The treatment of complex systems often requires the manipulation of vague, imprecise and uncertain information. Indeed, the human being is competent in handling of such systems in a natural way. Instead of thinking in mathematical terms, humans describes the behavior of the system by language proposals. In order to represent this type of information, Zadeh proposed to model the mechanism of human thought by approximate reasoning based on linguistic
variables. He introduced the theory of fuzzy sets in 1965, which provides an interface between language and digital worlds. In this paper, we propose a Boolean modeling of the fuzzy
reasoning that we baptized Fuzzy-BML and uses the characteristics of induction graph classification. Fuzzy-BML is the process by which the retrieval phase of a CBR is modelled not in the conventional form of mathematical equations, but in the form of a database with membership functions of fuzzy rules.
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAScscpconf
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
Neuro-Fuzzy Model for Strategic Intellectual Property Cost ManagementEditor IJCATR
Strategic Intellectual property (IP) management requires strategic IP creation cost management. It is ideal to
be able to proactively estimate the cost of creating IP. This would facilitate the alignment of IP creation activities in order
to meet strategic management objectives. This paper proposes the use of Neuro-fuzzy model for strategic management
of IP cost management. The extraction of the variables for the model is based on the Activity Based Costing techniques.
An Iterative Improved k-means ClusteringIDES Editor
Clustering is a data mining (machine learning),
unsupervised learning technique used to place data elements
into related groups without advance knowledge of the group
definitions. One of the most popular and widely studied
clustering methods that minimize the clustering error for
points in Euclidean space is called K-means clustering.
However, the k-means method converges to one of many local
minima, and it is known that the final results depend on the
initial starting points (means). In this research paper, we have
introduced and tested an improved algorithm to start the kmeans
with good starting points (means). The good initial
starting points allow k-means to converge to a better local
minimum; also the numbers of iteration over the full dataset
are being decreased. Experimental results show that initial
starting points lead to good solution reducing the number of
iterations to form a cluster.
A preliminary survey on optimized multiobjective metaheuristic methods for da...ijcsit
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
My presentation gives a brief overview about soft computing and it's concepts. Such as..Neural networks, Machine learning, Artificial Intelligence etc...
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAScscpconf
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
Neuro-Fuzzy Model for Strategic Intellectual Property Cost ManagementEditor IJCATR
Strategic Intellectual property (IP) management requires strategic IP creation cost management. It is ideal to
be able to proactively estimate the cost of creating IP. This would facilitate the alignment of IP creation activities in order
to meet strategic management objectives. This paper proposes the use of Neuro-fuzzy model for strategic management
of IP cost management. The extraction of the variables for the model is based on the Activity Based Costing techniques.
An Iterative Improved k-means ClusteringIDES Editor
Clustering is a data mining (machine learning),
unsupervised learning technique used to place data elements
into related groups without advance knowledge of the group
definitions. One of the most popular and widely studied
clustering methods that minimize the clustering error for
points in Euclidean space is called K-means clustering.
However, the k-means method converges to one of many local
minima, and it is known that the final results depend on the
initial starting points (means). In this research paper, we have
introduced and tested an improved algorithm to start the kmeans
with good starting points (means). The good initial
starting points allow k-means to converge to a better local
minimum; also the numbers of iteration over the full dataset
are being decreased. Experimental results show that initial
starting points lead to good solution reducing the number of
iterations to form a cluster.
A preliminary survey on optimized multiobjective metaheuristic methods for da...ijcsit
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
My presentation gives a brief overview about soft computing and it's concepts. Such as..Neural networks, Machine learning, Artificial Intelligence etc...
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
PLANNING BASED ON CLASSIFICATION BY INDUCTION GRAPHcsandit
In Artificial Intelligence, planning refers to an area of research that proposes to develop
systems that can automatically generate a result set, in the form of an integrated decisionmaking
system through a formal procedure, known as plan. Instead of resorting to the
scheduling algorithms to generate plans, it is proposed to operate the automatic learning by
decision tree to optimize time. In this paper, we propose to build a classification model by
induction graph from a learning sample containing plans that have an associated set of
descriptors whose values change depending on each plan. This model will then operate for
classifying new cases by assigning the appropriate plan.
PLANNING BASED ON CLASSIFICATION BY INDUCTION GRAPHcscpconf
In Artificial Intelligence, planning refers to an area of research that proposes to develop systems that can automatically generate a result set, in the form of an integrated decisionmaking system through a formal procedure, known as plan. Instead of resorting to the scheduling algorithms to generate plans, it is proposed to operate the automatic learning by decision tree to optimize time. In this paper, we propose to build a classification model by induction graph from a learning sample containing plans that have an associated set of descriptors whose values change depending on each plan. This model will then operate for classifying new cases by assigning the appropriate plan.
In everyday life, we are often faced with similar problems which we resolve with our
experience. Case-based reasoning is a paradigm of problem solving based on past experience.
Thus, case-based reasoning is considered as a valuable technique for the implementation of
various tasks involving solving planning problem. Planning is considered as a decision support
process designed to provide resources and required services to achieve specific objectives,
allowing the selection of a better solution among several alternatives. However, we propose to
exploit decision trees and k-NN combination to choose the most appropriate solutions. In a
previous work [1], we have proposed a new planning approach guided by case-based reasoning
and decision tree, called DTR, for case retrieval. In this paper, we use a classifier combination
for similarity calculation in order to select the best solution to the target case. Thus, the use of
the decision trees and k-NN combination allows improving the relevance of results and finding
the most relevant cases.
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
PLANNING BASED ON CLASSIFICATION BY INDUCTION GRAPHcsandit
In Artificial Intelligence, planning refers to an area of research that proposes to develop
systems that can automatically generate a result set, in the form of an integrated decisionmaking
system through a formal procedure, known as plan. Instead of resorting to the
scheduling algorithms to generate plans, it is proposed to operate the automatic learning by
decision tree to optimize time. In this paper, we propose to build a classification model by
induction graph from a learning sample containing plans that have an associated set of
descriptors whose values change depending on each plan. This model will then operate for
classifying new cases by assigning the appropriate plan.
PLANNING BASED ON CLASSIFICATION BY INDUCTION GRAPHcscpconf
In Artificial Intelligence, planning refers to an area of research that proposes to develop systems that can automatically generate a result set, in the form of an integrated decisionmaking system through a formal procedure, known as plan. Instead of resorting to the scheduling algorithms to generate plans, it is proposed to operate the automatic learning by decision tree to optimize time. In this paper, we propose to build a classification model by induction graph from a learning sample containing plans that have an associated set of descriptors whose values change depending on each plan. This model will then operate for classifying new cases by assigning the appropriate plan.
In everyday life, we are often faced with similar problems which we resolve with our
experience. Case-based reasoning is a paradigm of problem solving based on past experience.
Thus, case-based reasoning is considered as a valuable technique for the implementation of
various tasks involving solving planning problem. Planning is considered as a decision support
process designed to provide resources and required services to achieve specific objectives,
allowing the selection of a better solution among several alternatives. However, we propose to
exploit decision trees and k-NN combination to choose the most appropriate solutions. In a
previous work [1], we have proposed a new planning approach guided by case-based reasoning
and decision tree, called DTR, for case retrieval. In this paper, we use a classifier combination
for similarity calculation in order to select the best solution to the target case. Thus, the use of
the decision trees and k-NN combination allows improving the relevance of results and finding
the most relevant cases.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This survey reviews the latest literature related to scheduling problems which is closely related to load
balancing problems. It is noted that they are often used with the same meaning. In fact, it is not efficient to use one
without the other. This is because the scheduling problem is to determine the order of tasks execution on available
devices, while load balancing seeks to balance these tasks between these devices. The motivation of this work comes
from the need to have, in one paper, a comprehensive idea of these problems with an in-depth view of the involved
research tendencies. Several scheduling schemes under different constraints and optimization criteria are discussed.
We observed that the rapid technological development at the level of machinery and equipment is accompanied by
intensive use of these devices. This requires the enhancement and improvement of scheduling algorithms and the
tendency is more and more towards the heuristic and approximate algorithms. As the scheduling schemes range from
workshops to Cloud, Fog and Edge computing segments of the collaborative mobile computing, we argue that they
have not yet been used effectively in its third segment: individual mobile networks. These networks can play the most
effective role, in catastrophic situations, to overcome the problem of telephony/internet communication traffic with
the cheapest or free cost. We aim to motivate research on scheduling issues to this segment of collaborative mobile
computing that becomes indispensable in urgent these cases as: Oregon, floods, earthquake, terrorist attacks, etc.,
when almost everything is damaged or not accessible except our small mobile devices and ubiquitous resources.
CONSTRAINT-BASED AND FUZZY LOGIC STUDENT MODELING FOR ARABIC GRAMMARijcsit
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer) which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different questions that deal with the different concepts and have different difficulty levels. Constraint-based student modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
level the different grammar skills through the defined skill structures. The main contribution of this paper is the hierarchal representation of the system's basic grammar skills as domain knowledge. That representation is used as a mechanism for efficiently checking constraints to model the student knowledge
and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number of trails the student takes for answering each question and fuzzy logic decision system are used to determine the student learning level for each lesson as a long-term model. The results of the evaluation showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its
features and abilities.
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with
linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language
Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such
systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic
language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the
fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer)
which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different
questions that deal with the different concepts and have different difficulty levels. Constraint-based student
modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
level the different grammar skills through the defined skill structures. The main contribution of this paper
is the hierarchal representation of the system's basic grammar skills as domain knowledge. That
representation is used as a mechanism for efficiently checking constraints to model the student knowledge
and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number
of trails the student takes for answering each question and fuzzy logic decision system are used to
determine the student learning level for each lesson as a long-term model. The results of the evaluation
showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its
features and abilities.
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with
linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language
Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such
systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic
language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the
fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer)
which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different
questions that deal with the different concepts and have different difficulty levels. Constraint-based student
modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
level the different grammar skills through the defined skill structures. The main contribution of this paper
is the hierarchal representation of the system's basic grammar skills as domain knowledge. That
representation is used as a mechanism for efficiently checking constraints to model the student knowledge
and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number
of trails the student takes for answering each question and fuzzy logic decision system are used to
determine the student learning level for each lesson as a long-term model. The results of the evaluation
showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its
features and abilities.
Ontology Based PMSE with Manifold PreferenceIJCERT
International journal from http://www.ijcert.org
IJCERT Standard on-line Journal
ISSN(Online):2349-7084,(An ISO 9001:2008 Certified Journal)
iso nicir csir
IJCERT (ISSN 2349–7084 (Online)) is approved by National Science Library (NSL), National Institute of Science Communication And Information Resources (NISCAIR), Council of Scientific and Industrial Research, New Delhi, India.
Visual representation and organization of the knowledge have been utilized in different ways in tutoring
systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms,
for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses
what is way of the utilization of every formalism and the offering of the potential outcomes to assist the
student in education systems.
GRAPHICAL REPRESENTATION IN TUTORING SYSTEMSijcsit
Visual representation and organization of the knowledge have been utilized in different ways in tutoring systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms, for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses what is way of the utilization of every formalism and the offering of the potential outcomes to assist the student in education systems.
Visual representation and organization of the knowledge have been utilized in different ways in tutoring systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms, for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses what is way of the utilization of every formalism and the offering of the potential outcomes to assist the student in education systems.
A ROBUST JOINT-TRAINING GRAPHNEURALNETWORKS MODEL FOR EVENT DETECTIONWITHSYMM...kevig
Events are the core element of information in descriptive corpus. Although many progresses have beenmade in Event Detection (ED), it is still a challenge in Natural Language Processing (NLP) to detect
event information from data with unavoidable noisy labels. A robust Joint-training Graph ConvolutionNetworks (JT-GCN) model is proposed to meet the challenge of ED tasks with noisy labels in this paper. Specifically, we first employ two Graph Convolution Networks with Edge Enhancement (EE-GCN) tomake predictions simultaneously. A joint loss combining the detection loss and the contrast loss fromtwonetworks is then calculated for training. Meanwhile, a small-loss selection mechanism is introduced tomitigate the impact of mislabeled samples in networks training process. These two networks gradually
reach an agreement on the ED tasks as joint-training progresses. Corrupted data with label noise are
generated from the benchmark dataset ACE2005. Experiments on ED tasks has been conducted with bothsymmetry and asymmetry label noise on dif erent level. The experimental results show that the proposedmodel is robust to the impact of label noise and superior to the state-of-the-art models for EDtasks.
A Robust Joint-Training Graph Neural Networks Model for Event Detection with ...kevig
Events are the core element of information in descriptive corpus. Although many progresses have beenmade in Event Detection (ED), it is still a challenge in Natural Language Processing (NLP) to detect event information from data with unavoidable noisy labels. A robust Joint-training Graph ConvolutionNetworks (JT-GCN) model is proposed to meet the challenge of ED tasks with noisy labels in this paper. Specifically, we first employ two Graph Convolution Networks with Edge Enhancement (EE-GCN) tomake predictions simultaneously. A joint loss combining the detection loss and the contrast loss fromtwonetworks is then calculated for training. Meanwhile, a small-loss selection mechanism is introduced tomitigate the impact of mislabeled samples in networks training process. These two networks gradually reach an agreement on the ED tasks as joint-training progresses. Corrupted data with label noise are generated from the benchmark dataset ACE2005. Experiments on ED tasks has been conducted with bothsymmetry and asymmetry label noise on dif erent level. The experimental results show that the proposedmodel is robust to the impact of label noise and superior to the state-of-the-art models for EDtasks.
Case Study Based Software Engineering Project Development: State of ArtDr Sukhpal Singh Gill
Publised in International Journal of Scientific Research in Computer Science Applications and Management Studies (IJSRCSAMS), Volume 2, Issue 3 (May 2013).
Step by Step Development of Software Project
An approach to learn Software Project Management Practically.
SDLC phases of Software Engineering
Project Completed at Thapar University, Patiala, Punjab, India.
Download Link:
http://arxiv.org/ftp/arxiv/papers/1306/1306.2502.pdf
http://www.ijsrcsams.com/images/stories/Past_Issue_Docs/ijsrcsamsv2i3p31.pdf
SRS of this Project can be downloaded from :
http://www.slideshare.net/sukhpalsinghgill/software-requirements-specification-srs-for-online-tower-plotting-system-otps
An Iterative Model as a Tool in Optimal Allocation of Resources in University...Dr. Amarjeet Singh
In this paper, a study was carried out to aid in
adequate allocation of resources in the College of Natural
Sciences, TYZ University (not real name because of ethical
issue). Questionnaires were administered to the highranking officials of one the Colleges, College of Pure and
Applied Sciences, to examine how resources were allocated
for three consecutive sessions(the sessions were 2009/2010,
2010/2011 and 2011/2012),then used the data gathered and
analysed to generate contributory inputs for the three basic
outputs (variables)formed for the purpose of the study.
These variables are: 1
x
represents the quality of graduates
produced;
2
x
stands for research papers, Seminars,
Journals articles etc. published by faculties and
3
x
denotes service delivery within the three sessions under study.
Simplex Method of Linear Programming was used to solve
the model formulated.
Similar to PLANNING BY CASE-BASED REASONING BASED ON FUZZY LOGIC (20)
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
Using social media in education provides learners with an informal way for communication. Informal communication tends to remove barriers and hence promotes student engagement. This paper presents our experience in using three different social media technologies in teaching software project management course. We conducted different surveys at the end of every semester to evaluate students’ satisfaction and engagement. Results show that using social media enhances students’ engagement and satisfaction. However, familiarity with the tool is an important factor for student satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The amount of piracy in the streaming digital content in general and the music industry in specific is posing a real challenge to digital content owners. This paper presents a DRM solution to monetizing, tracking and controlling online streaming content cross platforms for IP enabled devices. The paper benefits from the current advances in Blockchain and cryptocurrencies. Specifically, the paper presents a Global Music Asset Assurance (GoMAA) digital currency and presents the iMediaStreams Blockchain to enable the secure dissemination and tracking of the streamed content. The proposed solution provides the data owner the ability to control the flow of information even after it has been released by creating a secure, selfinstalled, cross platform reader located on the digital content file header. The proposed system provides the content owners’ options to manage their digital information (audio, video, speech, etc.), including the tracking of the most consumed segments, once it is release. The system benefits from token distribution between the content owner (Music Bands), the content distributer (Online Radio Stations) and the content consumer(Fans) on the system blockchain.
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This paper discusses the importance of verb suffix mapping in Discourse translation system. In
discourse translation, the crucial step is Anaphora resolution and generation. In Anaphora
resolution, cohesion links like pronouns are identified between portions of text. These binders
make the text cohesive by referring to nouns appearing in the previous sentences or nouns
appearing in sentences after them. In Machine Translation systems, to convert the source
language sentences into meaningful target language sentences the verb suffixes should be
changed as per the cohesion links identified. This step of translation process is emphasized in
the present paper. Specifically, the discussion is on how the verbs change according to the
subjects and anaphors. To explain the concept, English is used as the source language (SL) and
an Indian language Telugu is used as Target language (TL)
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The using of information technology resources is rapidly increasing in organizations,
businesses, and even governments, that led to arise various attacks, and vulnerabilities in the
field. All resources make it a must to do frequently a penetration test (PT) for the environment
and see what can the attacker gain and what is the current environment's vulnerabilities. This
paper reviews some of the automated penetration testing techniques and presents its
enhancement over the traditional manual approaches. To the best of our knowledge, it is the
first research that takes into consideration the concept of penetration testing and the standards
in the area.This research tackles the comparison between the manual and automated
penetration testing, the main tools used in penetration testing. Additionally, compares between
some methodologies used to build an automated penetration testing platform.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several
algorithms have been introduced to extract these rules. However, these algorithms suffer from
the problems of utility, redundancy and large number of extracted fuzzy association rules. The
expert will then be confronted with this huge amount of fuzzy association rules. The task of
validation becomes fastidious. In order to solve these problems, we propose a new validation
method. Our method is based on three steps. (i) We extract a generic base of non redundant
fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis.
(ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules
using structural equation model.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
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Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
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Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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2. 54 Computer Science & Information Technology (CS & IT)
The CBR based on artificial intelligence techniques is an approach to problem solving that uses
past experiences to solve new problems by finding similar cases in its knowledge base and
adapting them to the particular case. All the experiences form a case basis. Each case is
represented by a knowledge experience. This experience is a lesson for the CBR system to solve
problems of various kinds. The CBR consists of five phases : 1-Elaboration of the case. 2-
Retrieval; 3-Adaptation; 4-Review and finally 5-Memory. For our project we are interested in the
second phase: retrieval.
Therefore our contribution in this area is double, on the one hand offer a reactive planning
module based on a CBR for the optimization of the scheduling, and on the other hand offer a
classification induction graph [10] for the acceleration of the indexing of cases : remembering.
The classification issue is to assign the various observations to categories or predefined classes
[16] [2]. In general classification methods consist in several stages. The most important step is to
develop the rules of classification from a priori knowledge; It is the learning phase [11].
The classification by inductive learning finds its originality in the fact that humans often use it to
resolve and to handle very complex situations in their lives daily [19]. However, the induction in
humans is often approximate rather than exact. Indeed, the human brain is able to handle
imprecise, vague, uncertain and incomplete information [18]. Also, the human brain is able to
learn and to operate in a context where uncertainty management is indispensable. In this paper,
we propose a Boolean model of fuzzy reasoning for indexing the sub-plans, based on
characteristics of the classification by inductive learning in humans [22].
This article is structured as follows. Section 2 presents a state of the art of work about planning
and data mining. Section 3 is devoted to the construction of the base of the case. In section 4 we
discuss classification by inductive learning from data and in particular the induction of rules by
graph of induction. In section 5 we introduce Boolean modeling [1]. Fuzzy logic is discussed in
section 6. Section 7 presents results of experimentation. Finally, we present the guidance of our
contribution and experimentation and we conclude in section 8.
2. STATE OF THE ART
We present previous work which have combined planning and data mining.
Kaufman and Michalski [15] propose an approach that involves the integration of various
processes of learning and inference in a system that automatically search for different data mining
tasks according to a high-level plan developed by a user. This plan is specified in a language of
knowledge production, called KGL (Knowledge Generation Language).
Kalousis and al. [14] propose a system that combines planning and metalearning to provide
support to users of a virtual laboratory data mining. The addition of meta-learning to planning
based data mining support will make the planner adaptive to changes in the data and capable of
improving its advice over time. Planner based on knowledge is based on ontology of data mining
workflow for planning knowledge discovery and determine the set of valid operator for each
stage of the workflow.
Záková and al. [20] have proposed a methodology that defines a formal conceptualization of the
types of knowledge and data mining algorithms as well as a planning algorithm that extracts the
constraints of this conceptualization according to the requirements given by the user. The task of
building automated workflow includes the following steps: converting the task of knowledge
discovery into a planning problem, plan generation using a planning algorithm, storing the
generated abstract workflow in form of semantic annotation, instantiating the abstract workflow
with specific configurations of the required algorithms and storing the generated workflow.
3. Computer Science & Information Technology (CS & IT) 55
Fernández and al. [12] presented a tool based on automated planning that helps users, not
necessarily experts on data minig, to perform data mining tasks. The starting point will be a
definition of the data mining task to be carried out and the output will be a set of plans. These
plans are executed with the data mining tool WEKA [19] to obtain a set of models and statistics.
First, the data mining tasks are described in PMML (Predictive Model Markup Language). Then,
from the PMML file a description of the planning problem is generated in PDDL (the standard
language in the planning community). Finally, the plan is being implemented in WEKA (Waikato
Environment for Knowledge Analysis).
3. CONSTRUCTION OF THE CASE BASE
Case-based reasoning is one of the currently most widely used artificial intelligence techniques.
Reasoning from cases is to solve a new problem, called problem target, using a set of problems
already solved. A source case refers to an episode from problem solving and a case one basis
together cases sources [3]. A case consists of two parts: the problem and the solution part. The
problem part is described by a set of indices that determine in what situation a case is applicable.
Case-based reasoning process generally operates under five sequential phases: development,
remembering (or indexing), adaptation, the revision and learning.
Scheduling based on the case is a planning approach that is based on a particular aspect of human
behaviour. Generally, the man does not generate plans (calendar) entirely new from basic
operations; he uses his experiences (success or failure) to help solve new problems that arise to
him. Establish a schedule returned to try to synthesize a solution plan, by reusing the best plans
already produced in similar situations and changing to adapt to the new situation. Planning from
case, a scheduling problem is the specification of an initial state and a goal to achieve. A solution
is a plan for achieving the purpose starting from the initial state.
For a system of case-based reasoning to work, it must start from a certain number of cases
constituting the basis of cases. These cases should cover the target area the best possible so
interesting solutions are found. Take the example of the treatment of a disease reportable:
tuberculosis. The treatment of tuberculosis differs depending on the patient's age and various
other factors. We are building the basis of cases passing through four steps: the project
description, modeling of the project by a graph or, the generation of the plans, the construction
and representation of cases [6].
The description of the project is to represent the sequence of tasks or actions as an array.
Then, a graph or is generated from the project. The graph is a graph whose nodes represent tasks
or arcs represent the relationships between tasks. The relationship between the tasks being to
satisfy constraints [4]. Constraints are criteria that may be taken into account in the development
of sub-plans. The choice of a solution plan depends on several criteria: time, probability, and the
cost. However a task represents the action carried out for a period of time [17].
After the construction of the graph, we and/or apply planning algorithms to determine the
possible plans. The scheduling algorithm we use is based on a course of the graph or back
chaining. It is to find possible paths between the initial node and the final node of the graph
and/or. The algorithm stops when the sought initial node is found [8].
To build the case, we will link a duration, a probability and a cost to each plan obtained in the
previous step according to its tasks. Therefore cases will be represented by descriptors (duration,
cost, likelihood) that describe the problem part and the corresponding plane that represents the
solution part. Table 1 presents the basis of 14 cases.
4. 56 Computer Science & Information Technology (CS & IT)
4. CLASSIFICATION OF PLANS BY INDUCTIVE LEARNING
Establish a plan, means being able to associate the subplan to a number of indices presented by
situations. In this type of problem, it identifies three essentials: problems, plans and indices.
Problems are the population, indices are the descriptions of the problems and plans are the
classes. It is assumed that there is a correct classification, meaning that there is an application that
associates any scheduling problem with a plan. Learn how to develop a plan, is to associate a plan
already drawn up a list of indices. To formalize this connection, we will use the following
notations: = {w1,w2,...,wn} to refer to a population of n scheduling problems. G={g1,g2,...,gd}
for all d descriptions (indices of the problem) and Q={q1,q2,...,qm} for all the plans m .
Is a population of individuals affected by the problem of classification. This population is a
special attribute called noted class attribute is associated. The variable Y is called the area of
variable statistics endogenous or simply class. At each individual w may be associated with its
class Y(w). They say that the function is takes its values in the set of labels Q, called also whole
classes. For example, if the population is diabetic patients and is the result of the identification
of diabetes type 1 noted q1, and type 2 noted q2; then Y(w) will be the result of the identification
of the type of diabetes the patient w [1].
The determination of the classification model ϕ is related to the assumption that the values taken
by the variable Y are not random, but certain specific situations that can characterize [22]. For this
the expert in the field concerned establishes a priori list of p variable statistical called variables
exogenous and rated X= {X1,X2,...,Xp}. These variables are also called predictive attributes or
explanatory. The value taken by a variable exogenous Xj is called modality or value of attribute Xj
of the problem w. We mean by lj the number of terms that a variable Xj can receive. To illustrate
this notation, consider the problem of planning. A problem can be described, for example, by
three exogenous variables:
X1: Duration, which can take three values 1
1x =Courte, 2
1x =Normale, 3
1x =Longue
X2: Probability, which can take three values 1
2x =Incertain, 2
2x =Douteux, 3
2x =Certain
X3: Cost, which can take three values 1
3x =Faible, 2
3x =Raisonnable, 3
3x =Elevé
Inductive learning aims to seek a classification model ϕ allowing for a new case w, for which we
do not know the class Y (w) but we know the State of all of its variables exogenous to predict this
value throughj. The development of ϕ requires in the population two samples graded A and
T. The first said of learning will be used for construction and the second said to test will be used
to test the validity of ϕ. Thus, for any case w, we assume known both its values X (w) in the space
of representation and its class Y (w) space labels Q.
Population A cases, taken into account for classification is nothing more than a sequence of n
case w i (situations) with their plan corresponding Y(wi). Suppose that the A sample is composed
of 14 situations (table 1):
5. Computer Science & Information Technology (CS & IT) 57
Table 1. Example of a learning sample.
Supervised inductive learning intends to provide tools for extracting the classification model
based on the information available on the sample of learning ϕ. The general process of inductive
learning includes typically three steps that we summarize below:
1. Development of the model: This is the step that uses a sample of noted learning A , which
all individuals wi are described in a space of representation and belong to one of the m
classes denoted cj, j= 1,...,m. It is building the application ϕ which allows calculating the
class from representation.
2 Validation of the model: This is to verify on a sample test T and which we know for each of
its individuals, representation and the class, if the classification model ϕ from step previous
gives of the class expected.
3 Generalization of the model: This is the stage which is to extend the application of the model
to all individuals of the population .
5. BOOLEAN MODELING OF THE INDUCTION GRAPH
In this section, we present the principles of construction, by Boolean modelling [1,5,9,23,24], of
induction graphs in the problems of discrimination and classification [1,2] : we want to explain
the class taken by one variable to predict categorical Y, attribute class or endogenous variable;
from a series of variables X1, X2,..., Xp, say variable predictive (descriptors) or exogenous, discrete
or continuous. According to the terminology of machine learning, we are therefore in the context
of supervised learning. The general process of learning than the cellular system CASI (Cellular
Automata for Symbolic Induction) [1] applies to a population is organized on three stages:
1) Boolean modeling of the induction graph;
2) Generation of the rules for cases indexing;
3) Validation and generalization;
Ω Y(ω) X1(ω) X2(ω) X3(ω) Ω Y(ω) X1(ω) X2(ω) X3(ω)
ω1 Plan1 75 0,70 70 ω8 Plan1 64 0,40 65
ω2 Plan2 80 0,80 90 ω9 Plan1 65 0,80 75
ω3 Plan2 85 0,85 85 ω10 Plan2 51 0,10 80
ω4 Plan2 72 0,20 95 ω11 Plan2 55 0,50 70
ω5 Plan1 79 0,69 70 ω12 Plan1 49 0,52 80
ω6 Plan1 71 0,70 90 ω13 Plan1 58 0,81 80
ω7 Plan1 63 0,30 78 ω14 Plan1 40 0,90 96
6. 58 Computer Science & Information Technology (CS & IT)
Figure 1 summarizes the general diagram of the Boolean modeling process in the CASI system.
Figure 1. General diagram of the system CASI
From the sample A we begin the symbolic treatment for the construction of the induction graph
(method SIPINA [21] [22].
1) Choose the extent of uncertainty (Shannon or quadratic);
2) Initialize the parameters λ, µ and the initial partition S0 ;
3) Use the SIPINA method to pass partition St to St + 1 and generate the graph of induction.
4) Finally, generation of prediction rules.
Method SIPINA [21] algorithm is a non tree heuristic for the construction of a graph of induction.
Its principle is to generate a succession of scores by merger or breakup of the nodes of the graph.
In what follows we describe the process on the fictional example of table 1. Suppose our sample
of learning A consists of 14 cases of scheduling which are divided into two classes plan1 - plan2
(see table 1). The initial partition S0 has one s0noted element, which includes the entire sample
learning with 9 situations belonging to the class plan1 and 5, class plan2. The next partition S1 is
generated by the variable X1 after discretization and individuals in each node si are defined as
follows: s1={ω∈ A|X1(ω)=Longue pour X1(ω) >=72}, s2={ω∈ A|X1(ω)=Normale pour X1(ω)
>=60 et X1(ω)<72} and s3={ω∈ A|X1(ω)=Courte pour X1(ω) <60}.
As well as in the s0 node, there are in s1, s2 and s3, individuals of the plan1 and plan2classes. The
figure 2 summarizes the steps of construction of s0, s1, s2 and s3. The S1partition, the process is
repeated looking for a S2 score which would be better.
Figure 2. Construction of the nodes s0, s1, s2 and s3
To illustrate the architecture and the operating principle of the BIG module, we consider figure 2
with the S0= (s0) partitions and S1= (s1,s2,s3). Figure 3 shows how the knowledge extracted from
SIPINA
Symbolic
Learning
BOG
Boolean
Opt & Gen
BIG
Boolean
Inferenc
Engine
BVG
Boolean
validation
Boolean KB
CASI
User interfaceΩA ΩT ΩE
Partition S0
X1
Plan1
Plan2
9
5
s0
Courte
3
2
s3
Norm
ale
4
0
s2
Longue
2
3
s1Partition S1
7. Computer Science & Information Technology (CS & IT) 59
RE ARC1 ARC2 ARC3
s0 1 1 1
X1=longue 0 0 0
s1 0 0 0
X1=normale 0 0 0
s2 0 0 0
X1=courte 0 0 0
s3 0 0 0
RS ARC1 ARC2 ARC3
s0 0 0 0
X1=longue 1 0 0
s1 1 0 0
X1=normale 0 1 0
s2 0 1 0
X1=courte 0 0 1
s3 0 0 1
this graph database is represented by the CELFACT and CELRULE layers. Initially, all entries in
cells in the CELFACT layer are passive (E= 0), except for those who represent the initial basis of
facts (E= 1).
Figure 3. Boolean partitions modeling S0 and S1
In figure 4 are, respectively, represented the impact of input matrices RE and exit RS the Boolean
model.
· the relationship entry, denoted i RE j, is formulated as follows:∀i∈{1,..., l},∀j∈{1,..., r}, if (the
fact i ∈ to the premise of the j rule) then RE(i, j) ← 1.
· the relationship of output, denoted i RS j, is formulated as follows:∀i∈{1,..l}, ∀ j∈ {1,..., r}, if
(the fact i ∈ the conclusion of rule j) then RS(i, j) ← 1.
Figure 4. Input/output incidences matrices
Incidence matrices RE and RS represent the relationship input/output of the facts and are used
in forward-chaining [1] [9]. You can also use RS as relationship of input and RE as relationship of
output to run a rear chaining inference. Note that no cells in the vicinity of a cell that belongs to
CELFACT (at CELRULE) does not belong to the layer CELFACT (at CELRULE).
The dynamics of the cellular automaton BIG [1,23], to simulate the operation of an Inference
engine uses two functions of transitions δfact and δrule, where δfact corresponds to the phase of
assessment, selection and filtering, and δrule corresponds to the execution phase [1,24]. To set the
two functions of transition we will adopt the following notation: EF, IF and SF to designate
CELFACT_E, _I and _S; Respectively ER, IR and SR to designate CELRULE_E, _I and _S.
-The transition function δfact: (EF, IF, SF, ER, IR, SR) (EF, IF, EF, ER+(RE
T
·EF), IR, SR)
-The transition function δrule : (EF, IF, SF, ER, IR, SR) (EF+(RS·ER), IF, SF, ER, IR, §ER)
Where RE
T
matrix is the transpose of RE and where §ER is the logical negation of ER. Operators +
and · used are respectively the or and the and logical.
We consider G0 initial configuration of our cellular automaton (see figure 4), and ∆ = δrule ο δfact
the global transition function: ∆ (G0) = G1 if δfact (G0) = G'0 and δrule (G'0) = G1. Suppose that
CELFACT E I S
s0 1 1 0
X1=longue 0 1 0
s1 0 1 0
X1=normale 0 1 0
s2 0 1 0
X1=courte 0 1 0
s3 0 1 0
CELRULE E I S
ARC1 0 1 0
ARC2 0 1 0
ARC3 0 1 0
ARC1 : Si s0 Alors (X1=longue) et s1.
ARC2 : Si s0 Alors (X1=normale) et s2.
ARC3 : Si s0 Alors (X1=courte) et s3.
8. 60 Computer Science & Information Technology (CS & IT)
G = {G0, G1,..., Gq} is the set of Boolean PLC configurations. Discrete developments plc, from
one generation to another, is defined by the sequence G0, G1,..., Gq, where Gi+1=∆(Gi)
[1,23,24].
6. FUZZY BOOLEAN MODELING
According to Lotfi Zadeh [19], founder of fuzzy logic, the limits of the classical theories applied
in artificial intelligence come because they require and manipulate only accurate information.
Fuzzy logic provides approximate reasoning modes rather than accurate. It is mainly the mode of
reasoning used in most cases in humans.
According to Zadeh, fuzzy logic is fuzzy sets theory which is a mathematical theory, whose main
objective is the modeling of the vague and uncertain of the natural language concepts. Thus, it
avoids the inadequacies of the traditional theory regarding the treatment of this kind of
knowledge. The fundamental characteristic of a classic set is rigid boundary between two classes
of elements: those who belong to all and those who do not belong to this set. they belong rather to
its complement. The relationship of belonging is represented in this case by a µ function that
takes truth values {0,1} pair. Thus, the membership of a classic set A function is defined by:
∉
∈
=
Axif
Aif
xA
0
x1
)(µ
This means that an element x in A (µA(x)=1) or not (µA(x)=0). However, in many situations, it is
sometimes ambiguous whether x belongs or not to A.
As an example for the definition of the membership functions, it takes the variable X3 = Coût of
table 2. According to the (logical Boolean) classical logic, which allows for variables that two
values 0 and 1, all costs less than 40 are considered low, and those over 70 as high. However such
logic of classification does not make sense. Why a cost of 75 is considered higher? in reality such
a passage is done gradually. Fuzzy logic variables may take any values between 0 and 1 to take
account of this reality.
In the simplest case, one can distinguish three values Faible, Raisonnable, and Elevé, of the
language variable “Coût” which forms three fuzzy sets (figure 5). Thus, a cost of 35 belongs with
a factor of belonging µ = 0.75 across ' Faible' and with µ = 0.25 to all 'Raisonnable'. Obviously,
the choice characterizing the trapezoidal shape of the membership function is quite arbitrary and
must take account of the particular circumstances. Often, it is necessary to introduce a subdivision
more fine, e.g. 5 values «très faible», «faible», «raisonnable», «élevé» and «très élevé» for the
language variable 'Cost', thus forming 5 sets. Thus a cost of 35 belongs, with µ = 0.25, across '
faible '.
9. Computer Science & Information Technology (CS & IT) 61
Figure 5. Classification of the cost according to the logic fuzzy
Suppose now that from the induction graph obtained with the method SIPINA we generated five
rules R1, R2, R3, R4 and R5 of classification that we'll use for the indexing of cases. The following
illustration shows the Boolean modelling of extracted knowledge base.
Figure 6. Boolean modelling of extracted knowledge base
40 70
35
0,75
0,25
µ
x
Faible Raisonnable Elevé
30
80
40 70
35
µ
x
Faible Raisonnable Elevé
R1 : Si (X1=Longue) et (X3=Faible) Alors plan1.
R2 : Si (X1=Longue) et (X3=Elevé) Alors plan2.
R3 : Si (X1=Normale) Alors plan1.
R4 : Si (X1=Courte) et (X2=Incertain) Alors plan2.
R5 : Si (X1=Courte) et (X2=Douteux) Alors plan1.
RE R1 R2 R3 R4 R5
X1=Longue 1 1 0 0 0
X1=Normale 0 0 1 0 0
X1=Courte 0 0 0 1 1
X2=Incertain 0 0 0 1 0
X2=Douteux 0 0 0 0 1
X2=Certain 0 0 0 0 0
X3=Faible 1 0 0 0 0
X3=Raisonnable 0 0 0 0 0
X3=Elevé 0 1 0 0 0
Plan1 0 0 0 0 0
Plan2 0 0 0 0 0
RE R1 R2 R3 R4 R5
X1=Longue 0 0 0 0 0
X1=Normale 0 0 0 0 0
X1=Courte 0 0 0 0 0
X2=Incertain 0 0 0 0 0
X2=Douteux 0 0 0 0 0
X2=Certain 0 0 0 0 0
X3=Faible 0 0 0 0 0
X3=Raisonnable 0 0 0 0 0
X3=Elevé 0 0 0 0 0
Plan1 1 0 1 0 1
Plan2 0 1 0 1 0
CELFACT E I S
X1=Longue 1 1 0
X1=Normale 0 1 0
X1=Courte 0 1 0
X2=Incertain 0 1 0
X2=Douteux 0 1 0
X2=Certain 0 1 0
X3=Faible 0 1 0
X3=Raisonnable 0 1 0
X3=Elevé 0 1 0
Plan1 0 1 0
Plan2 0 1 0
CELRULE E I S
R1 0 1 0
R2 0 1 0
R3 0 1 0
R4 0 1 0
R5 0 1 0
10. 62 Computer Science & Information Technology (CS & IT)
6.1. Boolean Fuzzification of Exogenous Variables
Fuzzy-BML modelling deals with the fuzzy input variables and provides results on output
variables themselves blurred. Fuzzification, illustrated by the following example, is the step that
consists of fuzzy quantification of actual values of a language variable.
Fuzzifier to: the universe of discourse, i.e. a range of possible variations of the corresponding
entry. A partition interval fuzzy from this universe, for the identification of the cost we
partitioned space of X3 to 7 with a Boolean modeling on 3 bits of 000 to 110. Finally, the duties
of membership classes.
6.2. Boolean Defuzzification
Output the Fuzzy-BML modeling cannot communicate to the user of the fuzzy values. The role of
the defuzzification is therefore to provide accurate values. During this step, the system will
perform tests to define the range of proven goal. This test will depend on the number of rules
candidates and the de facto number of each rule that participated in the inference according to the
following principle:
• Cases for a single rule and a single fact: "if then conclusion.
CELFACT _I (conclusion) = minimum (CELFACT_I (fact), CELRULE(rule) _I).
• Cases for a single rule with several facts: ' If fait1 and fait2 and... .' then conclusion» :
CELFACT_I (conclusion) = minimum (CELFACT_I (fait1), CELFACT(fait2) _I, ...).
The 'minimum' operator in Boolean logic represents the "and logical."
• Several rules:
CELFACT _I (goal) = maximum (CELRULE_I (rule1), CELRULE_I (rule2),...).
The 'maximum' operator in Boolean logic represents the "logical or". Figure 7 shows the Boolean
principle adopted by the Fuzzy-BML modeling.
Figure 7. Boolean for the defuzzification operator
Interface de fuzzification
Coût faible à 25%
Coût faible à 75%
Coût faible à 100%
X3 = 42
11. Computer Science & Information Technology (CS & IT) 63
7. EXPERIMENTATION
We have evaluated our approach on a case basis about the treatment of tuberculosis. The case
basis contains actual cases collected from the CHU of Oran [7]. The problem part of cases is
described by three descriptors given in Table 2 and the solution part is given in the form of a
treatment plan Y which takes its values in the set of plans C={T1, T2, T3, T4}.
Table 2. Values of descriptors.
Descriptors Meaning Values
X1 Age < 20, 20-30, 30-40, 40-50, >50
X2 Weight 30-39, 39-54, 54-70, >70
X3 Antecedent NT, T
To compare the proposed approach with other methods, we have applied the k-NN [13], the
decision tree and the Fuzzy-BML on the same case base. We show in Table 3 the rate of correctly
classified instances with each method using the supervised mode of discretization.
Table 3. Results of experimentation.
k-NN Decision tree Fuzzy-BML
66 % 73 % 81 %
The rate of correctly classified instances is 66 % with k-NN, 73 % with decision tree and 81 %
with Fuzzy-BML. From the obtained results, we note that the Fuzzy-BML method has provided
better results with a rate of 81 % of well classified instances.
8. CONCLUSIONS AND PERSPECTIVES
Several competing motivations have led us to define a Boolean model for CBR knowledge base
systems. Indeed, we have not only desired experiment with a new approach to indexing of cases
by decision tree, but we also wanted improve modeling of the vague and uncertain of the natural
language concepts. When it comes to planning guided by CBR, we must go through the following
steps:
- Build the base of cases by planning tools;
- Construct the graph of induction by symbolic learning and extract the rules;
- Import knowledge base in the platform WCSS [5].
- Launch the Boolean fuzzification [9] ;
- Launch the inference blurred for indexing in the basis of the cases;
- Finally, and if necessary run the Boolean defuzzification.
For the calculation of the similarity in the retrieval (cases indexing) phase, typically used k-
nearest neighbours. So we compared our Fuzzy Boolean Model with k-nearest neighbours (k-NN)
and decision tree. We noticed that the indexing of cases for the choice of a plan is significantly
better with Fuzzy-BML. Finally, we can say that the structure of the cases that we have used is
quite simple. We have described the part problem of cases by age, weight and a antecedent. By
adding other constraints could subsequently used a slightly more complex representation. As a
future perspective of this work, we propose to improve the other steps of the CBR process for the
proposed approach
12. 64 Computer Science & Information Technology (CS & IT)
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