Volume-8 Issue-2, May 2018 – International Journal of Soft Computing and Engineering (IJSCE)

B3135058218

IJSCE Journal — Wed, 29 Sep 2021 10:03:56 +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 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Review on Distinctive Image Features from Scale-Invariant Key-Points
Sonali S. Thangan¹, Ankit R. Mune²
¹Sonali S. Thangan, M.E. Student, Department of Computer Science & Engineering, Dr. Rajendra Gode Institute of Technology & Research, Amravati (Maharashtra)-444602, India.
²Prof. Ankit R. Mune, Department of Computer Science & Engineering, Dr. Rajendra Gode Institute of Technology & Research, Amravati (Maharashtra)-444602, India.
Manuscript received on April 20, 2018. | Revised Manuscript received on April 30, 2018. | Manuscript published on July 30, 2018. | PP: 25-29 | Volume-8 Issue-2, May 2018. | Retrieval Number: B3135058218/2018©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbour algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Keywords: Extracting Distinctive, Approach, Real-Time Performance, the Features Are Highly Distinctive,

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B3136058218

IJSCE Journal — Wed, 29 Sep 2021 09:58:10 +0000

Audio Steganography using RSA Algorithm
Harshal Chhadwa¹, Glynes D’souza², Swaradi Godane³, Pooja Sharma⁴
¹Harshal Chhadwa, Department of Computer Engineering, Xavier Institute of Engineering, Mumbai (Maharashtra), India.
²Glynes D’souza, Department of Computer Engineering, Xavier Institute of Engineering, Mumbai (Maharashtra), India.
³Swaradi Godane, Department of Computer Engineering, Xavier Institute of Engineering, Mumbai (Maharashtra), India.
⁴Pooja Sharma, Department of Computer Engineering, Xavier Institute of Engineering, Mumbai (Maharashtra), India.
Manuscript received on July 02, 2018. | Revised Manuscript received on July 03, 2018. | Manuscript published on July 30, 2018. | PP: 22-24 | Volume-8 Issue-2, May 2018. | Retrieval Number: B3136058218/2018©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: A significant amount of research is done to improve the effectiveness in data hiding. Audio steganography is an art of hiding secret information inside an audio file, such that the representation of audio file won’t be altered. Steganography is one of the safest ways of secret data transmissions in today’s digital world. In this paper, large embedding capacity steganography method is proposed using LSB substitution. Alongside, the power and security of the RSA cryptosystem is based on the fact that the factoring problem is “hard.” The public and private keys are related mathematically, but the parameters are chosen so that calculating the private key from the public key is either impossible or prohibitively expensive. Thus two level security and robustness is achieved. The original and stega-audio signals show resemblance hence there is minimum chance of detecting the secret message hidden in the stega-audio. MATLAB is used for proposed algorithm and proposed results have been shown.
Keywords: Cryptography; Steganography; LSB; RSA; Stega-Aud

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B3133058218

IJSCE Journal — Wed, 29 Sep 2021 09:51:56 +0000

Effects of Different Fine Aggregate on Concrete Strengt
Kiambigi Maina¹, Gwaya A.O², Koteng D.O³
¹Kiambigi MAINA, Department of Construction Management, JKUAT;
²Gwaya A.O, Department of Construction Management, JKUAT.
³Koteng D.O, Department of Construction Management, JKUAT
Manuscript received on April 21, 2018. | Revised Manuscript received on April 24, 2018. | Manuscript published on July 30, 2018. | PP: 16-21 | Volume-8 Issue-2, May 2018. | Retrieval Number: B3133058218/2018©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Fine aggregate has been extensively used in the construction industry as a key component of concrete production. Although river sand is one of the major sources of fine aggregate, different sources exhibit different properties by virtue of the geological formation of the drainage basin. Further, the use of river sand as the source of fine aggregate has resulted in over-exploitation leading to depletion and environmental degradation. This has led to exploration of alternative sources to safeguard depletion and reduce the negative impacts on the environment. This research was conducted on a variety of river sands and alternative fine aggregates to assess their suitability for concrete manufacture. A quantitative experimental approach was adopted to test the Physical, chemical and mineralogical properties of fine aggregates sourced from Machakos, Mwingi, Naivasha and Kajiado and the resultant concrete strength after 7, 14 and 28 days recorded. The fineness modulus of all the material samples ranged from 1.92 to 3.66, specific gravity 1.73 to 2.27 and silt content 2.06% to 11.9%. All the samples fell within the overall grading envelope. The silicon dioxide concentration ranged from 65% to 80%, Aluminium oxide 9% to 19% and Calcium oxide 1.3% to 2.5%. Machakos sand had the highest Silicon dioxide and calcium oxide concentration of 80% and 2.5% respectively, while quarry dust had the highest aluminium oxide concentration of 19%. It was observed that concrete produced from natural river sand obtained from Mwingi, Kajiado and Machakos achieved strengths of 41.899N/mm2 , 37.173N/mm2 and 33.645N/mm2 respectively comparative to 30 N/mm2 target characteristic strength after 28 days. On the other hand, concrete produced using fine aggregates obtained from Mlolongo rock sand, Naivasha sand and Mlolongo Quarry dust achieved strengths of 28.682 N/mm2 , 28.411 N/mm2 and 27.661 N/mm2 respectively falling short of the requisite compressive strength after 28 days.
Keywords: Concrete Mix Design, Concrete Strength, Fine Aggregates.

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B3134058218

IJSCE Journal — Wed, 29 Sep 2021 09:40:32 +0000

Darksort: A New Linear Sorting Algorithm
Blake MacKenzie Burns
Blake MacKenzie Burns, Department of Computer Science, University of Toronto, Toronto, Canada.

Manuscript received on April 12, 2018. | Revised Manuscript received on April 25, 2018. | Manuscript published on July 30, 2018. | PP: 12-15 | Volume-8 Issue-2, May 2018. | Retrieval Number: B3134058218
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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 is a new paper on the algorithm “Darksort”. It is a linear sorting algorithm that operates in O(n) timeand space complexity. It uses advanced data structures to be highly applicable in many Computer Science uses. Comparisons to other linear sorting algorithms are included.
Keywords: Bayesian Information Criterion, Akaike Information Computer Science, Data Structures, Algorithms, Sorting

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B3129058218

IJSCE Journal — Wed, 29 Sep 2021 09:34:05 +0000

Construction Site Management and its Influence on Project Implementation in Nairobi County
Bonface Maturi Nyabioge¹, Stephen Diang’a², Abednego Gwaya³
¹Mr. Bonface Maturi Nyabioge, Student, Masters in Construction Project Management, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya.
²Prof. Stephen Diang’a, Department of Construction Management, School of Architecture and Building Sciences (SABS), Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya.
³Dr. Abednego Gwaya, Department of Construction Management, School of Architecture and Building Sciences (SABS), Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya.
Manuscript received on April 12, 2018. | Revised Manuscript received on April 14, 2018. | Manuscript published on July 30, 2018. | PP: 7-12 | Volume-8 Issue-2, May 2018. | Retrieval Number: B3129058218/2018©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: Construction site management has become more complex than ever since a number of resource flows (operatives, information, finance as well as materials) have to converge simultaneously at the workface in order to ensure successful completion of construction projects. This research sought to assess the impact of site management factors on projects implementation in Nairobi County. The selected site management aspects that were investigated in relation to project implementation included; material management, labor management, health and safety management, commercial/cost management and information management.The study utilized a survey research design and the target population was made up of commercial/ mixed urban development buildings (only projects worth more than Kenya shillings 100 million were considered) under construction in Westlands constituency. A sample of 40 projects were picked randomly and questionnaires were administered to construction project managers. The survey achieved a 68% rate of return of questionnaires. Data analysis involved cleaning, sorting and coding of raw data collected from the field and processing for purposes of interpretation by use of R software version 3.4.3. The data analysis procedures adopted used both descriptive and inferential statistical methods and the results were presented in form of statistical equation models, tables, charts and graphs in a simple and comprehensive manner. The results indicated that, the selected site management factors contribute significantly to the prediction of successful project implementation. This findings were justified by the following statement; proper health and safety management had the greatest impact on probability of successful project implementation with a probability of 99.4%, cost management had a probability of 97.4% while labour management had a probability of 83.9%. The second highest probability of successful project implementation was material management with 98.9% while information management had a probability of project success of 93.6%. Finally, a well-defined site management system was suggested to aid construction project managers in achieving control over the flow of basic resources (personnel, finance and materials) and processes, eased information exchange and increased customer and stakeholder satisfaction (as a result of meeting the project objectives).
Keywords: Construction Site Management, Project Management, Project Implementation.

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B3117058218

IJSCE Journal — Wed, 29 Sep 2021 09:27:01 +0000

Forecasting Saudi Arabia Daily Stock Market Prices
Mahmoud Al-Zyood
¹Mahmoud Al-Zyood, King Abdulaziz University, Finance College of Business, P.O. Box 344, Rabigh, Jeddah 21911, Saudi Araba.
Manuscript received on March 14, 2018. | Revised Manuscript received on March 16, 2018. | Manuscript published on July 30, 2018. | PP: 1-6 | Volume-8 Issue-2, May 2018. | Retrieval Number: B3117058218/2018©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The Saudi stock market is regionally and globally important due to the country’s economic position as the world’s largest oil producer and its inherent socio-political role as a major world economy. Stock market prices are one of the main factors affecting the national economy, indicating economic strength and attracting investment. This paper inspects the best autoregressive integrated moving average (ARIMA) model to forecast daily stock market prices in Saudi Arabia. The results indicate that the optimum model is ARIMA (4, 4, 0, 0), due to the ACF having an exponential deterioration and the PACF having a spike at lag12, which is an indication of its being the best model to forecast Saudi Arabia stock market prices from 2000 to 2018. The least Akaike Information Criterion (AIC) value was used to select the appropriate model from 25 tentative ARMA models. The chosen model is the first one, AIC -5.404104. The selected ARIMA (4, 4) (0, 0) predicts the future values of time series (stock market prices) with 95% prediction intervals for the next year. It is important to focus on the improvement and development of other models to improve the forecasting process and improve the ability of companies to plan. The results expected by the model indicate economic strength in the near future, which stimulates the economic situation of the state and increases confidence in it.
Keywords: Bayesian Information Criterion, Akaike Information Criterion, Saudi Stock Exchange (Tadawul), Stock Prices, Prediction Models, Forecasting.

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