Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Singapore Management University (1112)
- TÜBİTAK (199)
- Wright State University (191)
- Technological University Dublin (182)
- Old Dominion University (126)
-
- Missouri University of Science and Technology (112)
- San Jose State University (111)
- City University of New York (CUNY) (93)
- Neutrosophic Systems with Applications (92)
- MBZUAI (70)
- Dartmouth College (67)
- Brigham Young University (65)
- University of Texas at El Paso (63)
- Chulalongkorn University (62)
- Zayed University (58)
- University of Nebraska - Lincoln (56)
- Edith Cowan University (50)
- Kennesaw State University (49)
- Boise State University (48)
- New Jersey Institute of Technology (44)
- California Polytechnic State University, San Luis Obispo (43)
- Chapman University (42)
- Embry-Riddle Aeronautical University (42)
- MMU Press (41)
- Purdue University (41)
- University of Nebraska at Omaha (40)
- University of Arkansas, Fayetteville (39)
- Montclair State University (37)
- University of Central Florida (36)
- University of South Florida (36)
- Keyword
-
- Machine learning (186)
- Natural language processing (184)
- Artificial intelligence (141)
- Natural Language Processing (134)
- Machine Learning (127)
-
- Deep learning (119)
- Artificial Intelligence (95)
- Large language models (84)
- Computer Science (80)
- Social media (80)
- Twitter (64)
- NLP (62)
- Deep Learning (61)
- Sentiment analysis (59)
- Large Language Models (56)
- Computational linguistics (53)
- Department of Computer Science and Engineering (53)
- Text mining (51)
- Neural networks (48)
- Generative AI (46)
- Data mining (44)
- AI (43)
- Computer science (40)
- Classification (37)
- Information retrieval (36)
- Social Media (36)
- Fuzzy logic (35)
- Semantics (34)
- Sentiment Analysis (34)
- LLM (32)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (1036)
- Turkish Journal of Electrical Engineering and Computer Sciences (199)
- Theses and Dissertations (162)
- Master's Projects (99)
- Conference papers (93)
-
- Neutrosophic Systems with Applications (92)
- Kno.e.sis Publications (84)
- Dissertations (70)
- Computer Science Faculty Research & Creative Works (66)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (62)
- Browse all Theses and Dissertations (61)
- All Works (58)
- Computer Science Faculty Publications (58)
- Faculty Publications (49)
- Computer Science Faculty Publications and Presentations (45)
- Publications and Research (44)
- Journal of Informatics and Web Engineering (41)
- Articles (40)
- Electronic Theses and Dissertations (39)
- Computer Science and Engineering Faculty Publications (38)
- Natural Language Processing Faculty Publications (38)
- Departmental Technical Reports (CS) (36)
- Dissertations and Theses Collection (Open Access) (35)
- USF Tampa Graduate Theses and Dissertations (34)
- Computer Science: Faculty Publications (33)
- Dissertations, Theses, and Capstone Projects (32)
- Theses (31)
- CCAC Theses and Dissertations (30)
- Faculty Scholarship (28)
- Masters Theses (26)
- Publication Type
- File Type
Articles 2401 - 2430 of 4507
Full-Text Articles in Entire DC Network
Fuzzy Genetic Based Dynamic Spectrum Allocation (Fgdsa) Approach For Cognitive Radio Sensor Networks, Ganesan Rajesh, Xavier Mercilin Raajini, Kulandairaj Martin Sagayam, Bharat Bhushan, Utku Kose
Fuzzy Genetic Based Dynamic Spectrum Allocation (Fgdsa) Approach For Cognitive Radio Sensor Networks, Ganesan Rajesh, Xavier Mercilin Raajini, Kulandairaj Martin Sagayam, Bharat Bhushan, Utku Kose
Turkish Journal of Electrical Engineering and Computer Sciences
Cognitive Radio Sensor Network (CRSN) is known as a distributed network of wireless cognitive radio sensor nodes. Such system senses an event signal and ensures collaborative dynamic communication processes over the spectrum bands. Here, the concept of Dynamic Spectrum Access (DSA) denes the method of reaching progressively to the unused range of spectrum band. As among the essential CRSN user types, the Primary User (PU) has the license to access the spectrum band. On the other hand, the Secondary User (SU) tries to access the unused spectrum eciently, by not disturbing the PU. Considering that issue, this study introduces a …
A Fuzzy Neural Network For Web Service Selection Aimed At Dynamic Software Rejuvenation, Kimia Rezaei Kalantari, Ali Ebrahimnejad, Homayun Motameni
A Fuzzy Neural Network For Web Service Selection Aimed At Dynamic Software Rejuvenation, Kimia Rezaei Kalantari, Ali Ebrahimnejad, Homayun Motameni
Turkish Journal of Electrical Engineering and Computer Sciences
Software rejuvenation is an effective technique to counteract software aging in continuously running applications such as web service-based systems. In these systems, web services are allocated based on the requirements of receivers and the facilities of servers. One of the challenges while assigning web services is how to select appropriate server to reduce faults. In this paper, we propose dynamic software rejuvenation as a proactive fault-tolerance technique based on the neural fuzzy system. While considering a threshold for the rejuvenation of each web service, we completed the training based on the features of the service providers as well as the …
Estimating Synthetic Load Profile Based On Student Behavior Using Fuzzy Inference System For Demand Side Management Application, Nadhirah Omar, Mohd Aifaa Mohd Ariff, Ainnur Farah Izzati Mohd Shah, Muhammad Syafiq Anwar Mustaza
Estimating Synthetic Load Profile Based On Student Behavior Using Fuzzy Inference System For Demand Side Management Application, Nadhirah Omar, Mohd Aifaa Mohd Ariff, Ainnur Farah Izzati Mohd Shah, Muhammad Syafiq Anwar Mustaza
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a novel approach of estimating synthetic load profiles based on the electrical usage behavior using the fuzzy inference system (FIS) for demand side management (DSM). In practice, DSM is utilized to change the pattern of electrical energy consumed by end-users to modify the load profile by manipulating the price of the electricity. This study focuses on the energy consumption consumed by students who are paying electricity bills indirectly. Therefore, the effectiveness of conventional DSM methods on this user requires further investigation. In this study, the FIS estimates the synthetic load profile based on the student?s behavior profile. …
A Detailed Survey Of Turkish Automatic Speech Recognition, Recep Si̇nan Arslan, Necaatti̇n Barişçi
A Detailed Survey Of Turkish Automatic Speech Recognition, Recep Si̇nan Arslan, Necaatti̇n Barişçi
Turkish Journal of Electrical Engineering and Computer Sciences
Significant improvements have been made in automatic speech recognition(ASR)systems in terms of both the general technology and the software used. Despite these advancements, however, there is still an important difference between the recognition performance of humans and machines. This work focuses on the studies conducted in the field of Turkish speech recognition, the progress made in such studies in recent years, the language-specific constraints, the performance results achieved in the applications developed to date, and the development of a general scheme for researchers wishing to develop an ASR system for the Turkish language. A comprehensive study on the Turkish language, …
Automated Labeling Of Terms In Medical Reports In Serbian, Aldina Avdic, Ulfeta Marovac, Dragan Jankovic
Automated Labeling Of Terms In Medical Reports In Serbian, Aldina Avdic, Ulfeta Marovac, Dragan Jankovic
Turkish Journal of Electrical Engineering and Computer Sciences
Nowadays, many electronic health reports (EHRs) are stored daily. They consist of the structured part and of an unstructured section written in natural language. Due to the limited time for medical examination, EHRs are short reports which often contain errors and abbreviations. Therefore it is a challenge to process an EHR and extract knowledge from this part of the text for different purposes. This paper compares the results of three proposed methods for automatic labeling of medical terms in unstructured parts of EHRs. All words are categorized as words within the medical domain (symptoms, diagnoses, therapies, anatomy, specialties etc.) and …
A Regular Expression Generator Based On Css Selectors For Efficient Extraction From Html Pages, Erdi̇nç Uzun
A Regular Expression Generator Based On Css Selectors For Efficient Extraction From Html Pages, Erdi̇nç Uzun
Turkish Journal of Electrical Engineering and Computer Sciences
Cascading style sheets (CSS) selectors are patterns used to select HTML elements. They are often preferred in web data extraction because they are easy to prepare and have short expressions. In order to be able to extract data from web pages by using these patterns, a document object model (DOM) tree is constructed by an HTML parser for a webpage. The construction process of this tree and the extraction process using this tree increase time and memory costs depending on the number of HTML elements and their hierarchies. For reducing these costs, regular expressions can be considered as a solution. …
Event-Based Summarization Of News Articles, Feri̇de Savaroğlu Tabak, Vesi̇le Evri̇m
Event-Based Summarization Of News Articles, Feri̇de Savaroğlu Tabak, Vesi̇le Evri̇m
Turkish Journal of Electrical Engineering and Computer Sciences
In recent years, with the increase of available digital information on the Web, the time needed to find relevant information is also increased. Therefore, to reduce the time spent on searching, research on automatic text summarization has gained importance. The proposed summarization process is based on event extraction methods and is called an event-based extractive single-document summarization. In this method, the important features of event extraction and summarization methods are analyzed and combined together to extract the summaries from single-source news documents. Among the tested features, six features are found to be the most effective in constructing good summaries. The …
Ternary Logical Naming Convention And Application In Ternary Optical Computers, Shuang Li, Yi Jin
Ternary Logical Naming Convention And Application In Ternary Optical Computers, Shuang Li, Yi Jin
Turkish Journal of Electrical Engineering and Computer Sciences
This article introduces the ternary logical naming convention, which was newly discovered in the study of logical units of ternary optical computers (TOCs). First, the design principle and design specification of the ternary logical naming convention are elaborated in detail and several examples are given to illustrate the use of the naming convention. Second, taking the modified signed-digit (MSD) adder of the TOC as an example, the naming convention is applied to build four ternary logical units of the MSD adder, and the implementation method of pipelined addition is introduced. Finally, the correctness of the ternary logical naming convention proposed …
Estimating Spatiotemporal Focus Of Documents Using Entropy With Pmi, Damla Yaşar, Selma Teki̇r
Estimating Spatiotemporal Focus Of Documents Using Entropy With Pmi, Damla Yaşar, Selma Teki̇r
Turkish Journal of Electrical Engineering and Computer Sciences
Many text documents are spatiotemporal in nature, i.e. contents of a document can be mapped to a specific time period or location. For example, a news article about the French Revolution can be mapped to year 1789 as time and France as place. Identifying this time period and location associated with the document can be useful for various downstream applications such as document reasoning or spatiotemporal information retrieval. In this paper, temporal entropy with pointwise mutual information (PMI) is proposed to estimate the temporal focus of a document. PMI is used to measure the association of words with time expressions. …
Satire Identification In Turkish News Articles Based On Ensemble Of Classifiers, Aytuğ Onan, Mansur Alp Toçoğlu
Satire Identification In Turkish News Articles Based On Ensemble Of Classifiers, Aytuğ Onan, Mansur Alp Toçoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Social media and microblogging platforms generally contain elements of figurative and nonliteral language, including satire. The identification of figurative language is a fundamental task for sentiment analysis. It will not be possible to obtain sentiment analysis methods with high classification accuracy if elements of figurative language have not been properly identified. Satirical text is a kind of figurative language, in which irony and humor have been utilized to ridicule or criticize an event or entity. Satirical news is a pervasive issue on social media platforms, which can be deceptive and harmful. This paper presents an ensemble scheme for satirical news …
Convolutional Auto Encoders For Sentence Representation Generation, Ali̇ Mert Ceylan, Vecdi̇ Aytaç
Convolutional Auto Encoders For Sentence Representation Generation, Ali̇ Mert Ceylan, Vecdi̇ Aytaç
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we have proposed an alternative approach for sentence modeling problem. The difficulty of the choice of answer, the semantically related questions and the lack of syntactic closeness of the answers give rise to the difficulty of selecting the answer. The deep learning field has recently achieved a pivotal success in semantic analysis, machine translation, and text summaries. The essence of this work, inspired by the human orthographic processing mechanism and using multiple convolution filters with pre-rendered 2-Dimension (2D) representations of sentences, input or output size is to learn the basic features of the language without concerns. For …
The Impact Of Text Preprocessing On The Prediction Of Review Ratings, Muhi̇tti̇n Işik, Hasan Dağ
The Impact Of Text Preprocessing On The Prediction Of Review Ratings, Muhi̇tti̇n Işik, Hasan Dağ
Turkish Journal of Electrical Engineering and Computer Sciences
With the increase of e-commerce platforms and online applications, businessmen are looking to have a rating and review system through which they can easily reveal the feelings of customers related to their products and services. It is undeniable from the statistics that online ratings and reviews attract new customers as well as increase sales by means of providing confidence, ratification, opinions, comparisons, merchant credibility, etc. Although considerable research has been devoted to the sentiment analysis for review classification, rather less attention has been paid to the text preprocessing which is a crucial step in opinion mining especially if convenient preprocessing …
Implicit Relation-Based Question Answering To Answer Simple Questions Overdbpedia, Maryam Jamehshourani, Afsaneh Fatemi, Mohammadali Nematbakhsh
Implicit Relation-Based Question Answering To Answer Simple Questions Overdbpedia, Maryam Jamehshourani, Afsaneh Fatemi, Mohammadali Nematbakhsh
Turkish Journal of Electrical Engineering and Computer Sciences
RDF-based question answering systems give users the capability of natural language querying over RDF data. In order to respond to natural language questions, it is necessary that the main concept of the question be interpreted correctly, and then it is mapped to RDF data. A natural language question includes entities, classes, and implicit and explicit relationships. In this article, by focusing on identification and mapping of implicit relationships in the question (in addition to the explicit relationships), the mapping step has been improved. In the proposed solution (IRQA), entities and implicit/explicit relationships are identified by means of the provided rules …
Net-Lda: A Novel Topic Modeling Method Based On Semantic Document Similarity, Eki̇n Eki̇nci̇, Sevi̇nç İlhan Omurca
Net-Lda: A Novel Topic Modeling Method Based On Semantic Document Similarity, Eki̇n Eki̇nci̇, Sevi̇nç İlhan Omurca
Turkish Journal of Electrical Engineering and Computer Sciences
Topic models, such as latent Dirichlet allocation (LDA), allow us to categorize each document based on the topics. It builds a document as a mixture of topics and a topic is modeled as a probability distribution over words. However, the key drawback of the traditional topic model is that it cannot handle the semantic knowledge hidden in the documents. Therefore, semantically related, coherent and meaningful topics cannot be obtained. However, semantic inference plays a significant role in topic modeling as well as in other text mining tasks. In this paper, in order to tackle this problem, a novel NET-LDA model …
Enhancing Learning With Primitive-Decomposed Cognitive Representations, Jamie C. Macbeth
Enhancing Learning With Primitive-Decomposed Cognitive Representations, Jamie C. Macbeth
Computer Science: Faculty Publications
This paper proposes work that applies insights from meaning representation systems for in-depth natural language understanding to representations for self-supervised learning systems, which show promise in developing complex, deeply-nested symbolic structures through self-motivated exploration of their environments. The core of the representation system transforms language inputs into language-free structures that are complex combinations of conceptual primitives, forming a substrate for human-like understanding and common-sense reasoning. We focus on decomposing representations of expectation, intention, planning, and decision-making which are essential to a self-motivated learner. These meaning representations may enhance learning by enabling a rich array of mappings between new experiences and …
Automatically Categorizing Software Technologies, Mathieu Nassif, Christoph Treude, Martin P. Robillard
Automatically Categorizing Software Technologies, Mathieu Nassif, Christoph Treude, Martin P. Robillard
Research Collection School Of Computing and Information Systems
Informal language and the absence of a standard taxonomy for software technologies make it difficult to reliably analyze technology trends on discussion forums and other on-line venues. We propose an automated approach called Witt for the categorization of software technologies (an expanded version of the hypernym discovery problem). Witt takes as input a phrase describing a software technology or concept and returns a general category that describes it (e.g., integrated development environment), along with attributes that further qualify it (commercial, php, etc.). By extension, the approach enables the dynamic creation of lists of all technologies of a given type (e.g., …
Emoco: Visual Analysis Of Emotion Coherence In Presentation Videos, Haipeng Zeng, Xingbo Wang, Aoyu Wu, Yong Wang, Quan Li, Alex Endert, Huamin Qu
Emoco: Visual Analysis Of Emotion Coherence In Presentation Videos, Haipeng Zeng, Xingbo Wang, Aoyu Wu, Yong Wang, Quan Li, Alex Endert, Huamin Qu
Research Collection School Of Computing and Information Systems
Emotions play a key role in human communication and public presentations. Human emotions are usually expressed through multiple modalities. Therefore, exploring multimodal emotions and their coherence is of great value for understanding emotional expressions in presentations and improving presentation skills. However, manually watching and studying presentation videos is often tedious and time-consuming. There is a lack of tool support to help conduct an efficient and in-depth multi-level analysis. Thus, in this paper, we introduce EmoCo, an interactive visual analytics system to facilitate efficient analysis of emotion coherence across facial, text, and audio modalities in presentation videos. Our visualization system features …
Entity-Sensitive Attention And Fusion Network For Entity-Level Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang
Entity-Sensitive Attention And Fusion Network For Entity-Level Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
Entity-level (aka target-dependent) sentiment analysis of social media posts has recently attracted increasing attention, and its goal is to predict the sentiment orientations over individual target entities mentioned in users' posts. Most existing approaches to this task primarily rely on the textual content, but fail to consider the other important data sources (e.g., images, videos, and user profiles), which can potentially enhance these text-based approaches. Motivated by the observation, we study entity-level multimodal sentiment classification in this article, and aim to explore the usefulness of images for entity-level sentiment detection in social media posts. Specifically, we propose an Entity-Sensitive Attention …
Synthesizing Aspect-Driven Recommendation Explanations From Reviews, Trung-Hoang Le, Hady W. Lauw
Synthesizing Aspect-Driven Recommendation Explanations From Reviews, Trung-Hoang Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Explanations help to make sense of recommendations, increasing the likelihood of adoption. However, existing approaches to explainable recommendations tend to rely on rigid, standardized templates, customized only via fill-in-the-blank aspect sentiments. For more flexible, literate, and varied explanations covering various aspects of interest, we synthesize an explanation by selecting snippets from reviews, while optimizing for representativeness and coherence. To fit target users' aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of several product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text …
Interaction, Collaboration And Content Creation In Informal Online Learning Environments: Multidimensional Analyses Of Longitudinal Data From The Scratch Coding Community, Seung B. Lee
Theses and Dissertations
Despite rising levels of participation by children and adolescents in large, informal online learning communities, there has been limited research examining the role that social dynamics play on the online behavior of young users. In this context, this mixed-methods longitudinal study aimed to investigate the relationship between interaction, collaboration and content creation through the analysis of user-generated comments and log-data from the Scratch platform. The research focused on more than 45,000 comments associated with the online activity of 200 randomly selected participants over a period of three months in early 2012. A combination of methodological techniques was applied in the …
Classifying Relations Using Recurrent Neural Network With Ontological-Concept Embedding, Mario J. Lorenzo
Classifying Relations Using Recurrent Neural Network With Ontological-Concept Embedding, Mario J. Lorenzo
CCAC Theses and Dissertations
Relation extraction and classification represents a fundamental and challenging aspect of Natural Language Processing (NLP) research which depends on other tasks such as entity detection and word sense disambiguation. Traditional relation extraction methods based on pattern-matching using regular expressions grammars and lexico-syntactic pattern rules suffer from several drawbacks including the labor involved in handcrafting and maintaining large number of rules that are difficult to reuse. Current research has focused on using Neural Networks to help improve the accuracy of relation extraction tasks using a specific type of Recurrent Neural Network (RNN). A promising approach for relation classification uses an RNN …
Selective Personalization And Group Profiles For Improved Web Search Personalization, Samira Karimi Mansoub, Gönenç Ercan, İlyas Çi̇çekli̇
Selective Personalization And Group Profiles For Improved Web Search Personalization, Samira Karimi Mansoub, Gönenç Ercan, İlyas Çi̇çekli̇
Turkish Journal of Electrical Engineering and Computer Sciences
Personalization is a common technique used in Web search engines to improve the effectiveness of retrieval. While personalizing some queries yields significant improvements in user experience by providing a ranking in line with the user preferences, it fails to improve or even degrades the effectiveness for less ambiguous queries. A potential personalization metric could improve search engines by selectively applying personalization. One such measure, click entropy uses the query history and the clicked documents for the query, which might be sparse for some queries. In this article, the topic entropy measure is improved by integrating the user distribution into the …
What Snippets Feel: Depression, Search, And Snippets, Ashlee Milton, Maria Soledad Pera
What Snippets Feel: Depression, Search, And Snippets, Ashlee Milton, Maria Soledad Pera
Computer Science Faculty Publications and Presentations
Mental health disorders (MHD) is a rising, yet stigmatized, topic in the United States. Individuals suffering from MHD are slowing starting to overcome this stigma by discussing how technology affects them. Researchers have explored behavioral nuances that emerge from interactions of individuals affected by MHD with persuasive technologies, mainly social media. Yet, there is a gap in the analysis pertaining to search engines, another persuasive technology, which is part of their everyday lives. In this paper, we report the results of an initial exploratory analysis conducted to understand the sentiment/emotion profiles of search engines handling the information needs of searchers …
Structural And Lexical Methods For Auditing Biomedical Terminologies, Rashmie Abeysinghe
Structural And Lexical Methods For Auditing Biomedical Terminologies, Rashmie Abeysinghe
Theses and Dissertations--Computer Science
Biomedical terminologies serve as knowledge sources for a wide variety of biomedical applications including information extraction and retrieval, data integration and management, and decision support. Quality issues of biomedical terminologies, if not addressed, could affect all downstream applications that use them as knowledge sources. Therefore, Terminology Quality Assurance (TQA) has become an integral part of the terminology management lifecycle. However, identification of potential quality issues is challenging due to the ever-growing size and complexity of biomedical terminologies. It is time-consuming and labor-intensive to manually audit them and hence, automated TQA methods are highly desirable. In this dissertation, systematic and scalable …
Transformer Neural Networks For Automated Story Generation, Kemal Araz
Transformer Neural Networks For Automated Story Generation, Kemal Araz
Dissertations
Towards the last two-decade Artificial Intelligence (AI) proved its use on tasks such as image recognition, natural language processing, automated driving. As discussed in the Moore’s law the computational power increased rapidly over the few decades (Moore, 1965) and made it possible to use the techniques which were computationally expensive. These techniques include Deep Learning (DL) changed the field of AI and outperformed other models in a lot of fields some of which mentioned above. However, in natural language generation especially for creative tasks that needs the artificial intelligent models to have not only a precise understanding of the given …
Identifying Online Sexual Predators Using Support Vector Machine, Yifan Li
Identifying Online Sexual Predators Using Support Vector Machine, Yifan Li
Dissertations
A two-stage classification model is built in the research for online sexual predator identification. The first stage identifies the suspicious conversations that have predator participants. The second stage identifies the predators in suspicious conversations. Support vector machines are used with word and character n-grams, combined with behavioural features of the authors to train the final classifier. The unbalanced dataset is downsampled to test the performance of re-balancing an unbalanced dataset. An age group classification model is also constructed to test the feasibility of extracting the age profile of the authors, which can be used as features for classifier training. The …
Pretraining Deep Learning Models For Natural Language Understanding, Han Shao
Pretraining Deep Learning Models For Natural Language Understanding, Han Shao
Honors Papers
Since the first bidirectional deep learn- ing model for natural language understanding, BERT, emerged in 2018, researchers have started to study and use pretrained bidirectional autoencoding or autoregressive models to solve language problems. In this project, I conducted research to fully understand BERT and XLNet and applied their pretrained models to two language tasks: reading comprehension (RACE) and part-of-speech tagging (The Penn Treebank). After experimenting with those released models, I implemented my own version of ELECTRA, a pretrained text encoder as a discriminator instead of a generator to improve compute-efficiency, with BERT as its underlying architecture. To reduce the number …
A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas
A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas
Dissertations
Over the last few years, email has met with enormous popularity. People send and receive a lot of messages every day, connect with colleagues and friends, share files and information. Unfortunately, the email overload outbreak has developed into a personal trouble for users as well as a financial concerns for businesses. Accessing an ever-increasing number of lengthy emails in the present generation has become a major concern for many users. Email text summarization is a promising approach to resolve this challenge. Email messages are general domain text, unstructured and not always well developed syntactically. Such elements introduce challenges for study …
An Empirical Evaluation Of The Inferential Capacity Of Defeasible Argumentation, Non-Monotonic Fuzzy Reasoning And Expert Systems, Lucas Rizzo, Luca Longo
An Empirical Evaluation Of The Inferential Capacity Of Defeasible Argumentation, Non-Monotonic Fuzzy Reasoning And Expert Systems, Lucas Rizzo, Luca Longo
Articles
Several non-monotonic formalisms exist in the field of Artificial Intelligence for reasoning under uncertainty. Many of these are deductive and knowledge-driven, and also employ procedural and semi-declarative techniques for inferential purposes. Nonetheless, limited work exist for the comparison across distinct techniques and in particular the examination of their inferential capacity. Thus, this paper focuses on a comparison of three knowledge-driven approaches employed for non-monotonic reasoning, namely expert systems, fuzzy reasoning and defeasible argumentation. A knowledge-representation and reasoning problem has been selected: modelling and assessing mental workload. This is an ill-defined construct, and its formalisation can be seen as a reasoning …
การพัฒนาตัวแบบรู้จำบุคลิกภาพจากเสียงสนทนาในบริบทของศูนย์ให้บริการข้อมูลลูกค้า, นคร ศรีณรงค์
การพัฒนาตัวแบบรู้จำบุคลิกภาพจากเสียงสนทนาในบริบทของศูนย์ให้บริการข้อมูลลูกค้า, นคร ศรีณรงค์
Chulalongkorn University Theses and Dissertations (Chula ETD)
ศูนย์บริการข้อมูลลูกค้าเป็นช่องทางสำคัญในการติดต่อระหว่างภาคธุรกิจและลูกค้า ตัวชี้วัดที่สำคัญของการทำงานของศูนย์บริการข้อมูลลูกค้าคือความพึงพอใจของลูกค้า ข้อมูลหลักที่ได้จากศูนย์บริการข้อมูลลูกค้าคือเสียงสนทนา ทำให้ผู้วิจัยสนใจที่จะศึกษาแนวทางในการสร้างตัวแบบรู้จำบุคลิกภาพจากเสียง ข้อมูลเสียงและเพศถูกเก็บจากหน่วยตัวอย่าง 92 คน พร้อมกับข้อมูลบุคลิกภาพโดยใช้แบบวัด MPI (Maudsley Personality Inventory) แบบวัดดังกล่าวแบ่งบุคลิกภาพออกเป็น 2 ด้าน คือด้าน E-scale (Extraversion และ Introversion) และ N-scale (Neuroticism และ Stability) ซึ่งนำมาใช้ในการพัฒนาตัวแบบจำแนกบุคลิกภาพทั้งสองด้าน เทคนิคที่ใช้ในการพัฒนาตัวแบบประกอบด้วย Logistic regression, SVM, Random forest และ Artificial neural network โดยพบว่าตัวแบบที่พัฒนาด้วยเทคนิค Artificial neural network มีประสิทธิภาพสูงสุดในการรู้จำ E-scale โดยมีค่า Positive predictive value (ค่าวัดประสิทธิภาพของ Introversion) เท่ากับ 0.71 และค่า Negative predictive value (ประสิทธิภาพของ Extraversion) เท่ากับ 0.75 ในส่วนของ N-scale ไม่พบตัวแบบที่พัฒนาด้วยเทคนิคใดมีประสิทธิภาพเพียงพอ ในการศึกษาครั้งนี้พบว่าบุคลิกภาพ Extraversion และ Introversion ซึ่งสามารถนำมาประยุกต์กับงานภาคธุรกิจ สามารถรู้จำจากเสียงสนทนาในบริบทศูนย์บริการข้อมูลลูกค้า โดยสามารถนำไปใช้มอบหมายพนักงานที่มีบุคลิกภาพเหมือนกับลูกค้าเพื่อเพิ่มความพึงพอใจของลูกค้าในการติดต่อสื่อสาร ภาคธุรกิจยังสามารถนำข้อมูลบุคลิกภาพเหล่านี้ไปใช้ต่อยอดในการแนะนำผลิตภัณฑ์ หรือโฆษณา ให้เหมาะสมกับลูกค้าแต่ละคน