International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1187
Sentiment Analysis: A Perspective on its Past, Present and Future
Статья научная
The proliferation of Web-enabled devices, including desktops, laptops, tablets, and mobile phones, enables people to communicate, participate and collaborate with each other in various Web communities, viz., forums, social networks, blogs. Simultaneously, the enormous amount of heterogeneous data that is generated by the users of these communities, offers an unprecedented opportunity to create and employ theories & technologies that search and retrieve relevant data from the huge quantity of information available and mine for opinions thereafter. Consequently, Sentiment Analysis which automatically extracts and analyses the subjectivities and sentiments (or polarities) in written text has emerged as an active area of research. This paper previews and reviews the substantial research on the subject of sentiment analysis, expounding its basic terminology, tasks and granularity levels. It further gives an overview of the state- of – art depicting some previous attempts to study sentiment analysis. Its practical and potential applications are also discussed, followed by the issues and challenges that will keep the field dynamic and lively for years to come.
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Sentiment Predictions using Support Vector Machines for Odd-Even Formula in Delhi
Статья научная
This paper analyzes the odd-even formula in Delhi using tweets posted on Twitter from December 2015 to August 2016. Twitter is a social network where users post their feelings, opinions and sentiments for any event using hashtags and mentions. The tweets posted publicly can be viewed by anyone interested. This paper transforms the unstructured tweets into structured information using open source libraries. Further objective is to build a model using Support Vector Machines (SVM) to classify unseen tweets on the same context. This paper collects tweets on this event under the hashtag "#oddeven formula". This study explores four freely available resources in the form of Application Programming Interfaces (APIs)/Packages for labeling tweets for academic research. Four machine learning models using SVM multi-class classifier were built using the labels provided by the APIs/Packages. The performances of these four models are evaluated through standard evaluation metrics. The experimental results reveal that TextBlob and Pattern python packages outperformed Vivekn and Meaning Cloud APIs. This study may also help in decision making of this event to some extent.
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Sequential Adaptive Fuzzy Inference System Based Intelligent Control of Robot Manipulators
Статья научная
The present paper is dedicated to the presentation and implementation of an optimized technique allowing an on-line estimation of a robot manipulator parameters to use them in a computed torque control. Indeed the proposed control law needs the exact robot model to give good performances. The complexity of the robot manipulator and its strong non-linearity makes it hard to know its parameters. Therefore, we propose in this paper to use neuro-fuzzy networks Sequential Adaptive Fuzzy Inference System (SAFIS) to estimate the parameters of the controlled robot manipulator.
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Sequential Adaptive RBF-Fuzzy Variable Structure Control Applied to Robotics Systems
Статья научная
In this paper, we present a combination of sequential trained radial basis function networks and fuzzy techniques to enhance the variable structure controllers dedicated to robotics systems. In this aim, four RBFs networks were used to estimate the model based part parameters (Inertia, Centrifugal and Coriolis, Gravity and Friction matrices) of a variable structure controller so to respond to model variation and disturbances, a sequential online training algorithm based on Growing-Pruning "GAP" strategy and Kalman filter was implemented. To eliminate the chattering effect, the corrective control of the VS control was computed by a fuzzy controller. Simulations are carried out to control three degrees of freedom SCARA robot manipulator where the obtained results show good disturbance rejection and chattering elimination.
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Short Term Electrical Load Forecasting Based on Weather Parameters under Multiple FIS of Processing
Статья научная
The electrical load forecasting plays a vital role on the economy of a country in context of fuel saving, working hours of employee and depreciation cost of equipment of power generating station. In this paper, we use several machine learning techniques relevant to fuzzy system to forecast the demand of electrical load on short-term basis. Here, we consider temperature, humidity, wind speed, types of day such as working day or holiday, barometric pressure as the parameters, which govern the demand of electrical load. To cope with the variables and the power demand, the previous data of Bangladesh Power Development Board (BPDB) and Bangladesh Space Research and Remote Sensing Organization (SPARRSO) were taken for training purpose and then data of current day was used as the test data. For each of the weather parameter several membership functions (MFs) were used as the fuzzy input and then Takagi-Sugeno, Mamdani rule, FCM + Mamdani and ANFIS were applied to acquire the output as the demand of load. The average percentage of error as the difference between forecasted demand and actual demand of test data was found 1.675% for Takagi-Sugeno, 1.91% for Mamdani (centroid), 2.56% for FCM + Mamdani and 3.62% for ANFIS, which were found superior to some previous research works.
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Simplified real-, complex-, and quaternion-valued neuro-fuzzy learning algorithms
Статья научная
The conventional real-valued neuro-fuzzy method (RNF) is based on classic fuzzy systems with antecedent membership functions and consequent singletons. Rules in RNF are made by all the combinations of membership functions; thus, the number of rules as well as total parameters increase rapidly with the number of inputs. Although network parameters are relatively less in the recently developed complex-valued neuro-fuzzy (CVNF) and quaternion neuro-fuzzy (QNF), parameters increase with number of inputs. This study investigates simplified fuzzy rules that constrain rapid increment of rules with inputs; and proposed simplified RNF (SRNF), simplified CVNF (SCVNF) and simplified QNF (SQNF) employing the proposed simplified fuzzy rules in conventional methods. The proposed simplified neuro-fuzzy learning methods differ from the conventional methods in their fuzzy rule structures. The methods tune fuzzy rules based on the gradient descent method. The number of rules in these methods are equal to the number of divisions of input space; and hence they require significantly less number of parameters to be tuned. The proposed methods are tested on function approximations and classification problems. They exhibit much less execution time than the conventional counterparts with equivalent accuracy. Due to less number of parameters, the proposed methods can be utilized for the problems (e.g., real-time control of large systems) where the conventional methods are difficult to apply due to time constrain.
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Simulation Model of Magnetic Levitation Based on NARX Neural Networks
Статья научная
In this paper, we present analysis of different training types for nonlinear autoregressive neural network, used for simulation of magnetic levitation system. First, the model of this highly nonlinear system is described and after that the Nonlinear Auto Regressive eXogenous (NARX) of neural network model is given. Also, numerical optimization techniques for improved network training are described. It is verified that NARX neural network can be successfully used to simulate real magnetic levitation system if suitable training procedure is chosen, and the best two training types, obtained from experimental results, are described in details.
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Simulation and Analysis of Umbilical Blood Flow using Markov-based Mathematical Model
Статья научная
The intra-uterine development of the fetus depends on various factors, one such critical factor is umbilical blood flow because the quantity of oxygen delivered to the placenta and to the fetus is directly limited by umbilical blood flow rate. Since the measurement of the hemodynamic quantities such as blood pressure and blood flow rate is not possible in utero hence the use of patient-specific mathematical modeling is beneficial for the assessment of feto-maternal well-being. A Markov model based mathematical model of fetal circulation is developed by taking three node concept. The fetus, the umbilical cord, and the placenta represent the 3 nodes of Markov model. A LabVIEW-based virtual instrument is designed to simulate the mathematical model which results in waveform similar to Doppler blood flow velocimetry of umbilical artery. The model is simulated at various degree of conductivity of the umbilical cord to the oxygenated blood. Simulation results show that the umbilical artery blood flow velocity waveform depends on gestation age, fetal heart rate, uterine contraction and placental insufficiency. The Doppler indices calculated from simulation helps in predicting both fetal and maternal abnormalities at various degrees of the conductivity to the blood flow passage. Therefore, integrating patient-specific models along with established medical equipments will be helpful in identifying true intra-uterine growth restricted fetuses from normal fetuses and helps clinicians to take timely interventions.
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Simulation for the reverse extrapolation of radar threats and their verification
Статья научная
Various and unpredictable electronic warfare situations drive the development of an integrated electronic warfare (EW) simulator that can perform electronic warfare modeling and simulation on radar threats. This paper introduces the basic components of simulation system that enables our agents to be operational in EW settings. In various simulation of EW environments, our agents can preset their path in the existence of enemy radars' surveillance and autonomously be aware of radar threats while they proceed in their own route. As reversely extrapolating radar threats given radio-active parameters received, our agents perform an appropriate jamming technique in order to deceive the enemy radar keeping track of our agents. Based upon the response of the radar threat attacked by the jamming techniques, our agents figure out the types of the radar threat and verify its identification. For the actual and helpful information, real radars with the probability of similarity could be prioritized from radar database. The integrated EW simulator that we have designed and developed in this paper enables our agents to perform such capabilities as reverse extrapolation of RF threats, its verification using jamming, and recommendation of similar radars, and to evaluate their autonomous behaviors in a tapestry of realistic scenarios.
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Статья научная
This paper introduces the status of parallel-type Electrostatic Fabric Filter was researched, and the factors influencing collection efficiency were analyzed in this paper. Using software Gambit, which also meshed the calculating region, a three-dimensional structure model of the precipitator was established. And then the numerical simulation of the air distribution characteristic was carried on with the software of fluent 6.2, which sets the boundary conditions, standard k-ε 2-equation model and SIMPLE algorithm; Then draw the path line and contour chart of the cross-section, obtained the mean square deviation value, analyzed the airflow distribution situation and the reasons for why its uneven. By setting an appropriate opening rate for the airflow distribution plates and collection plates to improve the air distribution. The results show that the airflow distribution can be uniformed by improving the opening rate of the collection plates. The numerical simulation result is more reasonable and can be used as the reference of optimizing the structural design of Electrostatic Fabric Filter.
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Simulation of Fuzzy Logic Based Shunt Hybrid Active Filter for Power Quality Improvement
Статья научная
This paper deals with the implementation of fuzzy logic based Shunt Hybrid Active Filter (SHAF) with non-linear load to minimize the source current harmonics and provide reactive power compensation. Comparison with Proportional Integral (PI) based SHAF is also analyzed. Shunt Hybrid Active Filter is constituted by Active Filter connected in shunt and shunt connected three phase single tuned LC filter for 5th harmonic frequency with rectifier load. The Active Filtering System is based on Synchronous Reference Frame. The proposed fuzzy logic based control strategy improves active filter operation and reduces the selective harmonic contents. The control strategies are demonstrated through MATLAB Simulated Environment.
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Simulation of High Step-Up DC–DC Converter for Photovoltaic Module Application using MATLAB/SIMULINK
Статья научная
As per the present scenario lot of power shortages are there in all over the world especially country like India the grid transferring problem is also high. Almost the power from the fossil fuels are becoming so less some of the examples of the fossil fuels are (coal, lignite, oil, and gases).So most of them looking in forward for the power from green or renewable based energies like solar, wind, biomass, tidal etc. Which does not cause any pollution to the environment. In this paper the simulation and analysis of the PV panel and also high efficient boost converter design and simulation is also performed. Even though the solar based systems are renewable based energies when compared to other renewable energies like wind, biomass it does not connect to more number of grid connections. Lot of necessary steps want to be taken one of the main important factor that high efficient boost converter is needed, here in this paper the input voltage to the boost converter is given as 15V and receives the output voltage of 55.64V
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Simulation on Different Proportions of Coal and Natural Gas Co-combustion in a Rotary Lime Kiln
Статья научная
Co-combustion of coal and natural gas is a promising technology in the production of active lime. For this technology, proper fuel proportion of coal and natural gas (α) is one of the key parameters that requires significant thought. By means of numerical simulation, contrast studies on co-combustion with five different fuel proportions were carried out. This paper firstly puts forward the models used to describe the system based on the actual conditions. Then, numerical simulation results were analysed in detail to illustrate the co-combustion process and the velocity and temperature distribution in the kiln. Finally, comparisons of high temperature region, char conversion, length of calcining zone, CO and NOx emission and total heat transfer rate to the material bed were made in order to make a decision on fuel proportion. Synthetically considering, α=30% is a balance between benefits and costs for the rotary lime kiln studied.
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Single and Multi-Area Optimal Dispatch by Modified Salp Swarm Algorithm
Статья научная
This paper presents modified salp swarm algorithm (MSSA) for solution of power system scheduling problems with diverse complexity level. Salp swarm algorithm (SSA) is a recently proposed efficient nature inspired (NI) optimization method inspired by foraging behaviour of salps found in deep ocean. SSA sometimes suffers to stagnation at local minima, to overcome this problem and enhancing searching capability by both exploration and exploitation MSSA is proposed in this paper. MSSA applied and tested on two types of problems. Type one is having five benchmark functions of diverse nature, whereas type two is related with real world problem of power system scheduling of a standard IEEE 114 bus system with 54 thermal units for (i) single area system, (ii) two area system and (iii) three area system. Finally Outcome of simulation results are validated with reported results by other method available in literature.
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Single and Multiple Hand Gesture Recognition Systems: A Comparative Analysis
Статья научная
With the evolution of higher computing speed, efficient communication technologies, and advanced display techniques the legacy HCI techniques become obsolete and are no more helpful in accurate and fast flow of information in present day computing devices. Hence the need of user friendly human machine interfaces for real time interfaces for human computer interaction have to be designed and developed to make the man machine interaction more intuitive and user friendly. The vision based hand gesture recognition affords users with the ability to interact with computers in more natural and intuitive ways. These gesture recognition systems generally consist of three main modules like hand segmentation, hand tracking and gesture recognition from hand features, designed using different image processing techniques which are further integrated with different applications. An increase use of new interfaces based on hand gesture recognition designed to cope up with the computing devices for interaction. This paper is an effort to provide a comparative analysis between such real time vision based hand gesture recognition systems which are based on interaction using single and multiple hand gestures. Single hand gesture based recognition systems (SHGRS) have fewer complexes to implement, with a constraint to the count of different gestures which is large enough with various permutations and combinations of gesture, which is possible with multiple hands in multiple hand gesture recognition systems (MHGRS). The thorough comparative analysis has been done on various other vital parameters for the recognition systems.
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Skin Diseases Expert System using Dempster-Shafer Theory
Статья научная
Based on World Health Organization (WHO) report in the 2011 Skin diseases still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin diseases are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we are using Dempster-Shafer Theory for detecting skin diseases and displaying the result of detection process. We describe five symptoms as major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer theory to quantify the degree of belief, our approach uses Dempster-Shafer theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. The result reveal that Skin Diseases Expert System has been successfully detecting skin diseases and displaying the result of identification process.
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Sky-CNN: a CNN-based learning approach for skyline scene understanding
Статья научная
Skyline scenes are a scientific matter of interest for some geographers and urbanists. These scenes have not been well-handled in computer vision tasks. Understanding the context of a skyline scene could refer to approaches based on hand-crafted features combined with linear classifiers; which are somewhat side-lined in favor of the Convolutional Neural Networks based approaches. In this paper, we proposed a new CNN learning approach to categorize skyline scenes. The proposed model requires a pre-processing step enhancing the deep-learned features and the training time. To evaluate our suggested system; we constructed the SKYLINEScene database. This new DB contains 2000 images of urban and rural landscape scenes with a skyline view. In order to examine the performance of our Sky-CNN system, many fair comparisons were carried out using well-known CNN architectures and the SKYLINEScene DB for tests. Our approach shows it robustness in Skyline context understanding and outperforms the hand-crafted approaches based on global and local features.
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Smart Parking System with Image Processing Facility
Статья научная
Smart Parking Systems obtain information about available parking spaces, process it and then place the car at a certain position. A prototype of the parking assistance system based on the proposed architecture was constructed here. The adopted hardware, software, and implementation solutions in this prototype construction are described in this paper. The effective circular design is introduced here having rack-pinion special mechanism which is used to lift and place the car in the certain position. The design of rack pinion mechanism is also simulated using AUTODESK INVENTOR and COMSOL software.
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Статья научная
Manually, to manage stocks amounts spending the every day in the rays to count for each product the number which it remains in stores, or to record by a scanner head barcode information dependent of each product. However, the mission become increasingly difficult if several warehouses are found, that involves much time to pass from a product to another, moreover that requires agents to carry out these spots. In this article we use a network architecture neuron combined with the readers bar code of technology vision, this method allows to know in real time information concerning each product in stock. It will allow besides introducing the concept of real stocks rather than physical. However The basic classical use of data and to feed it will be completely changed by the spheres of knowledge which generates the NN (Neural Network) to store information on the quantity at a given time (Dynamic inventory), the entries(delivery of suppliers ) and the outputs ( delivery or sale with the customers and use of manufacturing pieces or repair ).
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Solving Economic Load Dispatch Problem Using Particle Swarm Optimization Technique
Статья научная
Economic load dispatch (ELD) problem is a common task in the operational planning of a power system, which requires to be optimized. This paper presents an effective and reliable particle swarm optimization (PSO) technique for the economic load dispatch problem. The results have been demonstrated for ELD of standard 3-generator and 6-generator systems with and without consideration of transmission losses. The final results obtained using PSO are compared with conventional quadratic programming and found to be encouraging.
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