Volume-2 Issue-5, November 2012 – International Journal of Soft Computing and Engineering (IJSCE)

E1062102512

IJSCE Journal — Wed, 13 Oct 2021 11:27:20 +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 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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 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#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 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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 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Satellite Image Resolution Enhancement using DWT Technique
O. Harikrishna¹, A. Maheshwari²
¹O. Harikrishna, (M. Tech) Sktrmce, Kondair, Andhra Pradesh, India.
²A. Maheshwari, M. Tech Asst. Professor In Ece, Dept, Sktrmce, Kondair, A.P. , India.
Manuscript received on November 01, 2012. | Revised Manuscript received on November 03, 2012. | Manuscript published on November 05, 2012. | PP: 274-278 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1062102512/2012©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: Satellite images are used in many applications such as geosciences studies, astronomy, and geographical information systems. One of the most important quality factors in images comes from its resolution. Interpolation in image processing is a well-known method to increase the resolution of a digital image. Interpolation has been widely used in many image-processing applications such as facial reconstruction, multiple-description coding, and resolution enhancement. In this project, we propose a new satellite image resolution enhancement technique based on the interpolation of the high-frequency sub bands obtained by discrete wavelet transform (DWT) and the input image. The proposed resolution enhancement technique uses DWT to decompose the input image into different subbands. Then, the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new resolution-enhanced image by using inverse DWT. In order to achieve a sharper image, an intermediate stage for estimating the high-frequency sub bands has been proposed. The proposed technique has been tested on satellite benchmark images. The quantitative (peak signal-to-noise ratio and root mean square error) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.
Keywords: Discrete wavelet transform (DWT), interpolation, satellite image resolution enhancement, wavelet zero padding.(keywords)

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E1016102512

IJSCE Journal — Mon, 11 Oct 2021 06:36:13 +0000

Vehicle Collision Avoidance System using Wireless Sensor Networks
S.Ramesh¹, Ravi Ranjan², Ranjeet Mukherjee³, Swarnali Chaudhuri⁴
¹S.Ramesh, Assistant Professor, Dept. of Electrical & Electronics Engineering, SRM University, NCR Campus Modinagr, Ghaziabad (U.P.), India
²Ravi Ranjan, Department of Electronics & Communication Engineering, NCR Campus Modinagr, Ghaziabad (U.P.), India
³Ranjeet Mukherjee, Department of Electronics & Communication Engineering, NCR Campus Modinagr, Ghaziabad (U.P.), India
⁴Swarnali Chaudhuri, Department of Electronics & Communication Engineering, NCR Campus Modinagr, Ghaziabad (U.P.), India
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 300-303 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1016102512/2012©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: Collision Avoidance systems, as a subsequent step to collision mitigation, are one of the Great challenges in the area of active safety for road vehicles. In India the total annual deaths due to road accidents has crossed 1.18 lakh, according to the latest report of National Crime Records Bureau (NCRB). If these deficiencies are not controlled at early stages they might cause huge economical problems affecting the road side networks. The main part of the work was to carry out a feasibility study on vehicle collision avoidance system using wireless sensor networks. The collision avoidance can be done by Laser sensor. Vehicle collision avoidance system can be identified by using Laser rays with the laser transmitter and laser receiver. Laser transmitter is connected to the laser sensor. Can controller is connected to the all sides of the nodes and send the information via Zigbee and transmit the message to the LCD output on the driver side. Laser receiver is connected to the can controller.
Keywords: Can controller, Collision, Laser, Zigbee, LCD.

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E1053102512

IJSCE Journal — Mon, 11 Oct 2021 06:32:43 +0000

A Comparative Study Review of Soft Computing Approach in Weather Forecasting
Govind Kumar Rahul¹, Madhu Khurana²
¹Govind Kumar Rahul, M.Tech Student, ABES Engineering College Ghaziabad, MTU Noida, Uttar Pradesh, India
²Prof. Madhu Khurana, Assoc. Professor, ABES Engineering College Ghaziabad, MTU Noida, Uttar Pradesh, India
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 295-299 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1053102512/2012©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: In a developing country, like India where the agriculture & industries are base for the national economy, the weather conditions play leading role for their proper development and smooth running. Therefore having accurate weather forecasting information may allow farmers or industry managers to make better decisions on managing their farms. Soft computing using ANN is an innovative approach to construct a computationally intelligent system that is able to process non-linear weather conditions within a specific domain, and make prediction. A number of researches have been done or being done using Soft Computing Approach for forecasting. In this paper the presentation is all about to present the comparative study of several researches and some key findings that are initials for better start any soft computing model for prediction.
Keywords: Soft Computing, Artificial Neural Network (ANN), Back Propagation Algorithms, Multilayer Feed Forward Neural network (MLFFNN), Mean Square Error (MSE) etc.

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E1064102512

IJSCE Journal — Mon, 11 Oct 2021 06:28:22 +0000

Analysis of Sliding Window Protocol for Connected Node
Kapil Kumar Bansal¹, Rakesh Kumar Yadav²
¹Dr. Kapil Kumar Bansal, Head Research and Publication, SRM University, NCR Campus Modinagr, Ghaziabad (U.P.), India
²Rakesh Kumar Yadav, Department of Computer Science & Engineering, SRM University, NCR Campus Modinagr, Ghaziabad (U.P.), India
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 292-294 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1064102512/2012©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: Abstract- In conventional networks lack of reliable and efficient transmission of data over unreliable channels that can lose, reorder and duplicate messages due to router or receiver limited buffer space, are retransmitted by the source. These scenarios usually data blocks contain error detection codes to detect whether an error has occurred during data transmission or not. If an error is detected, the receiver can try to fix the error if the received data has enough redundant bits or request a retransmission of data, for fulfill this requirement sliding window protocol is use. This paper shows calculated link utilization is better than observed link utilization for sliding window protocol using Deft Netz2.0 network simulation.
Keywords: Network, Congestion, Buffer, Simulation, Bandwidth, Reliable, Efficient, Netz2.0

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E1065102512

IJSCE Journal — Mon, 11 Oct 2021 06:21:01 +0000

Design of High Breakdown Voltage and Power Dissipation of 6H-SIC DIMOSFET using Uniformly Doped Profile of Drift Region
Sanjay Sharma¹, Amarjeet Kaur², Shaweta Narula³, Meenu Jangir⁴, Ashish Sharma⁵, Hemchand Vashist⁶
¹Asst Professor Sanjay Sharma, Electronics & Comm. Engineering Department, ACME, India.
²Er. Amarjeet Kaur, Electronics & Comm. Engineering Department, ACME, India.
³Er. Shaweta Narula, Electronics & Comm. Engineering Department, ACME, India.
⁴Er. Meenu Jangir, Electronics & Comm. Engineering Department, AITM, India.
⁵Er. Ashish Sharma, Electronics & Comm. Engineering Department, ACME, India.
⁶Er. Hemchand Vashist, Electronics & Comm. Engineering Department, ACME, India.
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 286-291 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1065102512/2012©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: In this paper a novel approach for designing of High Breakdown Voltage and Power Dissipation of 6H-SiC DIMOSFET Using Uniformly Doped Profile of Drift Region has been presented. All the calculations& graphs for Forward Voltage, Power Dissipation, On Resistance and Drain to Source Voltage at different levels of doping for different values of Current Density have been observed using MATLAB 7.0
Keywords: IMOSFET, Forward Voltage, Power Dissipation, On Resistance, Drain to Source Voltage, Current Density, Doping, MATLAB

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E1067102512

IJSCE Journal — Mon, 11 Oct 2021 06:16:50 +0000

Design of Improved Built-In-Self-Test Algorithm (8n) for Single Port Memory
Manoj Vishnoi¹, Arun Kumar², Minakshi Sanadhya³
¹Manoj Vishnoi, Department of ECE, SRM University NCR Campus modinagar India.
²Arun Kumar, Department of ECE, SRM University NCR Campus modinagar India.
³Minakshi Sanadhya, Department of ECE, SRM University NCR Campus modinagar India
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 281-285 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1067102512/2012©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 presents a modified March (8n) test algorithm to the Built-In Self-Test (BIST) for Single Port Memory. In this algorithm, test patterns are complemented to generate state-transitions that are needed for the detection of frequently occurring as well as newer occurring faults with the shrinking of channel length. The test pattern will be generated for the single port memory. The use of 8n pattern to generate state transition allow to reducing both of time and energy for detection of faults. As a result, the number of test patterns required is very less than of the traditional method, while the extra hardware is negligible.
Keywords: March Algorithm, BIST (Built-In-Self-Test), Channel Length, Faults, Test-Pattern.

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E1066102512

IJSCE Journal — Mon, 11 Oct 2021 06:05:34 +0000

Design of a Rectangular Fractal Patch Geometry for Modern Communication Systems
Ruchika Jain¹, Deepak Bhatnagar², Kirti Vyas³
¹Ruchika Jain, Arya college of Engg & I.T,Jaipur, India.
²Deepak Bhatnagar, Professor, Department of Physics,University of Rajasthan, Jaipur, India.
³Kirti Vyas, Associate Professor,Arya college of Engg & I.T, Jaipur, India.
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 279-280 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1066102512/2012©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: In modern wireless communication systems, compact multiband antennas with wider bandwidth and low profile configuration are in great demand. This has initiated antenna research in many directions. One of such antennas is a fractal shaped antenna element. The fractal structures may be designed by stacking of numerous small units together in such a way that the final geometry has the same shape as that of the unit structure. In addition to their larger bandwidths, fractal antennas are compact in size relative to the conventional antennas because of their efficient volume filling nature. In the present paper, design of new compact fractal multiband antenna geometry is presented. This antenna is targeted for application in Wi-MAX communication systems which operates in three bands namely lower band (2.3 to 2.9 GHz), medium band (3.4 to 3.96 GHz) and upper band (5.25 to 5.85 GHz). Two iterations of the rectangular fractal multiband antenna, with probe feeding in 2-6 GHz band are examined. The proposed antenna resonates at three frequencies namely 2.8 GHz, 3.95 GHz and 5.8 GHz after 2nd iteration. These three frequencies lie in the three considered bands. From the return loss variations with frequency; it is realized that proposed antenna may also be used for Wi-Fi (5.1-5.825 GHz) applications. Finally this antenna is fabricated and its return loss, VSWR and input impedance variations with frequency are tested. Antenna Configuration – The design paramters of proposed antenna are as folllows: (i) The base antenna geometry is a rectangle with width length „a‟= 20.2mm and width „b‟ = 19.8mm. (i) The antenna structure is simulated on glass epoxy FR4 dielectric substrate having dielectric constant (εr) and loss tangent (tanδ)of the substrate equal to 4.4 and 0.025 respectively. The thickness of substrate is h = 1.59mm and the patch thickness is 0.0035mm. (ii) The antenna is fed through a 50Ω coaxial probe of diameter 1.24mm.
Keywords: The antenna structure is simulated on glass epoxy FR4 dielectric substrate having dielectric constant (εr) and loss tangent (tanδ)of the substrate equal to 4.4 and 0.025 respectively. The thickness of substrate is h = 1.59mm and the patch thickness is 0.0035mm.

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E1050102512

IJSCE Journal — Mon, 11 Oct 2021 06:00:02 +0000

Artificial Intelligence in Games
Raju¹, Nitin Sikka², Sanjeev Kumar³, Rahul Gupta⁴
¹Raju, Pursuing Bachelor of technology degree from Dronacharya College of Engineering, Gurgaon, Haryana.
²Nitin Sikka, Pursuing Bachelor of technology degree from Dronacharya College of Engineering, Gurgaon, Haryana.
³Sanjeev Kumar, Pursuing Bachelor of technology degree from Dronacharya College of Engineering, Gurgaon, Haryana.
⁴Rahul Gupta, Pursuing Bachelor of technology degree from Dronacharya College of Engineering, Gurgaon, Haryana.
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 269-273 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1050102512/2012©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: Based on the recent surge in interest in the both academic and games industry in character- based artificial intelligence. Although the games are mainly related with entertainments, but with this there are other serious applications of gaming, including military training, educational games, driving training, medical training and games that reflect social consciousness or advocate for a cause. Artificial intelligence in games is a concept of taking game applications beyond the limits of interactive gaming. Such system learn about the player’s behaviours during game play and beyond the pre-programmed set provides and interactively develop and provide a best experience to the players. General Terms The main aim of our research is to develop such artificial intelligence techniques that can have a substantial impact in the game industry. In the following research paper we are going to study about Case Based Reasoning (CBR), automatic behaviour adjustment for believing characters, drama management and user modelling for interactive stories and strategic behaviour planning for real time strategy games. We include problems in adopting artificial intelligence in games and some algorithms for respective games. Future aspect of artificial intelligence in games is also mentioned in the paper.
Keywords: Need of Artificial Intelligence, Problem in Computer Games Artificial Intelligence, Behavior Adaption for Believing Characters Artificial Intelligence, Action transformation System, Case Base Planing For Strategy Games, Drama Management In Intractive Stories, Integrate user modelling In with drama management.

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E1061102512

IJSCE Journal — Mon, 11 Oct 2021 05:44:22 +0000

Tumors Classification using PNN Methods
M. Vinay Kumar¹, Sharad Kulkarini²
¹M.Vinay Kumar, Electronics & Communication Engineering, Sri Kottam Tulasi Reddy College Of Engineering & Technology, India.
²Sharad Kulkarini, Electronics & Communication Engineering, Sri Kottam Tulasi Reddy College Of Engineering & Technology, India.
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 266-268 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1061102512/2012©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: In this paper, pnn with image and data processing techniques was employed to implement an automated brain tumor classification. The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Medical Resonance images contain a noise caused by Operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques for instant, neural networks, and fuzzy logic shown great potential in this field. Hence, in this paper the Probabilistic Neural Network was applied for the purposes. Decision making was performed in two stages: feature extraction using the principal component analysis and the Probabilistic Neural Network (PNN). The performance of the PNN classifier was evaluated in terms of training performance and classification accuracies. Probabilistic Neural Network gives fast and accurate classification and is a promising tool for classification of the tumors.
Keywords: PNN, Neural Network, PCA, MRI, Classification

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E1058102512

IJSCE Journal — Mon, 11 Oct 2021 05:38:01 +0000

Component Based Approach for Technically Feasible and Economically Viable E-Content Design and Development
J.Senthil Kumar¹, S.K.Srivatsa²
¹J.Senthil Kumar, Research Scholar, Vels University, India
²D.B.Jain College, Chennai, India. S.K.Sriv, St.Joseph’s College of Engineering, Chennai, India
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 264-265 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1058102512/2012©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 world has undergone a transition from the Industrial Age to the Information Age and to the present Knowledge Age in a rapid way. In this era, wherein the economy is knowledge-based; continuous learning will decide the success or failure of every organization and individual. E-learning, it is believed, would mark the zenith of the evolution of learning. Socio-economic changes in the world have been causing drastic changes in the way people look at education and training as we have progressed from agriculturist mode of economy to the information age. E-learning has had a broadly positive pedagogic impact. The “learning object” model is perhaps the most prominent “revolutionary” approach. This paper aims to address the issues associated with different types of pedagogical approaches and the key benefits of content development using learning object.
Keywords: E-learning, learning object, pedagogic,ict.

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