Volume-4 Issue-2, May 2014 – International Journal of Soft Computing and Engineering (IJSCE)

B2247054214

IJSCE Journal — Thu, 14 Oct 2021 09:01:45 +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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#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 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Monitoring Company Status on Single dashboard by using GRC
Sasi Kumar Gurumurthy¹, T. Siva Shankar², Niranjan Babu³¹Sasi Kumar Gurumurthy, is a M.Tech, student in School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India.
³Siva Shankar, is a M.Tech, student in School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India.
²Niranjan Babu, is a M.Tech, student in School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India.
Manuscript received on May 02, 2014. | Revised Manuscript received on May 03, 2014. | Manuscript published on May 05, 2014. | PP: 218-220 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2247054214/2014©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: In this paper we are going to describe how to organize a company based records securely. The company contains several module such as audit, asset, policy and so on. Nowadays every company maintains their records using XL Sheet, So we need to enter our each and every data manually. This contains several setbacks such as litter of time, entering and recoup time also gets high, high manpower required, not secured and even we may also enter our data in wrong fields. Here this system provides full security of maintaining company relevant records and creating an application for maintaining company records by using fully role based access manner. In which only authorized user can access respective action and suppose unauthorized user trying to access someone’s data at that point of time it sends an alert message to respective authorized user. Overall company status can be seen in an single dashboard and based upon that status we can act accordingly. Means each and every module such as asset, audit, policy and so on status can be monitored in a single dashboard at a same time.
Keywords: GRC, Dash board, Asset ,policy, Risk business continuity management, audit ,standards.

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B2246054214

IJSCE Journal — Thu, 14 Oct 2021 08:57:31 +0000

An Approach for Security and Privacy Enhancing by Making Use of Distinct Clouds
Sasikumar Gurumurthy¹, T. Niranjan Babu², G. Siva Shankar³¹Prof. Sasikumar Gurumurthy, School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India.
²T. Niranjan Babu, School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India.
³G.Siva Shankar, School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India.
Manuscript received on April 26, 2014. | Revised Manuscript received on May 04, 2014. | Manuscript published on May 05, 2014. | PP: 214-217 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2246054214/2014©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: Security challenges are the major concern when we considering the acceptence of cloud service. A lot of research activities regarding to cloud security resulting in an amount of application and targeting the cloud security threats. The cloud concept comes with a new set of unique features, techniques and architectures. This paper is related to security and privacy enhancing by making use of multiple distinct clouds. Based on the different cloud architecture, the security and privacy capabilities can be approximated. Cloud computing refers to applications and services that run on a distributed network using virtualized resources and accessed by common internet protocols. In this paper, we are introduced different clouds for encryption, decryption and storage process.
Keywords: Cloud, Security, Privacy, Multicloud, data partitioning.

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B2256054214

IJSCE Journal — Thu, 14 Oct 2021 08:53:28 +0000

Design and Study of Enhanced Parallel FIR Filter using Various Adders for 16 Bit Length
D.Ashok Kumar¹, P.Samundiswary²¹Mr. D. Ashok kumar, M.Tech. Student, Department of Electronics Engineering, Pondicherry University, Pondicherry, India.
²Dr. P. Samundiswary, Assistant Professor, Department of Electronics Engineering, Pondicherry University, Pondicherry, India.
Manuscript received on May 02, 2014. | Revised Manuscript received on May 03, 2014. | Manuscript published on May 05, 2014. | PP: 207-213 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2256054214/2014©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: Now a day’s parallel Finite Impulse Response (FIR) filter plays very important role in the Digital Signal Processing (DSP) based applications. FIR filters are one of the most widely used fundamental filters in the DSP systems. The parallel FIR filters are derived from FIR digital filter. In this paper, design and study of enhanced parallel FIR filter with various adders using the structure of Fast FIR Algorithm (FFA) based FIR filter and symmetric convolution based FIR filter structures considering 2-parallel and 3-parallel filters is done. These entire filter structures are also designed using Ripple Carry Adder (RCA), Carry save Adder (CSA) and Carry Increment Adder (CIA) by replacing the existing adders with the input bit length and coefficient length of 16-bits. Then the performance metrics of the above two structures is done by designing using Verilog HDL. Further, they are simulated and synthesized using Xilinx ISE 13.2 for Vertex family device of speed -12.
Keywords: Parallel FIR filter, FFA, symmetric convolution, Ripple Carry Adder (RCA), Carry Save Adder (CSA), Carry Increment Adder (CIA).

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B2253054214

IJSCE Journal — Thu, 14 Oct 2021 08:49:33 +0000

PID Control Tuning VIA Particle Swarm Optimization for Coupled Tank System
S.Y.S Hussien¹, H.I Jaafar², N.A Selamat³, F.S Daud⁴, A.F.Z Abidin⁵¹S.Y.S. Hussein, Faculty of Electrical Engineering (FKE), Universiti Teknikal Malaysia Melaka (UTeM).
²H.I. Jaafar, Faculty of Electrical Engineering (FKE), Universiti Teknikal Malaysia. Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Malaysia .
³N.A. Selamat, Faculty of Electrical Engineering (FKE), Universiti Teknikal Malaysia Melaka (UTeM).
⁴F.S. Daud, Faculty of Electrical Engineering (FKE), Universiti Teknikal Malaysia Melaka (UTeM).
⁵A.F.Z. Abidin, Faculty of Electrical Engineering (FKE), Universiti Teknologi MARA (UiTM).
Manuscript received on April 26, 2014. | Revised Manuscript received on May 03, 2014. | Manuscript published on May 05, 2014. | PP: 202-206 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2253054214/2014©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 the use of meta-heuristic technique to obtain three parameters (KP, KI and KD) of PID controller for Coupled Tank System (CTS). Particle Swarm Optimization (PSO) is chosen and Sum Squared Error is selected as objective function. This PSO is implemented for controlling desired liquid level of CTS. Then, the performances of the system are compared to various conventional techniques which are Trial and Error, Auto-Tuning, Ziegler-Nichols (Z-N) and Cohen-Coon (C-C) method. Simulation is conducted within Matlab environment to verify the transient response specifications in terms of Rise Time (TR), Settling Time (TS), Steady State Error (SSE) and Overshoot (OS). Result obtained shows that performance of CTS can be improved via PSO as PID tuning methods.
Keywords: Coupled Tank System (CTS), Particle Swarm Optimization (PSO), PID Controller, PID Tuning Method.

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B2258054214

IJSCE Journal — Thu, 14 Oct 2021 08:44:10 +0000

Motioned Facial Recognition from Live Feed for Surveillance Solutions
Sarah Bal¹, Anmol Kalra², Rishi Kumar³¹Sarah Bal, Amity School of Engineering and Technology, Amity University Uttar Pradesh, New Delhi, India.
²Anmol Kalra, Amity Institute of Aerospace Engineering, Amity University Uttar Pradesh, New Delhi, India.
³Anmol Kalra, Amity Institute of Aerospace Engineering, Amity University Uttar Pradesh, New Delhi, India.
Manuscript received on May 01, 2014. | Revised Manuscript received on May 03, 2014. | Manuscript published on May 05, 2014. | PP: 195-201 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2258054214/2014©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 paper focuses on how face recognition can be done on live video stream (using a webcam-inbuilt or USB attached).The live video is checked for any human face. If a human face is detected, a rectangular box is formed around the face. If nothing is found for the face detection method, a text box showing the error is presented in front of the user. If the face is detected this face is then matched with the already saved database which was priorly created having images of different faces. This is the training database which is then matched with the face image extracted from the live video stream. Initially the project shows the process of face detection and matching procedure from images and then proceeds to face recognition and matching through a live video streaming. The live video here considered is the webcam, the face is detected through the webcam and if any match is found from the train database previously stored in the computer or the device is found then both the detected image and the current image are displayed on the graphical user interface. The GUI being made consists of three axes windows, one showing the continuous live streaming of video, the second shows the screenshot or singular frame of the face detected in the live stream and the third has the image got from the database that somewhat matches to the current image being displayed. The two databases are there, one the train database where the images of different faces of people are stored which would then be used for matching from live video stream for the purpose of security and authentication. The test database consists of the images that are being received from the live video stream, the video stream as soon as it detects the face of human, takes the snapshot of the frame and saves it to the test database, these images are then checked for authentication by matching them with the images in the train database.
Keywords: Graphical user interface (GUI).

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B2257054214

IJSCE Journal — Thu, 14 Oct 2021 08:38:01 +0000

Cooperative Mobile Robotics
Ameya V. Mane¹, Yogesh Ankurkar², Pratik K. Bajaria³¹Ameya V Mane, Electrical Engineering, Veermata Jijabai Technological Institute, Mumbai, India.
²Yogesh Ankurkar, Electrical Engineering, Veermata Jijabai Technological Institute, Mumbai, India.
³Pratik K Bajaria, Electrical Engineering, Veermata Jijabai Technological Institute, Mumbai, India.
Manuscript received on May 01, 2014. | Revised Manuscript received on May 02, 2014. | Manuscript published on May 05, 2014. | PP: 191-194 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2257054214/2014©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: “Cooperative control” is a term which is used to capture those problem areas in which some type of repetition of identical or non-identical subsystems, which are interconnected together, occurs. Such systems are often found in nature, i.e. in the motion of clusters of birds, fish, insects, etc. moving together, in the cell structure of mammals and life-forms, and also in the man-made systems such as in transportation systems. In such systems, a decentralized control configuration is often applied to control the overall system, so that some common objective is achieved. In this paper two non-linear models in multi-agent systems are proposed. These models operate on the principles of distributed control and cascade control
Keywords: About four key words or phrases in alphabetical order, separated by commas.

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B2254054214

IJSCE Journal — Thu, 14 Oct 2021 07:29:41 +0000

Caries Detection Technique for Radiographic and Intra Oral Camera Images
Adhar Vashishth¹, Bipan Kaushal², Abhishek Srivastava³¹Adhar Vashishth, Department of Electronics and Communication Engineering, PEC University of Technology, Chandigarh, India.
²Prof. Bipan Kaushal, Department of Electronics and Communication Engineering, PEC University of Technology, Chandigarh, India.
³Abhishek Srivastava, Department of Electronics and Communication Engineering, PEC University of Technology, Chandigarh, India.
Manuscript received on May 01, 2014. | Revised Manuscript received on May 05, 2014. | Manuscript published on May 05, 2014. | PP: 188-190 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2254054214/2014©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 the modern times, caries is one of the most prevelent disease of the teeth in the whole world. A large percentage of population is affected by them. Dentists try their level best to identify the problem at an earlier stage, but, with poor dentist to patient ratio , the problem becomes compounded. To provide them a helping hand various machines and techniques are developed. Prominent among them is the DIFOTI(digital imaging fiber-optic transillumination) technique, but it requires very expensive machinery to work with which can not be afforded by most of the dentists. We are proposing a method that can provide the needed diagnostic help without requiring the kind of machinery currently in use. We have used image processing technique to identify the caries that provide the dentists with the precise results about caries and the area affected. This method can detect caries in radiographic images as well as in intra oral camera images. This will not only help in countering the low man power problem but will also provide an accurate and cost effective method in identifying and treating caries.
Keywords: Binarization, caries, mask, RGB plane, MATLAB.

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B2250054214

IJSCE Journal — Thu, 14 Oct 2021 07:25:02 +0000

Feature Extraction Values for Digital Mammograms
Arpana M.A¹, Prathiba Kiran²¹Mrs. Arpana M.A, is currently pursuing her M.Tech Degree in Digital Electronics and Communications at AMC College of Engineering, Bangalore which is affiliated to VTU, Belgaum.
²Mrs.Prathibha Kiran, was awarded with M.Tech in Biomedical Signal Processing and Instrumentation during 2011 from Dayanand Sagar College, which is affiliated to VTU, Belgaum.
Manuscript received on May 01, 2014. | Revised Manuscript received on May 03, 2014. | Manuscript published on May 05, 2014. | PP: 183-187 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2250054214/2014©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: Currently digital mammography is the most efficient and widely used technology for early breast cancer detection. The major diagnosing elements such as masses, lesions in the digital mammograms are noisy and of low contrast. The aim of the proposal is to enhance the mammogram images by reducing the noise using median filter, image sharpening and image smoothing. The data clustering algorithm i,e Fuzzy C means clustering is used to segment the region of interest from which various statistical, gradient and geometrical features are extracted. The features extracted from the few images of the data base are used to train the neural networks for classification. The evaluated algorithm is tested on the digital mammograms from the Mammogram Image Analysis Society (MIAS) database. The experimental results show that the breast region extracted by the presented algorithm approximately follows that extracted by an expert radiologist. The detected mass is classified as normal or abnormal. Further abnormal can be classified into benign or malignant.
Keywords: Bio Medical Image processing, Mammograms, Breast Cancer, High Pass Spatial Filter, Fuzzy C means Clustering, Median Filtering, Gradient features.

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B2245054214

IJSCE Journal — Thu, 14 Oct 2021 07:18:54 +0000

IT Project Management According to the PMBoK Adaptation and Application in a Set of Computing Projects in a Moroccan Public Body
Safaa Errihani¹, Said Elfezazi², Khalid Benhida³¹Ing. Safaa ERRIHANI, PHD student, University Cadi Ayyad. Safi, Morocco.
²Dr. Said Elfezazi, Professor in Cadi Ayyad University Higher School of Technology in Safi, Morocco.
³Dr. Khalid BENHIDA, Professor in Cadi Ayyad University Higher School of Technology in Safi, Morocco.
Manuscript received on May 02, 2014. | Revised Manuscript received on May 03, 2014. | Manuscript published on May 05, 2014. | PP: 174-182 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2167054214/2014©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: With the growth of the computing projects and the limited visibility by the administrators regarding the use of the resources as well as the technologies which lead to the abundance of projects, along with the trend to the subcontracting in external suppliers, the project management becomes the key function (office) for the success of all the technical projects. So the Project Management Body Of Knowledge (Guide PMBOK)) of PMI (Project Management Institute) is in phase to become an essential tool for the practitioners in all organizations and business sectors. The main axis of this article is the PMBoK’s analysis and modeling in order to reach a uniform model of project management. The obtained model will be projected on a set of projects within a Moroccan public department. Let’s recall that the PMBoK defines the project management in terms of: integration, scope, time, cost, quality, human resources, communication, risk, procurement and the stakeholders of the project.
Keywords: IT project, Modeling, PMBOK, Project Management.

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B2244054214

IJSCE Journal — Thu, 14 Oct 2021 07:11:36 +0000

Moving Object Detection using Differential Evolution
Amlan Raychaudhuri¹, Arkadev Roy², Ashesh Das³, Gourav Kumar Shaw⁴, Pratik Kumar Mitra⁵¹Amlan Raychaudhuri, Department of CSE, B. P. Poddar Institute of Management & Technology, Kolkata, India.
²Arkadev Roy, Department of CSE, B. P. Poddar Institute of Management & Technology, Kolkata, India.
³Ashesh Das, Department of CSE, B. P. Poddar Institute of Management & Technology, Kolkata, India.
⁴Gourav Kumar Shaw, Department of CSE, B. P. Poddar Institute of Management & Technology, Kolkata, India.
⁵Pratik Kumar Mitra, Department of CSE, B. P. Poddar Institute of Management & Technology, Kolkata, India.
Manuscript received on May 01, 2014. | Revised Manuscript received on May 05, 2014. | Manuscript published on May 05, 2014. | PP: 170-173 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2244054214/2014©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: Moving Object detection is the process of detecting a change in position of an object relative to its surroundings or the change in the surroundings relative to an object. Different complex algorithms are employed to detect a moving object in a video. It has large number of applications in video surveillance and other security systems that are used to process video information. We have achieved it using Differential Evolution (DE). The proposed method is successfully tested over two video sequences.
Keywords: Clustering, Differential Evolution, Moving Object Detection, Temporal video segmentation.

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