Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1159
Placement and Sizing of DG Using PSO&HBMO Algorithms in Radial Distribution Networks
Статья научная
Optimal placement and sizing of DG in distribution network is an optimization problem with continuous and discrete variables. Many researchers have used evolutionary methods for finding the opti-mal DG placement and sizing. This paper proposes a hybrid algorithm PSO&HBMO for optimal placement and sizing of distributed generation (DG) in radial distribution system to minimize the total power loss and improve the voltage profile. The proposed method is tested on a standard 13 bus radial distribution system and simulation results carried out using MATLAB software. The simulation results indicate that PSO&HBMO method can obtain better results than the simple heuristic search method and PSO algorithm. The method has a potential to be a tool for identifying the best location and rating of a DG to be installed for improving voltage profile and line losses reduction in an electrical power system. Moreover, current reduction is obtained in distribution system.
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Статья научная
This paper presents multi-objective function for optimally determining the size and location of distributed generation (DG) and capacitor in distribution systems for power loss minimization, reliability and voltage improvement. The objective function proposed in this paper includes reliability index, active power loss index, DG's and capacitor's investment cost index and voltage profile index which is minimized using binary particle swarm optimization algorithm (BPSO). The effectiveness of the proposed method is examined in the 10 and 33 bus test systems and comparative studies are conducted before and after DG and capacitor installation in the test systems. Results illustrate significant losses reduction and voltage profile and reliability improvement with presence of DG unit and capacitor.
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Plant Disease Detection Using Deep Learning
Статья научная
Agricultural development is a critical strategy for promoting prosperity and addressing the challenge of feeding nearly 10 billion people by 2050. Plant diseases can significantly impact food production, reducing both quantity and diversity. Therefore, early detection of plant diseases through automatic detection methods based on deep learning can improve food production quality and reduce economic losses. While previous models have been implemented for a single type of plant to ensure high accuracy, they require high-quality images for proper classification and are not effective with low-resolution images. To address these limitations, this paper proposes the use of pre-trained model based on convolutional neural networks (CNN) for plant disease detection. The focus is on fine-tuning the hyperparameters of popular pre-trained model such as EfficientNetV2S, to achieve higher accuracy in detecting plant diseases in lower resolution images, crowded and misleading backgrounds, shadows on leaves, different textures, and changes in brightness. The study utilized the Plant Diseases Dataset, which includes infected and uninfected crop leaves comprising 38 classes. In pursuit of improving the adaptability and robustness of our neural networks, we intentionally exposed them to a deliberately noisy training dataset. This strategic move followed the modification of the Plant Diseases Dataset, tailored to better suit the demands of our training process. Our objective was to enhance the network's ability to generalize effectively and perform robustly in real-world scenarios. This approach represents a critical step in our study's overarching goal of advancing plant disease detection, especially in challenging conditions, and underscores the importance of dataset optimization in deep learning applications.
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Статья научная
Traffic is a medium to move from one point to another. Therefore, the role of traffic is very important to support vehicle mobility. If congestion occurs, mobility will be hampered so that it gives influence to other sectors such as financial, air pollution and traffic violations. This study aims to create a model to predict vehicle queue at the traffic lights when its status is red. The prediction is conducted by using Neural Network with Extreme Learning Machine method to predict the length of the vehicle queue, and Correlation Analysis was used to measure the correlation between the connected roads. The conducted experiments use data of the length of the vehicle queue at the traffic lights which was obtained from DISHUB (Transportation Bureau) DI Yogyakarta. Several experiments were carried out to determine the optimum prediction model of vehicle queue length. The experiments found that the optimum model had an average MAPE value of 15.5882% and an average Running Time of 5.2226 seconds.
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Polymorphic Radial Basis Functions Neural Network
Статья научная
The work is devoted to the development of the radial basis functions (RBF networks) neural network new architecture – a polymorphic RBF network in which the one-dimensional radial basis functions (RBFs) in the hidden layer instead, multidimensional RBFs are used, which makes it possible to better approximate complex functions that depend on several independent variables. Moreover, in its second layer, the summing the RBF outputs one by one from each group instead, multiplication is used, which allows the polymorphic RBF network to better identify relations between independent variables. Based on the training classical RBF networks evolutionary algorithm, the polymorphic RBF network training algorithm was created, which, through the initializing weight coefficients methods use taking into account the tasks structure and preliminary values, using the mutations tournament selection, adding additional criteria to the fitness function to take into account stability and speed training a polymorphic RBF network, as well as using an evolutionary mutation strategy, allowed us to obtain the lowest errors in training and testing a polymorphic RBF network compared to known RBF network architectures. The created polymorphic RBF network practical application possibility is demonstrated experimentally using the helicopters turboshaft engines (using the example, the TV3-117 turboshaft engine) operating process parameters optimizing solving task using a multicriteria optimization algorithm. The optimal Pareto front was obtained, which made it possible to obtain the engine operation three additional modes: maximum reduction of specific fuel consumption at the total pressure in the compressor increase degree increased value by 5.0 %, specific fuel consumption minimization at the total pressure in the compressor increase degree reduced value by 1.0 %, the total pressure in the compressor increases degree optimal value with a slight increase in specific fuel consumption by 10.5 %. Future research prospects include adapting the developed methods and models into the general concept for monitoring and controlling helicopter turboshaft engines during flight operations. This concept is implemented in the neural network expert system and the on-board automatic control system.
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Статья научная
This paper presents a comparison study of different control strategies for stabilizing highly non-linear Gantry inverted pendulum (GIP) system. The control objective was achieved using three different soft-computing techniques i.e. Fuzzy logic (FL), Adaptive neuro fuzzy inference system (ANFIS) and Neural networks (NN's). The results obtained from fuzzy controller were further optimized using ANFIS and NN's controllers. The performance parameters considered for analysis were Settling time (seconds), Maximum Overshoot (degree) and Steady state error. The simulation results that both fuzzy and ANFIS controllers were able to stabilize the non-linear GIP system within specified time. It was also observed that ANFIS controller shows better learning ability as compared to NN's controller. The study also elaborates the relationship between Membership functions (MF's) and training error tolerance for ANFIS controller and relation between hidden neurons and Mean squared error (MSE) and Regression (R) value for NN's controller.
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Статья научная
Fuzzy clustering procedures for categorical data are proposed in the paper. Most of well-known conventional clustering methods face certain difficulties while processing this sort of data because a notion of similarity is missing in these data. A detailed description of a possibilistic fuzzy clustering method based on frequency-based cluster prototypes and dissimilarity measures for categorical data is given.
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Potential halal tourism destinations with applying K-Means clustering
Статья научная
The clustering application can be used to develop a variety of tourism potential. Currently, halal tourism is a national income that increases every year and is a favorite for Indonesia. The development of halal tourism is supported by a majority population Muslim and as a halal tourist destination in the world. The objective of this study is to investigate the number of clustering with partitioning approach i.e. K-Means (KM) with two simulation scenarios. The characteristics similarity of this method refers to 11 indicators in 2017 Global Muslim Travel Index (GMTI). The output of this study is to display the information in the form of a map and make it easier for the public to determine which halal tourism destinations are high, medium, and low potential.
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Power Optimized Multiplier Using Shannon Based Multiplexing Logic
Статья научная
In Digital Image Processing, Median Filter is used to reduce the noise in an image. The median filter considers each pixel in the image and replaces the noisy pixel by the median of the neighbourhood pixels. The median value is calculated by sorting the pixels. Sorting in turn consists of comparator which includes adders and multiplier. Multiplication is a fundamental operation in arithmetic computing systems and is used in many DSP applications such as FIR Filters. The adder circuit is used as a main component in the multiplier circuits. The Carry Save Array (CSA) multiplier is designed by using the proposed adder cell based on multiplexing logic. The proposed adder circuit is designed by using Shannon theorem.The multiplier circuits are schematised and their layouts are generated by using VLSI CAD tools. The proposed adder based multiplier circuits are simulated and results are compared with CPL and other circuit designed using Shannon based adder cell in terms of power and area and the intermediate state involved in the circuit is eliminated.The proposed adder based multiplier circuits are simulated by using 90nm feature size and with various supply voltages. The Shannon full adder circuit based multiplier circuits gives better performance than other published results in terms of power dissipation and area due to less number of transistors used in Shannon adder circuit.
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Статья научная
Low frequency oscillations result due to heavy loading conditions line faults, sudden change of generator output and also due to poor damping of interconnected power systems. There are different types of disturbances in the power system like sudden change of load, generation, faults, switching of lines. This hampers the power transmission capacity of the lines and the stability of the system There are significant impacts on the system stability during the charging and discharging operation of Electric Vehicle (EV). In the present work the charging operation of EV is considered as a load disturbance. The introduction of these vehicles in the system creates the problem of low frequency oscillation and endanger the system stability and security. In the present work the Single machine infinite bus system (SMIB) is first developed using mathematical modelling with consideration of EV disturbance. The LQR approach from optimal control theory is then applied in the system to damp the system oscillations, improving the system eigenvalues and enhancing the stability. The stability is seen in the system after LQR from various figures. In the second work the plotting of variation of different state variables is done using three different methods which are the transfer function model method, using code and then using state space representation of the system. The work is further extended by adding Power system stabilizer (PSS) to the system, again considering the EV disturbance. The time domain simulation results showed the improvement in stability using PSS device. Thus, in the present work the oscillations problems created due to the introduction of electric vehicles are solved by two methods. The first is implementing LQR approach from optimal control theory in the system and the second method is by adding PSS device in the same system.
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Статья научная
In this paper, power quality at the distribution system has been examined by introducing an observer based control technique with fuzzy logic controller for wind energy conversion systems with constant wind velocity, for combination of linear, nonlinear loads and with load removal in one of the phases. The power quality improvement, including voltage regulation and reactive power management on the distribution side is achieved and the device used here is a distributed static compensator (DSTATCOM) a voltage source converter(VSC) based power electronic device. The performance is found to be satisfactory with the implementation of DSTATCOM for better voltage regulation with self-sustained DC link voltage at VSC of DSTATCOM. The fuzzy logic controller is used to generate gate pulses to VSC for power quality improvement and is simulated in MATLAB environment and results are studied.
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Статья научная
In this paper an enhanced approach based on a modified biogeography optimization with predator and prey behavior (PMBBO) is presented. The approach uses several predators with new proposed prey’s movement formula. The potential of using a modified predator and prey model is to increase the diversification along the optimization process so to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems (Mass spring damper and an inverted pendulum) and has given remarkable results when compared to genetic algorithm and classical BBO.
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Predication and Optimization of Maintenance Resources for Weapon System
Статья научная
Maintenance resources are important part of the maintenance support system. The whole efficiency of weapon system is directly affected by the allocation of maintenance resources. Joint support for weapon system of multi-kinds of equipments is the main fashion of maintenance support in the future. However, there is a lack of the efficiency tools and methods for predication and optimization of weapon system maintenance resources presently. For the prediction requirement of maintenance resources of weapon system, the primary infection factors for the requirement of maintenance resources were analyzed. According to the different characteristics of maintenance resources and the analysis for the traditional classification methods, a kind of classification for weapon system’s maintenance resources was given. A prediction flow for the maintenance resources requirement was designed. Four kinds of models for predicting the maintenance resources requirement in a weapon system were designed and described in detail. In this paper, approaches of the optimal selection from the simulation schemes and reverse simulation for the resources allocation optimization were analyzed; some optimization models for maintenance resources such as spare parts and personnel were constructed. Further more, an optimization and decision-making system was not only designed but also developed. At last, an example was presented, which proved the prediction and optimization methods were applicability and feasibility, the decision-making system for the optimization of maintenance resources was a supportable and efficient tool.
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Predicting Automobile Stock Prices Index in the Tehran Stock Exchange Using Machine Learning Models
Статья научная
This paper analyses the performance of machine learning models in forecasting the Tehran Stock Exchange's automobile index. Historical daily data from 2018-2022 was pre-processed and used to train Linear Regression (LR), Support Vector Regression (SVR), and Random Forest (RF) models. The models were evaluated on mean absolute error, mean squared error, root mean squared error and R2 score metrics. The results indicate that LR and SVR outperformed RF in predicting automobile stock prices, with LR achieving the lowest error scores. This demonstrates the capability of machine learning techniques to model complex, nonlinear relationships in financial time series data. This pioneering study on a previously unexplored dataset provides empirical evidence that LR and SVR can reliably forecast automobile stock market prices, holding promise for investing applications.
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Predicting Financial Prices of Stock Market using Recurrent Convolutional Neural Networks
Статья научная
Financial time-series prediction has been long and the most challenging issues in financial market analysis. The deep neural networks is one of the excellent data mining approach has received great attention by researchers in several areas of time-series prediction since last 10 years. “Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for financial predictions. In this paper, we proposed to combine architectures, which exploit the advantages of CNN and RNN simultaneously, for the prediction of trading signals. Our model is essentially presented to financial time series predicting signals through a CNN layer, and directly fed into a gated recurrent unit (GRU) layer to capture long-term signals dependencies. GRU model perform better in sequential learning tasks and solve the vanishing gradients and exploding issue in standard RNNs. We evaluate our model on three datasets for stock indexes of the Hang Seng Indexes (HSI), the Deutscher Aktienindex (DAX) and the S&P 500 Index range 2008 to 2016, and associate the GRU-CNN based approaches with the existing deep learning models. Experimental results present that the proposed GRU-CNN model obtained the best prediction accuracy 56.2% on HIS dataset, 56.1% on DAX dataset and 56.3% on S&P500 dataset respectively.
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Predicting Future Products Rate using Machine Learning Algorithms
Статья научная
Opinion mining in social networks data is considered as one of most important research areas because a large number of users interact with different topics on it. This paper discusses the problem of predicting future products rate according to users’ comments. Researchers interacted with this problem by using machine learning algorithms (e.g. Logistic Regression, Random Forest Regression, Support Vector Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression and Decision Tree). However, the accuracy of these techniques still needs to be improved. In this study, we introduce an approach for predicting future products rate using LR, RFR, and SVR. Our data set consists of tweets and its rate from 1:5. The main goal of our approach is improving the prediction accuracy about existing techniques. SVR can predict future product rate with a Mean Squared Error (MSE) of 0.4122, Linear Regression model predict with a Mean Squared Error of 0.4986 and Random Forest Regression can predict with a Mean Squared Error of 0.4770. This is better than the existing approaches accuracy.
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Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis
Статья научная
Stock market prediction has become an attractive investigation topic due to its important role in economy and beneficial offers. There is an imminent need to uncover the stock market future behavior in order to avoid investment risks. The large amount of data generated by the stock market is considered a treasure of knowledge for investors. This study aims at constructing an effective model to predict stock market future trends with small error ratio and improve the accuracy of prediction. This prediction model is based on sentiment analysis of financial news and historical stock market prices. This model provides better accuracy results than all previous studies by considering multiple types of news related to market and company with historical stock prices. A dataset containing stock prices from three companies is used. The first step is to analyze news sentiment to get the text polarity using naïve Bayes algorithm. This step achieved prediction accuracy results ranging from 72.73% to 86.21%. The second step combines news polarities and historical stock prices together to predict future stock prices. This improved the prediction accuracy up to 89.80%.
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Predicting Student Academic Performance at Degree Level: A Case Study
Статья научная
Universities gather large volumes of data with reference to their students in electronic form. The advances in the data mining field make it possible to mine these educational data and find information that allow for innovative ways of supporting both teachers and students. This paper presents a case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The data of four academic cohorts comprising 347 undergraduate students have been mined with different classifiers. The results show that it is possible to predict the graduation performance in 4th year at university using only pre-university marks and marks of 1st and 2nd year courses, no socio-economic or demographic features, with a reasonable accuracy. Furthermore courses that are indicators of particularly good or poor performance have been identified.
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Prediction of Adsorption of Cadmium by Hematite Using Fuzzy C-Means Clustering Technique
Статья научная
Clustering is partitioning of data set into subsets (clusters), so that the data in each subset share some common trait. In this paper, an algorithm has been proposed based on Fuzzy C-means clustering technique for prediction of adsorption of cadmium by hematite. The original data elements have been used for clustering the random data set. The random data have been generated within the minimum and maximum value of test data. The proposed algorithm has been applied on random dataset considering the original data set as initial cluster center. A threshold value has been taken to make the boundary around the clustering center. Finally, after execution of algorithm, modified cluster centers have been computed based on each initial cluster center. The modified cluster centers have been treated as predicted data set. The algorithm has been tested in prediction of adsorption of cadmium by hematite. The error has been calculated between the original data and predicted data. It has been observed that the proposed algorithm has given better result than the previous applied methods.
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Prediction of Drought Resistance Gene with Clustered Amino Acid Features
Статья научная
Drought resistant gene plays important role in molecular breeding while little is known for its genetic mechanism. By extracting the clustered amino acids features, crucial numerical features are inferred for the resistance property of the given gene. Support vector machine algorithm is used to testify the reliability of feature extraction method. After carefully parameters choosing, the accuracy of the predictor achieves 79.36% in Jack-knife test, and the Mathews correlation coefficient achieves 0.5636.
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