Volume-6 Issue-4, September 2016 – International Journal of Soft Computing and Engineering (IJSCE)

D2909096416

IJSCE Journal — Thu, 30 Sep 2021 05:13:38 +0000

The International Journal of Soft Computing and Engineering (IJSCE) has ISSN: 2231-2307 (online), an open-access, peer-reviewed, periodical bi-monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in January, March, May, July, September and November.The journal aims to publish high-quality peer–reviewed original articles in the area of Soft Computing and Engineering that covers Data Communication and Networking, Artificial Intelligence and Neural Networking, Image Processing and Recognition, Software Engineering and Project Management (SEPM), Computer Graphics and Multimedia (CGM), Data Mining, Cryptography and Network Security, Smart Computing and Information Processing, Internet of Things (IoT), Computation Intelligence and Analytics, Machine Learning (ML) and Knowledge Mining (KM), Operating System, Data Base Management System (DBMS), Data Structure and Algorithm (DSA), Object Oriented Analysis and Design (OOAD), and all interdisciplinary streams of Soft Computing and Engineering. #Addressing, Routing, Switching and Location Management #Broadband, Mobile, Wired, Wireless Internet #Networks: Adhoc and Sensor Networks, High Speed Networks, Mobile and #Wireless Networks #Network Architectures #Network Based Applications #Network Protocols #Network Operations & Management #Network Security Trust & Privacy #Cross Layer Design and Optimization #Internet and Web Applications #Network Measurement & Performance Analysis #Next Generation Internet & Web Architectures #Peer to Peer and Overlay Networks #QOS And Resource Management #Recent Trends & Developments in Computer Networks #Self-Organizing Networks and Networked Systems #Ubiquitous Networks #Virtual & Overlay Networks #Wireless Mesh Networks and Protocols #Wireless Multimedia Systems #Personal and Wearable Networks #Information-Centric Networking #Embedded Networks #Opportunistic Networking #Delay-Tolerant Networks #Cognitive Radio Networks #Vehicular Networks #Smart Grid Communications #Underwater Sensor Networks #Cyber-Physical Systems #Social Networks #Green Networking #Overlay and Peer-To-Peer Networks #Local-Area and Metropolitan-Area Networks #Storage-Area Networks #Routing and Transport Protocols #Big Data Networking #Cloud Computing and Networking #Software Defined Networking and Network Function Virtualization #Internet of Things #Link Technologies #Adaptive Networking Applications #Authentication, Authorization, Accounting #Security and Privacy #Cross-Layer Optimization #Multimedia and Real-Time Communication #Machine-To-Machine Communications for Smart Environments #Smart Cities #Network Traffic Characterization and Measurements #Network Management, Reliability and QoS #Performance Evaluation of Networks #Testbeds for Network Experiments #Network Coding #Optical and High-Speed Access Networks #High Performance Computing #Digital System and Logic Design #Networked-Driven Multicourse Chips #Information-Centric Networking #Opportunistic Networking #Mobile and Ubiquitous Networking #Big Data Networking #Cloud Computing and Networking #Software Defined Networking and Network Function Virtualization #Adaptive Networking Applications #Antenna and Communications Devices #Microstrip Antenna #Microwave #Radar and Satellite #Smart Antenna #MIMO Antenna #Wireless Communication #RFID Network and Applications #5G Communication #6G Communication #Neural Networks #Fuzzy Logic #Simulated Biological Evolution Algorithms (Like Genetic Algorithm) #Ant Colony Optimization(etc) #Reasoning and Evolution #Intelligence Applications #Computer Vision and Speech Understanding #Multimedia and Cognitive Informatics #Data Mining and Machine Learning Tools #Heuristic and Ai Planning Strategies and Tools #Computational Theories of Learning #Technology and Computing (Like Particle Swarm Optimization) #Intelligent System Architectures #Knowledge Representation #Bioinformatics #Natural Language Processing #Multiagent Systems #Supervised Learning #Unsupervised Learning #Deep Learning #Big Data and Ai Approaches #Reinforcement Learning #And Learning with Generative Adversarial Networks #Regression and Prediction #Problem Solving and Planning #Clustering #Classification #Neural Information Processing #Vision and Speech Perception #Heterogeneous and Streaming Data #Probabilistic Models and Methods #Reasoning and Inference #Marketing and Social Sciences #Knowledge Discovery #Web Mining #Information Retrieval #Design and Diagnosis #Game Playing #Streaming Data #Music Modelling and Analysis #Robotics and Control #Multi-Agent Systems #Active Vision and Robotic Systems #Architecture of Imaging and Vision Systems #Autonomous Vehicles #Character and Handwritten Text Recognition #Chemical and Spectral Sensitization #Chemistry of Photosensitive Materials #Coating Technologies #Coding and Transmission #Cognitive Aspects of Image Understanding #Color Imaging #Communication of Visual Data #Data Fusion from Multiple Sensor Inputs #Display and Printing #Document Image Understanding # Generation and Display #Holography #Image Analysis and Interpretation #Image Capturing #Image Databases #Image Generation #Image Manipulation #Image Permanence #Image Processing #Image Processing Applications #Image Processing: Coding Analysis and Recognition #Image Representation #Image Sensing #Imaging Systems and Image Scanning #Implementation and Architectures #Latent Image #Materials for Electro-Photography #Monitoring and Surveillance #Network Architecture for Real-Time Video Transport #New Visual Services Over ATM/Packet Network #Non-Impact Printing Technologies #Object Modeling and Knowledge Acquisition #Photoconductors #Photographic Emulsions #Photopolymers #Prepress and Printing Technologies #Processing and Analysis #Protocols for Packet Video #Remote Image Sensing #Retrieval and Multimedia #Scene Modeling #Signal-Processing Aspects of Image Processing #Storage and Transmission #Video Coding Algorithms and Technologies for ATM/P #Ethics of Software Engineering and Management #Software Life Cycles Development Models #Project Planning and Management #Software Requirements Analysis #Software Design Analysis #Software Testing and Integration #Software Implementation and Maintenance #Software Maintenance and Maintainability #Software Metrics or Attributes #Software Risk Assessment and Containment #Reliability #Software Quality and Quality Assurance #Software Cost Estimation #Object Oriented Developments #Object Oriented Metrics #Object Oriented Testing #Formal Notations #3D Reconstruction #Computer Animation #Computational Fabrication #Computational Geometry #Computational Photography #Computer Vision for Computer Graphics #Data Compression for Graphics #Geometric Modelling #Geometric Processing #HCI and Computer Graphics #Human Modelling #Image Analysis #Image Based Rendering #Image Processing #Machine Learning for Graphics #Medical Imaging #Pattern Recognition #Physically Based Modelling #Illumination and Rendering ethods #Robotics and Vision #Saliency Methods #Scientific Visualization #Shape and Surface Modelling #Shape Analysis and Image Retrieval #Shape Matching #Sketch-based Modelling #Solid Modelling #Stylized rendering #Textures #Virtual and Augmented Reality #Visual Analytics #Volume Rendering #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #K-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #Cryptographic Protocols #Cryptography and Coding #Untrace-Ability #Privacy and Authentication #Key Management #Authentication #Trust Management #Quantum Cryptography #Computational Intelligence in Security #Artificial Immune Systems #Biological & Evolutionary Computation #Intelligent Agents and Systems #Reinforcement & Unsupervised Learning #Autonomy-Oriented Computing #Coevolutionary Algorithms #Fuzzy Systems #Biometric Security #Trust Models and Metrics #Regulation and Trust Mechanisms #Data Integrity #Models for Authentication #Trust and Authorization #Wireless Network Security #Information Hiding #Data & System Integrity #Access Control and Intrusion Detection #Intrusion Detection and Vulnerability Assessment #Authentication and Non-Repudiation #Identification and Authentication #Insider Threats and Countermeasures #Intrusion Detection & Prevention #Secure Cloud Computing #Security Information Systems Architecture and Design and Security Patterns #Security Management #Security Requirements (Threats, Vulnerabilities, Risk, Formal Methods, etc.) #Sensor and Mobile ad hoc Network Security #Service and Systems Design and QOS Network Security #System Security #Intrusion Detection #Secure end Systems #Secure Operating Systems #Data Base Security #Security Infrastructures #Security Evaluation #Software Security #Security and Privacy in Mobile Systems#Security and Privacy in Pervasive/Ubiquitous Computing #Security and Privacy in Web Services #Security and Privacy Policies #Security Area Control #Security Deployment #Security Engineering #Security for Grid Computing #Security in Distributed Systems #Network Security #Internet Security #Firewalls #Mobile Security #Security Agents #Protocols #Anti-Virus and Anti-Hacker Measures #Content Protection #Software Protection #Tamper Resistant Software #Electronic Commerce #Electronic Government #Health #Telecommunications #Mobility #Foundations #Privacy #Access Control #Authentication #Identification #Applied Cryptography #Formal Methods in Information Security #IoT & IoE & Edge Computing #Distributed Mobile Applications Utilizing IoT #Security, Privacy and Trust in IoT & IoE #Standards for IoT Applications #Ubiquitous Computing #Blockchain-enabled IoT Device and Data Security and Privacy #Application of WSN in IoT #Cloud Resources Utilization in IoT #Wireless Access Technologies for IoT #Mobile Applications and Services for IoT #Machine/ Deep Learning with IoT & IoE #Smart Sensors and Internet of Things for Smart City #Logic, Functional programming and Microcontrollers for IoT #Sensor Networks, Actuators for Internet of Things #Data Visualization using IoT #IoT Application and Communication Protocol #Big Data Analytics for Social Networking using IoT #IoT Applications for Smart Cities #Emulation and Simulation Methodologies for IoT #IoT Applied for Digital Contents #Soft Computing #Advance Ubiquitous Computing #Parallel Computing #Distributed Computing #Machine Learning #Information Retrieval #Expert Systems #Data Mining #Text Mining #Data Warehousing #Predictive Analysis #Data Management #Big Data Analytics #Big Data Security #Nature Inspired Computing #Reconfigurable Computing #Cloud, Cluster, Grid and P2P Computing #Biomedical Computing #Green Computing #Mobile Computing #Nano Ubiquitous Computing #Context Awareness and Personalization, Autonomic and Trusted Computing #Internet Computing #Sequential, Parallel and Distributed Algorithms and Data Structures #Approximation and Randomized Algorithms #Graph Algorithms And Graph Drawing #On-Line and Streaming Algorithms #Analysis of Algorithms and Computational Complexity #Algorithm Engineering #Web Algorithms #Exact And Parameterized Computation #Algorithmic Game Theory #Computational Biology #Foundations of Communication Networks #Computational Geometry #Discrete Optimization #Software Engineering Methodologies #Agent-based Software Engineering #Artificial Intelligence Approaches to Software Engineering #Component-based Software Engineering #Embedded and Ubiquitous Software Engineering #Aspect-based Software Engineering #Empirical Software Engineering #Search-Based Software Engineering #Automated Software Design and Synthesis #Computer-Supported Cooperative Work #Automated Software Specification #Reverse Engineering #Software Engineering Techniques and Production Perspectives #Requirements Engineering #Software Analysis, Design and Modelling #Software Maintenance and Evolution #Software Engineering Tools and Environments #Software Engineering Decision Support #Software Design Patterns #Software Product Lines #Process and Workflow Management #Reflection and Metadata Approaches #Program Understanding and System Maintenance #Software Domain Modelling and Analysis #Software Economics #Multimedia and Hypermedia Software Engineering #Software Engineering Case Study and Experience Reports #Enterprise Software, Middleware, and Tools #Artificial Intelligent Methods, Models, Techniques #Artificial Life and Societies #Swarm Intelligence #Smart Spaces #Autonomic Computing and Agent-Based Systems #Autonomic Computing #Adaptive Systems #Agent Architectures, Ontologies, Languages and Protocols #Multi-Agent Systems #Agent-Based Learning and Knowledge Discovery #Interface Agents #Agent-Based Auctions and Marketplaces #Secure Mobile and Multi-Agent Systems #Mobile agents #SOA and Service-Oriented Systems #Service-Centric Software Engineering #Service Oriented Requirements Engineering #Service Oriented Architectures #Middleware for Service Based Systems #Service Discovery and Composition #Service Level Agreements (Drafting, Negotiation, Monitoring and Management) #Runtime Service Management #Semantic web #Data Modelling, Mining and Data Analytics #Aggregation, Integration, and Transformation #Web and Text Mining #Data Mining Methods, Techniques, and Tools #Data Analytics Modelling and Algorithms #Patterns and Frameworks #Data Visualization #Knowledge Systems and Engineering #Knowledge Acquisition #Knowledge-based and Expert Systems #Knowledge Representation and Retrievals #Knowledge Engineering Tools and Techniques #Time and Knowledge Management Tools #Knowledge Modelling, Integration, Transformation, and Management #Uncertainty Knowledge Management #Knowledge Visualization #Mobile Computing and Mobile System Engineering #Mobile App Design and Development #Innovative Mobile Applications #Pervasive Computing #Mobile System Validation and Test Automation #Software & System Quality of Service #Soft Computing #Software and System Testing Methods #Quality Assurance Process, Standards, and Systems #Software Safety Systems #Software Test Automation and Tools #Software Dependability, Reliability, Scalability #Software & System Security #Software and System Security and Privacy #Mobile App Security and Privacy #Encryption Methods and Tools #Security Service Systems #Cloud, Sensor Cloud and Mobile Cloud Security #Mobile System Integrity, Security, And Fault Tolerance #Emergent Topics #Cloud Computing and Innovative Cloud-Based Application Systems #Mobile Cloud Computing and Application Services #Big Data Analytics Application Systems #Innovative Sensing Cloud and Systems #Large-Scale Cyber Systems #IoT and Smart City Application Systems #Big Data Quality Validation #Big Data Application Quality Services #Smart Learning and Innovative Education Systems #Learning Software Design Engineering #Smart Learning Methods and Environments #Online Learning Systems #Mobile Enabled Learning Systems and Tools #PhD Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLon

Implementation of FBG Mechanism for the Removal of Optical Signal Spreading
Mohammed AbdALLA Adam Elmaleeh¹, Fadalalla Suleiman Mahmoud Gamer²
¹Mohammed AbdALLA Adam Elmaleeh, Faculty of Engineering, Alneelain University, Khartoum, Sudan.
²Fadalalla Suleiman Mahmoud Gamer, Faculty of Engineering, Alneelain University, Khartoum, Sudan.
Manuscript received on August 05, 2016. | Revised Manuscript received on August 06, 2016. | Manuscript published on September 05, 2016. | PP: 69-76 | Volume-6 Issue-4, September 2016. | Retrieval Number: D2909096416/2016©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The most significant segments of software development is software testing. Automated software testing is an effective testing process that reduces the effort and cost of manual testing. However, it is difficult to select a suitable software testing tool from the huge list of freely available tools. The main contribution of this work is to conduct a comparative study of three readily available automated software testing tools such as QTP, Silk4j and Load Runner. The selected tools are evaluated and compared on the basis of their usability, maintenance and effectiveness. For this purpose we have taken an existing JAVA based applications to perform automated testing on these three tools. The results will assist testers to effectively select the best automated software testing tool for related applications.
Keywords: Significant segments, However, Automated software, QTP, JAVA based

Download PDF

The post D2909096416 appeared first on International Journal of Soft Computing and Engineering (IJSCE).

D2910096416

IJSCE Journal — Thu, 30 Sep 2021 05:08:40 +0000

Module Allocation for Maximizing Reliability of Distributed Computing Systems using Genetic Algorithms
Surinder Kumar
Surinder Kumar, Assistant Professor, Department of Mathematics, D.A.V. College, Chandigarh-160011, India.
Manuscript received on August 05, 2016. | Revised Manuscript received on August 06, 2016. | Manuscript published on September 05, 2016. | PP: 44-48 | Volume-6 Issue-4, September 2016. | Retrieval Number: D2910096416/2016©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The problem of the module allocation in distributed computing system is to need to allocate a number of modules to different processors for execution. The paper deals with the problem of module allocation in heterogeneous distributed computing systems with the goal of maximizing the system reliability. We present a genetic algorithm to obtain the optimal solution for this problem. In the performance of the algorithm we consider more one parameter such as the number of modules, the number of processors, and module interaction density of applications. The experimental results illustrate the effectiveness of this algorithm over conventional algorithms.
Keywords: Distributed computing systems, Genetic algorithms, Module allocations and Maximizing reliability.

Download PDF

The post D2910096416 appeared first on International Journal of Soft Computing and Engineering (IJSCE).

D2902096416

IJSCE Journal — Thu, 30 Sep 2021 05:05:10 +0000

Using Spray Pyrolysis Technique to Prepare PBS Lead Sulfide Thin Films and Study Their Structural and Electrical Properties as Function of Thickness
Rana Kadhim Abdulnabi
Rana Kadhim Abdulnabi, MSC, Department of Applied Physics, Iraq, Baghdad, 101, Iraq.
Manuscript received on August 05, 2016. | Revised Manuscript received on August 06, 2016. | Manuscript published on September 05, 2016. | PP: 60-63 | Volume-6 Issue-4, September 2016. | Retrieval Number: D2897096416/2016©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Lead sulfide (PbS) thin films are prepared by “solution “from, lead acetate” pb (CH3COO)2 and Thiourea CS(NH2) using Spray Pyrolysis Technique (SPT) on glassy substrates at temperature of 200Cº with a variable thicknesses of 100, 200, 300 nm. The “structural properties are approved by XRay Refraction (XRR), which helped in displaying the films having a cubic structure such as NaCl The size of the crystal grains of PbS films are shown to be increased when increasing the thickness of PbS films. Roughness of the films was measured according to the Root Mean Square (RMS). This was achieved using Scanning Microscope (SM). With increasing film thickness to the mentioned values, results showed an increase with RMS, electrical conductivity of the PbS film to 0.0186*10- ³, 0.4166*10-3 and 0.9090*10- ³ (Ω.cm)- ¹ and with charge-carrier concentration as well to 0.298*10¹¹, 1.1*1011 , 12*10¹¹ cm− 3 الخالصة:. في هذا البحث تم تحضير اغشية كبريتيد الرصاص) PbS ) بأستخدام محلول من NH2(CS على قاعدة زجاجية ) والثايوريا PbS ) CH3COO(2 خالت الرصاص بدرجة حرارة بطريقة الرش الكيميائي الحراري . تم دراسة الخصائص التركيبية عند اسماك متغيرة (300,200,100 (بأستخدام اشعة Ray- X التي اوضحت الشكل البلوري لالغشية وقد الحظنا زيادة الحجم الحبيبي لالغشية بزيادة سمك الغشاء , كما تم قياس خشونة السطح لالغشية بأستخدام فحص STM. كما تم دراسة الخصائص الكهربائية ومنها التوصيلية الكهربائية والتي قيمها والتي لالغشية الشحن وحامالت . ( 186, .4166, .9 9 )*1 ˉ³( Ω.cm)ˉ¹ ( . 98,1.1,1 )*1 ¹¹ cmˉ³ قيمها
Keywords: lead sulfide, chemical pyrolysis, thin film, semiconductor

Download PDF

The post D2902096416 appeared first on International Journal of Soft Computing and Engineering (IJSCE).

D2905096416

IJSCE Journal — Thu, 30 Sep 2021 05:01:21 +0000

On using Adaptive Hybrid Intelligent Systems in PM10 Prediction
S. A. Asklany¹, Khaled Elhelow², M .Abd El-Wahab³
¹Dr. S. A. Asklany, Department of Computer Science, Modern Academy Maadi, Cairo, Egypt.
²Dr. Khaled Elhelow, Department of Mathematics, Elmagmaa University, Elmagmaa, Saudi Arabia.
³Prof. M.Abd El-Wahab, Astronomy and Meteorology department, Cairo University, Cairo, Egypt.
Manuscript received on August 05, 2016. | Revised Manuscript received on August 06, 2016. | Manuscript published on September 05, 2016. | PP: 54-59 | Volume-6 Issue-4, September 2016. | Retrieval Number: D2905096416/2016©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: A comparative study based on producing two intelligence systems applied to PM10 prediction was presented in this work. Adaptive Network –based Fuzzy Inference System (ANFIS) used in build a system has three weather elements as input variables (Wind Speed, Wind Direction and Temperature) and the PM10 as output variable for PM10 nowcast model. Another technique used ANFIS in prediction of chaotic time series to get 6 hours forecast for PM10 from the present data. For developing the models, thirteen years hourly data for Mansoria station coordinates 29° 300′ 0″ N, 45° 45′ 0″ E from 1995to 2007 has been used. Different models employing a different training and testing data sets had been studied. The criteria of performance evaluation are calculated for estimating and comparing the performances of the two techniques. The results show that the two presented models success tools in PM10 prediction with acceptable root mean square error (RMSE); the model built on using ANFIS for chaotic time series prediction has smaller error compared with the adaptive network fuzzy inference system.
Keywords: Air quality, artificial intelligence, pollution, ANFIS, soft computing

Download PDF

The post D2905096416 appeared first on International Journal of Soft Computing and Engineering (IJSCE).

D2904096416

IJSCE Journal — Thu, 30 Sep 2021 04:57:45 +0000

Loan Sanctioning Prediction System
Aditi Kacheria¹, Nidhi Shivakumar², Shreya Sawkar³, Archana Gupta⁴
¹Aditi Kacheria, Student, Department of Computer Engineering, K. J. Somaiya College of Engineering, Mumbai, India.
²Nidhi Shivakumar, Student, Department of Computer Engineering, K. J. Somaiya College of Engineering, Mumbai, India.
³Shreya Sawkar, Student, Department of Computer Engineering, K. J. Somaiya College of Engineering, Mumbai, India.
⁴Prof. Archana Gupta, Assistant Professor, Department of Computer Engineering, K. J. Somaiya College of Engineering, Mumbai, India.
Manuscript received on August 05, 2016. | Revised Manuscript received on August 06, 2016. | Manuscript published on September 05, 2016. | PP: 50-53 | Volume-6 Issue-4, September 2016. | Retrieval Number: D2904096416/2016©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: People operating in banks face lots of issues which involve approval of a loan. In the 21st century, people often rely on technology to tackle such issues. This paper proposes a loan sanctioning system which determines whether or not a loan should be given to a person, based on certain attributes. In spite of banks following stringent rules and regulations and conducting meticulous background checks while sanctioning a loan and keeping in mind the probability of the person’s ability to return the loan, often such situations are faced where in, the person is unable to repay the loan that has been given to him. In this paper, the system that we propose for the bankers will help them predict the credible customers who have applied for loan, thereby improving the chances of their loans being repaid in time. This classification is done using Naïve Bayesian algorithm. In order to improve the classification accuracy, the quality of the data is improved before classifying it by using K-NN and Binning algorithms. This system uses these algorithms in order to yield a better efficiency so as to reduce the possibility of such a problem. The proposed system additionally facilitates self-confirmation regarding the same for the commoner.
Keywords: Binning, Data mining, K-NN, Naïve Bayesian

Download PDF

The post D2904096416 appeared first on International Journal of Soft Computing and Engineering (IJSCE).

D2897096416

IJSCE Journal — Thu, 30 Sep 2021 04:45:51 +0000

Comparative Analysis of Automated Software Testing Tools
Majid Khan¹, Abdus Salam², Javed Iqbal³, Syed Irfan Ullah⁴
¹Majid Khan, Department of Computing and Technology, Abasyn University, Peshawar Pakistan.
²Abdus Salam, Department of Computing and Technology, Abasyn University, Peshawar Pakistan.
³Javed Iqbal, Department of Computing and Technology, Abasyn University, Peshawar Pakistan.
⁴Syed Irfan Ullah, Department of Computing and Technology, Abasyn University, Peshawar Pakistan.
Manuscript received on August 05, 2016. | Revised Manuscript received on August 06, 2016. | Manuscript published on September 05, 2016. | PP: 46-49 | Volume-6 Issue-4, September 2016. | Retrieval Number: D2897096416/2016©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Download PDF

The post D2897096416 appeared first on International Journal of Soft Computing and Engineering (IJSCE).

D2890096416

IJSCE Journal — Thu, 30 Sep 2021 04:37:57 +0000

Comparison between Algorithms of MRI Image Segmentation
Hayfa Masghouni
Hayfa Masghouni, National School of Engineers in Carthage, Rades, Tunisia
Manuscript received on August 02, 2016. | Revised Manuscript received on August 09, 2016. | Manuscript published on September 05, 2016. | PP: 38-45 | Volume-6 Issue-4, September 2016. | Retrieval Number: D2890096416/2016©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this article, we present different algorithms of MRI image segmentation based on classification of pixels. First, we present FCM (Fuzzy C_Means) and its different extensions with a comparison between them, after we present GMM (Gaussian Mixture Model) and EM (Expectation Maximization) and its extensions with a comparison between them.
Keywords: FCM, EM, GMM, MRI image segmentation

Download PDF

The post D2890096416 appeared first on International Journal of Soft Computing and Engineering (IJSCE).

D2900096416

IJSCE Journal — Thu, 30 Sep 2021 04:34:15 +0000

Review of the Self-Organizing Map (SOM) Approach in the Field of Environmental Engineering
B. Sowmiya¹, S. Amal Raj²
¹B. Sowmiya, Research Scholar, Centre for Environmental Studies, Anna University, Kotturpuram, Chennai (Tamil Nadu). India.
²Dr. S. Amal Raj, Professor, Centre for Environmental Studies, Anna University, Kotturpuram, Chennai (Tamil Nadu). India.
Manuscript received on July 25, 2016. | Revised Manuscript received on July 29, 2016. | Manuscript published on September 05, 2016. | PP: 32-37 | Volume-6 Issue-4, September 2016. | Retrieval Number: D2900096416/2016©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In environmental engineering field, the use of artificial neural networks (ANNs) has received steadily increasing interest over the last decade or so. In ANN, selforganizing map (SOM) is an unsupervised learning method to analyze, cluster, and model various types of large databases. There is, however, still a notable lack of comprehensive literature review for SOM along with training and data handling procedures, and potential applicability. As a result the present paper, first explains the basic structure and algorithm of selforganizing map (SOM) and secondly, to review published applications with special importance on environmental engineering related problems in order to assess how well SOM can be used to solve a particular problem. Finally, concluded that self-organizing map (SOM) is a hopeful technique suitable to investigate, model, and rule environmental related problems. However, in recent years, self-organizing map (SOM) has displayed a steady increase in the number of applications in environmental engineering related problems due to the robustness of the method.
Keywords: Linear and non- linear process, Artificial Neural Network, Self Organizing Map, Environmental Engineering, Review

Download PDF

The post D2900096416 appeared first on International Journal of Soft Computing and Engineering (IJSCE).

D2901096416

IJSCE Journal — Thu, 30 Sep 2021 04:30:05 +0000

Influence of the Particle Size on the Microstructure and the Curie Temperature (TC) of Nano-Iron Particles Model
Nguyen Trong Dung¹, Nguyen Chinh Cuong²
¹PhD student. Nguyen Trong Dung, Faculty of Physics, Hanoi National University of Education, Address: 136 Xuan Thuy Street, Cau Giay District, Hanoi, Vietnam.
²PhD. Nguyen Chinh Cuong, Faculty of Physics, Hanoi National University of Education, Address: 136 Xuan Thuy Street, Cau Giay District, Hanoi, Vietnam
Manuscript received on July 25, 2016. | Revised Manuscript received on July 28, 2016. | Manuscript published on September 05, 2016. | PP: 26-32 | Volume-6 Issue-4, September 2016. | Retrieval Number: D2901096416/2016©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper studies the influence of the particle size (with the chosen sizes 2.122 nm; 2.49 nm; 2.884 nm; 3.128 nm; 3.254 nm; 4.07 nm; 4.68 nm; 4.978 nm; 5.3 nm; 6.602 nm; 7.774 nm; 8.392 nm) on the microstructure and the Curie temperature (Tc) of nano-iron particles model. The nano-iron particles were created by Molecular Dynamics Simulation method with the Pak-Doyama pair interaction potential and aperiodic boundary conditions which is called soft boundary or free boundary. The microstructure characteristics were analyzed through the radial distribution function (RDF), the energy and the coordination number. The Curie temperature (Tc) is the point at which materials switch from the ferromagnetic phase to the paramagnetic phase and it is determined through the Ising model. The study purpose of this paper is to determine the relationship between the particle size and the Curie temperature (Tc) of the model. The obtained results showed that there was specific influence of the particle size on the microstructure and the Curie temperature (Tc) of the nano-iron particles model: when the size of the nano-iron particles was increased from 2.122 nm to 2.49 nm; 2.884 nm; 3.128 nm; 3.254 nm; 4.07 nm; 4.68 nm; 4.978 nm; 5.3 nm; 6.602 nm; 7.774 nm; 8.392 nm, the phase transition temperature of the model increased from 8.9 K to 9.3 K; 9.5 K; 9.6 K; 9.7 K; 10 K; 10.1 K; 10.2 K; 10.3 K; 10.4 K; 10.5 K; 10.6 K respectively. The results have also been compared with the results from the theoretical – experimental model showing the significant influence of the particle size on the Curie temperature of the nano-iron particles model. In addition, the nano-iron particles model at different sizes had the different microstructure characteristics and different Curie temperatures.
Keywords: Particle size, microstructure, Curie temperature (Tc), nano-iron particles model, Molecular Dynamics.

Download PDF

The post D2901096416 appeared first on International Journal of Soft Computing and Engineering (IJSCE).

C2855076316

IJSCE Journal — Thu, 30 Sep 2021 04:26:28 +0000

Constructive Initialization of a Genetic Algorithm for the Solution of a Highly Constrained Departmental Timetabling Problem
Peter. U. Eze¹, Dawn. C. Walker², Ifeyinwa E. Achumba³
¹Peter. U. Eze, Department of Computer Science, University of Sheffield, Sheffield S10 2TN, United Kingdom, Europe.
²Dr. Dawn. C. Walker, Department of Computer Science, University of Sheffield, Sheffield S10 2TN, United Kingdom, Europe.
³Dr. Ifeyinwa E. Achumba, Department of Electrical & Electronic Engineering, Federal University of Technology Owerri, Imo State, Nigeria.
Manuscript received on July 15, 2016. | Revised Manuscript received on July 25, 2016. | Manuscript published on September 05, 2016. | PP: 18-25 | Volume-6 Issue-4, September 2016. | Retrieval Number: C2855076316/2016©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The University or Departmental Timetabling Problem (UTP or DTP) is a scheduling problem ridden with numerous constraints. Each of the constraints has a complex effect on the ideal solution and their combined effect makes the problem harder to solve. As a solution to this problem, a genetic algorithm (GA) approach was augmented by a process of constructive initialisation and applied to an exemplar scheduling problem in the Department of Computer Science at the University of Sheffield. The problem entailed scheduling of timetabled slots for 33 modules across a range of taught programmes at various levels, delivered by 29 lecturers in 10 lecture theatres and 6 laboratories. A total of eight hard constraints and four soft constraints were considered, for problems of five levels of increasing complexity. It was found that the synergistic solution satisfied all the hard constraints, achieved up to 75% optimisation of the soft constraints, and converged within 500 iterations or an average of 2.74 minutes. These results indicate that the GA, when combined with constructive initialization, will give efficient solution to the DTP problem with constrained variables.
Keywords: Departmental Timetabling Problem, Constructive Initialization, Genetic Algorithm, Scheduling, Constraints

Download PDF

The post C2855076316 appeared first on International Journal of Soft Computing and Engineering (IJSCE).