Volume-3, Issue-2, May 2013 – International Journal of Soft Computing and Engineering (IJSCE)

B1491053213

IJSCE Journal — Mon, 11 Oct 2021 11:40:49 +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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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 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#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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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

Comprehensive Study of Weighted Sequential Pattern Mining
Niti Desai¹, Amit Ganatra²
¹Niti Desai, Deprtment of Computer Engg, Uka Tarsadia University, Bardoli, Surat, Gujarat, India.
²Amit Ganatra, U and P U Patel Department of Computer Engineering, Charotar University of Science and Technology, Changa, Anand, Gujarat, India.
Manuscript received on April 05, 2013. | Revised Manuscript received on April 27, 2013. | Manuscript published on May 05, 2013. | PP: 185-188 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1491053213/2013©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: Extensive growth of data gives the motivation to find meaningful patterns among the huge data. Sequential pattern provides us interesting relationships between different items in sequential database. In the real world, there are several applications in which specific sequences are more important than other sequences. Traditional Sequential pattern approaches are suffering from two disadvantages: Firstly, all the items and sequences are treated uniformly. Second, conventional algorithms are generating large number of patterns for lower support. In addition, the unimportant patterns with low weights can be detected. This paper addresses problem of traditional framework and various framework of weighted sequential pattern. Paper also discuses how algorithm mines sequential pattern which reduces the search space and new pruning technique prune the unimportant pattern and pick only those patterns which leads to important and emerging pattern. Later section of paper discuses results of simulation study and how researcher can lead current research.
Keywords: Weighted Sequential Pattern Mining, Weighted Association Mining Framework, Weighted sequential pattern Mining Framework.

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B1569053213

IJSCE Journal — Sat, 09 Oct 2021 05:09:21 +0000

QoS and Cost Aware Service Brokering Using Pattern Based Service Selection in Cloud Computing
Mandeep Devgan¹, Kanwalvir Singh Dhindsa²
¹Mandeep Devgan, Department of Information Technology, Chandigarh Engineering College, Landran, Mohali, India.
²Kanwalvir Singh Dhindsa, Department of CSE/IT, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib.
Manuscript received on April 04, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 441-447 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1569053213/2013©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 an effective Services selection mechanism has been introduced for creating a practically useful Service Broker. Selection of Services is based on characteristics such as performance, reliability and cost, ranking and integrity are also considered. In Cloud computing a service broker is responsible for routing the user requests to the most appropriate Services. Traditionally, user of a service issues service request with some specific characteristics to a service broker and the broker searches all available Services with specified service and with a certain level of the user satisfaction. But, how can we select a set of available services from a query of service user with the some restriction? To solve this issue, we propose a service selector for service broker that can denote the restriction of similar services into a service test data, which is a set of similar cloud services, and select a set of services that provide a certain level of service consumer’s satisfaction. We first identify the performance, reliability and cost of services which could be important for a cloud service consumer while requesting and then represent them in a knowledge base. And then we implement a Usage Pattern based selection mechanism to handle a service request with Limitation and the selection method is experimented on a simulated service test data. F i r s t Part also involves the testing and comparison of the Usage Pattern mechanism with traditional selection mechanism. In Part II, some of functionally similar Cloud services with different non-functional characteristics are modeled and each web service is differentiated with their non-functional properties. The usage pattern based mechanism is then incorporated with a user interface for consumer, so that user can request the service-broker for a set of best cloud services in terms of required levels of non-functional characteristics. The Usage Pattern based service selection mechanism in the service-broker will give a set of best services according to the required level of consumer satisfaction. The consumer can then select any service from the set and invoke it through its Uniform Resource Locator (URL) address. This last Part leads to the concept of an automated service broker satisfying the needs of the consumer with usage pattern. This usage pattern-based Broker would be able to satisfy the consumer requests better than a traditional broker by finding more cloud services and at the same time giving consumer the flexibility to describe its requirements in a flexible and more realistic manner.
Keywords: Effective Services selection, Uniform Resource Locator.

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B1555053213

IJSCE Journal — Sat, 09 Oct 2021 05:02:55 +0000

Suitable Areas for Economic Crops Based on GIS and Physical Land Evaluation Model
Teerawong Laosuwan¹, Satith Sangpradid², Poramate Chunpang³
¹Teerawong Laosuwan, Department of Physics, Faculty of Science Mahasarakham University, Khamreang, Kantarawichai, Mahasarakham, Thailand.
²Sathit Sangpradid, Department of Informatics, Faculty of Geoinformatics, Mahasarakham University, Khamreang, Kantarawichai, Mahasarakham, Thailand.
³Pormate Chunpang, Department of Physics, Faculty of Science Mahasarakham University, Khamreang, Kantarawichai, Mahasarakham, Thailand.
Manuscript received on April 03, 2013. | Revised Manuscript received on April 29, 2013. | Manuscript published on May 05, 2013. | PP: 437-440 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1555053213/2013©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 aim of this research was to locate suitable areas for economic crops of sesame based on Geographic Information System (GIS) and Physical Land Evaluation Model in Chi watershed, North-east of Thailand. The authors used decision tools or rules to locate suitable areas for economic crops plantation by analyzing six variables from physical evaluation model (S), (W), (R), (L), (SA), and (SL). These six factors were analyzed by using overlay function technique including union and intersect scheme. The variables then were determined their score via Physical Land Evaluation Model. The results of this work found that areas for sesame plantation included high, moderate, low and non suitable areas, which accounted for 30.47%, 19.61%, 8.92% and 1.66%, respectively.
Keywords: Suitable Areas, Economic Crops Plantation, GIS, Physical Land Evaluation.

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B1585053213

IJSCE Journal — Sat, 09 Oct 2021 04:56:24 +0000

Principal Components Analysis Based Iris Recognition and Identification System
E. Mattar
E. Mattar, Department of Electrical and Electronics Engineering, College of Engineering, University of Bahrain, P. O. Box 13184, Kingdom of Bahrain.
Manuscript received on April 05, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 430-436 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1585053213/2013©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 article focuses on the employment of iris recognition technique and their application in security systems. The implementation of such a system is based on the processing of an iris (scene) using Principle Component Analysis known in the literature as (PCA). This is done by an iris segmentation algorithm of (Libor Masek). Libor Masek algorithm is utilized here to segment an iris from some undesired noises and ingredients in an eye image. This rather helps to acquire the most accurate iris scene. Eigen irises are hence obtained using the PCA method. Eigen of irises are then utilized to train an Artificial Neural Network (ANN) recognition system. This is followed by transforming a set of irises into a new space. Transformed irises are accumulated in a database, where they are compared with a set of test irises transformed in the same state of the recognition cycle. The proposed system has resulted in accurate results up to 91% for identifying a pre-stored individuals.
Keywords: Iris recognition, Principal Component Analysis, Pattern recognition, Eigen vectors.

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B1579053213

IJSCE Journal — Sat, 09 Oct 2021 04:52:38 +0000

Identification of Handwritten Simple Mathematical Equation Based on SVM and Projection Histogram
Sanjay S. Gharde¹, Pallavi V. Baviskar², K. P. Adhiya³
¹Sanjay S. Gharde, is currently working as assistant professor in Computer Engineering Department, North Maharashtra University/ SSBT College of Engineering and Technology, Jalgaon India.
²Pallavi V. Baviskar is currently pursuing master’s degree in computer science engieeringin from North Maharashtra University, Jalgaon, India
³K. P Adhiya is currently Associate Professor in Computer Engineering Department of SSBT College of Engineering and Technology, Jalgaon, India.
Manuscript received on April 03, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 425-429 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1579053213/2013©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: Recognition of simple mathematical equation can applied on off-line handwritten samples. For smooth implementation database is prepaid with total 237 symbols which are collected from 28 different simple mathematical equations. The dataset 1and dataset 2 are training using most popular classifier named Support Vector Machine. In particular, this work tries to spotlight on evaluation of various methods used for feature extraction and recognition system. Moreover, some essential issues in simple mathematical handwritten equation recognition will be addressed in deepness. This paper discusses various steps of recognition process for simple mathematical handwritten equations. In that, pre-processing, segmentation, feature extraction, classification and recognition for handwritten mathematical symbol as well as for simple expression is described. Among the different phases applied in recognition system, features extraction and classification method may influence the overall accuracy and recognition rate of the system. Therefore, various techniques applied in this context are studied and comparative analysis is prepared. This evaluation study suggests projection histogram most suitable feature extraction technique and support vector machine is appropriate classification technique for implementation. Using projection profile and support vector machine two different dataset are recognized then 97.58% and 98.40% (as an average it resulted into 98.26%) recognition rate is achieved for simple handwritten mathematical equation.
Keywords: Classification, mathematical expression, projection histogram, support vector machine.

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B1576053213

IJSCE Journal — Sat, 09 Oct 2021 04:45:38 +0000

Survey of Load Balancing Approachs in Peer-To-Peer Network
Vishakha Patange¹, D.D.Gatade²
¹Vishakha Patange, M.E. (Computer Networks), Pune University, S.C.O.E. Pune., India.
²D. D. Gatade, M.E. (Computer Networks), Pune University, S.C.O.E. Pune., India.
Manuscript received on April 05, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 422-424 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1576053213/2013©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 recent years, structured peer-to-peer (P2P) has gained an important role in the design of large-scale distributed systems. However, due to their strict data placement rules, they are often prone to three main load imbalances, i.e., range, data, and execution skew. Further imbalance may result due to non-uniform distribution of objects in the identifier space and a high degree of heterogeneity in object loads and node capacities. A node’s load may vary greatly over time since the system can be expected to experience continuous insertions and deletions of objects, skewed object arrival patterns, and continuous arrival and departure of nodes. A virtual server looks like a single peer to the underlying DHT, but each physical node can be responsible for more than one virtual server. Load balancing among application layer peer-to-peer (P2P) networks is critical for its effectiveness but, is considered to be the most important development for next-generation internet infrastructure. Most structured P2P systems rely on ID-space partitioning schemes to solve the load imbalance problem. P2P system harnesses the resources of large populations – networked computers in a cost-effective manner such as the storage, bandwidth, and computing power. In structured P2P systems, data items are spread across distributed computers (nodes), and the location of each item is determined in a decentralized manner.
Keywords: About four key words or phrases in alphabetical order, separated by commas.

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B1575053213

IJSCE Journal — Sat, 09 Oct 2021 04:38:18 +0000

An Equivalence Relation to Reduce Data Redundancy Based on Fuzzy Object Oriented Database System
Sujoy Dutta¹, Laxman Sahoo², Debasis Dwibedy³
¹Sujoy Dutta, Computer Science and Engineering, Kalinga Institute of Industrial Technology (KIIT University), Bhubaneswar, India.
²Dr. Laxman Sahoo, Computer Science and Engineering, Kalinga Institute of Industrial Technology (KIIT University), Bhubaneswar, India.
³Dr. Laxman Sahoo, Computer Science and Engineering, Kalinga Institute of Industrial Technology (KIIT University), Bhubaneswar, India.
Manuscript received on April 04, 2013. | Revised Manuscript received on April 29, 2013. | Manuscript published on May 05, 2013. | PP: 417-421 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1575053213/2013©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 fuzzy equivalence relation is defined, generally, superseding most of the established results. The technique of employing sets of values for tuple components to express imprecision and redundancy in relational databases was proposed by Buckles and Petry in their classic works on fuzzy relational databases [1], [2]. By employing finite scalar domains with similarity relations and special fuzzy number domains, Buckles and Petry have demonstrated that the classical properties of uniqueness of tuple interpretations and well-definedness of the relational algebra can be retained in the fuzzy relational database model. The key to the preservation of these properties is the fact that scalar domains with similarity relations and the fuzzy number domains can be partitioned into equivalence classes. However, since equivalence classes can be constructed by assuming the existence of similarity relations, it is desirable to generalize the fuzzy relational database model to one based only on equivalence classes. In this work, we show that the important properties of classical relational databases (and of fuzzy relational databases) are preserved in a generalized model built on equivalence relations on finite database domains.
Keywords: Domain partitions, Equivalence classes, Equivalence relations, Fuzzy relational databases, Relational algebra.

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B1567053213

IJSCE Journal — Sat, 09 Oct 2021 04:33:12 +0000

Quantum Circuit Automatic Synthesizer(QCAS)
Ali Moghadaszadeh¹, Majid Mohamadi², Ali Akbar Niknafs³, Peyman Keshavarzian⁴
¹Ali Moghadaszadeh, Computer Engineering Department, Science and Research branch, Islamic Azad University, Kerman, Iran.
²Majid Mohamadi, Computer Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.
³Ali Akbar Niknafs, Computer Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.
⁴Peyman Keshavarzian, Computer Engineering Department, Science and Research branch, Islamic Azad University, Kerman, Iran.
Manuscript received on April 04, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 412-416 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1567053213/2013©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: Quantum and reversible circuit synthesis has been important concern of designers in recently passed decades. Considering the application of garbage bit in optimization of quantum circuit, GA-Based techniques have been introduced. This project provides an extensible infrastructure for quantum circuit automatic synthesis and optimization with the enhancement of interoperability features. The problem domain begins from complex computing model up to a synthesis of reversible quantum circuit. Optimization criterias are whole considered separately from software processing logic which cause capability of applying variable optimization critria in different problem domains. The new introduced methodology utilize PSO technique for circuit synthesis evolutionary computation in optimization step. A software library has been developed which implements this technique. It provides a simple flexible software for researchers in order to automatic synthesis of quantum circuit that implements an open source library of prerequisite of quantum circuit synthesis.
Keywords: Quantum circuits, gate, PSO, Automatic synthesis.

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B1561053213

IJSCE Journal — Fri, 08 Oct 2021 12:03:14 +0000

Adaptive Filtering and Voice Compression Using Neural Networks
Kalyan Chatterjee¹, Mandavi², Prasannjit³, Nilotpal Mrinal⁴, Vikash Kumar Saw⁵, Kumari Rupmala⁶, Priyadarshani⁷
¹Mandavi, Information Technology, Bengal College of Engineering and Technology, Durgapur, India.
²Kalyan Chatterjee, Computer Science Engineering, Bengal College of Engineering And Technology, Durgapur, India.
³Prasannjit, Information Technology, Bengal College of Engineering and Technology, Durgapur, India.
⁴Nilotpal Mrinal, Information Technology, Bengal College of Engineering and Technology, Durgapur, India.
⁵Kumari Rupmala, Computer Science Engineering, Bengal College of Engineering And Technology, Durgapur, India.
⁶Vikash Kumar Saw, Computer Science Engineering, Bengal College of Engineering And Technology, Durgapur, India.
⁷Priyadarshani, Computer Science Engineering, Bengal College of Engineering And Technology, Durgapur, India.
Manuscript received on April 03, 2013. | Revised Manuscript received on April 29, 2013. | Manuscript published on May 05, 2013. | PP: 409-411 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1561053213/2013©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: Voice data compression is about a process which reduces the data rate or file size of digital audio signals. This process reduces the dynamic range without changing the amount of digital data of audio signals. Voice compression is one of the leading vicinity of digital signal processing that spotlight on dipping the bit rate of speech signals for transmission and storage devoid of considerable loss of quality. This paper attempts to present an adaptive filtering technique for the removal of useless noise from the audio signals. After the noise removal has been done, the filtered audio signal is taken as the input to the neural network. Finally, the back propagation algorithm is applied for the compression of the audio signals.
Keywords: Adaptive Filtering, Audio signals, Back propagation algorithm, Neural Network, Recursive Least Square Algorithm (RLC).

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B1560053213

IJSCE Journal — Fri, 08 Oct 2021 11:52:55 +0000

Chemical Sensors Employed In Electronic Noses: A Review
Syeda Erfana Zohora¹, A.M.Khan², A.K.Srivastava³, Nisar Hundewale⁴
¹S. E. Zohora, N. Hundewale is with College of Computers & Information Technology, Taif University, Taif, and KSA.
²A. M. Khan is with Mangalore University, Mangalore, India.
³A. K. Srivastava is with LightField Corporation Philadelphia, PA, USA.
⁴A. K. Srivastava is with LightField Corporation Philadelphia, PA, USA.
Manuscript received on April 03, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 405-408 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1560053213/2013©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: Electronic noses utilizes an array of chemical sensors of different specificities which responds to the volatile organic compounds present in the gases. The use of electronic chemical sensors in an array design with coupled signal conditioning and appropriate pattern recognition system is capable of identifying complex odours.Such an artificial gas sensing system is called ‘electronic nose’. The requirement for the sensors in a electronic nose is that they have a partial sensitivity, i.e. that they can respond broadly to a range or class of gases rather than to a specific one. However, The electronic nose will categorize many odours that contain many chemical components. Different types of gas sensors in the sensor array includes metal oxide semiconductors,optical and amperometric gas sensor, surface acoustic sensors,piezoelectric gas sensors. In this review paper,we discuss the operating principle of each chemical sensor type and its use in electronic nose system.
Keywords: Chemical sensors, Electronic noses, E-nose, Volatile Organic Compounds(VOC).

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