Pub Date : 2018-07-01DOI: 10.1109/ICSCEE.2018.8538416
SK Ahammad Fahad, Abdulsamad E. Yahya
In the age of knowledge, Natural Language Processing (NLP) express its demand by a huge range of utilization. Previously NLP was dealing with statically data. Contemporary time NLP is doing considerably with the corpus, lexicon database, pattern reorganization. Considering Deep Learning (DL) method recognize artificial Neural Network (NN) to nonlinear process, NLP tools become increasingly accurate and efficient that begin a debacle. Multi-Layer Neural Network obtaining the importance of the NLP for its capability including standard speed and resolute output. Hierarchical designs of data operate recurring processing layers to learn and with this arrangement of DL methods manage several practices. In this paper, this resumed striving to reach a review of the tools and the necessary methodology to present a clear understanding of the association of NLP and DL for truly understand in the training. Efficiency and execution both are improved in NLP by Part of speech tagging (POST), Morphological Analysis, Named Entity Recognition (NER), Semantic Role Labeling (SRL), Syntactic Parsing, and Coreference resolution. Artificial Neural Networks (ANN), Time Delay Neural Networks (TDNN), Recurrent Neural Network (RNN), Convolution Neural Networks (CNN), and Long-Short-Term-Memory (LSTM) dealings among Dense Vector (DV), Windows Approach (WA), and Multitask learning (MTL) as a characteristic of Deep Learning. After statically methods, when DL communicate the influence of NLP, the individual form of the NLP process and DL rule collaboration was started a fundamental connection.
{"title":"Inflectional Review of Deep Learning on Natural Language Processing","authors":"SK Ahammad Fahad, Abdulsamad E. Yahya","doi":"10.1109/ICSCEE.2018.8538416","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538416","url":null,"abstract":"In the age of knowledge, Natural Language Processing (NLP) express its demand by a huge range of utilization. Previously NLP was dealing with statically data. Contemporary time NLP is doing considerably with the corpus, lexicon database, pattern reorganization. Considering Deep Learning (DL) method recognize artificial Neural Network (NN) to nonlinear process, NLP tools become increasingly accurate and efficient that begin a debacle. Multi-Layer Neural Network obtaining the importance of the NLP for its capability including standard speed and resolute output. Hierarchical designs of data operate recurring processing layers to learn and with this arrangement of DL methods manage several practices. In this paper, this resumed striving to reach a review of the tools and the necessary methodology to present a clear understanding of the association of NLP and DL for truly understand in the training. Efficiency and execution both are improved in NLP by Part of speech tagging (POST), Morphological Analysis, Named Entity Recognition (NER), Semantic Role Labeling (SRL), Syntactic Parsing, and Coreference resolution. Artificial Neural Networks (ANN), Time Delay Neural Networks (TDNN), Recurrent Neural Network (RNN), Convolution Neural Networks (CNN), and Long-Short-Term-Memory (LSTM) dealings among Dense Vector (DV), Windows Approach (WA), and Multitask learning (MTL) as a characteristic of Deep Learning. After statically methods, when DL communicate the influence of NLP, the individual form of the NLP process and DL rule collaboration was started a fundamental connection.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115141538","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 : 2018-07-01DOI: 10.1109/ICSCEE.2018.8538421
Yuan Jia
This paper presents a novel annotation scheme of Chinese discourse structures to model the complex interactions among grammar, semantics and phonology. The scheme mainly contains three layers, i.e., grammatical, semantic and prosodic layers. Within each layer, the representations of dependency relations, rhetorical structure, information structure, topic chain, prosodic boundaries and stress distributions are specified. Based on the scheme, a large scale corpus of transcribed speech data is constructed and annotated. We further propose a machine learning methodology to learn from the annotated corpus a computational representation of the internal structure of each layer and the interactions across different layers. Specifically, we employ the Recursive Neural Network (RNN) to model the fine-grained structure in natural language information, through learning a distributed representation of the structural units. The proposed annotation scheme and machine learning methodology to expected to underpin more effective and intelligent speech engineering and understanding technologies of the future.
{"title":"A Multi-layered Annotation Scheme and Computational Model for Co-Learning Semantic and Prosodic Structures of Chinese Discourse","authors":"Yuan Jia","doi":"10.1109/ICSCEE.2018.8538421","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538421","url":null,"abstract":"This paper presents a novel annotation scheme of Chinese discourse structures to model the complex interactions among grammar, semantics and phonology. The scheme mainly contains three layers, i.e., grammatical, semantic and prosodic layers. Within each layer, the representations of dependency relations, rhetorical structure, information structure, topic chain, prosodic boundaries and stress distributions are specified. Based on the scheme, a large scale corpus of transcribed speech data is constructed and annotated. We further propose a machine learning methodology to learn from the annotated corpus a computational representation of the internal structure of each layer and the interactions across different layers. Specifically, we employ the Recursive Neural Network (RNN) to model the fine-grained structure in natural language information, through learning a distributed representation of the structural units. The proposed annotation scheme and machine learning methodology to expected to underpin more effective and intelligent speech engineering and understanding technologies of the future.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"10 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125930898","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 : 2018-07-01DOI: 10.1109/ICSCEE.2018.8538369
Y. Al-Gumaei, K. Noordin, Ali Mohammed Mansoor, K. Dimyati
In cognitive radio networks, each cognitive radio CR's signal represent a source of interference to other users that sharing the same spectrum. Amount of interference that should be below the interference temperature and battery power of cognitive devices are the critical issues that require an efficient power control algorithms. These algorithms aimed to attain two objectives: achieve the quality of service (QoS) and increase the system capacity. The power control problem in CRN is obviously suitable to formulation as a non-cooperative game in which CRs choose to the balance between signal-to interference ratio (SIR) error and power usage. We considered perversely the problem of power control by using the static Nash game formulation based on a sigmoid function. The solution obtained from proposed game led to a system of nonlinear algebraic sigmoid equations. In this paper, we present the distributed power control game using Newton iterations to solve the slow of convergence problem. The effectiveness result of the new improved algorithm is demonstrated in simulation on a small and pragmatic cognitive radio system. The results indicates that the new development algorithm based on Newton iteration can reduce the number of iterations up to 58% comparing with traditional fixed point algorithm.
{"title":"Acceleration Improvement of a Sigmoid Power Control Game Algorithm in Cognitive Radio Networks","authors":"Y. Al-Gumaei, K. Noordin, Ali Mohammed Mansoor, K. Dimyati","doi":"10.1109/ICSCEE.2018.8538369","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538369","url":null,"abstract":"In cognitive radio networks, each cognitive radio CR's signal represent a source of interference to other users that sharing the same spectrum. Amount of interference that should be below the interference temperature and battery power of cognitive devices are the critical issues that require an efficient power control algorithms. These algorithms aimed to attain two objectives: achieve the quality of service (QoS) and increase the system capacity. The power control problem in CRN is obviously suitable to formulation as a non-cooperative game in which CRs choose to the balance between signal-to interference ratio (SIR) error and power usage. We considered perversely the problem of power control by using the static Nash game formulation based on a sigmoid function. The solution obtained from proposed game led to a system of nonlinear algebraic sigmoid equations. In this paper, we present the distributed power control game using Newton iterations to solve the slow of convergence problem. The effectiveness result of the new improved algorithm is demonstrated in simulation on a small and pragmatic cognitive radio system. The results indicates that the new development algorithm based on Newton iteration can reduce the number of iterations up to 58% comparing with traditional fixed point algorithm.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123500800","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 : 2018-07-01DOI: 10.1109/ICSCEE.2018.8538374
Bayan Omar Mohammed, S. Shamsuddin
The power of multi-biometric fusion for identical twins at the feature-level with Dis-Mean algorithm is addressed in this work. A feature-fusion framework is geared toward improving identical twins identification accuracy for multiple biometrics. A novel multi-biometric system is thus designed based on the framework, which serves as fusion guidelines for multi-biometric applications that fuse at the feature-level with identical twins. This framework was applied to the twin handwriting and fingerprint to 30 twins with 480 images, when using MAE for intra-class and inter-class for accuracy. The result provides an alternative mechanism to detect identical twin besides using the traditional methods.
{"title":"Feature level Fusion for Multi-biometric with identical twins","authors":"Bayan Omar Mohammed, S. Shamsuddin","doi":"10.1109/ICSCEE.2018.8538374","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538374","url":null,"abstract":"The power of multi-biometric fusion for identical twins at the feature-level with Dis-Mean algorithm is addressed in this work. A feature-fusion framework is geared toward improving identical twins identification accuracy for multiple biometrics. A novel multi-biometric system is thus designed based on the framework, which serves as fusion guidelines for multi-biometric applications that fuse at the feature-level with identical twins. This framework was applied to the twin handwriting and fingerprint to 30 twins with 480 images, when using MAE for intra-class and inter-class for accuracy. The result provides an alternative mechanism to detect identical twin besides using the traditional methods.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126499200","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 : 2018-07-01DOI: 10.1109/ICSCEE.2018.8538363
Tooba Samaad, G. Tahir, Mansoor-ur-Rahman, Murtaza Ashraf
The uncertain and devastating outcomes of natural disasters require pre-planning and timely coordination to reduce human and economic loss. While predictive capabilities remain limited - especially in the event of earthquakes, significant research efforts have been made towards increasing the efficiency and preparedness of rescue and relief operations. In this respect, different applications of agent-based modeling exhibits substantial promise for enabling humanitarian committees in terms of better planning and responsiveness in case of a disaster. While the usefulness of more conventional disaster management systems that focus primarily on communication and coordination efforts have a significant role, the ability of agent - based systems to aid decision making under unforeseen circumstances adds a new dimension to the overall effectiveness of such systems. Hence, the following study surveys conventional and agent-based disaster management systems, and aims to determine the performance of both systems
{"title":"Comparative Performance Analysis Between Agent-Based And Conventional Diaster Management System","authors":"Tooba Samaad, G. Tahir, Mansoor-ur-Rahman, Murtaza Ashraf","doi":"10.1109/ICSCEE.2018.8538363","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538363","url":null,"abstract":"The uncertain and devastating outcomes of natural disasters require pre-planning and timely coordination to reduce human and economic loss. While predictive capabilities remain limited - especially in the event of earthquakes, significant research efforts have been made towards increasing the efficiency and preparedness of rescue and relief operations. In this respect, different applications of agent-based modeling exhibits substantial promise for enabling humanitarian committees in terms of better planning and responsiveness in case of a disaster. While the usefulness of more conventional disaster management systems that focus primarily on communication and coordination efforts have a significant role, the ability of agent - based systems to aid decision making under unforeseen circumstances adds a new dimension to the overall effectiveness of such systems. Hence, the following study surveys conventional and agent-based disaster management systems, and aims to determine the performance of both systems","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129896110","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}
Complex human activity is hard to classify with observational logic. The advent of fabric sensors with discernible and steady response have opened a new avenue for classifying physical activity of humans. Our goal is to construct a smart jacket for human activity/posture classification and to apply machine learning models on the fabric sensor reading to predict physical events. The core concept is in placing stretch sensors, pressure sensors and accelerometer at strategic location to collect the responses. The sensor's response is studied toidentify linearity and repeatability, using which, reliability of data is determined. Further, appropriate Machine learning algorithms can be employed to classify different set of activities. It also important to follow a proper procedure to record data from fabric sensors which create a voltage fluctuation when stretched. We propose a systematic way of design, development, testing and integration of fabric sensors for reliable data collection in this paper.
{"title":"Design and Development of Smart-Jacket for Posture Detection","authors":"Princy Randhawa, Vijay Shanthagiri, Rishabh Mour, Ajay Kumar","doi":"10.1109/ICSCEE.2018.8538384","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538384","url":null,"abstract":"Complex human activity is hard to classify with observational logic. The advent of fabric sensors with discernible and steady response have opened a new avenue for classifying physical activity of humans. Our goal is to construct a smart jacket for human activity/posture classification and to apply machine learning models on the fabric sensor reading to predict physical events. The core concept is in placing stretch sensors, pressure sensors and accelerometer at strategic location to collect the responses. The sensor's response is studied toidentify linearity and repeatability, using which, reliability of data is determined. Further, appropriate Machine learning algorithms can be employed to classify different set of activities. It also important to follow a proper procedure to record data from fabric sensors which create a voltage fluctuation when stretched. We propose a systematic way of design, development, testing and integration of fabric sensors for reliable data collection in this paper.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134489942","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 : 2018-07-01DOI: 10.1109/ICSCEE.2018.8538409
A. M. Najar, M. I. Irawan, D. Adzkiya
Dengue Hemorrhagic Fever (DHF) is one of the major health problems in Indonesia. With increasing mobility and population density, weather changes, other epidemic factors, the number of dengue fever patients also increases. In order to optimize the prevention of DHF outbreaks, it is important to obtain predictions related to the risk level of DHF outbreak, because each region needs to be treated according to its risk level. The spread of DHF is closely related to weather conditions. Therefore in this study, we apply extreme learning machine (ELM) method to predict the risk of outbreak based on weather condition. We Develop ELM architecture with weather variables as input nodes and risk level of DHF outbreak as the target. We use binary sigmoid activation function and bipolar sigmoid with a number of hidden neurons between 5- 200 nodes. The results show that ELM can predict the level of risk of DHF with the best performance of ELM network using a binary sigmoid activation function with 50 hidden neurons.
{"title":"Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Prediction","authors":"A. M. Najar, M. I. Irawan, D. Adzkiya","doi":"10.1109/ICSCEE.2018.8538409","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538409","url":null,"abstract":"Dengue Hemorrhagic Fever (DHF) is one of the major health problems in Indonesia. With increasing mobility and population density, weather changes, other epidemic factors, the number of dengue fever patients also increases. In order to optimize the prevention of DHF outbreaks, it is important to obtain predictions related to the risk level of DHF outbreak, because each region needs to be treated according to its risk level. The spread of DHF is closely related to weather conditions. Therefore in this study, we apply extreme learning machine (ELM) method to predict the risk of outbreak based on weather condition. We Develop ELM architecture with weather variables as input nodes and risk level of DHF outbreak as the target. We use binary sigmoid activation function and bipolar sigmoid with a number of hidden neurons between 5- 200 nodes. The results show that ELM can predict the level of risk of DHF with the best performance of ELM network using a binary sigmoid activation function with 50 hidden neurons.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133527992","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 : 2018-07-01DOI: 10.1109/ICSCEE.2018.8538410
Siti Nur Adibah binti Kassim, Saifullizam Puteh
Massive open online courses (MOOCs) are a very recent development that is being applied to enhance the importance of green technology innovation management as it springs up with practice and academia alike. To researcher's knowledge, a recent and comprehensive literature review is lacking. In this paper we contributed to a clarification of the concept assessing the MOOC innovation and offer an overview of the existing body of literature in the area of green technology innovations, H0 of the set hypothesis should be accepted, that, the impact of MOOC's attitudinal knowledge and practise is not positively impacted green technology. This finding was carried out among 300 students of TVET, the questionnaire was disseminated to 300 students, while 287 was retrieved and analysed on SPSS. The finding shows that, MOOC's attitudinal knowledge and practise have insufficiently impacted the attitude of Malaysians in various ways that contributes to environmentally sustainable and habitable society.
{"title":"Assessing The Role of MOOC on Knowledge, Attitude, and Practice of Green Technology Among TVET Students in Malaysia","authors":"Siti Nur Adibah binti Kassim, Saifullizam Puteh","doi":"10.1109/ICSCEE.2018.8538410","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538410","url":null,"abstract":"Massive open online courses (MOOCs) are a very recent development that is being applied to enhance the importance of green technology innovation management as it springs up with practice and academia alike. To researcher's knowledge, a recent and comprehensive literature review is lacking. In this paper we contributed to a clarification of the concept assessing the MOOC innovation and offer an overview of the existing body of literature in the area of green technology innovations, H0 of the set hypothesis should be accepted, that, the impact of MOOC's attitudinal knowledge and practise is not positively impacted green technology. This finding was carried out among 300 students of TVET, the questionnaire was disseminated to 300 students, while 287 was retrieved and analysed on SPSS. The finding shows that, MOOC's attitudinal knowledge and practise have insufficiently impacted the attitude of Malaysians in various ways that contributes to environmentally sustainable and habitable society.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128282125","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 : 2018-07-01DOI: 10.1109/ICSCEE.2018.8538390
S. Srivastava, Ayush Sharma, Harsh Balot
India recorded at least 4,80,652 accidents in 2016, leading to 1,50,785 deaths which were caused by speeding, overtaking, drunk driving, speed breakers and potholes. Potholes are not just structural failure in a road surface, here in India these days is a major cause of road side causalities. A constant detection and repair in proper time can not only result in ensure road surface quality but can also save many lives. The proposal describes one such road maintenance system which uses Basic Ultrasonic Sensors, Raspberry Pi and A Mobile Phone with Internet Capabilities which are connected to an Internet-of-Things platform over the Internet. In addition to providing a generic Internet-of-Things based platform, the proposed solution brings objective real time data about the state of the roads in a particular region which can be sent to the local authorities to take further actions upon.
{"title":"Analysis and Improvements on Current Pothole Detection Techniques","authors":"S. Srivastava, Ayush Sharma, Harsh Balot","doi":"10.1109/ICSCEE.2018.8538390","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538390","url":null,"abstract":"India recorded at least 4,80,652 accidents in 2016, leading to 1,50,785 deaths which were caused by speeding, overtaking, drunk driving, speed breakers and potholes. Potholes are not just structural failure in a road surface, here in India these days is a major cause of road side causalities. A constant detection and repair in proper time can not only result in ensure road surface quality but can also save many lives. The proposal describes one such road maintenance system which uses Basic Ultrasonic Sensors, Raspberry Pi and A Mobile Phone with Internet Capabilities which are connected to an Internet-of-Things platform over the Internet. In addition to providing a generic Internet-of-Things based platform, the proposed solution brings objective real time data about the state of the roads in a particular region which can be sent to the local authorities to take further actions upon.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123674397","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 : 2018-07-01DOI: 10.1109/ICSCEE.2018.8538403
Murtaza Ashraf, G. Tahir, Sundus Abrar, Mustafa Abdulaali, Saqib Mushtaq, Hamid Mukthar
Staying updated about global events is a necessity for the modern day man. Many sources for latest news are available on the web, and individuals can make use of their favorite news source to get the daily news, but most of the time they are unable to get the news of desired interest. There is a need to analyze the news and rank them according to user’s interest. Social media can provide an insight on a user’s likes and dislikes, which used for news recommendation. This paper presents a multi-agent framework [1] that uses a novel methodology for ranking news articles on the basis of user’s interests fetched from social media [2]. To do so, we have modeled the relationship between user’s social media preferences and news categories: we have extracted categories from social media, mapped with general news categories. Our developed solution provides 28% better results than current news websites recommendation. Further experimentations show that our solution provides effective news recommendation as it makes use of the user’s social media profile [3], which always updated and maintained by the user firsthand. Another important objective is to increase positivity in one’s life. These days the world is in turmoil due to terrorism activities [4].These activities naturally attract media coverage, presenting an unpleasant view of various regions of the world. Although there are many good things/activities happening around us, we mostly see violence and hate speech everywhere on the web [5]. Sentiment analysis is a technique used to extract the impact of the statement i.e. weather the statement is positive or negative [6], [7], and [8]. Sentiment analysis used to filter news based on harmful negative activities and displaying positive news of latest inventions in world, advancement in the industry, relief packages from governments and other growth opportunities. Based on these ideas, we have developed an android application and performed a pilot study. Our results show higher satisfaction levels for users when searching news articles through the proposed system.
{"title":"Personalized News Recommendation based on Multi-agent framework using Social Media Preferences","authors":"Murtaza Ashraf, G. Tahir, Sundus Abrar, Mustafa Abdulaali, Saqib Mushtaq, Hamid Mukthar","doi":"10.1109/ICSCEE.2018.8538403","DOIUrl":"https://doi.org/10.1109/ICSCEE.2018.8538403","url":null,"abstract":"Staying updated about global events is a necessity for the modern day man. Many sources for latest news are available on the web, and individuals can make use of their favorite news source to get the daily news, but most of the time they are unable to get the news of desired interest. There is a need to analyze the news and rank them according to user’s interest. Social media can provide an insight on a user’s likes and dislikes, which used for news recommendation. This paper presents a multi-agent framework [1] that uses a novel methodology for ranking news articles on the basis of user’s interests fetched from social media [2]. To do so, we have modeled the relationship between user’s social media preferences and news categories: we have extracted categories from social media, mapped with general news categories. Our developed solution provides 28% better results than current news websites recommendation. Further experimentations show that our solution provides effective news recommendation as it makes use of the user’s social media profile [3], which always updated and maintained by the user firsthand. Another important objective is to increase positivity in one’s life. These days the world is in turmoil due to terrorism activities [4].These activities naturally attract media coverage, presenting an unpleasant view of various regions of the world. Although there are many good things/activities happening around us, we mostly see violence and hate speech everywhere on the web [5]. Sentiment analysis is a technique used to extract the impact of the statement i.e. weather the statement is positive or negative [6], [7], and [8]. Sentiment analysis used to filter news based on harmful negative activities and displaying positive news of latest inventions in world, advancement in the industry, relief packages from governments and other growth opportunities. Based on these ideas, we have developed an android application and performed a pilot study. Our results show higher satisfaction levels for users when searching news articles through the proposed system.","PeriodicalId":265737,"journal":{"name":"2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117350703","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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