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23 results on '"Saadi, Younes"'

1. A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption

Author

Chiroma, Haruna, Abdullahi, Usman Ali, Hashem, Ibrahim Abaker Targio, Saadi, Younes, Al-Dabbagh, Rawaa Dawoud, Ahmad, Muhammad Murtala, Dada, Gbenga Emmanuel, Danjuma, Sani, Maitama, Jaafar Zubairu, Abubakar, Adamu, Abdulhamid, Shafi’i Muhammad, Herawan, Tutut, editor, Chiroma, Haruna, editor, and Abawajy, Jemal H., editor

Published
2019
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2. A Framework for Clustering of Web Users Transaction Based on Soft Set Theory

Author

Sutoyo, Edi, Yanto, Iwan Tri Riyadi, Saadi, Younes, Chiroma, Haruna, Hamid, Suraya, Herawan, Tutut, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Abawajy, Jemal H., editor, Othman, Mohamed, editor, Ghazali, Rozaida, editor, Deris, Mustafa Mat, editor, Mahdin, Hairulnizam, editor, and Herawan, Tutut, editor

Published
2019
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3. Estimation of Middle-East Oil Consumption Using Hybrid Meta-heuristic Algorithms

Author

Chiroma, Haruna, Khan, Abdullah, Abubakar, Adamu I., Saadi, Younes, Muaz, Sanah Abdullahi, Gital, Abdulsalam Ya’u, Shuib, Liyana Mohd, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Abawajy, Jemal H., editor, Othman, Mohamed, editor, Ghazali, Rozaida, editor, Deris, Mustafa Mat, editor, Mahdin, Hairulnizam, editor, and Herawan, Tutut, editor

Published
2019
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4. Bio-inspired Algorithm Optimization of Neural Network for the Prediction of Dubai Crude Oil Price

Author

Chiroma, Haruna, Abdul-kareem, Sameem, Saadi, Younes, Khan, Abdullah, Abubakar, Adamu I., Muaz, Sanah Abdullahi, Gital, Abdulsalam Ya’u, Shuib, Liyana Mohd, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Abawajy, Jemal H., editor, Othman, Mohamed, editor, Ghazali, Rozaida, editor, Deris, Mustafa Mat, editor, Mahdin, Hairulnizam, editor, and Herawan, Tutut, editor

Published
2019
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5. A New Framework for Metaheuristic Search Based on Animal Foraging

Author

Saadi, Younes, Yanto, Iwan Tri Riyadi, Sutoyo, Edi, Mungad, Mungad, Chiroma, Haruna, Herawan, Tutut, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Abawajy, Jemal H., editor, Othman, Mohamed, editor, Ghazali, Rozaida, editor, Deris, Mustafa Mat, editor, Mahdin, Hairulnizam, editor, and Herawan, Tutut, editor

Published
2019
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8. A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption

Author

Chiroma, Haruna, primary, Abdullahi, Usman Ali, additional, Hashem, Ibrahim Abaker Targio, additional, Saadi, Younes, additional, Al-Dabbagh, Rawaa Dawoud, additional, Ahmad, Muhammad Murtala, additional, Dada, Gbenga Emmanuel, additional, Danjuma, Sani, additional, Maitama, Jaafar Zubairu, additional, Abubakar, Adamu, additional, and Abdulhamid, Shafi’i Muhammad, additional

Published
2019
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13. An Intelligent Modeling of Oil Consumption

Author

Chiroma, Haruna, Abdulkareem, Sameem, Muaz, Sanah Abdullahi, Abubakar, Adamu I., Sutoyo, Edi, Mungad, Mungad, Saadi, Younes, Sari, Eka Novita, Herawan, Tutut, Kacprzyk, Janusz, Series editor, El-Alfy, El-Sayed M., editor, Thampi, Sabu M., editor, Takagi, Hideyuki, editor, Piramuthu, Selwyn, editor, and Hanne, Thomas, editor

Published
2015
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18. Tracking Dengue on Twitter Using Hybrid Filtration-Polarity and Apache Flume.

Author

Ghani, Norjihan Binti Abdul, Hamid, Suraya, Ahmad, Muneer, Saadi, Younes, Jhanjhi, N. Z., Alzain, Mohammed A., and Masud, Mehedi

Subjects
DENGUE, SOCIAL media, COMPUTER algorithms
Abstract

The world health organization (WHO) terms dengue as a serious illness that impacts almost half of the world's population and carries no specific treatment. Early and accurate detection of spread in affected regions can save precious lives. Despite the severity of the disease, a few noticeable works can be found that involve sentiment analysis to mine accurate intuitions from the social media text streams. However, the massive data explosion in recent years has led to difficulties in terms of storing and processing large amounts of data, as reliable mechanisms to gather the data and suitable techniques to extract meaningful insights from the data are required. This research study proposes a sentiment analysis polarity approach for collecting data and extracting relevant information about dengue via Apache Hadoop. The method consists of two main parts: the first part collects data from social media using Apache Flume, while the second part focuses on querying and extracting relevant information via the hybrid filtration-polarity algorithm using Apache Hive. To overcome the noisy and unstructured nature of the data, the process of extracting information is characterized by pre and post-filtration phases. As a result, only with the integration of Flume and Hive with filtration and polarity analysis, can a reliable sentiment analysis technique be offered to collect and process large-scale data from the social network. We introduce how the Apache Hadoop ecosystem - Flume and Hive - can provide a sentiment analysis capability by storing and processing large amounts of data. An important finding of this paper is that developing efficient sentiment analysis applications for detecting diseases can be more reliable through the use of the Hadoop ecosystem components than through the use of normal machines. [ABSTRACT FROM AUTHOR]

Published
2022
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22. Contour matching using ant colony optimization and curve evolution

Author

Saadi, Younes and Saadi, Younes

Abstract

Shape retrieval is a very important topic in computer vision. Image retrieval consists of selecting images that fulfil specific criteria from a collection of images. This thesis concentrates on contour-based image retrieval, in which we only explore the information located on the shape contour. There are many different kinds of shape retrieval methods. Most of the research in this field has till now concentrated on matching methods and how to achieve a meaningful correspondence. The matching process consist of finding correspondence between the points located on the designed contours. However, the huge number of incorporated points in the correspondence makes the matching process more complex. Furthermore, this scheme does not support computation of the correspondence intuitively without considering noise effect and distortions. Hence, heuristics methods are convoked to find acceptable solution. Moreover, some researches focus on improving polygonal modelling methods of a contour in such a way that the resulted contour is a good approximation of the original contour, which can be used to reduce the number of incorporated points in the matching. In this thesis, a novel approach for Ant Colony Optimization (ACO) contour matching that can be used to find an acceptable matching between contour shapes is developed. A polygonal evolution method proposed previously is selected to simplify the extracted contour. The main reason behind selecting this method is due to the use of a stopping criterion which must be predetermined. The match process is formulated as a Quadratic Assignment Problem (QAP) and resolved by using ACO. An approximated similarity is computed using original shape context descriptor and the Euclidean metric. The experimental results justify that the proposed approach is invariant to noise and distortions, and it is more robust to noise and distortion compared to the previously introduced Dominant Point (DP) Approach.

Published
2013

23. Ant colony matching: A curve evolution approach.

Author

Saadi, Younes, Rathiah Binti Hashim, and Abdul-Kahar, Rosmila

Abstract

In this paper we present a novel shape matching approach for 2D shapes. Finding correspondence for a huge number of points can be very complex. In order to resolve the complexity of matching between the huge number of points, we need to reduce the number of points incorporated in shape correspondence. Discrete Curve Evolution (DCE) proposed previously is selected to simplify the extracted contour. Then the correspondence between shapes is formulated as a Quadratic assignment problem (QAP) and resolved by using Ant Colony optimization (ACO). The experimental results demonstrate the effectiveness of our approach and give an intuitive shape similarity invariant to noise and distortions. [ABSTRACT FROM PUBLISHER]

Published
2012

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