https://reunir.unir.net/handle/123456789/12352 2024-11-08T12:22:56Z https://reunir.unir.net/handle/123456789/12367 IJIMAI Editor's Note - Vol. 5 Issue 1 Khari, Manju The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) provides an interdisciplinary forum in which scientists and professionals share their research results and report new advances on tools that use AI with interactive multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T08:46:13Z No. of bitstreams: 1 ijimai_5_1_0_pdf_13503.pdf: 170828 bytes, checksum: 7a816025c5da6bc51e3ba54cc2735c0e (MD5); Made available in DSpace on 2022-01-27T08:46:13Z (GMT). No. of bitstreams: 1 ijimai_5_1_0_pdf_13503.pdf: 170828 bytes, checksum: 7a816025c5da6bc51e3ba54cc2735c0e (MD5) https://reunir.unir.net/handle/123456789/12366 MSA for Optimal Reconfiguration and Capacitor Allocation in Radial/Ring Distribution Networks Mohamed, Emad; Mohamed, Al-Attar Ali; Mitani, Yasunori This work presents a hybrid heuristic search algorithm called Moth Swarm Algorithm (MSA) in the context of power loss minimization of radial distribution networks (RDN) through optimal allocation and rating of shunt capacitors for enhancing the performance of distribution networks. With MSA, different optimization operators are used to mimic a set of behavioral patterns of moths in nature, which allows for flexible and powerful optimizer. Hence, a new dynamic selection strategy of crossover points is proposed based on population diversity to handle the difference vectors Lévy-mutation to force MSA jump out of stagnation and enhance its exploration ability. In addition, a spiral motion, adaptive Gaussian walks, and a novel associative learning mechanism with immediate memory are implemented to exploit the promising areas in the search space. In this article, the MSA is tested to adapt the objective function to reduce the system power losses, reduce total system cost and consequently increase the annual net saving with inequity constrains on capacitor size and voltage limits. The validation of the proposed algorithm has been tested and verified through small, medium and large scales of standard RDN of IEEE (33, 69, 85-bus) systems and also on ring main systems of 33 and 69-bus. In addition, the obtained results are compared with other algorithms to highlight the advantages of the proposed approach. Numerical results stated that the MSA can achieve optimal solutions for losses reduction and capacitor locations with finest performance compared with many existing algorithms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T08:42:39Z No. of bitstreams: 1 ijimai_5_1_14_pdf_75537.pdf: 2952774 bytes, checksum: 15abbd0a4a638e1080a3a712c78fedbd (MD5); Made available in DSpace on 2022-01-27T08:42:39Z (GMT). No. of bitstreams: 1 ijimai_5_1_14_pdf_75537.pdf: 2952774 bytes, checksum: 15abbd0a4a638e1080a3a712c78fedbd (MD5) https://reunir.unir.net/handle/123456789/12365 EEG Signal Analysis of Writing and Typing between Adults with Dyslexia and Normal Controls Perera, Perera, Harshani Harshani; Shiratuddin, Mohd Fairuz; Wong, Kok Wai; Fullarton, Kelly EEG is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, plays a vital role in detecting neurological conditions. In this paper, we identify some unique EEG patterns pertaining to dyslexia, which is a learning disability with a neurological origin. Although EEG signals hold important insights of brain behaviours, uncovering these insights are not always straightforward due to its complexity. We tackle this using machine learning and uncover unique EEG signals generated in adults with dyslexia during writing and typing as well as optimal EEG electrodes and brain regions for classification. This study revealed that the greater level of difficulties seen in individuals with dyslexia during writing and typing compared to normal controls are reflected in the brainwave signal patterns. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T08:36:05Z No. of bitstreams: 1 ijimai_5_1_8_pdf_17747.pdf: 1445633 bytes, checksum: 3ac9512ea438440d1f3c88d45adc48ad (MD5); Made available in DSpace on 2022-01-27T08:36:05Z (GMT). No. of bitstreams: 1 ijimai_5_1_8_pdf_17747.pdf: 1445633 bytes, checksum: 3ac9512ea438440d1f3c88d45adc48ad (MD5) https://reunir.unir.net/handle/123456789/12364 Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training Suliman, Azizah; Omarov, Batyrkhan Neural network is widely used for image classification problems, and is proven to be effective with high successful rate. However one of its main challenges is the significant amount of time it takes to train the network. The goal of this research is to improve the neural network training algorithms and apply and test them in classification and recognition problems. In this paper, we describe a method of applying Bayesian regularization to improve Levenberg-Marquardt (LM) algorithm and make it better usable in training neural networks. In the experimental part, we qualify the modified LM algorithm using Bayesian regularization and use it to determine an appropriate number of hidden layers in the network to avoid overtraining. The result of the experiment was very encouraging with a 98.8% correct classification when run on test samples. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T08:28:30Z No. of bitstreams: 1 ijimai_5_1_9_pdf_32121.pdf: 687098 bytes, checksum: 0ece36f7c9507e56324e1207a046025d (MD5); Made available in DSpace on 2022-01-27T08:28:30Z (GMT). No. of bitstreams: 1 ijimai_5_1_9_pdf_32121.pdf: 687098 bytes, checksum: 0ece36f7c9507e56324e1207a046025d (MD5) https://reunir.unir.net/handle/123456789/12363 Conceptual Model Development of Big Data Analytics Implementation Assessment Effect on Decision-Making Adrian, Cecilia; Abdullah, Rusli; Atan, Rodziah; Jusoh, Yusmadi Yah The significance of big data advancement has benefited various organizations to leverage the potential insights and capabilities of big data in organizational performance and decision-making. However, the analytics outcome and quality of big data analytics (BDA) implementation has yet to be addressed. Therefore the aims of this paper are to identify and analyze the affecting factors and elements of BDA implementation and to propose a conceptual model for effective decision-making through BDA implementation assessment. The model is developed based on three dimensions such as performing data strategy (organization), collaborative knowledge worker (people) and executing data analytics (technology). The findings of this ongoing study proceeds with designing a proposed conceptual model with the research hypothesis and may provide a better assessment model for effective decision-making in the long run. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T08:13:13Z No. of bitstreams: 1 ijimai_5_1_13_pdf_81158.pdf: 487442 bytes, checksum: 52e42f27aa17f01834a104976bc364a2 (MD5); Made available in DSpace on 2022-01-27T08:13:13Z (GMT). No. of bitstreams: 1 ijimai_5_1_13_pdf_81158.pdf: 487442 bytes, checksum: 52e42f27aa17f01834a104976bc364a2 (MD5) https://reunir.unir.net/handle/123456789/12362 A Study on Persuasive Technologies: The Relationship between User Emotions, Trust and Persuasion Ahmad, Wan Nooraishya Wan; Ali, Nazlena Mohamad A successful persuasive technology is able to persuade people to change from one state to a more well known state. Therefore, to allow for a change, persuasive technology must be able to affect users’ emotion and make the user trust the technology so that they will adopt the persuasive technology into their daily life routine, as well as continue to use the technology for long period. This paper is aimed to study the relation between users’ emotion with trust and persuasion and how they may contribute to the success of changing a person attitude or behavior towards a certain context or issue. Twenty five participants have completed the study in 6 weeks by using two types of persuasive technology that were assessed at three different interaction stages: pre, during and post. Result shows that emotions have a significant effect on trust, whereas the effect of emotions on persuasion using the persuasive technology was mediated by trust. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-27T07:58:49Z No. of bitstreams: 1 ijimai_5_1_7_pdf_14509.pdf: 372681 bytes, checksum: df5fb93cf1b9580e8b37412c5f4b0911 (MD5); Made available in DSpace on 2022-01-27T07:58:49Z (GMT). No. of bitstreams: 1 ijimai_5_1_7_pdf_14509.pdf: 372681 bytes, checksum: df5fb93cf1b9580e8b37412c5f4b0911 (MD5) https://reunir.unir.net/handle/123456789/12361 A Novel Smart Grid State Estimation Method Based on Neural Networks Abdel-Nasser, Mohamed; Mahmoud, Karar; Kashef, Heba The rapid development in smart grids needs efficient state estimation methods. This paper presents a novel method for smart grid state estimation (e.g., voltages, active and reactive power loss) using artificial neural networks (ANNs). The proposed method which is called SE-NN (state estimation using neural network) can evaluate the state at any point of smart grid systems considering fluctuated loads. To demonstrate the effectiveness of the proposed method, it has been applied on IEEE 33-bus distribution system with different data resolutions. The accuracy of the proposed method is validated by comparing the results with an exact power flow method. The proposed SE-NN method is a very fast tool to estimate voltages and re/active power loss with a high accuracy compared to the traditional methods. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-26T13:26:31Z No. of bitstreams: 1 ijimai_5_1_12_pdf_18312.pdf: 980918 bytes, checksum: b29143ba71e0d61592542210c26c4ead (MD5); Made available in DSpace on 2022-01-26T13:26:31Z (GMT). No. of bitstreams: 1 ijimai_5_1_12_pdf_18312.pdf: 980918 bytes, checksum: b29143ba71e0d61592542210c26c4ead (MD5) https://reunir.unir.net/handle/123456789/12360 Users Integrity Constraints in SOLAP Systems. Application in Agroforestry Charef, Abdallah Bensalloua; Djamila, Hamdadou SpatialData Warehouse and Spatial On-Line Analytical Processing are decision support technologies which offer the spatial and multidimensional analysis of data stored in multidimensional structure. They are aimed also at supporting geographic knowledge discovery to help decision-maker in his job related to make the appropriate decision . However, if we don’t consider data quality in the spatial hypercubes and how it is explored, it may provide unreliable results. In this paper, we propose a system for the implementation of user integrity constraints in SOLAP namely “UIC-SOLAP”. It corresponds to a methodology for guaranteeing results quality in an analytical process effectuated by different users exploiting several facts tables within the same hypercube. We integrate users Integrity Constraints (IC) by specifying visualization ICs according to their preferences and we define inter-facts ICs in this case. In order to validate our proposition, we propose the multidimensional modeling by UML profile to support constellation schema of a hypercube with several fact tables related to subjects of analysis in forestry management. Then, we propose implementation of some ICs related to users of such a system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-26T13:15:19Z No. of bitstreams: 1 ijimai_5_1_6_pdf_24536.pdf: 2042302 bytes, checksum: 02a7329e6a5c358002e74e27dd226dd2 (MD5); Made available in DSpace on 2022-01-26T13:15:19Z (GMT). No. of bitstreams: 1 ijimai_5_1_6_pdf_24536.pdf: 2042302 bytes, checksum: 02a7329e6a5c358002e74e27dd226dd2 (MD5) https://reunir.unir.net/handle/123456789/12359 Spectral Restoration Based Speech Enhancement for Robust Speaker Identification Saleem, Nasir; Tareen, Tayyaba Gul Spectral restoration based speech enhancement algorithms are used to enhance quality of noise masked speech for robust speaker identification. In presence of background noise, the performance of speaker identification systems can be severely deteriorated. The present study employed and evaluated the Minimum Mean-Square-Error Short-Time Spectral Amplitude Estimators with modified a priori SNR estimate prior to speaker identification to improve performance of the speaker identification systems in presence of background noise. For speaker identification, Mel Frequency Cepstral coefficient and Vector Quantization is used to extract the speech features and to model the extracted features respectively. The experimental results showed significant improvement in speaker identification rates when spectral restoration based speech enhancement algorithms are used as a pre-processing step. The identification rates are found to be higher after employing the speech enhancement algorithms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-26T13:07:47Z No. of bitstreams: 1 ijimai_5_1_4_pdf_16417.pdf: 1957139 bytes, checksum: 3b9701b41dca56086367fdaf9a829b94 (MD5); Made available in DSpace on 2022-01-26T13:07:47Z (GMT). No. of bitstreams: 1 ijimai_5_1_4_pdf_16417.pdf: 1957139 bytes, checksum: 3b9701b41dca56086367fdaf9a829b94 (MD5) https://reunir.unir.net/handle/123456789/12358 Spatial and Textural Aspects for Arabic Handwritten Characters Recognition Boulid, Youssef; Souhar, Abdelghani; Ouagague, Mly. The purpose of the present paper is the recognition of handwritten Arabic characters in their isolated form. The specificity of Arabic characters is taken into consideration, each of the proposed feature extraction method integrates one of the two aspects: spatial and textural. In the first step, a modified Bitmap Sampling method is proposed, which converts the character’s images into a binary Matrix and then constructs a Mask for each class. A matching rate is used between the input binary matrix and the masks to determinate the corresponding class. In the second step we investigate the use of an Artificial Neural Network as classifier with the binary matrices as features and then the histograms of Local Binary Patterns to capture the texture aspect of the characters. Finally, the results of these two methods are combined to take into consideration both aspects at the same time. Tested on the Arabic set of the Isolated Farsi Handwritten Character Database, the proposed method has 2.82% error rate. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-26T13:00:17Z No. of bitstreams: 1 ijimai_5_1_11_pdf_70968.pdf: 923665 bytes, checksum: 57bbc905e4dbac48be784f4a5b0c78e9 (MD5); Made available in DSpace on 2022-01-26T13:00:17Z (GMT). No. of bitstreams: 1 ijimai_5_1_11_pdf_70968.pdf: 923665 bytes, checksum: 57bbc905e4dbac48be784f4a5b0c78e9 (MD5) https://reunir.unir.net/handle/123456789/12357 Real Time Facial Expression Recognition Using Webcam and SDK Affectiva Magdin, Martin; Prikler, F Facial expression is an essential part of communication. For this reason, the issue of human emotions evaluation using a computer is a very interesting topic, which has gained more and more attention in recent years. It is mainly related to the possibility of applying facial expression recognition in many fields such as HCI, video games, virtual reality, and analysing customer satisfaction etc. Emotions determination (recognition process) is often performed in 3 basic phases: face detection, facial features extraction, and last stage - expression classification. Most often you can meet the so-called Ekman’s classification of 6 emotional expressions (or 7 - neutral expression) as well as other types of classification - the Russell circular model, which contains up to 24 or the Plutchik’s Wheel of Emotions. The methods used in the three phases of the recognition process have not only improved over the last 60 years, but new methods and algorithms have also emerged that can determine the ViolaJones detector with greater accuracy and lower computational demands. Therefore, there are currently various solutions in the form of the Software Development Kit (SDK). In this publication, we point to the proposition and creation of our system for real-time emotion classification. Our intention was to create a system that would use all three phases of the recognition process, work fast and stable in real time. That’s why we’ve decided to take advantage of existing Affectiva SDKs. By using the classic webcamera we can detect facial landmarks on the image automatically using the Software Development Kit (SDK) from Affectiva. Geometric feature based approach is used for feature extraction. The distance between landmarks is used as a feature, and for selecting an optimal set of features, the brute force method is used. The proposed system uses neural network algorithm for classification. The proposed system recognizes 6 (respectively 7) facial expressions, namely anger, disgust, fear, happiness, sadness, surprise and neutral. We do not want to point only to the percentage of success of our solution. We want to point out the way we have determined this measurements and the results we have achieved and how these results have significantly influenced our future research direction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-25T10:54:44Z No. of bitstreams: 1 ijimai_5_1_1_pdf_82035.pdf: 1222831 bytes, checksum: be0c3502763a7b93fc0f6eb2d0b16408 (MD5); Made available in DSpace on 2022-01-25T10:54:44Z (GMT). No. of bitstreams: 1 ijimai_5_1_1_pdf_82035.pdf: 1222831 bytes, checksum: be0c3502763a7b93fc0f6eb2d0b16408 (MD5) https://reunir.unir.net/handle/123456789/12356 Novel Clustering Method Based on K-Medoids and Mobility Metric Hamzaoui, Y; Amnai, M; Choukri, A; Fakhri, Y The structure and constraint of MANETS influence negatively the performance of QoS, moreover the main routing protocols proposed generally operate in flat routing. Hence, this structure gives the bad results of QoS when the network becomes larger and denser. To solve this problem we use one of the most popular methods named clustering. The present paper comes within the frameworks of research to improve the QoS in MANETs. In this paper we propose a new algorithm of clustering based on the new mobility metric and K-Medoid to distribute the nodes into several clusters. Intuitively our algorithm can give good results in terms of stability of the cluster, and can also extend life time of cluster head. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-25T10:29:55Z No. of bitstreams: 1 ijimai_5_1_3_pdf_41024.pdf: 785747 bytes, checksum: 12a58e3d9212a360c0165802e94d744e (MD5); Made available in DSpace on 2022-01-25T10:29:55Z (GMT). No. of bitstreams: 1 ijimai_5_1_3_pdf_41024.pdf: 785747 bytes, checksum: 12a58e3d9212a360c0165802e94d744e (MD5) https://reunir.unir.net/handle/123456789/12355 Object Detection and Tracking using Modified Diamond Search Block Matching Motion Estimation Algorithm Kamble, Shailesh; Thakur, Nileshsingh; Samdurkar, Apurva; Patharkar, Akshay Object tracking is one of the main fields within computer vision. Amongst various methods/ approaches for object detection and tracking, the background subtraction approach makes the detection of object easier. To the detected object, apply the proposed block matching algorithm for generating the motion vectors. The existing diamond search (DS) and cross diamond search algorithms (CDS) are studied and experiments are carried out on various standard video data sets and user defined data sets. Based on the study and analysis of these two existing algorithms a modified diamond search pattern (MDS) algorithm is proposed using small diamond shape search pattern in initial step and large diamond shape (LDS) in further steps for motion estimation. The initial search pattern consists of five points in small diamond shape pattern and gradually grows into a large diamond shape pattern, based on the point with minimum cost function. The algorithm ends with the small shape pattern at last. The proposed MDS algorithm finds the smaller motion vectors and fewer searching points than the existing DS and CDS algorithms. Further, object detection is carried out by using background subtraction approach and finally, MDS motion estimation algorithm is used for tracking the object in color video sequences. The experiments are carried out by using different video data sets containing a single object. The results are evaluated and compared by using the evaluation parameters like average searching points per frame and average computational time per frame. The experimental results show that the MDS performs better than DS and CDS on average search point and average computation time. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-25T10:15:07Z No. of bitstreams: 1 ijimai_5_1_10_pdf_12412.pdf: 1985614 bytes, checksum: 7b67fc33ccfae0a04afc9d3bf450af57 (MD5); Made available in DSpace on 2022-01-25T10:15:07Z (GMT). No. of bitstreams: 1 ijimai_5_1_10_pdf_12412.pdf: 1985614 bytes, checksum: 7b67fc33ccfae0a04afc9d3bf450af57 (MD5) https://reunir.unir.net/handle/123456789/12354 Hybrid Model for Passive Locomotion Control of a Biped Humanoid:The Artificial Neural Network Approach Bhaskar-Semwal, Vijay; Raj, Manish; Nandi, G C Developing a correct model for a biped robot locomotion is extremely challenging due to its inherently unstable structure because of the passive joint located at the unilateral foot-ground contact and varying configurations throughout the gait cycle, resulting variation of dynamic descriptions and control laws from phase to phase. The present research describes the development of a hybrid biped model using an Open Dynamics Engine (ODE) based analytical three link leg model as a base model and, on top of it, an Artificial Neural Network based learning model which ensures better adaptability, better limits cycle behaviors and better generalization while negotiating along a down slope. The base model has been configured according to the individual subjects and data have been collected using a novel technique through an android app from those subjects while walking down a slope. The pattern between the deviation of the actual trajectories and the base model generated trajectories has been found using a back propagation based artificial neural network architecture. It has been observed that this base model with learning based compensation enables the biped to better adapt in a real walking environment, showing better limit cycle behaviors. We also observed the bounded nature of deviation which led us to conclude that the strategy for biped locomotion control is generic in nature and largely dominated by learning. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-25T09:46:34Z No. of bitstreams: 1 ijimai_5_1_5_pdf_20509.pdf: 1122752 bytes, checksum: 35d27cb4355b571f07b82e8ee741f9ee (MD5); Made available in DSpace on 2022-01-25T09:46:34Z (GMT). No. of bitstreams: 1 ijimai_5_1_5_pdf_20509.pdf: 1122752 bytes, checksum: 35d27cb4355b571f07b82e8ee741f9ee (MD5) https://reunir.unir.net/handle/123456789/12353 Self-Organized Hybrid Wireless Sensor Network for Finding Randomly Moving Target in Unknown Environment Nighot, Mininath; Ghatol, Ashok; Thakare, Vilas Unknown target search, in an unknown environment, is a complex problem in Wireless Sensor Network (WSN). It does not have a linear solution when target’s location and searching space is unknown. For the past few years, many researchers have invented novel techniques for finding a target using either Static Sensor Node (SSN) or Mobile Sensor Node (MSN) in WSN i.e. Hybrid WSN. But there is a lack of research to find a solution using hybrid WSN. In the current research, the problem has been addressed mostly using non-biological techniques. Due to its complexity and having a non-linear solution, Bio-inspired techniques are most suited to solve the problem. This paper proposes a solution for searching of randomly moving target in unknown area using only Mobile sensor nodes and combination of both Static and Mobile sensor nodes. In proposed technique coverage area is determined and compared. To perform the work, novel algorithms like MSNs Movement Prediction Algorithm (MMPA), Leader Selection Algorithm (LSA), Leader’s Movement Prediction Algorithm (LMPA) and follower algorithm are implemented. Simulation results validate the effectiveness of proposed work. Through the result, it is shown that proposed hybrid WSN approach with less number of sensor nodes (combination of Static and Mobile sensor nodes) finds target faster than only MSN approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-01-25T09:19:28Z No. of bitstreams: 1 ijimai_5_1_2_pdf_19352.pdf: 3409132 bytes, checksum: c031351811b430c938336ec2b69ea658 (MD5); Made available in DSpace on 2022-01-25T09:19:29Z (GMT). No. of bitstreams: 1 ijimai_5_1_2_pdf_19352.pdf: 3409132 bytes, checksum: c031351811b430c938336ec2b69ea658 (MD5)