IJARAI Volume 2 Issue 2

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: Emotional Belief-Desire-Intention Agent Model: Previous Work And Proposed Architecture

Abstract: Research in affective computing shows that agents cannot be truly intelligent, nor believable or realistic without emotions. In this paper, we present a model of emotional agents that is based on a BDI architecture. We show how we can integrate emotions, resources and personality features into an artificial intelligent agent so as to obtain a human-like behavior of this agent. We place our work in the general context of existing research in emotional agents, with emphasis on BDI emotional models.

Author 1: Mihaela- Alexandra Puica
Author 2: Adina-Magda Florea

Keywords: Affective Computing; Agent architecture; Belief-Desire-Intention model

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Paper 2: A Kabbalah System Theory of Ontological and Knowledge Engineering for Knowledge Based Systems

Abstract: Using the Kabbalah system theory (KST) developed in [1], [2], we propose an ontological engineering for knowledge representation of domains in terms of concept systems in knowledge based systems in artificial intelligence. KST is also used for the knowledge engineering of the knowledge model building based on ontology. KST provides thus an integrative, unifying, domain independent framework for both the knowledge representation via ontologies and knowledge model building via knowledge engineering in knowledge based systems.

Author 1: Gabriel Burstein
Author 2: Constantin Virgil Negoita

Keywords: knowledge based system; knowledge representation; ontological engineering; knowledge modeling; knowledge engineering; artificial intelligence; Kabbalah; system theory; category theory

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Paper 3: Genetic Programming for Document Segmentation and Region Classification Using Discipulus

Abstract: Document segmentation is a method of rending the document into distinct regions. A document is an assortment of information and a standard mode of conveying information to others. Pursuance of data from documents involves ton of human effort, time intense and might severely prohibit the usage of data systems. So, automatic information pursuance from the document has become a big issue. It is been shown that document segmentation will facilitate to beat such problems. This paper proposes a new approach to segment and classify the document regions as text, image, drawings and table. Document image is divided into blocks using Run length smearing rule and features are extracted from every blocks. Discipulus tool has been used to construct the Genetic programming based classifier model and located 97.5% classification accuracy.

Author 1: Priyadharshini N
Author 2: Vijaya MS

Keywords: Document analysis; Information retrieval; Classification; Feature extraction; Document segmentation.

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Paper 4: Moving Domestic Robotics Control Method Based on Creating and Sharing Maps with Shortest Path Findings and Obstacle Avoidance

Abstract: Control method for moving robotics in closed areas based on creation and sharing maps through shortest path findings and obstacle avoidance is proposed. Through simulation study, a validity of the proposed method is confirmed. Furthermore, the effect of map sharing among robotics is also confirmed together with obstacle avoidance with cameras and ultrasonic sensors.

Author 1: Kohei Arai

Keywords: domestic robotics; obstacle avoidance; place identifierl ultrasonic sensor; web camera

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Paper 5: Eye-Base Domestic Robot Allowing Patient to Be Self-Services and Communications Remotely

Abstract: Eye-based domestic helper is proposed for helping patient self-sufficient in hospital circumstance. This kind of system will benefit for those patient who cannot move around, it especially happen to stroke patient who in the daily they just lay on the bed. They could not move around due to the body malfunction. The only information that still could be retrieved from user is eyes. In this research, we develop a new system in the form of domestic robot helper controlled by eye which allows patient self-service and speaks remotely. First, we estimate user sight by placing camera mounted on user glasses. Once eye image is captured, the several image processing are used to estimate the sight. Eye image is cropped from the source for simplifying the area. We detect the centre of eye by seeking the location of pupil. The pupil and other eye component could be easily distinguished based on the color. Because pupil has darker color than others, we just apply adaptive threshold for its separation. By using simple model of eye, we could estimate the sight based on the input from pupil location. Next, the obtained sight value is used as input command to the domestic robot. User could control the moving of robot by eye. Also, user could send the voice through text to speech functionality. We use baby infant robot as our domestic robot. We control the robot movement by sending the command via serial communication (utilizing the USB to serial adapter). Three types of command consist of move forward, turn left, and turn right are used in the system for moving the robot. In the robot, we place another camera for capturing the scenery in the front of robot. Between robot and user, they are separated by distance. They are connected over TCP/IP network. The network allows user control the robot remotely. We set the robot as server and user’s computer as client. The robot streams the scenery video and receives command sending by the client. In the other place, client (user) receives video streaming from server and control the robot movement by sending command via the network. The user could control the robot remotely even in the long distance because user could see the scenery in the front of robot. We have tested the performance of our robot controlled over TCP/IP network. An experiment measuring the robot maneuverability performance from start point avoiding and passing obstacles has been done in our laboratory. By implementing our system, patient in hospital could self-service by them self.

Author 1: Kohei Arai
Author 2: Ronny Mardiyanto

Keywords: eye-based Human-Computer Interaction (HCI); domestic robot; communication aid; virtual travel

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Paper 6: Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification

Abstract: This paper presents a comparative account of unsupervised and supervised learning models and their pattern classification evaluations as applied to the higher education scenario. Classification plays a vital role in machine based learning algorithms and in the present study, we found that, though the error back-propagation learning algorithm as provided by supervised learning model is very efficient for a number of non-linear real-time problems, KSOM of unsupervised learning model, offers efficient solution and classification in the present study.

Author 1: R. Sathya
Author 2: Annamma Abraham

Keywords: Classification; Clustering; Learning; MLP; SOM; Supervised learning; Unsupervised learning;

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Paper 7: Hybrid of Rough Neural Networks for Arabic/Farsi Handwriting Recognition

Abstract: Handwritten character recognition is one of the focused areas of research in the field of Pattern Recognition. In this paper, a hybrid model of rough neural network has been developed for recognizing isolated Arabic/Farsi digital characters. It solves the neural network problems; proneness to overfitting, and the empirical nature of model development using rough sets and the dissimilarity analysis. Moreover the perturbation in the input data is violated using rough neuron. This paper describes an evolutionary rough neural network based technique to recognize Arabic/Farsi isolated handwritten digital characters. This method involves hierarchical feature extraction, data clustering and classification. In contrast with conventional neural network, a comparative study is appeared. Also, the details and limitations are discussed.

Author 1: Elsayed Radwan

Keywords: Rough Sets; Rough Neural Network; Arabic/Farsi Digit Recognition; Dissimilarity Analysis; and Classification.

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Paper 9: AutoBeeConf : A swarm intelligence algorithm for MANET administration

Abstract: In a mobile ad-hoc network (MANET) nodes are self-organized without any infrastructure support: they move arbitrarily causing the network to experience quick and random topology changes, have to act as routers as well as forwarding nodes, some of them do not communicate directly with each other. Routing and IP address auto-configuration are among the most challenging tasks in the MANET domain. Swarm Intelligence is a property of natural and artificial systems involving minimally skilled individuals that exhibit a collective intelligent behavior derived from the interaction with each other by means of the environment. Colonies of ants and bees are the most prominent examples of swarm intelligence systems. Flexibility, robustness, and self-organization make swarm intelligence a successful design paradigm for difficult combinatorial optimization problems, such as routing and IP address allocation in MANET. This paper proposes AutoBeeConf, a new IP address auto-configuration algorithm based on a bee swarm labor that may be applied to large scale MANET with low complexity, low communication overhead, even address distribution, and low latency. Both the protocol description and the simulation experiments are presented to demonstrate the advantages of AutoBeeConf over two known algorithms, namely Buddy and Antbased protocols. Eventually, future research directions are established, especially toward the principle that swarm intelligence paradigms may be usefully employed in the redefinition or modifications of each layer in the TCP/IP suite in such a way that it can efficiently work even in the infrastructure-less and dynamic nature of MANET environment.

Author 1: Luca Caputo
Author 2: Cristiano Davino
Author 3: Filomena de Santis
Author 4: Vincenzo Ferri

Keywords: MANET; Routing protocols; IP address auto-configuration; Swarm intelligence

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Paper 10: Improved Scatter Search Using Cuckoo Search

Abstract: The Scatter Search (SS) is a deterministic strategy that has been applied successfully to some combinatorial and continuous optimization problems. Cuckoo Search (CS) is heuristic search algorithm which is inspired by the reproduction strategy of cuckoos. This paper presents enhanced scatter search algorithm using CS algorithm. The improvement provides Scatter Search with random exploration for search space of problem and more of diversity and intensification for promising solutions. The original and improved Scatter Search has been tested on Traveling Salesman Problem. A computational experiment with benchmark instances is reported. The results demonstrate that the improved Scatter Search algorithms produce better performance than original Scatter Search algorithm. The improvement in the value of average fitness is 23.2% comparing with original SS. The developed algorithm has been compared with other algorithms for the same problem, and the result was competitive with some algorithm and insufficient with another.

Author 1: Ahmed T.Sadiq Al-Obaidi

Keywords: component; Metaheuristic; Scatter Search; Cuckoo Search; Combinatorial Problems; Traveling Salesman Problem

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Paper 11: Texture Based Image Retrieval Using Framelet Transform–Gray Level Co-occurrence Matrix(GLCM)

Abstract: This paper presents a novel content based image retrieval (CBIR) system based on Framelet Transform combined with gray level co-occurrence matrix (GLCM).The proposed method is shift invariant which captured edge information more accurately than conventional transform domain methods as well as able to handle images of arbitrary size. Current system uses texture as a visual content for feature extraction. First Texture features are obtained by computing the energy, standard deviation and mean on each sub band of the Framelet transform decomposed image .Then a new method as a combination of the Framelet transform-Gray level co-occurrence matrix (GLCM) is applied. The results of the proposed methods are compared with conventional methods. We have done the comparison of results of these two methods for image retrieval. Euclidean distance, Canberra distance, city black distance is used as similarity measure in the proposed CBIR system.

Author 1: S. Sulochana
Author 2: R.Vidhya

Keywords: Content Based image Retrieval (CBIR); Discrete Wavelet transform (DWT); Framelet Transform; Gray level- co occurrence matrix(GLCM)

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