Volume-5 Issue-5, November 2016 – International Journal of Soft Computing and Engineering (IJSCE)

E2753115515

IJSCE Journal — Tue, 05 Oct 2021 05:38:52 +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

Automatic Face Detection using RGB Color Model for Authentication
Chiranjit Dutta¹, Ranjeet Singh²¹Mr. Chiranjit Dutta, Faculty of Information Technology, SRM University, NCR Campus, Modinagar, India.
²Mr. Ranjeet Singh, Faculty of Information Technology, SRM University, NCR Campus, Modinagar, India.
Manuscript received on October 16, 2015. | Revised Manuscript received on October 29, 2015. | Manuscript published on November 05, 2015. | PP: 70-73 | Volume-5 Issue-5, November 2015 . | Retrieval Number: E2753115515/2015©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 paper entitled Automatic face detection using RGB color model is the application developed to recognise the face and use it as a biometric security in the email protection. The face is captured through the webcam and stored in the database with the email id, password and then if we try to login then it again ask for face input and if the face matches then we can login . The face that we capture during login is matched with the image present in the database by matching the R,G,B component . This paper provides more secure and robust email security using biometric as a password. Biometric security comes with its own unique set of challenges. While face recognition have been used as a biometric security, which make the email more secure; its accuracy is still a problem. As it provide only 80 to 90 % accuracy. Therefore, we must find a way to make it more accurate and secure.This application may also be used to prevent various crimes going on in the country. Even it can be used in capturing the images of suspected person and matching it with the databases of criminals to catch them.Further, the algorithm can detect both dark skin-tone and bright skin-tone using YUC Color Space model.
Keywords: Euclidean space, face Detection, Principal Component Analysis.

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E2752115515

IJSCE Journal — Tue, 05 Oct 2021 05:34:28 +0000

Shape-Based Retrieval of Arbitrarily Shaped Video Objects
Mohammed Jassim Mohammed JassimDr. Mohammed Jassim Mohammed Jassim, College of Science and Information Technology, Iraq.
Manuscript received on October 14, 2015. | Revised Manuscript received on October 27, 2015. | Manuscript published on November 05, 2015. | PP: 67-69 | Volume-5 Issue-5, November 2015 . | Retrieval Number: D094071411/2015©BEIESP
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©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 increasing availability of object-based video content requires new technologies for automatically extracting and matching of the low level features of arbitrarily shaped video. In this paper, methods for shape retrieval of arbitrarily shaped video objects are proposed. Along with still shape features the shape deformations that may occur in an object’s life span is also taken. In this paper a novel method is used for measuring shape similarities between arbitrarily shaped video objects by comparing the low-level still shape features of the representative frames of the video objects.
Keywords: Object-based, video, extracting, shape similarities, video objects.

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E2745115515

IJSCE Journal — Tue, 05 Oct 2021 05:29:10 +0000

Finite Element Modelling of Mixed Mode Crack Propagation
Abdulnaser M. AlshoaibiDr. Author Abdulnaser M. Alshoaibi, Department of Mechanical Engineering, Jazan University, Jazan, P.O. Box, Kingdom of Saudi Arabia.
Manuscript received on October 14, 2015. | Revised Manuscript received on October 27, 2015. | Manuscript published on November 05, 2015. | PP: 61-66 | Volume-5 Issue-5, November 2015 . | Retrieval Number: E2745115515/2015©BEIESP
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©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 illustrates an algorithm for automatic simulations of crack propagations in 2D linear elastic finite element representation. The crack tip singularity and stress intensity factors around the crack tip are obtained by using the displacement extrapolation method. The crack propagation direction can be predicted by using the maximum circumferential stress theory criterion. The developed program has been examined with two different types of geometries namely: single edge cracked plate with one hole and double edge cracked plate with two holes with different types of loading. The results obtained by the current program have been assessed by comparing with the relevant works.
Keywords: Finite element method, crack trajectories, holes, stress intensity factors, adaptive mesh.

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E2744115515

IJSCE Journal — Tue, 05 Oct 2021 05:24:22 +0000

A Comparative Study of Stanford NLP and Apache Open NLP in the view of POS Tagging
Jay Nanavati¹, Yogesh Ghodasara²¹Jay Nanavati, Research Scholar, School of Science, RK University, Rajkot, (Gujarat) India.
²Dr. Yogesh Ghodasara, Associate Professor, College of Agricultural I.T., Anand Agriculture University, Anand, (Gujarat) India.
Manuscript received on October 19, 2015. | Revised Manuscript received on October 29, 2015. | Manuscript published on November 05, 2015. | PP: 57-60 | Volume-5 Issue-5, November 2015 . | Retrieval Number: E2744115515/2015©BEIESP
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©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: To perform a comparative study of two popular Natural Language Processing tools – Stanford NLPand Apache Open NLP, is the main objective of this paper. This paper also provides an insight into use of these two tools for analysis of requirements specification expressed in Natural Language English.
Keywords: NLP, Requirements Specifications, Part Of Speech (POS) tagging, Tokens, Tag set.

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E2749115515

IJSCE Journal — Tue, 05 Oct 2021 05:17:59 +0000

Tour Guide System using Augmented Reality: A Review
Umashankar Thakur¹, Vaibhav Nagare², Mita Shimpi³, Mubarak Pathan⁴, Nuzhat F. Shaikh⁵¹Mr. Umashankar Thakur, pursuing degree in Computer Science and Engineering, from Savitribai Phule Pune University, Modern Education Society’s College of Engineering, Pune, India..
²Mr. Umashankar Thakur, pursuing degree in Computer Science and Engineering, from Savitribai Phule Pune University, Modern Education Society’s College of Engineering, Pune, India.
³Ms. Mita Shimpi, pursuing degree in Computer Science and Engineering, from Savitribai Phule Pune University, Modern Education Society’s College of Engineering, Pune, India.
⁴Mr. Mubarak Pathan, pursuing degree in Computer Science and Engineering, from Savitribai Phule Pune University, Modern Education Society’s College of Engineering, Pune, India.
⁵Nuzhat F. Shaikh, Associate Professor and Head, Department of Computer Engineering, Modern Education Society’s College of Engineering, Pune (Maharashtra). India.
Manuscript received on October 19, 2015. | Revised Manuscript received on October 27, 2015. | Manuscript published on November 05, 2015. | PP: 52-56 | Volume-5 Issue-5, November 2015 . | Retrieval Number: E2749115515/2015©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: One of the largest, most important and constantly growing industries in the world is, without doubt, tourism. Such rapid growth calls for enticing ways to engage tourists and keep them interested, which is a big challenge for the professionals, having to maintain standards and increasing expectations of tourists. Tools such as augmented reality (AR) hold a vast potential in attracting and retaining visitors. The rise in smart mobile devices only boosts this further as it becomes possible to have information and tour generation at one’s fingertips. In this paper, we first try to enlist all the limitations and challenges encountered while utilizing concepts of AR to develop a tour guide system. We describe various state-of-the-art AR applications that provide such service, having their own set of drawbacks, and we give a brief introduction to our proposed system. All the facts and literature we have surveyed and studies related to various aspects of developing an AR-based tourism system are presented in the paper, which include several methods and algorithms that can be used for image comparison required to recognize objects of interest.
Keywords: Augmented reality (AR), image comparison, tourism, mobile AR.

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E2751115515

IJSCE Journal — Tue, 05 Oct 2021 05:10:48 +0000

Test for Efficient Increase/ Decrease Factors for Resilient Backpropagation using Combustion Engine Vibration Signals
N. D. Liyanagedera¹, A. Ratnaweera², D. I. B. Randeniya³¹N. D. Liyanagedera Department of Computing & Information Systems, Wayamba University of Sri Lanka Kuliyapitiya, Sri Lanka.
²A. Ratnaweera Department of Mechanical Engineering, University of Peradeniya, Peradeniya, Sri Lanka.
³D. I. B. Randeniya, Department of Mechanical Engineering, University of Peradeniya, Peradeniya, Sri Lanka.
Manuscript received on October 15, 2015. | Revised Manuscript received on October 29, 2015. | Manuscript published on November 05, 2015. | PP: 45-51 | Volume-5 Issue-5, November 2015 . | Retrieval Number: E2751115515/2015©BEIESP
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©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: Resilient backpropagation is a recently emerging neural network with a high potential. This neural network is capable of handling a network structure with a large number of input nodes which many other networks fail. This is achieved based on the way the weights are updated in the algorithm. Resilient backpropagation uses two constant values, decrease factor [ ] and increase factor [ ] to update the weights and to get the optimal solution. This experiment checks on to find how the neural network performs when different values were used for these constants. The training, testing and validation of the neural networks were done using vibration signal data collected from a combustion engine corresponding to 16 different fault combinations available in the combustion engine. The performances of the networks were compared using mean square error, time and epoch. The final results indicate that, when the decrease factor is in the range of 0.5 to 0.6 and when the increase factor is in the range of 1.2 to 1.3 the resilient backpropagation algorithm has the best performance.
Keywords: Resilient Backpropagation Algorithm, Increase/Decrease Factor, Combustion Engine, Vibration Signals.

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E2746115515

IJSCE Journal — Tue, 05 Oct 2021 05:04:24 +0000

Actuated Micro-Sensor for Magnetic Field Detection Based on Piezoresistor Transduction
Waddah Abdelbagi Talha¹, Mohammed A. A. Emaleeh², John Ojur Dennis³¹Waddah Abdelbagi Talha, Department of Electronic Engineering, University of Gazira Sudan, Wad Madani, (Sudan) North Africa.
² Mohammed A. A. Emaleeh, Department of Control Engineering, University of Khartoum, (Sudan) North Africa.
³John Ojur Dennis, Departments of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Perak, Malaysia.
Manuscript received on October 16, 2015. | Revised Manuscript received on October 27, 2015. | Manuscript published on November 05, 2015. | PP: 40-44 | Volume-5 Issue-5, November 2015 . | Retrieval Number: E2746115515/2015©BEIESP
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©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: One of the techniques used to measure the external magnetic field is by the transduction of the deflection produced by the Lorentz force into electrical signal using Piezoresistor technology. It is stated that the piezoresistive effect in silicon depends strongly on the crystal orientation, doping type, and concentration. In this paper the piezoresistive property of the Polysilicon is used as Piezoresistor transducer to study the transduction procedures. The results obtained observe the response of the different samples of piezoresistor transducer to transfer the deflection of the cantilever to electrical signal (voltage output). The sensitivity of the system is calculated for different samples at a fixed value of the applied force the percentage rate of change of the resistivity (ΔRp %) for different values of the cantilever deflections were obtained. It is observed that the change in resistance of the piezoresistor increases nonlinearly with the increase of the cantilever displacement. The Polysilicon Piezoresistor in Whetstone’s bridge configuration is used to transducer the response of the cantilever to electrical measurements at various voltages. Various dimensions of the cantilever were considered in the measurements. The highest sensitivity of the measurements (64mV/mT) is obtained for a thin beam of 0.6 µm polysilicon embedded in 2 µm thick silicon cantilever beam.
Keywords: Lorentz force; Polysilicon Piezoresistor; Bulk Micromachining; Wheatstone bridge.

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E2747115515

IJSCE Journal — Tue, 05 Oct 2021 04:57:18 +0000

A Survey on Applications of Big Data Analytics in Healthcare
Shubham Borikar¹, Mohan Bhagchandani², Raunak Kochar³, Ketansing Pardeshi⁴, Manisha Gahirwal⁵.¹Shubham Borikar, Bachelor of Engineering, Student at Vivekanand Education Society’s Institute of Technology, University of Mumbai, Mumbai (Maharashtra). India.
²Mohan Bhagchandani, Bachelor of Engineering Student at Vivekanand Education Society’s Institute of Technology, University of Mumbai, Mumbai (Maharashtra). India.
³Raunak Kochar, Bachelor of Engineering Student at Vivekanand Education Society’s Institute of Technology, University of Mumbai, Mumbai (Maharashtra). India.
⁴Ketansing Pardeshi, Bachelor of Engineering Student at Vivekanand Education Society’s Institute of Technology, University of Mumbai, Mumbai (Maharashtra). India.
⁵Manisha Gahirwal, Professor, Department of Computer Engineering, Vivekanand Education Society’s Institute of Technology, University of Mumbai, Mumbai (Maharashtra). India.
Manuscript received on October 14, 2015. | Revised Manuscript received on October 27, 2015. | Manuscript published on November 05, 2015. | PP: 36-39 | Volume-5 Issue-5, November 2015 . | Retrieval Number: E2747115515/2015©BEIESP
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©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 data in healthcare is increasing rapidly and is expected to increase significantly in coming years. Healthcare services although armed with modern technologies for curing the diseases grapples when it comes to preventing the diseases beforehand. Adoption of Big Data solutions will play an important role in transforming the outcomes of the healthcare industry by promoting evidence based reasoning and providing patient centric treatment. In this age of Big Data we can provide solutions to identify individuals who are prone to certain lifestyle diseases. Think of identifying an individual having an increased risk of diabetes after 10 years, now. With the advent of new big data analysis tools and technologies, such predictive systems can be designed which can identify individuals with increased risk. This paper provides an overview of big data analytics, different technologies that can be used in big data and its impact on healthcare domain to make some useful predictions based upon analyzing a variety of datasets. Finally we provide a model which can be used for predictive analytics using data mining and machine learning algorithms to predict the chances for a person to be prone to a disease.
Keywords: Big Data;Healthcare;Prediction system.

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E2742115515

IJSCE Journal — Tue, 05 Oct 2021 04:48:19 +0000

A Client Centric Hadoop Ecosystem
Raj Kushal Ananth Kumar¹, Pooja Singh², Naman Pradhan³, Afroz Pasha⁴¹RajKushal Ananth Kumar, Manhattan Associates and aims to pursue his masters in computer science. Nitte Meenakshi Institute of Technology, Govindpura, Gollahalli, Yelahanaka, Bengaluru, (Karnataka). India.
²Pooja Singh, Capgemini and aims to pursue her Masters in Computer Science. Nitte Meenakshi Institute of Technology, Govindpura, Gollahalli, Yelahanaka, Bengaluru, (Karnataka). India.
³Naman Pradhan, Larsen and Tubro. Cloud Computing and Databases, Nitte Meenakshi Institute of Technology, Govindpura, Gollahalli, Yelahanaka, Bengaluru, (Karnataka). India.
⁴Naman Pradhan, Larsen and Tubro. Cloud Computing and Databases, Nitte Meenakshi Institute of Technology, Govindpura, Gollahalli, Yelahanaka, Bengaluru, (Karnataka). India.
Manuscript received on October 13, 2015. | Revised Manuscript received on October 25, 2015. | Manuscript published on November 05, 2015. | PP: 32-35 | Volume-5 Issue-5, November 2015 . | Retrieval Number: E2742115515/2015©BEIESP
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©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: Various public cloud service offerings such as Google App Eng, Amazon Web Services, Microsoft Azure, etc. are increasingly gaining popularity as a means to perform network, storage and compute operations. Also the Hadoop framework is rapidly becoming the industry standard for most organizations due to its scalability, fault tolerant and cost effective design. Hence deploying Hadoop on the cloud will allow leveraging extremely elastic and flexible operations all while being cost and time effective. However various cloud attacks can bypass the current Hadoop security mechanisms, and threaten the confidentiality of the client’s data and the overall system. In this paper we propose a Hadoop System that maintains the privacy and security of the information stored on the cloud through Client side validation and encryption along with a more resilient public cloud-based Hadoop model.
Keywords: Client encryption; Hadoop; Public cloud; Resiliency; Security and privacy.

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E2736115515

IJSCE Journal — Tue, 05 Oct 2021 04:39:20 +0000

Development of Prosthethic Hand Hardware and Its Control System
M.H.Jali¹, M.K.Alias², R.Ghazali³, T.A.Izzuddin⁴, H.I.Jaafar⁵¹M.H. Jali, Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Malacca, Malaysia.
²M. K. Alias, Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Malacca, Malaysia.
³M. K. Alias, Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Malacca, Malaysia.
⁴T.A. Izzuddin, Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Malacca, Malaysia.
⁵H.I. Jaafar, Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Malacca, Malaysia.
Manuscript received on October 16, 2015. | Revised Manuscript received on October 29, 2015. | Manuscript published on November 05, 2015. | PP: 28-31 | Volume-5 Issue-5, November 2015 . | Retrieval Number: E2736115515/2015©BEIESP
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©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 described the development of prosthetic hand and its control system. Initially the research start by reviewing the existing prosthetic design. After investigating of those designs, a better design approach has been presented in this work. The objective of this research is to design and develop the prosthetic hand control system in order to provide solutions for the amputee who lose their hand. This project describes an under actuated artificial hand for functional replacement of the natural hand. The hand comprised of an actuator embedded in an under actuated mechanical system. After the prosthetic hand hardware completed the fabrication process, it is modelled using system identification method. An input data (voltage) and output data (position) is collected using MyRIO. Then, the PID controller is designed based on Zigler Nichols method. Finally, the prosthetic control system is verified via simulation. It is expected that the PID controller could perform well for the prosthetic system.
Keywords: Prosthetic hand, Ziegler Nichols method, system identification, PID controller.

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