https://reunir.unir.net/handle/123456789/12670 Mon, 28 Oct 2024 00:48:28 GMT 2024-10-28T00:48:28Z https://reunir.unir.net/handle/123456789/12717 Fuzzy C-Means Clustering with Histogram based Cluster Selection for Skin Lesion Segmentation using Non-Dermoscopic Images Devi, Salam Shuleenda; Laskar, Rabul Hussain; Singh, Ngangbam Herojit Purpose – Pre-screening of skin lesion for malignancy is highly demanded as melanoma being a life-threatening skin cancer due to unpaired DNA damage. In this paper, lesion segmentation based on Fuzzy C-Means clustering using non-dermoscopic images has been proposed. Design/methodology/approach – The proposed methodology consists of automatic cluster selection for FCM using the histogram property. The system used the local maxima along with Euclidean distance to detect the binomial distribution property of the image histogram, to segment the melanoma from normal skin. As the Value channel of HSV color image provides better and distinct histogram distribution based on the entropy, it has been used for segmentation purpose. Findings – The proposed system can effectively segment the lesion region from the normal skin. The system provides a segmentation accuracy of 95.69 % and the comparative analysis has been performed with various segmentation methods. From the analysis, it has been observed that the proposed system can effectively segment the lesion region from normal skin automatically. Originality/Value – This paper suggests a new approach for skin lesion segmentation based on FCM with automatic cluster selection. Here, different color channel has also been analyzed using entropy to select the better channel for segmentation. In future, the classification of melanoma from benign naevi can be performed. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T13:34:44Z No. of bitstreams: 1 ijimai20206_1_3_pdf_17885.pdf: 1265459 bytes, checksum: 62187387480274425fa7ca793d82bbb9 (MD5); Made available in DSpace on 2022-03-24T13:34:44Z (GMT). No. of bitstreams: 1 ijimai20206_1_3_pdf_17885.pdf: 1265459 bytes, checksum: 62187387480274425fa7ca793d82bbb9 (MD5) https://reunir.unir.net/handle/123456789/12717 https://reunir.unir.net/handle/123456789/12716 Learning Models for Semantic Classification of Insufficient Plantar Pressure Images Dey, Nilanjan; Wu, Yao; Wu, Qun; Sherratt, Simon Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T13:07:50Z No. of bitstreams: 1 ijimai20206_1_6_pdf_49479.pdf: 1043257 bytes, checksum: c2abae0a912570173755f564dba3b789 (MD5); Made available in DSpace on 2022-03-24T13:07:50Z (GMT). No. of bitstreams: 1 ijimai20206_1_6_pdf_49479.pdf: 1043257 bytes, checksum: c2abae0a912570173755f564dba3b789 (MD5) https://reunir.unir.net/handle/123456789/12716 https://reunir.unir.net/handle/123456789/12715 Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems Bobadilla, Jesús; Ortega, Fernando; Gutiérrez, Abraham; Alonso, Santiago This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ratings dataset. The learning process is based on the binary relevant/non-relevant vote and the binary voted/non-voted item information. This data reduction provides a new level of abstraction and it makes possible to design the classification-based architecture. In addition to the original architecture, its prediction process has a novel approach: it does not need to make a large number of predictions to get recommendations. Instead to run forward the neural network for each prediction, our approach runs forward the neural network just once to get a set of probabilities in its categorical output layer. The proposed neural architecture has been tested by using the MovieLens and FilmTrust datasets. A state-of-the-art baseline that outperforms current competitive approaches has been used. Results show a competitive recommendation quality and an interesting quality improvement on large number of recommendations, consistent with the architecture design. The architecture originality makes it possible to address a broad range of future works. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T12:48:31Z No. of bitstreams: 1 ijimai20206_1_8_pdf_28842.pdf: 2786476 bytes, checksum: b9e4a8262fddfe10d36c1e971d60bd68 (MD5); Made available in DSpace on 2022-03-24T12:48:31Z (GMT). No. of bitstreams: 1 ijimai20206_1_8_pdf_28842.pdf: 2786476 bytes, checksum: b9e4a8262fddfe10d36c1e971d60bd68 (MD5) https://reunir.unir.net/handle/123456789/12715 https://reunir.unir.net/handle/123456789/12714 Voltage Stability Assessment of Radial Distribution Systems Including Optimal Allocation of Distributed Generators Selim, Ali; Kamel, Salah; Jurado, Francisco; Nasrat, Loai Assessment of power systems voltage stability is considered an important assignment for the operation and planning of power system. In this paper, a voltage stability study using Continuous Power Flow (CPF) is introduced to evaluate the impact of Distribution Generator (DG) on radial distribution systems. On the way to allocate the DG, a hybrid between the Voltage Stability Index (VSI) and Whale Optimization Algorithm (WOA) is developed. The main purpose of using VSI is to find the most sensitive buses for allocating the DG in the system. Hence, Fuzzy logic control with the Normalized VSI (NVSI) and the voltage magnitude at each bus are used to determine the candidate buses. However, the best DG size is calculated using WOA. Four standard radial distribution systems are used in this paper; 12, 33, 69, and 85-bus. The developed hybrid optimization method is compared with other existing analytical and metaheuristic optimization techniques to prove its efficiency. The results prove the ability of the developed method in the allocation of DG. In addition, the influence of the DG integration on enhancing the voltage stability through injecting the proper active and reactive powers is studied. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T11:11:45Z No. of bitstreams: 1 ijimai20206_1_4_pdf_13565.pdf: 1437028 bytes, checksum: 7532b8f58bfe4e38be53eab06767b5ca (MD5); Made available in DSpace on 2022-03-24T11:11:45Z (GMT). No. of bitstreams: 1 ijimai20206_1_4_pdf_13565.pdf: 1437028 bytes, checksum: 7532b8f58bfe4e38be53eab06767b5ca (MD5) https://reunir.unir.net/handle/123456789/12714 https://reunir.unir.net/handle/123456789/12713 Editor's Note Morente-Molinera, Juan Antonio Soft Computing is an AI branch that focuses on solving problems that have incomplete, inexact or fuzzy information. In other words, Soft Computing area includes algorithms and methods that are typically used when the imprecision or lack of the dealt data make other type of methods to become useless. Deep Learning, Machine learning and Fuzzy Systems related methods have achieved really good results even when the available data is not as good as desired. This success has converted the Soft Computing area in one of the most important ones inside the AI field. This special issue’s goal is to reunite some of the most recent research on the Soft Computing area. The selected research covers different aspects and problems on the AI area in an effort to provide a clear overview of the state of the art on the topic. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T10:37:50Z No. of bitstreams: 0; Made available in DSpace on 2022-03-24T10:37:50Z (GMT). No. of bitstreams: 0 https://reunir.unir.net/handle/123456789/12713 https://reunir.unir.net/handle/123456789/12712 Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak González-Crespo, Rubén; Herrera-Viedma, Enrique; Dey, Nilanjan; Fong, Simon James; Li, Gloria Epidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include closing borders, schools, suspending community services and commuters. Resuming such curfews depends on the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty involved and the complex societal-political factors that influence the widespread of the new virus, any forecast is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model which offers forecast at the best efforts is a big challenge in machine learning. In the past, three popular methods have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the best forecasting model from several models, and 3) fine-tuning the parameters of an individual forecasting model for the highest possible accuracy. In this paper, a methodology that embraces these three virtues of data mining from a small dataset is proposed. An experiment that is based on the recent coronavirus outbreak originated from Wuhan is conducted by applying this methodology. It is shown that an optimized forecasting model that is constructed from a new algorithm, namely polynomial neural network with corrective feedback (PNN+cf) is able to make a forecast that has relatively the lowest prediction error. The results showcase that the newly proposed methodology and PNN+cf are useful in generating acceptable forecast upon the critical time of disease outbreak when the samples are far from abundant. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T10:21:50Z No. of bitstreams: 1 ijimai20206_1_15_pdf_18033.pdf: 1038427 bytes, checksum: 72331956a944f7572b62fd73c1db177b (MD5); Made available in DSpace on 2022-03-24T10:21:50Z (GMT). No. of bitstreams: 1 ijimai20206_1_15_pdf_18033.pdf: 1038427 bytes, checksum: 72331956a944f7572b62fd73c1db177b (MD5) https://reunir.unir.net/handle/123456789/12712 https://reunir.unir.net/handle/123456789/12711 An Intelligent Technique for Grape Fanleaf Virus Detection Mohammadpoor, Mojtaba; Nooghabi, Mohadese Gerami; Ahmedi, Zahra Grapevine Fanleaf Virus (GFLV) is one of the most important viral diseases of grapes, which can damage up to 85% of the crop, if not treated at the right time. The aim of this study is to identify infected leaves with GFLV using artificial intelligent methods using an accessible database. To do this, some pictures are taken from infected and healthy leaves of grapes and labeled by technical specialists using conventional laboratory methods. In order to provide an intelligent method for distinguishing infected leaves from healthy ones, the area of unhealthy parts of each leaf is highlighted using Fuzzy C-mean Algorithm (FCM), and then the percentages of the first two segments area are fed to a Support Vector Machines (SVM). To increase the diagnostic reliability of the system, K-fold cross validation method with k = 3 and k =5 is applied. After applying the proposed method over all images using K-fold validation technique, average confusion matrix is extracted to show the True Positive, True Negative, False Positive and False Negative percentages of classification. The results show that specificity, as the ability of the algorithm to really detect healthy images, is 100%, and sensitivity, as the ability of the algorithm to correctly detect infected images is around 97.3%. The average accuracy of the system is around 98.6%. The results imply the ability of the proposed method compared to previous methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T10:15:16Z No. of bitstreams: 1 ijimai20206_1_7_pdf_85267.pdf: 775171 bytes, checksum: f558ff64eeb7c4e5b697209e58a9f338 (MD5); Made available in DSpace on 2022-03-24T10:15:16Z (GMT). No. of bitstreams: 1 ijimai20206_1_7_pdf_85267.pdf: 775171 bytes, checksum: f558ff64eeb7c4e5b697209e58a9f338 (MD5) https://reunir.unir.net/handle/123456789/12711 https://reunir.unir.net/handle/123456789/12710 Comparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT) Kumar, Sumit; Kumar-Solanki, Vijender; Kumar Choudhary, Saket; Selamat, Ali; González-Crespo, Rubén The concept of Internet of Things (IoT) was proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology (MIT) in 1999. IoT is an environment that people understand in many different ways depending on their requirement, point of view and purpose. When transmitting data in IoT environment, distribution of network traffic fluctuates frequently. If links of the network or nodes fail randomly, then automatically new nodes get added frequently. Heavy network traffic affects the response time of all system and it consumes more energy continuously. Minimization the network traffic/ by finding the shortest path from source to destination minimizes the response time of all system and also reduces the energy consumption cost. The ant colony optimization (ACO) and K-Means clustering algorithms characteristics conform to the auto-activator and optimistic response mechanism of the shortest route searching from source to destination. In this article, ACO and K-Means clustering algorithms are studied to search the shortest route path from source to destination by optimizing the Quality of Service (QoS) constraints. Resources are assumed in the active and varied IoT network atmosphere for these two algorithms. This work includes the study and comparison between ant colony optimization (ACO) and K-Means algorithms to plan a response time aware scheduling model for IoT. It is proposed to divide the IoT environment into various areas and a various number of clusters depending on the types of networks. It is noticed that this model is more efficient for the suggested routing algorithm in terms of response time, point-to-point delay, throughput and overhead of control bits. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T09:55:24Z No. of bitstreams: 1 ijimai20206_1_12_pdf_15248.pdf: 1138748 bytes, checksum: 3677e79d590223438312a4e59f5b9c40 (MD5); Made available in DSpace on 2022-03-24T09:55:24Z (GMT). No. of bitstreams: 1 ijimai20206_1_12_pdf_15248.pdf: 1138748 bytes, checksum: 3677e79d590223438312a4e59f5b9c40 (MD5) https://reunir.unir.net/handle/123456789/12710 https://reunir.unir.net/handle/123456789/12709 A Multicriteria Optimization for Flight Route Networks in Large-Scale Airlines Using Intelligent Spatial Information Borhani, Mostafa; Akbari, Kamal; Matkan, Aliakbar; Tanasan, Mohammad Air route network optimization, one of the airspace planning challenges, effectively manages airspace resources toward increasing airspace capacity and reducing air traffic congestion. In this paper, the structure of the flight network in air transport is analyzed with a multi-objective genetic algorithm regarding Geographic Information System (GIS) which is used to optimize this Iran airlines topology to reduce the number of airways and the aggregation of passengers in aviation industries organization and also to reduce changes in airways and the travel time for travelers. The proposed model of this study is based on the combination of two topologies – point-to-point and Hub-and-spoke – with multiple goals for causing a decrease in airways and travel length per passenger and also to reach the minimum number of air stops per passenger. The proposed Multi-objective Genetic Algorithm (MOGA) is tested and assessed in data of the Iran airlines industry in 2018, as an example to real-world applications, to design Iran airline topology. MOGA is proven to be effective in general to solve a network-wide flight trajectory planning. Using the combination of point-to-point and Hub-and-spoke topologies can improve the performance of the MOGA algorithm. Based on Iran airline traffic patterns in 2018, the proposed model successfully decreased 50.8% of air routes (184 air routes) compared to the current situations while the average travel length and the average changes in routes were increased up to 13.8% (about 100 kilometers) and up to 18%, respectively. The proposed algorithm also suggests that the current air routes of Iran can be decreased up to 24.7% (89 airways) if the travel length and the number of changes increase up to 4.5% (32 kilometers) and 5%, respectively. Two intermediate airports were supposed for these experiments. The computational results show the potential benefits of the proposed model and the advantage of the algorithm. The structure of the flight network in air transport can significantly reduce operational cost while ensuring the operation safety. According to the results, this intelligent multi-object optimization model would be able to be successfully used for a precise design and efficient optimization of existing and new airline topologies. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-24T09:07:19Z No. of bitstreams: 1 IJIMAI20206_1_14.pdf: 3851211 bytes, checksum: 94e85c4b574e6f1eb76e0df81d928791 (MD5); Made available in DSpace on 2022-03-24T09:07:19Z (GMT). No. of bitstreams: 1 IJIMAI20206_1_14.pdf: 3851211 bytes, checksum: 94e85c4b574e6f1eb76e0df81d928791 (MD5) https://reunir.unir.net/handle/123456789/12709 https://reunir.unir.net/handle/123456789/12696 Multilayer Feedforward Neural Network for Internet Traffic Classification Harish, B S; Nagadarshan, N; Manju, N Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes. In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset. In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively. To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes. Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset. The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-21T11:33:54Z No. of bitstreams: 1 ijimai20206_1_13_pdf_16647.pdf: 369125 bytes, checksum: 5d56354cb826788e282d3f6cb037a7be (MD5); Made available in DSpace on 2022-03-21T11:33:54Z (GMT). No. of bitstreams: 1 ijimai20206_1_13_pdf_16647.pdf: 369125 bytes, checksum: 5d56354cb826788e282d3f6cb037a7be (MD5) https://reunir.unir.net/handle/123456789/12696 https://reunir.unir.net/handle/123456789/12695 Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection Hans, Rahul; Kaur, Harjot Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-21T11:10:28Z No. of bitstreams: 1 ijimai20206_1_11_pdf_31520.pdf: 937506 bytes, checksum: 4883488ef6b19fedd05372ccf2eecd10 (MD5); Made available in DSpace on 2022-03-21T11:10:28Z (GMT). No. of bitstreams: 1 ijimai20206_1_11_pdf_31520.pdf: 937506 bytes, checksum: 4883488ef6b19fedd05372ccf2eecd10 (MD5) https://reunir.unir.net/handle/123456789/12695 https://reunir.unir.net/handle/123456789/12694 Mamdani Fuzzy Expert System Based Directional Relaying Approach for Six-Phase Transmission Line Kumar, Naresh; Sanjay, Ch.; Chakravarthy, M Traditional directional relaying methods for 6-phase transmission lines have complex effort, and so there is still a need for novel direction relaying estimation scheme. This study presents a Mamdani-fuzzy expert system (MFES) approach for discriminating faulty section/zone, classifying faults and locating faults in 6-phase transmission lines. The 6-phase fundamental component of currents, voltages and phase angles are captured at single bus and are used in the protection scheme. Simulation results substantiate that the protection scheme is very successful against many parameters such as different fault types, fault resistances, transmission line fault locations and inception angles. A large number of fault case studies have been carried out to evaluate reach setting and % error of proposed method. It provides primary protection to transmission line length and also offers backup protection for a reverse section of transmission line. The experimental results show that the scheme performs better than the other schemes. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-21T10:44:28Z No. of bitstreams: 1 ijimai20206_1_5_pdf_10077.pdf: 957893 bytes, checksum: e61637c9f5939a170ec2f079a9975fba (MD5); Made available in DSpace on 2022-03-21T10:44:28Z (GMT). No. of bitstreams: 1 ijimai20206_1_5_pdf_10077.pdf: 957893 bytes, checksum: e61637c9f5939a170ec2f079a9975fba (MD5) https://reunir.unir.net/handle/123456789/12694 https://reunir.unir.net/handle/123456789/12693 Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments Saleem, Nasir; Khattak, Muhammad Irfan In this paper, we considered the problem of the speech enhancement similar to the real-world environments where several complex noise sources simultaneously degrade the quality and intelligibility of a target speech. The existing literature on the speech enhancement principally focuses on the presence of one noise source in mixture signals. However, in real-world situations, we generally face and attempt to improve the quality and intelligibility of speech where various complex stationary and nonstationary noise sources are simultaneously mixed with the target speech. Here, we have used deep learning for speech enhancement in complex-noisy environments and used ideal binary mask (IBM) as a binary classification function by using deep neural networks (DNNs). IBM is used as a target function during training and the trained DNNs are used to estimate IBM during enhancement stage. The estimated target function is then applied to the complex-noisy mixtures to obtain the target speech. The mean square error (MSE) is used as an objective cost function at various epochs. The experimental results at different input signal-to-noise ratio (SNR) showed that DNN-based complex-noisy speech enhancement outperformed the competing methods in terms of speech quality by using perceptual evaluation of speech quality (PESQ), segmental signal-to-noise ratio (SNRSeg), log-likelihood ratio (LLR), weighted spectral slope (WSS). Moreover, short-time objective intelligibility (STOI) reinforced the better speech intelligibility. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-21T09:41:51Z No. of bitstreams: 1 ijimai20206_1_10_pdf_20073.pdf: 1358294 bytes, checksum: 34c71933dfa29a7278a5ce6308de80aa (MD5); Made available in DSpace on 2022-03-21T09:41:51Z (GMT). No. of bitstreams: 1 ijimai20206_1_10_pdf_20073.pdf: 1358294 bytes, checksum: 34c71933dfa29a7278a5ce6308de80aa (MD5) https://reunir.unir.net/handle/123456789/12693 https://reunir.unir.net/handle/123456789/12692 A Convolution Neural Network Engine for Sclera Recognition Harish, B S; Maheshan, M S; Nagadarshan, N The world is shifting to the digital era in an enormous pace. This rise in the digital technology has created plenty of applications in the digital space, which demands a secured environment for transacting and authenticating the genuineness of end users. Biometric systems and its applications has seen great potentials in its usability in the tech industries. Among various biometric traits, sclera trait is attracting researchers from experimenting and exploring its characteristics for recognition systems. This paper, which is first of its kind, explores the power of Convolution Neural Network (CNN) for sclera recognition by developing a neural model that trains its neural engine for a recognition system. To do so, the proposed work uses the standard benchmark dataset called Sclera Segmentation and Recognition Benchmarking Competition (SSRBC 2015) dataset, which comprises of 734 images which are captured at different viewing angles from 30 different classes. The proposed methodology results showcases the potential of neural learning towards sclera recognition system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-21T09:18:49Z No. of bitstreams: 1 ijimai20206_1_9_pdf_52009.pdf: 558901 bytes, checksum: 43647f9ab1756054fb1233e9b5648f2f (MD5); Made available in DSpace on 2022-03-21T09:18:49Z (GMT). No. of bitstreams: 1 ijimai20206_1_9_pdf_52009.pdf: 558901 bytes, checksum: 43647f9ab1756054fb1233e9b5648f2f (MD5) https://reunir.unir.net/handle/123456789/12692 https://reunir.unir.net/handle/123456789/12688 Soft Computing Modelling of Urban Evolution: Tehran Metropolis Borhani, Mostafa; Ghasemloo, Nima Exploring computational intelligence, geographic information systems and statistical information, a creative and innovative model for urban evolution is presented in this paper. The proposed model employs fuzzy logic and artificial neural network as forecasting tools for describing the urban growth. This dynamic urban evolution model considers the spatial data of population, as well as its time changes and the building usage patterns. For clustering the spatial features, fuzzy algorithms were implemented to represent different levels of urban growth and development. Then, these fuzzy clusters were modeled by the multi-layer neural networks to estimate the urban growth. Based on this novel intelligent model, the current state of development of Tehran’s population and the future of this urban evolution were evaluated by empirical data and the achieved outcomes were detailed in qualitative charts. The input data-set includes four censuses with five-year intervals. Tehran's demographic evolution model forecasts the next five years with an overall accuracy of 81% and Cohen's kappa coefficient up to 74% beside the qualitative charts. These performance indicators are higher than the previous advanced models. The primary objective of this proposed model is to aid planners and decision makers to predict the development trend of urban population. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-18T13:13:49Z No. of bitstreams: 1 ijimai20206_1_1_pdf_16471.pdf: 757262 bytes, checksum: 8ecbe1ebc84543f5409a143f7c009930 (MD5); Made available in DSpace on 2022-03-18T13:13:49Z (GMT). No. of bitstreams: 1 ijimai20206_1_1_pdf_16471.pdf: 757262 bytes, checksum: 8ecbe1ebc84543f5409a143f7c009930 (MD5) https://reunir.unir.net/handle/123456789/12688 https://reunir.unir.net/handle/123456789/12687 Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network Harish, B S; Roopa, C K Cardio-vascular diseases are one of the foremost causes of mortality in today’s world. The prognosis for cardiovascular diseases is usually done by ECG signal, which is a simple 12-lead Electrocardiogram (ECG) that gives complete information about the function of the heart including the amplitude and time interval of P-QRST-U segment. This article recommends a novel approach to identify the location of thrombus in culprit artery using the Information Fuzzy Network (IFN). Information Fuzzy Network, being a supervised machine learning technique, takes known evidences based on rules to create a predicted classification model with thrombus location obtained from the vast input ECG data. These rules are well-defined procedures for selecting hypothesis that best fits a set of observations. Results illustrate that the recommended approach yields an accurateness of 92.30%. This novel approach is shown to be a viable ECG analysis approach for identifying the culprit artery and thus localizing the thrombus. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-18T13:07:26Z No. of bitstreams: 1 ijimai20206_1_2_pdf_13999.pdf: 771524 bytes, checksum: 891c8db38b0693dd2735f81ba123140f (MD5); Made available in DSpace on 2022-03-18T13:07:26Z (GMT). No. of bitstreams: 1 ijimai20206_1_2_pdf_13999.pdf: 771524 bytes, checksum: 891c8db38b0693dd2735f81ba123140f (MD5) https://reunir.unir.net/handle/123456789/12687