International Journal of Recent Technology and Engineering (IJRTE)

E1937017519

IJRTE Journal — Mon, 17 Oct 2022 06:12:25 +0000

Enhance Properties of Autoclaved Aerated Concreteby Adding Silica Fume
Ilham Abid Almajeed,1 Samer K. Turki2
¹Ilham Abid Almajeed, Ceramic Engineering and building materials Dept., Eng. Materials College, University of Babylon, Iraq.
².Samer K. Turki, ASAD-Babylon for producing building materials, Iraq.
Manuscript received on 01 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 11-14 | Volume-7 Issue-6, March 2019 | Retrieval Number: E1937017519/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. 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: Autoclaved aerated concrete (AAC) is important, building materials which has unique physical and mechanical properties compare with normal concrete. In this research silica fume used as the chemical additive to increase compressive strength, the percentage of silica fume added in this study is (4, 8, 12 % and 16% by weight of cement that used in recipe) the result shows the compressive strength increased with increasing silica fume to a specific limit than the compressive decreased with an additional amount of silica fume. It has been found that the density are not affected with silica fume due to less amount of silica compare with the total amount of the mix, and for thermal conductivity the same behavior was shown for the same reason as the lack of density. Index Terms: AAC, Autoclaved aerated concrete, cellular concrete, Silica fume.
Scope of the Article:

Download PDF

The post E1937017519 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

E1936017519

IJRTE Journal — Mon, 17 Oct 2022 05:30:06 +0000

Preparation of Titania Thin Films Using Sol-Gel Techniques
K Abd Al-Abass Abd Al-Ameer Shakir Al-Hamadani¹, Khelfa Fadelshedhan Muselmawe²
¹Abd Al-Abass Abd Al-Ameer Shakir Al-Hamadani, Directorate General of Education in Najaf Governorate, Iraq.
².Khelfa Fadelshedhan Muselmawe, Directorate General of Education in Babylon Governorate, Iraq.
Manuscript received on 01 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 7-9 | Volume-7 Issue-6, March 2019 | Retrieval Number: E1924017519/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. 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: Sol-gel technique have been used to prepare the anastane titania thin films and it is deposited on glass substrates. Under vigorous stirring, substrates are dipped into the solution. The films were dried at atmospheric temperature. Oley amine (OM) was used as a chemical agent within the synthesis part and it had been aggressively stirred with titanium tetraisopropoxide (TTIP). Samples of thin films are annealed under 5500C for 15 hours. To study the properties of structure, X-Ray diffraction (XRD) analysis and Raman Spectroscopy has been taken place. Also by using Ultraviolet (UV) Spectroscopy, optical properties are analyzed for the samples. XRD results illustrated the amorphous structure and section of TiO2 and these results were additional confirmed by Raman spectrum analysis. Then ultraviolet light spectrographic analysis has been used for preparation of TiO2 lean films in association with the energy band three 3.26 and 3.22 eV, it is easy refers to make different in crystal structure for anatase titania. In addition, each samples have high transmission.
Keywords: Optical properties, Raman spectroscopy, Sol-Gel technique, Titania Nanoparticle, X-Ray diffraction design.
Scope of the Article:

Download PDF

The post E1936017519 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

E1924017519

IJRTE Journal — Mon, 17 Oct 2022 05:03:21 +0000

REDIC K –Prototype Clustering Algorithm for Mixed Data (Numerical and Categorical Data)
Khyati R. Nirmal¹, K.V.V.Satyanarayana²
¹Khyati R. NIrmal, Research Scholar, Department of CSE, KoneruLakshmaiah Education Foundation, Green Fields, Vaddeswaram, (A.P), India.
²K.V.V.Satyanarayana, Department of CSE, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram (A.P), India.
Manuscript received on 01 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 1-6 | Volume-7 Issue-6, March 2019 | Retrieval Number: E1924017519/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. 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 unsupervised learning Clustering is the task to find hidden structure without any prior knowledge of data and derive the interesting patterns from the given data objects. Furthermost the real word dataset is the combination of numerical and categorical data attributes. The K-prototype Clustering algorithm is widely used to group the mixed data because of ease of implementation. The efficiency of the algorithm depends on the selection strategy of initial centroids, and here the initial centroids are randomly selected. Other constraint of this algorithm is to provide number of clusters as input, which requires the domain specific knowledge. Inappropriate choice for number of clusters will affect the complexity of algorithm. In this paper the REDIC (Removal Dependency on K and Initial Centroid Selection) K-prototype clustering algorithm is proposed which will eliminate the dependency on input parameter and creates the cluster using incremental approach. Here as a replacement for the bit by bit comparison of categorical attributes, the frequency-based method is used to calculate the dissimilarity measurement between two categorical instances. Experiments are conducted with standard datasets and the results are compared with traditional K-prototype algorithm. The better results of REDIC K -prototypes clustering algorithm proves the efficiency of algorithm and removes the dependency on initial parameter selection.
Keywords: Cluster Analysis; K- Prototype Clustering; Initial Centroid; Number of Cluster; Frequency based Similarity Measurement.
Scope of the Article:

Download PDF

The post E1924017519 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

E1924017519

IJRTE Journal — Mon, 17 Oct 2022 04:47:44 +0000

Khyati R. Nirmal,1 K.V.V.Satyanarayana2
¹Shehna Sherafudeen, Research Scholar, ICFAI Business School, IBS Hyderabad (Telangana), India.
²Dr. Debajani Sahoo*, Associate Professor, Department of Marketing and Strategy, ICFAI Business School, IBS Hyderabad (Telangana), India.
Manuscript received on 23 May 2022 | Revised Manuscript received on 28 May 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 21-28 | Volume-11 Issue-2, July 2022 | Retrieval Number: E1924017519/19©BEIES
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. 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 unsupervised learning Clustering is the task to
find hidden structure without any prior knowledge of data and
derive the interesting patterns from the given data objects.
Furthermost the real word dataset is the combination of
numerical and categorical data attributes. The K-prototype
Clustering algorithm is widely used to group the mixed data
because of ease of implementation. The efficiency of the algorithm
depends on the selection strategy of initial centroids, and here the
initial centroids are randomly selected. Other constraint of this
algorithm is to provide number of clusters as input, which requires
the domain specific knowledge. Inappropriate choice for number
of clusters will affect the complexity of algorithm. In this paper the
REDIC (Removal Dependency on K and Initial Centroid
Selection) K-prototype clustering algorithm is proposed which will
eliminate the dependency on input parameter and creates the
cluster using incremental approach. Here as a replacement for the
bit by bit comparison of categorical attributes, the
frequency-based method is used to calculate the dissimilarity
measurement between two categorical instances. Experiments are
conducted with standard datasets and the results are compared
with traditional K-prototype algorithm. The better results of
REDIC K -prototypes clustering algorithm proves the efficiency of
algorithm and removes the dependency on initial parameter
selection.
Keywords: Cluster Analysis; K- Prototype Clustering; Initial
Centroid; Number of Cluster; Frequency based Similarity
Measurement.

Download PDF

The post E1924017519 appeared first on International Journal of Recent Technology and Engineering (IJRTE).