology strategy. It is shown in this paper that the definition of search fields and search tasks are suitable methods to define topics of interest and thus to direct monitoring activities. Current as well as planned product, production and material technologies as well as existing skills, capabilities and resources form the basis of the described derivation of relevant search areas. To further improve the concept of technology monitoring the proposed concept should be extended during future research e.g. by the definition of relevant monitoring parameters.Keywords: monitoring radar, search field, technology intelligence, technology monitoring
Procedia PDF Downloads 4591229 Argumentation Frameworks and Theories of Judging
Authors: Sonia Anand Knowlton
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With the rise of artificial intelligence, computer science is becoming increasingly integrated in virtually every area of life. Of course, the law is no exception. Through argumentation frameworks (AFs), computer scientists have used abstract algebra to structure the legal reasoning process in a way that allows conclusions to be drawn from a formalized system of arguments. In AFs, arguments compete against each other for logical success and are related to one another through the binary operation of the attack. The prevailing arguments make up the preferred extension of the given argumentation framework, telling us what set of arguments must be accepted from a logical standpoint. There have been several developments of AFs since its original conception in the early 90’s in efforts to make them more aligned with the human reasoning process. Generally, these developments have sought to add nuance to the factors that influence the logical success of competing arguments (e.g., giving an argument more logical strength based on the underlying value it promotes). The most cogent development was that of the Extended Argumentation Framework (EAF), in which attacks can themselves be attacked by other arguments, and the promotion of different competing values can be formalized within the system. This article applies the logical structure of EAFs to current theoretical understandings of judicial reasoning to contribute to theories of judging and to the evolution of AFs simultaneously. The argument is that the main limitation of EAFs, when applied to judicial reasoning, is that they require judges to themselves assign values to different arguments and then lexically order these values to determine the given framework’s preferred extension. Drawing on John Rawls’ Theory of Justice, the examination that follows is whether values are lexical and commensurable to this extent. The analysis that follows then suggests a potential extension of the EAF system with an approach that formalizes different “planes of attack” for competing arguments that promote lexically ordered values. This article concludes with a summary of how these insights contribute to theories of judging and of legal reasoning more broadly, specifically in indeterminate cases where judges must turn to value-based approaches.Keywords: computer science, mathematics, law, legal theory, judging
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