Pub Date : 2019-04-20DOI: 10.11591/IJICT.V8I2.PP63-70
A. Ramaswamyreddy, S. Shivaprasad, K. V. Rangarao, A. Saranya
In the present generation, majority of the people are highly affected by kidney diseases. Among them, chronic kidney is the most common life threatening disease which can be prevented by early detection. Histological grade in chronic kidney disease provides clinically important prognostic information. Therefore, machine learning techniques are applied on the information collected from previously diagnosed patients in order to discover the knowledge and patterns for making precise predictions. A large number of features exist in the raw data in which some may cause low information and error; hence feature selection techniques can be used to retrieve useful subset of features and to improve the computation performance. In this manuscript we use a set of Filter, Wrapper methods followed by Bagging and Boosting models with parameter tuning technique to classify chronic kidney disease. The capability of Bagging and Boosting classifiers are compared and the best ensemble classifier which attains high stability with better promising results is identified.
{"title":"Efficient datamining model for prediction of chronic kidney disease using wrapper methods","authors":"A. Ramaswamyreddy, S. Shivaprasad, K. V. Rangarao, A. Saranya","doi":"10.11591/IJICT.V8I2.PP63-70","DOIUrl":"https://doi.org/10.11591/IJICT.V8I2.PP63-70","url":null,"abstract":"In the present generation, majority of the people are highly affected by kidney diseases. Among them, chronic kidney is the most common life threatening disease which can be prevented by early detection. Histological grade in chronic kidney disease provides clinically important prognostic information. Therefore, machine learning techniques are applied on the information collected from previously diagnosed patients in order to discover the knowledge and patterns for making precise predictions. A large number of features exist in the raw data in which some may cause low information and error; hence feature selection techniques can be used to retrieve useful subset of features and to improve the computation performance. In this manuscript we use a set of Filter, Wrapper methods followed by Bagging and Boosting models with parameter tuning technique to classify chronic kidney disease. The capability of Bagging and Boosting classifiers are compared and the best ensemble classifier which attains high stability with better promising results is identified.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134038851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-20DOI: 10.11591/IJICT.V8I2.PP%P
Dr.Lenin Kanagasabai
In this paper, Tailored Flower Pollination (TFP) algorithm is proposed to solve the optimal reactive power problem. Comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight, the performance of the flower pollination algorithm has been improved. Proposed TFP algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
{"title":"Tailored flower pollination (TFP) algorithm for diminution of real power loss","authors":"Dr.Lenin Kanagasabai","doi":"10.11591/IJICT.V8I2.PP%P","DOIUrl":"https://doi.org/10.11591/IJICT.V8I2.PP%P","url":null,"abstract":"In this paper, Tailored Flower Pollination (TFP) algorithm is proposed to solve the optimal reactive power problem. Comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight, the performance of the flower pollination algorithm has been improved. Proposed TFP algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122075541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.11591/IJICT.V8I1.PP39-49
Chetna Kaushal, D. Koundal
Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbor algorithm is discreetly chosen among them and described along with an example.
{"title":"Recent trends in big data using hadoop","authors":"Chetna Kaushal, D. Koundal","doi":"10.11591/IJICT.V8I1.PP39-49","DOIUrl":"https://doi.org/10.11591/IJICT.V8I1.PP39-49","url":null,"abstract":"Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbor algorithm is discreetly chosen among them and described along with an example. ","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124510916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.11591/IJICT.V8I1.PP50-55
A. Ghiasian, Majid Jamali
Virtual Output Queuing (VOQ) is a well-known queuing discipline in data switch architecture that eliminates Head Of Line (HOL) blocking issue. In VOQ scheme, for each output port, a separate FIFO is maintained by each input port. Consequently, a scheduling algorithm is required to determine the order of service to virtual queues at each time slot. Maximum Weight Matching (MWM) is a well-known scheduling algorithm that achieves the entire throughput region. Despite of outstanding attainable throughput, high complexity of MWM makes it an impractical algorithm for implementation in high-speed switches. To overcome this challenge, a number of randomized algorithms have been proposed in the literature. But they commonly perform poorly when input traffic does not uniformly select output ports. In this paper, we propose two randomized algorithms that outperform the well-known formerly proposed solutions. We exploit a method to keep a parametric number of heavy edges from the last time matching and mix it by randomly generated matching to produce a new schedule. Simulation results confirm the superior performance of the proposed algorithms.
{"title":"Randomized scheduling algorithm for virtual output queuing switch at the presence of non-uniform traffic","authors":"A. Ghiasian, Majid Jamali","doi":"10.11591/IJICT.V8I1.PP50-55","DOIUrl":"https://doi.org/10.11591/IJICT.V8I1.PP50-55","url":null,"abstract":"Virtual Output Queuing (VOQ) is a well-known queuing discipline in data switch architecture that eliminates Head Of Line (HOL) blocking issue. In VOQ scheme, for each output port, a separate FIFO is maintained by each input port. Consequently, a scheduling algorithm is required to determine the order of service to virtual queues at each time slot. Maximum Weight Matching (MWM) is a well-known scheduling algorithm that achieves the entire throughput region. Despite of outstanding attainable throughput, high complexity of MWM makes it an impractical algorithm for implementation in high-speed switches. To overcome this challenge, a number of randomized algorithms have been proposed in the literature. But they commonly perform poorly when input traffic does not uniformly select output ports. In this paper, we propose two randomized algorithms that outperform the well-known formerly proposed solutions. We exploit a method to keep a parametric number of heavy edges from the last time matching and mix it by randomly generated matching to produce a new schedule. Simulation results confirm the superior performance of the proposed algorithms.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121966694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.11591/IJICT.V8I1.PP13-18
Bahae Abidi, A. Jilbab, M. Haziti
Even in difficult places to reach, the new networking technique allows the easy deployment of sensor networks, although these wireless sensor networks confront a lot of constraints. The major constraint is related to the quality of information sent by the network. The wireless sensor networks use different methods to achieve data to the base station. Data aggregation is an important one, used by these wireless sensor networks. But this aggregated data can be subject to several types of attacks and provides security is necessary to resist against malicious attacks, secure communication between severely resource constrained sensor nodes while maintaining the flexibility of the topology changes. Recently, several secure data aggregation schemes have been proposed for wireless sensor networks, it provides better security compared with traditional aggregation. In this paper, we try to focus on giving a brief statement of the various approaches used for the purpose of secure data aggregation in wireless sensor networks.
{"title":"Security in wireless sensor networks","authors":"Bahae Abidi, A. Jilbab, M. Haziti","doi":"10.11591/IJICT.V8I1.PP13-18","DOIUrl":"https://doi.org/10.11591/IJICT.V8I1.PP13-18","url":null,"abstract":"Even in difficult places to reach, the new networking technique allows the easy deployment of sensor networks, although these wireless sensor networks confront a lot of constraints. The major constraint is related to the quality of information sent by the network. The wireless sensor networks use different methods to achieve data to the base station. Data aggregation is an important one, used by these wireless sensor networks. But this aggregated data can be subject to several types of attacks and provides security is necessary to resist against malicious attacks, secure communication between severely resource constrained sensor nodes while maintaining the flexibility of the topology changes. Recently, several secure data aggregation schemes have been proposed for wireless sensor networks, it provides better security compared with traditional aggregation. In this paper, we try to focus on giving a brief statement of the various approaches used for the purpose of secure data aggregation in wireless sensor networks.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129344128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.11591/IJICT.V8I1.PP19-24
Mohammed Alruqimi, N. Aknin
Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.
{"title":"Enabling social WEB for IoT inducing ontologies from social tagging","authors":"Mohammed Alruqimi, N. Aknin","doi":"10.11591/IJICT.V8I1.PP19-24","DOIUrl":"https://doi.org/10.11591/IJICT.V8I1.PP19-24","url":null,"abstract":"Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117211040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.11591/IJICT.V8I1.PP25-28
Ahed M. F. Al Sbou
There is a huge content of Arabic text available over online that requires an organization of these texts. As result, here are many applications of natural languages processing (NLP) that concerns with text organization. One of the is text classification (TC). TC helps to make dealing with unorganized text. However, it is easier to classify them into suitable class or labels. This paper is a survey of Arabic text classification. Also, it presents comparison among different methods in the classification of Arabic texts, where Arabic text is represented a complex text due to its vocabularies. Arabic language is one of the richest languages in the world, where it has many linguistic bases. The research in Arabic language processing is very few compared to English. As a result, these problems represent challenges in the classification, and organization of specific Arabic text. Text classification (TC) helps to access the most documents, or information that has already classified into specific classes, or categories to one or more classes or categories. In addition, classification of documents facilitate search engine to decrease the amount of document to, and then to become easier to search and matching with queries.
{"title":"A survey of arabic text classification models","authors":"Ahed M. F. Al Sbou","doi":"10.11591/IJICT.V8I1.PP25-28","DOIUrl":"https://doi.org/10.11591/IJICT.V8I1.PP25-28","url":null,"abstract":"There is a huge content of Arabic text available over online that requires an organization of these texts. As result, here are many applications of natural languages processing (NLP) that concerns with text organization. One of the is text classification (TC). TC helps to make dealing with unorganized text. However, it is easier to classify them into suitable class or labels. This paper is a survey of Arabic text classification. Also, it presents comparison among different methods in the classification of Arabic texts, where Arabic text is represented a complex text due to its vocabularies. Arabic language is one of the richest languages in the world, where it has many linguistic bases. The research in Arabic language processing is very few compared to English. As a result, these problems represent challenges in the classification, and organization of specific Arabic text. Text classification (TC) helps to access the most documents, or information that has already classified into specific classes, or categories to one or more classes or categories. In addition, classification of documents facilitate search engine to decrease the amount of document to, and then to become easier to search and matching with queries.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114875283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.11591/IJICT.V8I1.PP1-12
Jing Zhang, Baosheng Kang, Bo Jiang, Di Zhang
Since the skeleton represents the topology structure of the query sketch and 2D views of 3D model, this paper proposes a novel sketch-based 3D model retrieval algorithm which utilizes skeleton characteristics as the features to describe the object shape. Firstly, we propose advanced skeleton strength map (ASSM) algorithm to create the skeleton which computes the skeleton strength map by isotropic diffusion on the gradient vector field, selects critical points from the skeleton strength map and connects them by Kruskal's algorithm. Then, we propose histogram feature comparison algorithm which adopts the radii of the disks at skeleton points and the lengths of skeleton branches to extract the histogram feature, and compare the similarity between two skeletons using the histogram feature matrix of skeleton endpoints. Experiment results demonstrate that our approach which combines these two algorithms significantly outperforms several leading sketch-based retrieval approaches.
{"title":"A novel sketch-based 3D model retrieval approach based on skeleton","authors":"Jing Zhang, Baosheng Kang, Bo Jiang, Di Zhang","doi":"10.11591/IJICT.V8I1.PP1-12","DOIUrl":"https://doi.org/10.11591/IJICT.V8I1.PP1-12","url":null,"abstract":"Since the skeleton represents the topology structure of the query sketch and 2D views of 3D model, this paper proposes a novel sketch-based 3D model retrieval algorithm which utilizes skeleton characteristics as the features to describe the object shape. Firstly, we propose advanced skeleton strength map (ASSM) algorithm to create the skeleton which computes the skeleton strength map by isotropic diffusion on the gradient vector field, selects critical points from the skeleton strength map and connects them by Kruskal's algorithm. Then, we propose histogram feature comparison algorithm which adopts the radii of the disks at skeleton points and the lengths of skeleton branches to extract the histogram feature, and compare the similarity between two skeletons using the histogram feature matrix of skeleton endpoints. Experiment results demonstrate that our approach which combines these two algorithms significantly outperforms several leading sketch-based retrieval approaches.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"755 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122979814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.11591/IJICT.V8I1.PP29-38
Sunita Panda, R. Samantaray, P. Mohapatra, R. N. Panda, P. Sahu
To design most reliable wireless communication system we need an efficient method which can be proposed in this paper is V-BLAST technique which is most powerful tool in MIMO system. To improve the channel capacity and data rate efficiently we need manifold antennas together with the transmitter and receiver. In this paper we have analyzed different equalizers performance using V-BLAST algorithm. We have proposed two methods i.e. low complexity QR algorithm and another is consecutive iterations reduction method. This methods compare with traditional finding methods such as ZF, MMSE, SIC and ML. The proposed algorithm shows that it not only reduce the computational complexity but we can achieve significant bit error rate (BER) and probability error compared to traditional VBLAST techniques.
{"title":"A new complexity reduction methods of V-BLAST MIMO system in a communication channel","authors":"Sunita Panda, R. Samantaray, P. Mohapatra, R. N. Panda, P. Sahu","doi":"10.11591/IJICT.V8I1.PP29-38","DOIUrl":"https://doi.org/10.11591/IJICT.V8I1.PP29-38","url":null,"abstract":"To design most reliable wireless communication system we need an efficient method which can be proposed in this paper is V-BLAST technique which is most powerful tool in MIMO system. To improve the channel capacity and data rate efficiently we need manifold antennas together with the transmitter and receiver. In this paper we have analyzed different equalizers performance using V-BLAST algorithm. We have proposed two methods i.e. low complexity QR algorithm and another is consecutive iterations reduction method. This methods compare with traditional finding methods such as ZF, MMSE, SIC and ML. The proposed algorithm shows that it not only reduce the computational complexity but we can achieve significant bit error rate (BER) and probability error compared to traditional VBLAST techniques.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131886598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.11591/ijict.v9i1.pp31-39
Mahmoud Al-Odeh
In this paper, the author provides insights and lessons that can be learned from colleagues at American universities about their online education experiences. The literature review and previous studies of online educations gains are explored and summarized in this research. Emerging trends in online education are discussed in detail, and strategies to implement these trends are explained. The author provides several tools and strategies that enable universities to ensure the quality of online education. At the end of this research paper, the researcher provides examples from Arab universities who have successfully implemented online education and expanded their impact on the society. This research provides a strategy and a model that can be used by universities in the Middle East as a roadmap to implement online education in their regions.
{"title":"What universities in the Middle East can learn from the American online education system","authors":"Mahmoud Al-Odeh","doi":"10.11591/ijict.v9i1.pp31-39","DOIUrl":"https://doi.org/10.11591/ijict.v9i1.pp31-39","url":null,"abstract":"In this paper, the author provides insights and lessons that can be learned from colleagues at American universities about their online education experiences. The literature review and previous studies of online educations gains are explored and summarized in this research. Emerging trends in online education are discussed in detail, and strategies to implement these trends are explained. The author provides several tools and strategies that enable universities to ensure the quality of online education. At the end of this research paper, the researcher provides examples from Arab universities who have successfully implemented online education and expanded their impact on the society. This research provides a strategy and a model that can be used by universities in the Middle East as a roadmap to implement online education in their regions.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122148697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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