International Journal of Intelligent Systems and Applications @ijisa
Journal articles - International Journal of Intelligent Systems and Applications
All articles: 1251
Aggregation Operators Review - Mathematical Properties and Behavioral Measures
Scientific article
A problem that humans must face very often is that of having to add, melt or synthesize information, that is, combine together a series of data from various sources to reach a certain conclusion or make a certain decision. This involves the use of one or more aggregation operators capable to provide a collective preference relation. These operators must be chosen according to specific criteria taking into account the characteristic properties of each operator. Some conditions to be taken into account to identify them are the following: axiomatic strength, empirical setting, adaptability, numerical efficiency, compensation and compensation range, added behavior and scale level required of the membership functions. It is possible to establish a general list of possible mathematical properties whose verification might be desirable in certain cases: boundary conditions, continuity, not decreasing monotony, symmetry, idempotence, associativity, bisymmetry, self-distributivity, compensation, homogeneity, translativity, stability, ϕ-comparability, sensitivity and locally internal functions. For analyze the attitudinal character of the aggregation operator the following measures are studied: disjunction degree (orness), dispersion, balance and divergence. In this paper, a review of these issues is presented.
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Air quality prediction in Visakhapatnam with LSTM based recurrent neural networks
Scientific article
The research activity considered in this paper concerns about efficient approach for modeling and prediction of air quality. Poor air quality is an environmental hazard that has become a great challenge across the globe. Therefore, ambient air quality assessment and prediction has become a significant area of study. In general, air quality refers to quantification of pollution free air in a particular location. It is determined by measuring different types of pollution indicators in the atmosphere. Traditional approaches depend on numerical methods to estimate the air pollutant concentration and require lots of computing power. Moreover, these methods cannot draw insights from the abundant data available. To address this issue, the proposed study puts forward a deep learning approach for quantification and prediction of ambient air quality. Recurrent neural networks (RNN) based framework with special structured memory cells known as Long Short Term Memory (LSTM) is proposed to capture the dependencies in various pollutants and to perform air quality prediction. Real time dataset of the city Visakhapatnam having a record of 12 pollutants was considered for the study. Modeling of temporal sequence data of each pollutant was performed for forecasting hourly based concentrations. Experimental results show that proposed RNN-LSTM frame work attained higher accuracy in estimating hourly based air ambience. Further, this model may be enhanced by adopting bidirectional mechanism in recurrent layer.
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Scientific article
The article is devoted to the study of the regularities of the statistical organization of nucleotide sequences of single-stranded DNAs in genomes of higher and lower organisms, as well as their connections with cyclic Gray codes and the problem of holistic structures (gestalts) in physiology. The author presents stable statistical structures of an algebraic nature, which are found in many genomic DNAs and are called algebraic gestalt-archetypes of probabilities in genomic DNAs. They are discussed as a possible basis for several genetically inherited physiological and psychophysical properties. The numerical rules of these genomic archetypes realized in nature for a representative class of genomic DNAs, whose initial data were taken by the author from the publicly available genomic data bank “GenBank”, are formulated. The analysis of single-stranded genomic DNAs was carried out using the author's method of "hierarchies of multilayer statistics", representing the nucleotide sequence of DNA as a multilayer text structure, in which each n-th layer is a sequence of n-plets (that is, of monoplets, or duplets, or triplets, etc.). In each such layer, the percentages of each of the types of its n-plets are calculated, the values of which are inserted into the so-called genetic (2n∙2n) Karnaugh matrices, whose columns and rows are numbered by n-bit Gray codes by analogy with Karnaugh maps from the Boolean algebra of logic. The data of the analysis of the nucleotide sequence of DNA of the human chromosome № 1, containing about 250 million nucleotides, are represented as an example. The obtained data are discussed in light of the problem of genetically inherited holistic structures in biology and the tasks of developing algebraic biology, genetic biomechanics, quantum bioinformatics, artificial intelligence, and genetic algorithms.
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Aligning molecular sequences by wavelet transform using cross correlation similarity metric
Scientific article
The first fact of sequence analysis is sequence alignment for the study of structural and functional analysis of the molecular sequence. Owing to the increase in biological data, there is a trade-off between accuracy and the computation of sequence alignment process. Sequences can be aligned both in locally and globally to gives vital information for biologists. Focusing these issues, in this work the local and global alignment are focused on aligning multiple molecular sequences by applying a wavelet transform. Here, the sequence is converted into numerical values using the electron-ion interaction potential model. This is decomposed using a type of wavelet transform and the similarity between the sequences is found using the cross- correlation measure. The significance of the similarity is evaluated using two scoring function namely Position Specific Matrix and a new function called Count score. The work is compared with Fast Fourier Transform based approach and the result shows that the proposed method improves the alignment.
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Scientific article
The frequent poor service network experienced by some mobile phone users within some deadlock areas in Nigeria is an issue which has been identified by different researchers due to wrong positioning and planning of the evolved NodeB (eNodeB) transmitter using existing propagation loss models. To effectively contribute towards this potential issue constantly experienced in some part of Nigeria, an adaptive hybrid propagation loss model that is based on wavelet transform and genetic algorithm methods has been developed for cellular network planning and optimization, with the capacity to resolve the problems absolutely. First, the signal strengths were measured within four selected eNodeB cell sites in long term evolution (LTE) at 2600MHz using drive-test method. Secondly, the measured data were denoised through wavelet tools. Thirdly, COST231 model was optimize and deduced to generic model with parameters. Fourthly, genetic optimization algorithm automatically developed the propagation loss models for denoised signal data (designated as wavelet-GA model) and unprocessed signal data (designated as GA model). The hybrid wavelet-GA propagation loss model, GA propagation loss model, and COST231 propagation loss model were compared based on three error metrics such as root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R). The developed hybrid wavelet-GA model estimated the lowest RMSEs of 2.8813 dB, 3.9381 dB, 4.7643 dB, 6.9366 dB, whereas, COST231 model gave highest value of RMSE. The developed hybrid wavelet-GA model also derived the least value of MAE as compared with COST231 and the GA models, such as, 2.2016 dB, 2.8672 dB, 3.4766 dB, 5.8235 dB. The correlation coefficients were also compared, and it showed that the developed hybrid wavelet-GA model were 90.04%, 78.61%, 92.21% and 91.23% for the four cell sites. The developed hybrid wavelet-GA model was also validated to account for the performance level by checking for the correlation coefficient using another measured signal data from different eNodeB cell sites other than the once used for the developed of the hybrid wavelet-GA model. It was noticed that the developed hybrid wavelet-GA propagation loss model is 97.41% valid. Existing standard COST231 model are not able to predict propagation loss with high level of accuracy, as such not efficient to be applied within part of Port Harcourt, Nigeria. The proposed hybrid wavelet-GA model has proven to achieve high performance level and it is relevant to be utilized for cellular network planning and optimization. In future purposes, more regions and locations should be considered to form a broader view in the development of more robust propagation loss models.
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An Algorithm for Detecting the Minimal Sample Frequency for Tracking a Preset Motion Scenario
Scientific article
Inertial sensors are used for human motion capture in a wide range of applications. Some kinds of human motion can be tracked by inertial sensors incorporated in smartphones or smartwatches. However, the latter can scarcely be used if misclassification of user activities is highly undesirable. In this case electronics and embedded software engineers should design, implement and verify their own human motion capture embedded systems, and oftentimes they have to do so from scratch. One of the issues the engineers should face is selection of suitable components, primarily accelerometers, gyroscopes and magnetometers, after thorough examination of commercially available items. Among technical characteristics of inertial sensors their sample frequency determines whether the sensor will be able to capture a specific motion kind or not. We propose a novel algorithm that allows the researcher or embedded software engineer to calculate the minimal sample frequency sufficient for tracking a prescribed motion scenario without significant signal losses. The algorithm utilizes the Poisson equation for motion of a triaxial rigid body, the Shoemake’s algorithm for interpolating quaternions on the unit hypersphere, and the frequency analysis of a discrete-time signal. One can use the proposed algorithm as an argument for acceptance or rejection of a gyroscope when selecting hardware components for a human motion tracking system.
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An Analysis of Fuzzy Approaches for COCOMO II
Scientific article
Software cost estimation is one of the most challenging task in project management. However, the process of estimation is uncertain in nature as it largely depends upon some attributes that are quite unclear during the early stages of development. In this paper a soft computing technique is explored to overcome the uncertainty and imprecision in estimation. The main objective of this research is to investigate the role of fuzzy logic technique in improving the effort estimation accuracy using COCOMO II by characterizing inputs parameters using Gaussian, trapezoidal and triangular membership functions and comparing their results. NASA (93) dataset is used in the evaluation of the proposed Fuzzy Logic COCOMO II. After analyzing the results it had been found that effort estimation using Gaussian member function yields better results for maximum criterions when compared with the other methods.
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An Analysis of the Atlantic Ocean Wave Via Random Cosine and Sine Alternate Wavy ARIMA Functions
Scientific article
In this research, alternate random wave sine and cosine for discrete time-varying processes via Autoregressive Integrated Moving Average (ARIMA) in a deterministic manner were developed. The mean and variance of the cosine and sine periodical time-varying wavy functions were derived such that Maclaurin series via full Taylor series expansion was used to rewrite the mean and variance functions. Wavy buoys of sea temperature, significant wave height, and mean wave direction of Belmullet Inner (Berth B) and Belmullet Outer (Berth A) of the Atlantic Ocean based on the west coastal of Ireland were subjected to the random sine and cosine wave functions of ARIMA. Cosine-ARIMA (1, 1, 3) and cosine-ARIMA (0, 1, 1) were the sea temperature inner and outer oceanic climate wave buoys of Berth B and A with time-periods of 8437.5 and 8035.714 respectively. Cosine-ARIMA (5, 1, 0) gave minimum performance for peak direction of inner and outer oceanic climate wave buoys of both Berth B and A, but with different time-periods of 168750 and 56250 respectively. Lastly, cosine-ARIMA (2, 1, 2) and sine-ARIMA (0, 1, 5) put in the ideal generalization for wave height of Berth B and A with the same associated wave time-periods of 56250, that is, it takes 56250 seconds to complete one swaying cycle.
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An Analysis of the Effect of Communication for Multi-agent Planning in a Grid World Domain
Scientific article
Agent-based technology has generated a lot of attention in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. Some problems in real world cannot be handled by a single agent. Multiple agents work together to accomplish some task. Although multi-agent systems (MASs) provide many potential advantages, they also present many difficult challenges. This paper illustrates the importance of communication for planning in a multi-agent setting by considering a grid world domain that consists of obstacles at different locations. This paper provides a theoretical framework that is validated by the experimental results. Performance analysis with respect to plan size and execution time is also reported.
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An Analysis on Qualitative Bankruptcy Prediction Rules using Ant-Miner
Scientific article
Qualitative bankruptcy prediction rules represent experts' problem-solving knowledge to predict qualitative bankruptcy. The objective of this research is predicting qualitative bankruptcy using ant-miner algorithm. Qualitative data are subjective and more difficult to measure. This approach uses qualitative risk factors which include fourteen internal risk factors and sixty eight external risk factors associated with it. By using these factors qualitative prediction rules are generated using ant-miner algorithm and the influence of these factors in bankruptcy is also analyzed. Ant-Miner algorithm is a application of ant colony optimization and data mining concepts. Qualitative rules generated by ant miner algorithm are validated using measure of agreement. These prediction rules yields better accuracy with lesser number of terms than previously applied qualitative bankruptcy prediction methodologies.
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An Android-based Remote Desktop for IOS Platforms
Scientific article
Thanks to globalization, mobile devices have become an inseparable entity of our daily life. We often expect our smart devices (mobile phone, tablet, portable media player) to possess the functionalities of a personal computer. As the technology is getting cheaper, owning multiple mobile devices, each for a specific purpose, is becoming the current trend. For instance, an Android smartphone to fulfill a user’s communication needs on-the-go, an iPad could serve the user’s reading hobby, and lastly, a laptop for productivity activities. As such, to switch among different devices could be the emerging problem of the current generation. With our proposed Android based remote control app, a user does not only able to control his Windows based office laptop, but he could access to his IOS based devices too. Besides click event and text input, this application also supports panning and zooming gesture inputs.
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Scientific article
Origin–destination (OD) matrix estimation largely depends on the quality and quantity of the input data, which in turn depends on the number and sites of count locations. In this paper, we focus on the network count location problem (NCLP), namely the identification of informative links in the road network. Also we employ opposition based colonial competitive algorithm (OCCA), which originally inspired by imperialistic competition, to determine the desirable number and locations of counting points satisfying location rules. The model and algorithm is illustrated with numerical examples.
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Scientific article
Gestures are natural means of communication between humans, and therefore their application would benefit to many fields where usage of typical input devices, such as keyboards or joysticks is cumbersome or unpractical (e.g., in noisy environment). Recently, together with emergence of new cameras that allow obtaining not only colour images of observed scene, but also offer the software developer rich information on the number of seen humans and, what is most interesting, 3D positions of their body parts, practical applications using body gestures have become more popular. Such information is presented in a form of skeletal data. In this paper, an approach to gesture recognition based on skeletal data using nearest neighbour classifier with dynamic time warping is presented. Since similar approaches are widely used in the literature, a few practical improvements that led to better recognition results are proposed. The approach is extensively evaluated on three publicly available gesture datasets and compared with state-of-the-art classifiers. For some gesture datasets, the proposed approach outperformed its competitors in terms of recognition rate and time of recognition.
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Scientific article
Energy saving and improve the life time of the sensor node is main focus in the recent years for the researchers hence one of the application domain (Street light monitoring and controlling) of sensor required attention towards this direction. For contributing in this domain we have proposed a scheme for Street light controlling using distributed topology control (TC). The optimize version of A3 protocol reduces the number of messages send/received by the sensors which ultimately leads to the reduction of energy requirement. Experiments are carried on street light scenario for different no. of nodes by maintaining communication using Zigbee protocol. The performance of our extension is evaluated using, no. of messages send/receive & energy consumed during topology building and our approach is having good results as compared to the approach used for this type of network.
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An Automated Real-Time System for Opinion Mining using a Hybrid Approach
Scientific article
In this paper, a novel idea is being presented to perform Opinion Mining in a very simple and efficient manner with the help of the One-Level-Tree (OLT) based approach. To recognize opinions specific for features in customer reviews having a variety of features commingled with diverse emotions. Unlike some previous ventures entirely using one-time structured or filtered data but this is solely based on unstructured data obtained in real-time from Twitter. The hybrid approach utilizes the associations defined in Dependency Parsing Grammar and fully employs Double Propagation to extract new features and related new opinions within the review. The Dictionary based approach is used to expand the Opinion Lexicon. Within the dependency parsing relations a new relation is being proposed to more effectively catch the associations between opinions and features. The three new methods are being proposed, termed as Double Positive Double Negative (DPDN), Catch-Phrase Method (CPM) & Negation Check (NC), for performing criteria specific evaluations. The OLT approach conveniently displays the relationship between the features and their opinions in an elementary fashion in the form of a graph. The proposed system achieves splendid accuracy across all domains and also performs better than the state-of-the-art systems.
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An Automatic Number Plate Recognition System under Image Processing
Scientific article
Automatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image , it display the number plate information into text. Mainly the ANPR system consists of 4 phases: - Acquisition of Vehicle Image and Pre-Processing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. The overall accuracy and efficiency of whole ANPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Further the accuracy of Number Plate Extraction phase depends on the quality of captured vehicle image. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The existing methods of ANPR works well for dark and bright/light categories image but it does not work well for Low Contrast, Blurred and Noisy images and the detection of exact number plate area by using the existing ANPR approach is not successful even after applying existing filtering and enhancement technique for these types of images. Due to wrong extraction of number plate area, the character segmentation and character recognition are also not successful in this case by using the existing method. To overcome these drawbacks I proposed an efficient approach for ANPR in which the input vehicle image is pre-processed firstly by iterative bilateral filtering , adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, image binarization/thresholding, sobel vertical edge detection and by boundary box analysis. Sometimes the extracted plate area also contains noise, bolts, frames etc. So the extracted plate area is enhanced by using morphological operations to improve the quality of extracted plate so that the segmentation phase gives more successful output. The character segmentation is done by connected component analysis and boundary box analysis and finally in the last character recognition phase, the characters are recognized by matching with the template database using correlation and output results are displayed. This approach works well for low contrast, blurred, noisy as well as for dark and light/bright category images. The comparison is done between the ANPR with Adaptive Histogram Equalization and Iterative Bilateral Filtering that is the proposed approach and the existing ANPR approach using metrics: MSE, PSNR and Success rate.
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An Efficient Algorithm for Mining Weighted Frequent Itemsets Using Adaptive Weights
Scientific article
Weighted frequent itemset mining is more practical than traditional frequent itemset mining, because it can consider different semantic significance (weight) of items. Many models and algorithms for mining weighted frequent itemsets have been proposed. These models assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of the items may vary with time. Therefore, reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. Recently, Chowdhury F. A. et al. have introduced a novel concept of adaptive weight for each item and propose an algorithm AWFPM (Adaptive Weighted Frequent Pattern Mining). AWFPM can handle the situation where the weight (price or significance) of an item may vary with time. In this paper, we present an improved algorithm named AWFIMiner. Experimental computations show that our AWFIMiner is more efficient and scalable for mining weighted frequent itemsets using adaptive weights. Moreover, because it only requires one single database scan, the AWFIMiner is applicable for mining these itemsets on data streams.
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Scientific article
In recent years, the mining research over data stream has been prominent as they can be applied in many alternative areas in the real worlds. In [20], a framework for mining frequent itemsets over a data stream is proposed by the use of weighted slide window model. Two algorithms of single pass (WSW) and the WSW-Imp (improving one) using weighted sliding model were proposed in there to solve the data stream problems. The disadvantage of these algorithms is that they have to seek all data stream many times and generate a large set of candidates. In this paper, we have proposed a process of mining frequent itemsets with weights over a data stream. Based on the downward closure property and FP-Growth method [8,9] an alternative algorithm called WSWFP-stream has been proposed. This algorithm is proved working more efficiently regarding to computing time and memory aspects.
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An Efficient Method of Steganography using Matrix Approach
Scientific article
A large number of the world business is going on using “INTERNET” and the data over the internet which is vulnerable for attacks from the hackers. Thus, uses of highly efficient methods are required for sensitive data transmission over the internet to ensure data security. One of the solutions to data security is to use an efficient method of steganography. The goal of steganography is to hide messages inside other ‘harmless’ messages in a way that does not allow any enemy to even detect that there is a second message present. Steganography can be used with a large number of file formats most commonly used in the digital world of today. The different file formats popularly used are .bmp, .gif, .txt etc. Thus the techniques of steganography are going to play a very important part in the future of data security and privacy on open systems such as the Internet. This paper presents an efficient method for hiding data into an image and send to the destination in a safe manner. This technique does not need any key for embedding and extracting data. Also, it allows hiding four bits in a block of size 5×5 with minimal distortion. The proposed algorithm ensures security and safety of the hidden information. The experimental results presented in this paper show the efficacy of the proposed method.
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Scientific article
Automated, i.e. machine vision based fabric defect inspection systems have been drawing plenty of attention of the researchers in order to replace manual inspection. Two difficult problems are mainly posed by automated fabric defect inspection systems. They are defect detection and defect classification. Counterpropagation neural network (CPN) is a robust classifier and very promising for defect classification. In general, works reported to date have claimed varying level of successes in detection and classification of different types of defects through CPN; but in particular, no claimed has been made for successful application of CPN for fabric defects detection and classification. In those published works, no investigation has been reported regarding to the variation of major performance parameters of NN based classifiers such as learning time and classification accuracy based on network topology and training parameters. As a result, application engineer has little or no guidance to take design decisions for reaching to optimum structure of NN based defect classifiers in general and CPN based in particular. Our work focuses on empirical investigation of interrelationship between design parameters and performance of CPN based classifier for fabric defect classification. It is believed that such work will be laying the ground to empower application engineers to decide about optimum values of design parameters for realizing most appropriate CPN based classifier.
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