Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1159
Improved K-means Clustering based Distribution Planning on a Geographical Network
Статья научная
This paper presents a distribution planning on a geographical network, using improved K-means clustering algorithm and is compared with the conventional Euclidean distance based K-means clustering algorithm. The distribution planning includes optimal placement of substation, minimization of expansion cost, optimization of network parameters such as network topology, routing of single/multiple feeders, and reduction in network power losses. The improved K-means clustering is an iterative weighting factor based optimization algorithm which locates the substation optimally and improves the voltage drop at each node. For feeder routing shortest path based algorithm is proposed and the modified load flow method is used to calculate the active and reactive power losses in the network. Simulation is performed on 54 nodes based geographical network with load points and the results obtained show significant power loss minimization as compared to the conventional K-means clustering algorithm.
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Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization
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Krill herd algorithm (KHA) is a novel nature inspired (NI) optimization technique that mimics the herding behavior of krill, which is a kind of fish found in nature. The mathematical model of KHA is based on three phenomena observed in the behavior of a herd of krills, which are, moment induced by other krill, foraging motion and random physical diffusion. These three key features of the algorithm provide a good balance between global and local search capability, which makes the algorithm very powerful. The objective is to minimize the distance of each krill from the food source and also from the point of highest density of the herd, which helps in convergence of population around the food source. Improvisation has been made by introducing neighborhood distance concept along with genetic reproduction mechanism in basic KH Algorithm. KHA with mutation and crossover is called as (KHAMC) and KHA with neighborhood distance concept is referred here as (KHAMCD). This paper compares the performance of the KHA with its two improved variants KHAMC and KHAMCD. The performance of the three algorithms is compared on eight benchmark functions and also on two real world economic load dispatch (ELD) problems of power system. Results are also compared with recently reported methods to establish robustness, validity and superiority of the KHA and its variant algorithms.
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Статья научная
As elliptic curve cryptography is one of the popular ways of constructing an encoding and decoding processes, public-key algorithms as its basis provide people a comfortable way of exchanging pieces of encoded information. As the time goes by, a lot of algorithms have emerged, some of them are still in use today; some others are still being developed into new forms. The main point of algorithm innovation is to reduce the number of processed operations during every possible step to find maximum efficiency and highest speed while performing the calculations. This article describes an improved method of the López-Dahab-Montgomery (LD-Montgomery) scalar point multiplication in terms of working with binary elliptic curves. It is shown in the article that the possible improvement lies in reordering the set of operations which is used in LD-Montgomery scalar point multiplication algorithm. The algorithm is used to compute point multiplication results of the curves over binary Galois Fields featuring the following m values: . The article also presents the experimental results based on different scalars.
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Improvement of GVSRM with Addressing the Interoperability Issues in Global Village
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In today's globally networked environment, enterprises need collaborating using Information Technology (IT) and other tools to succeed in this dynamic and heterogeneous business environment. The Global Village Services Reference Model (GVSRM) is a model based on SOSA (Service Oriented Strategies and Architectures) ontology for global village services realization. In this model, three architectural abstraction layers have been considered for global village: ‘infrastructure for global village services’, ‘global village services provisioning’, and ‘using global village services’. Despite of relative completeness of this model, one of its obvious shortcomings is lack of attention to the crucial issue of interoperability in the global village. Based on this model, the grid of global village is comprised of VHGs (Virtual Holding Governance). The VHG is a temporary, scalable, dynamic cluster/association comprising of existing or newly service provider organizations which its aim is satisfying the requirements of global village actors through electronic processes. In this paper, we will propose a federated approach for interoperability among the VHGs of the global village and then improve the GVSRM by adding the corresponding interoperability components to it.
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Статья научная
The electrical distances between reactive power sources and the loads that need reactive compensation are not too much in microgrids. Thus, a coordinated compensation of reactive sources should be implemented to avoid a fast voltage collapse and improve the dynamic voltage profile by proposing a MicroGrid Voltage Stabilizer (MGVS). This stabilizer was used in microgrids with synchronous machine based resources. Main purpose of this research is verify the performance of the stabilizer by applying it to microgrids containing power-electronic converter based distributed generations (DGs). So that a 21-bus IEEE microgrid with three photovoltaic (PV) resources is tested. At first, PV resource and all of its needed equipments, are modeled. Then a control model of the stabilizer with appropriate parameters, is presented. Voltage deficiency of the system is the input of the stabilizer, and the output signal of the stabilizer, is divided between the DGs in order to provide required reactive power. The dynamic voltage profile of buses in presence of MGVS and its absence has been compared by implying disturbances. Simulation results in MATLAB/SIMULINK show that the dynamic voltage profile of buses improves satisfactorily with the addition of MGVS.
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Improving Cloud Data Encryption Using Customized Genetic Algorithm
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Data Encryption is widely utilized for ensuring data privacy, integrity, and confidentiality. Nowadays, a large volume of data is uploaded to the cloud, which increases its vulnerability and adds to security breaches. These security breaches include circumstances where sensitive information is being exposed to third parties or any access to sensitive information by unauthorized personnel. The objective of this research is to propose a method for improving encryption by customizing the genetic algorithm (GA) with added steps of encryption. These added steps of encryption include the data being processed with local information (chromosome's value calculated with computer-generated random bits without human intervention). The improvement in the randomness of the key generated is based on altering the population size, number of generations, and mutation rate. The first step of encrypting is to convert sample data into binary form. Once the encryption process is complete, this binary result is converted back to get the encrypted data or cipher-text. Foremost, the GA operators (population size, number of generations, and mutation rate) are changed to determine the optimal values of each operator to bring forth a random key in the minimum possible time, then local intelligence is headed in the algorithm to further improve the outcomes. Local Intelligence consists of local information and a random bit generated in each iteration. Local Information is the current value of a parent in each iteration at the gene level. Both local information and random bit are then applied in a mathematical pattern to generate a randomized key. The local intelligence-based algorithm can operate better in terms of time with the same degree of randomness that is generated with the conventional GA technique. The result showed that the proposed method is at least 80% more efficient in terms of time while generating the secret key with the same randomness level as generated by a conventional GA. Therefore, when large data are intended to be encrypted, then using local intelligence can demonstrate to be better utilized time.
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Improving Genetic Algorithm to Solve Multi-objectives Optimal of Upgrading Infrastructure in NGWN
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A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, I propose a new genetic algorithm based on a combination of two populations to solve multi-objective optimization infrastructure upgrade problem in NGWN. Network topology model has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. My objective function is the costs of connection from sources to concentrators such as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. I generate two populations satisfies constraints and combine its to build solutions and evaluate the performance of my algorithm with data randomly generated. The experimental results show that this approach is appropriate and effective Finally, I have applied this algorithm to planning of upgrade infrastructure in telecommunication networks in Haiphong city.
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Improving classification by using MASI algorithm for resampling imbalanced dataset
Статья научная
At present, financial fraud detection is interested by many machine learning researchers. This is because of existing a big ratio between normal transactions and abnormal ones in data set. Therefore, a good result of prediction rate does not mean that there is a good detection result. This is explained that the experimental result might be effected by the imbalance in the dataset. Resampling a dataset before putting to classification process can be seen as the required task for researching in financial fraud detection area. An algorithm, so-called as MASI, is proposed in this paper in order to improve the classification results. This algorithm breaks the imbalance in the data set by re-labelling the major class samples (normal transactions) to the minor class ones basing the nearest neighbor’s samples. This algorithm has been validated with UCI machine learning repository data domain. Then, the algorithm is also used with data domain, which is taken from a Vietnamese financial company. The results show the better in sensitivity, specificity, and G-mean values compared to other publication control methods (Random Over-sampling, Random Under-sampling, SMOTE and Borderline SMOTE). The MASI also remains the training dataset whereas other methods do not. Moreover, the classifiers using MASI resampling training dataset have detected better number of abnormal transactions compared to the one using no resampling algorithm (normal training data).
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Providing spontaneously personalized services to users, at anytime, anywhere and through any devices represent the main objective of pervasive computing. Smart home is an intelligent environment that can provide dozens or even hundreds of smart services. In this paper, we propose an approach to present spontaneously and continuously the most relevant services to the user in response to any significant change of his context. Our approach allows, firstly to assist proactively the user in the tasks of his/her daily life and secondly to help him/her to save energy in the smart home environment. The proposed approach is based on the use of context history information together with user profiling and machine learning techniques. Experimental results show that our approach can efficiently provide the most useful services to the user in a smart home environment.
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Статья научная
Early diabetes diagnosis allows patients to begin treatment on time, reducing or eliminating the risk of serious consequences. In this paper, we propose the Neutrosophic-Adaptive Neuro-Fuzzy Inference System (N-ANFIS) for the classification of diabetes. It is an extension of the generic ANFIS model. Neutrosophic logic is capable of handling the uncertain and imprecise information of the traditional fuzzy set. The suggested method begins with the conversion of crisp values to neutrosophic sets using a trapezoidal and triangular neutrosophic membership function. These values are fed into an inferential system, which compares the most impacted value to a diagnosis. The result demonstrates that the suggested model has successfully dealt with vague information. For practical implementation, a single-value neutrosophic number has been used; it is a special case of the neutrosophic set. To highlight the promising potential of the suggested technique, an experimental investigation of the well-known Pima Indian diabetes dataset is presented. The results of our trials show that the proposed technique attained a high degree of accuracy and produced a generic model capable of effectively classifying previously unknown data. It can also surpass some of the most advanced classification algorithms based on machine learning and fuzzy systems.
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Статья научная
Limited applicability of classical optimization methods influence the popularization of stochastic optimization techniques such as evolutionary algorithms (EAs). EAs are a class of probabilistic optimization techniques inspired by natural evolution process, witch belong to methods of Computational Intelligence (CI). EAs are based on concepts of natural selection and natural genetics. The basic principle of EA is searching optimal solution by processing population of individuals. This paper presents the results of simulation analysis of global optimization of benchmark function by Individually Directional Evolutionary Algorithm (IDEA) and other EAs such as Real Coded Genetic Algorithm (RCGA), elite RCGA with the one elite individual, elite RCGA with the number of elite individuals equal to population size. IDEA is a newly developed algorithm for global optimization. Main principle of IDEA is to monitor and direct the evolution of selected individuals of population to explore promising areas in the search space. The idea of IDEA is an independent evolution of individuals in current population. This process is focused on indicating correct direction of changes in the elements of solution vector. This paper presents a flowchart, selection method and genetic operators used in IDEA. Moreover, similar mechanisms and genetic operators are also discussed.
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Indoor Localization Enhancement Based on Time of Arrival Using Sectoring Method
Статья научная
The indoor wireless communication in general, suffers from several challenges like, signal reflection, diffraction, and attenuation. With these problems, the error range is increased significantly and the accuracy will be lost. To address those problems, Mini Zone (MZ)e technique propos in this paper which aim to partition building into small areas lead to more simplicity and flexibility to assign suitable parameters for specific area rather than whole building. To do that, case study building separated to seven zone (A-G). Each zone has its specific characteristics related to its contents such as, objects, walls, windows and any types of materials in addition to the distance between transmitters and each zone. We took in account these specific parameters to estimate the correct position. 56 receivers (8 for each zone) and 3 transmitters deployed in the case study building. The Wireless Insite Package has been used to design the chosen building and measure the required parameters. The target position has been estimated depending on RSS and ToA methods The objectives of this study are to implement a dynamic system that has capabilities to estimate position under deference conditions like LOS or NLO with the same accuracy. In addition, study the suitability of TOA and RSS methods to estimate position. These objectives were done based on the proposed technique by decrease error in the whole system to an acceptable level to be (0.293502m). Also, the results confirm that the TOA method was better than RSS by using propos technique.
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Indoor Thermal Comfort Optimization by Field Synergy Principle for Air-Conditioning
Статья научная
The principle of field synergy is to simulate the comfortableness of room in this study. The parameters U, V, W and input position of wind are calculated to simulate comfortableness of room. Temperature and velocity fields are simulated by COMSOL Multiphysics software. Comfortable degree is calculated by field synergy mean square root method in this research. The simulation result shows that field synergy angle decreases while comfortable degree increases. It is very obvious that the right input position of wind leads lower field synergy angle.
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Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization
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With the large quantity of information offered on-line, it's equally essential to retrieve correct information for a user query. A large amount of data is available in digital form in multiple languages. The various approaches want to increase the effectiveness of on-line information retrieval but the standard approach tries to retrieve information for a user query is to go looking at the documents within the corpus as a word by word for the given query. This approach is incredibly time intensive and it's going to miss several connected documents that are equally important. So, to avoid these issues, stemming has been extensively utilized in numerous Information Retrieval Systems (IRS) to extend the retrieval accuracy of all languages. These papers go through the problem of stemming with Web Page Categorization on Gujarati language which basically derived the stem words using GUJSTER algorithms [1]. The GUJSTER algorithm is based on morphological rules which is used to derived root or stem word from inflected words of the same class. In particular, we consider the influence of extracted a stem or root word, to check the integrity of the web page classification using supervised machine learning algorithms. This research work is intended to focus on the analysis of Web Page Categorization (WPC) of Gujarati language and concentrate on a research problem to do verify the influence of a stemming algorithm in a WPC application for the Gujarati language with improved accuracy between from 63% to 98% through Machine Learning supervised models with standard ratio 80% as training and 20% as testing
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In this paper, motion differential equation of the two degrees of freedom(2-DOF) vehicle is established based on the linear two degrees of freedom vehicle model and is derived without simplifying the front wheel steering angle(FWSA), then we analyze the vehicle's steady-state response , transient response and the amplitude-frequency characteristic of yaw velocity under different FWSA with the help of the matlab software and finally compare the results with the simplified ones to determine how the FWSA influences the level of the vehicle handling and stability(VHS). At the same time in order to better improve the VHS, this paper proposes a set of active control theory to optimize vehicle’s steady-state performance.The results show that: while the FWSA is small, it has a less influence on vehicle handling and stability, the FWSA is large,it has a greater influence on vehicle handling and stability and the active control can make the vehicle in the best response state when it is in the steady-state.
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Статья научная
A concept targeting information technology is considered. The model of selection of the optimal amount of advertising on various Internet resources, in order to maximize the desired reach to the target audience is analyzed which. This model is different from traditional. A chance constrained target programming model was developed after considering the parameter that corresponds to reach for different media as random variables. The random variables in this case has been considered as the values with known mean and standard deviation. The reachability parameter can be determined by finding the ideal solution and the law on which the parameter values change. The method of multicriteria optimization is examined with determination of resulting objective function, which allows to consider various aspects of the problems of media choice and optimal budgeting and budget allocation simultaneously to get a satisfactory solution of the problem.
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Infrared Images Spectra Multi-class Classification Model Based on Deep Learning
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The classification of Fourier Transform Infrared spectra images is crucial in chemometrics. This paper proposes an efficient model based on deep learning approaches for enhancement and classification of the Fourier Transform Infrared Spectra (FTIR) images. The proposed model integrates three deep learning models including ResNet101, EfficientNetB0, and Wavelet Scattering transform (WST) to extract several features from FTIR. Then the obtained features were fused in conjunction with standard statistical feature extraction. It followed by a subsequent classification phase that employs a Convolutional Neural Network (CNN) architecture, which demonstrates high accuracy in classifying the infrared spectra images into six different classes of ligands and their metal complexes. During the training phase, the network’s weights are iteratively updated using the Adam optimization algorithm. This model addresses the challenge of small and imbalanced datasets through an image oversampling process. Using random over-sampling technique, it enhances the training process and overall classification performance. The extracted features were analyzed using t-distributed Stochastic Neighbor Embedding (t-SNE) to visualize high-dimensional data in two dimensions. The results of the proposed model show high classification accuracy of 0.91%, low error rate of 0.08%, a sensitivity of 0.89% and a precision of 0.89%, false positive rate of 0.01%, F1 score of 0.89, Matthews Correlation Coefficient of 0.87 and Kappa of 0.68.
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Статья научная
Owing to its outstanding performance and rich features, Smart phones have been the rapid development over the past decade, more and more people love to use its mobile Application to process the day-to-day affairs instead of PC, including the normal call and Short Messaging Service, Personal Information Management, send and receive e-mail, browse Web, multimedia Applications and online shopping. In April 2011, the Taipei people began to use the free smart phone Apps “Good Travel in Taipei” to check the real-time traffic information of Taipei City Department of Transportation and get the best route plans according to your location, the Apps software brings together road, bus, subway, bike, high-speed rail, airport, parking and other traffic information, can be easily, simple and fast delivery to the public. The papers will introduce the case of “Good Travel in Taipei” firstly, then Zhengzhou is as an example in China to illustrate the Application of integrated traffic information service system for public travel based on smart phones, we hope it can provide a reference for the future construction of the similar mobile App of traffic information service system in the other cities for public travel.
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Integrating Face and the Both Irises for Personal Authentication
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The biometric authentication, which use the characteristic of persons to verify their identity by using their behavioral and physiological characteristics are an important application of the pattern recognition. There are different biometric modalities used to achieve the task of recognition. Among the most popular traits biometric currently used in several applications are the face and the iris. This paper proposes a multi-biometric technique which combines the face modality with the both irises (the left and the right irises) to authenticate the persons. The fusion of these two traits biometrics combines the advantages of the both instances of the iris modality with the face modality. The wavelets are used for the extraction of the biometrics features and the Support Vector Machine is used to obtain scores for fusion. Then, the Min-Max operator is used to normalize these scores. The fusion is operated at score level by the combination of two methods: a combination method and a classification method. So, we used the five rules (Sum, Product, Max, Min, Mean) combined with a classification method for the fusion. The Fusion is tested using the SDUMLA-HMT database. The experimental results show that multi-biometric systems achieve the task of recognition better than the mono-modal systems.
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We report on the development of an automated acoustic bird recognizer with improved noise robustness, which is part of a long-term project, aiming at the establishment of an automated biodiversity monitoring system at the Hymettus Mountain near Athens, Greece. In particular, a typical audio processing strategy, which has been proved quite successful in various audio recognition applications, was amended with a simple and effective mechanism for integration of temporal contextual information in the decision-making process. In the present implementation, we consider integration of temporal contextual information by joint post-processing of the recognition results for a number of preceding and subsequent audio frames. In order to evaluate the usefulness of the proposed scheme on the task of acoustic bird recognition, we experimented with six widely used classifiers and a set of real-field audio recordings for two bird species which are present at the Hymettus Mountain. The highest achieved recognition accuracy obtained on the real-field data was approximately 93%, while experiments with additive noise showed significant robustness in low signal-to-noise ratio setups. In all cases, the integration of temporal contextual information was found to improve the overall accuracy of the recognizer.
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