Natural Language Interpretation Research Articles

Computational linguistics at the crossroads: A comprehensive review of NLP advancements

New NLP breakthroughs have put Computational Linguistics at a crossroads. NLP's past, present, and future are covered. This review explains computational linguistics' creation with a brief history of linguistics and computer science. Early solutions processed and understood natural language using rule-based systems using manually constructed linguistic rules. Over time, these tactics became increasingly problematic as language became more complex and obscure. Statistical approaches transformed operations. Neural network-based machine learning methods are leading the area because they can learn complicated patterns and representations from large text collections. A data-driven model revolution in natural language processing enhanced language modelling, machine translation, and sentiment analysis. Next, NLP improvements for several tasks and applications are evaluated. Language understanding models that capture semantic nuances and contextual relationships use deep learning frameworks. Word embeddings and transformer-based architectures like GPT and BERT perform well on benchmark datasets for text classification, question answering, and named item identification. The paper also shows how NLP interacts with computer vision, voice processing, and other domains to show the merits and cons of cross-disciplinary research. Multimodal techniques that combine text, graphics, and audio may increase natural language processing and interpretation. The review discusses NLP's effects on prejudice, justice, and privacy. Responsible development and implementation are needed when NLP technology becomes widespread due to algorithmic bias and data privacy concerns. NLP research directions and concerns are reviewed. Existing models may meet standards but fail in practice.

Campus Companion : Creating a Supportive Chat – Assistant for Students

This article explores the creation and usage of chatbots, or intelligent conversational agents, for online communication. Python and other machine learning methods are employed in the chatbot's design. To be more explicit, TensorFlow/Keras is used for natural language processing, MySQL is utilized for database administration, and Flask is used for web hosting. The initiative attempts to accomplish its goals by enhancing the user experience and fostering more effective communication on the website. Using massive datasets, the chatbot will be educated to comprehend users' objectives and offer replies that fit with them. Natural language processing technologies, including voice recognition, natural language interpretation, and natural language production, will be used to foster natural discussions. A number of machine learning approaches, such as transformers, attention processes, and recurrent neural networks, will be investigated in order to classify intent and deliver replies. The objective is to construct a powerful and versatile chatbot that can grasp arguments in their context, preserve the debate's status, and answer in a manner that sounds human. Assessment criteria such as accuracy, recall, and precision will be utilized to develop and adapt the chatbot. Numerous businesses, including education, entertainment, customer service, and other disciplines, may profit from the employment of this chatbot. The implementation of intelligent conversational interfaces on websites has the potential to greatly enhance user experience. Keywords— Chatbot, Flask, MySQL, TensorFlow, Keras, Website Interaction, Natural Language Processing (NLP), Conversational AI, Intent Classification, Response Generation, Machine Learning Models, Recurrent Neural Networks (RNNs), Transformer Models, Attention Mechanisms, User Experience (UX), Contextual Conversations, Dialogue State Management, Human-like Responses, Customer Service Automation, Educational Chatbot, Entertainment Applications.

Terminology in the Age of AI: The Transformation of Terminology Theory and Practice

Abstract This article explores the necessary transformation of terminology theory and practice induced by advances in artificial intelligence (AI), in particular generative AI and large language models (LLMs). It underlines the centrality of terminology for accurate and efficient multilingual communication, and it studies the interaction between two different forms of language, the embodied language of humans and the algorithmic and mathematical language of AI. The analysis traces the evolution of terminology, from its normative, concept-centric origins to contemporary challenges such as the concept-context gap, multilingual conceptual modeling and enriching terminologies with contextual relations. The article then highlights how AI and LLM are revolutionizing terminology work, by proposing the construction of relations between terms around prototypical contexts. This innovative approach aims to infuse a more deterministic understanding into human-AI interactions, guiding the latter to generate responses more aligned with human intent and context. Advocating a re-evaluation of current methodologies to interact better with AI's algorithmic mathematical interpretation of natural language, the article envisions a future in which terminology science and AI co-create a framework for improving multilingual knowledge exchange and understanding.

Assessment of Artificial Intelligence Language Models and Information Retrieval Strategies for QA in Hematology

Introduction Large language models (LLMs) have gained popularity due to their natural language generation and interpretation capabilities. Integrating these models in medicine enables multiple tasks like summarizing medical histories, synthesizing literature, and suggesting diagnoses. Models like ChatGPT, GPT-4, and Med-PaLM2 (Singhal et al., 2023) have demonstrated their proficiency by achieving high scores in medical tests like the United States Medical Licensing Examination (USMLE) (Kung et al., 2023). However, LLMs may sometimes be inaccurate, providing unverified and erroneous information. In this study, we investigate the potential uses of LLMs in hematology, assessing their knowledge through hematology questions from the USMLE. Additionally, we propose augmenting LLMs with retrieval capabilities for medical guidelines in order to eliminate incorrect information. By extracting relevant information from specified medical documents, this approach holds the potential to streamline decision-making processes. Methods For comparative purposes, all experiments were conducted using both GPT 3.5-turbo and GPT-4 models. In a first step, we evaluated the general knowledge and performance of LLM in the field of hematology by testing it in a collected dataset of 127 question-answer pairs from the hematology section (covering various aspects of the field) of the USMLE. In a second step, we evaluated the proposed information retrieval framework using a set of 120 multiple-choice questions. These questions were specifically focused on the 4th revision of the World Health Organization classification of myeloid neoplasms and acute leukemia guidelines (subsequently called WHO 2017). By testing the framework on this domain-specific dataset, we aimed to assess its ability to extract specific clinical context and relevant information from complex clinical guidelines. Each question from the WHO 2017 guideline dataset was subjected to a comprehensive evaluation using two techniques. First, the questions were assessed using a zero-shot approach (the question together with the different options are directly posed to the model) to assess the LLM's capability to respond based on its own knowledge. Second, we employed our proposed retrieval information approach, enabling the system to conduct in-depth searches throughout the external documents (WHO 2017 guideline) to identify relevant (and similar) extracts about each question. Subsequently, the system provided answers based on the retrieved contexts from the document, thus facilitating more accurate and contextually informed responses. To achieve this, we created an embedding space containing the document's content and conducted a cosine-similarity search between a given question and all the content extracts from the document. The top three relevant extracts, based on similarity to the given question, were used as context for the LLM. Results In the evaluation of 127 hematology questions from the USMLE, GPT-3.5 in zero-shot mode achieved 63% accuracy, while GPT-4 demonstrated a higher accuracy rate of 82%. The evaluation of the WHO 2017 questions dataset revealed that the zero-shot approach achieved accuracy rates of 51% for GPT-3.5 and 71% for GPT-4. Incorporating information retrieval, retrieving the three most relevant extracts from the guidelines, substantially improved performance, with GPT-3.5 achieving 86% accuracy and GPT-4 demonstrating 97% accuracy. Conclusions LLMs have great potential, with current models showcasing substantial knowledge in hematology. However, ensuring their consistency and safety in responses is critical for their reliable application in medical settings (Thirunavukarasu et al., 2023). To address this, we demonstrated the benefits of information retrieval for question-answering in the field of hematology, significantly improving response reliability and accuracy by empowering LLMs to deliver more informed and contextually appropriate answers. The concept was effectively validated using the WHO 2017 guideline, and it can be effortlessly adapted to answer questions based on any set of hematology-related documents. Leveraging LLMs has the potential to significantly enhance the efficiency and effectiveness of clinical, educational, and research work in hematology.

Natural Language Processing for Enterprise Applications

Researchers are concentrating on more efficient communication technologies that can emulate human interactions and comprehend natural languages and human emotions as a result of people's growing reliance on computer-assisted systems. Unstructured data, which is deemed useless, has increased due to the issue of information overload in every industry, including business, healthcare, education, etc. In this context, natural language processing (NLP) is one of the efficient technologies that may be used with more sophisticated technologies, such as machine learning, artificial intelligence, and deep learning, to enhance the interpretation and processing of natural language. In addition to improving human-computer interaction, this can also enable massive amounts of useless and unstructured data to be analyzed and formatted in numerous industrial applications. This will produce significant results that can improve decision-making and hence increase operational effectiveness. This chapter introduces the idea of NLP, its background, and its current state while also going through examples of its use in various industrial fields.
 Keywords: Natural Language Processing, Artificial Intelligence, Machine Learning.

Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT.

Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML kinetic models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein-protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.

Towards Natural Language-Based Visualization Authoring.

A key challenge to visualization authoring is the process of getting familiar with the complex user interfaces of authoring tools. Natural Language Interface (NLI) presents promising benefits due to its learnability and usability. However, supporting NLIs for authoring tools requires expertise in natural language processing, while existing NLIs are mostly designed for visual analytic workflow. In this paper, we propose an authoring-oriented NLI pipeline by introducing a structured representation of users' visualization editing intents, called editing actions, based on a formative study and an extensive survey on visualization construction tools. The editing actions are executable, and thus decouple natural language interpretation and visualization applications as an intermediate layer. We implement a deep learning-based NL interpreter to translate NL utterances into editing actions. The interpreter is reusable and extensible across authoring tools. The authoring tools only need to map the editing actions into tool-specific operations. To illustrate the usages of the NL interpreter, we implement an Excel chart editor and a proof-of-concept authoring tool, VisTalk. We conduct a user study with VisTalk to understand the usage patterns of NL-based authoring systems. Finally, we discuss observations on how users author charts with natural language, as well as implications for future research.

A Pilot Study of the Application of Natural Language Interpretation to Identify Insights on Acute Lymphocytic Leukemia and Its Treatment from Social Media Data

Social media (SM) data is emerging as an important source of information on patient disease and treatment experiences. SM data provide researchers access to unsolicited information shared by patients/caregivers (CGs)/healthcare providers (HCPs), without burdens associated with traditional research methods or influence of interviewer bias. SM facilitates open communication on serious illness in an unstructured forum; offering snap shots of information that highlight important issues prioritized by patients/CGs/HCPs. SM data is growing exponentially, manual review of this noisy “Big Data” is time consuming and impractical. Natural language understanding (NLU) provides an optimal approach to extracting structured data elements from unstructured text. This pilot study aimed to use NLU to aggregate, analyze, and better understand various aspects of patients/CGs/HCPs experiences of Acute lymphocytic leukemia (ALL). ALL is a blood and bone marrow cancer with a significant patient symptom burden and detrimental impact on patient/CG health-related quality of life (HRQOL).Publicly available, English-language SM data were indexed from a 25-month period (Jul.2018-Aug.2020). Data were extracted using Sprinklr (Twitter), and Board Reader (boards/blogs/forums/reviews). NLU was applied to capture insights (e.g., emerging patterns/trends/relationships). Advanced analytics identified and analyzed relevant insights, and advanced logic determined user type and post contributor (e.g., patient/CGs/HCP). Posts underwent further manual evaluation using thematic content analysis to explore the experience of ALL shared by users and to support NLU-identified insights.6,365 ALL-related posts were identified; 289 (4.5%) were contributed by patients, 657 (10.3%) by CGs, 1224 (19.2%) by HCPS and 4,195 (65.9%) by organizations. Topics included expressions of positive support/encouragement for patients/monetary donation requests/treatment type/impact of side effects. NLU identified notable discussion areas: patient/CG commentaries on treatment experiences, decisions, side effects and outcomes, and patient knowledge regarding ALL treatments. 189 patient/CG posts discussed ALL treatments in general, including in-hospital time: missing life events due to intensive treatment regimens and extended hospital stays. 195 patient/CG posts discussed specific ALL treatment experiences: chemotherapy, n=120; bone marrow transplant, n=22; stem cell transplant, n=16; immunotherapy, n=14; Kymriah, n=11; CAR-T, n=8; radiation, n=4. Chemotherapy was mentioned in relation to daily and/or durable short-and long-term effects (e.g., sickness, pain) which had major impacts for patients' and their family's HRQOL. Chemotherapy was associated with unforeseen impacts related to patients compromised immune systems (e.g., restricted social functioning due to increased risk of infection). Fear of infection and impact of treatment notably increased patients' needs to isolate which had a substantial impact on the broader family life. Bone marrow transplant posts described it as ‘lifesaving’. Despite uncertainty of treatment success and negative treatment effects, patient/CG posts noted that side effects were an acceptable part of the journey to become cancer free. Posts also illustrated a general shift in patient/CG perception of ALL treatments, specifically that no one treatment works for everyone in the same way, and recent treatment developments mean that ALL is no longer perceived as a death sentence. 66 patient/CG posts commented on the cessation of treatment or lack of treatment due to remission, alternative treatments or end of life. Financial burden due to ALL treatment was an important issue for patients/CGs as it prevented the start of treatment or impaired patients‘/CGs’ lives.This pilot study shows how NLU can effectively extract SM data expressed by patients/CGs/HCPs relating to ALL. It is important to note that SM data are unregulated, not peer reviewed and inherently reliant on user self-identification. Thus, caution should be used interpreting SM data particularly as the information is not generalizable and/or reflective of the whole ALL patient population. Nevertheless, the pilot demonstrated how user-generated SM data can offer valuable insights on the experience and impact of ALL and its treatments that exist outside of the formal research context. DisclosuresCrawford: Pfizer Inc: Consultancy. Welch: Pfizer Inc: Current Employment. Shah: Pfizer Inc: Current Employment. Doward: Pfizer Inc: Consultancy. Truchan: Pfizer Inc: Current Employment.

Design and Development of a Prototype Heads-Up Display: Supporting Context-Aware, Semi-Automated, Hands-Free Medical Documentation

Military and civilian medical personnel across all echelons of medical care play a critical role in evaluating, caring for, and treating casualties. Accurate medical documentation is critical to effective, coordinated care and positive patient outcomes. We describe our prototype, Context-Aware Procedure Support Tools and User Interfaces for Rapid and Effective Workflows (CAPTURE). Leveraging human factors and usercentered design methods, and advanced artificial intelligence and computer vision capabilities, CAPTURE was designed to enable Tactical Combat Causality Care (TCCC) providers to more efficiently and effectively input critical medical information through hands-free interaction techniques and semiautomated data capture methods. We designed and prototyped a heads-up display that incorporates: multimodal interfaces, including augmented reality-based methods for input and information display to support visual image capture and heads-up interaction; post-care documentation support (e.g., artifacts to support post-care review and documentation); context-aware active and passive data capture methods, specifically natural language interpretation using systemic functional grammars; and computer vision technologies for semi-automated data capture capabilities. During the course of this project we encountered challenges towards effective design which fall into three main categories: (1) challenges related to designing novel multimodal interfaces; (2) technical challenges related to software and hardware development to meet design needs; and (3) challenges as a result of domain characteristics and operational constraints. We discuss how we addressed some of these challenges and provide additional considerations necessary for future research regarding next generation technology design for medical documentation in the field.

Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 6. The Еxternal Logic

Introduction. The article continues a series of publications on the linguistics of the relationship (hereafter R-linguistics) and is concerned with the semantic interpretation in terms of the linguistic model that is the initial stage to consider the logic of natural language (external logic).Methodology and sources. The results obtained in the previous parts of the series are used as research tools. In particular, the verbal categorization method is used to represent concepts and verbs. To develop the necessary mathematical representations in the field of logic and semantics of natural language, the previously formulated concept of the interpretation operator is used. The interpretation operator maps the sentences of the language into the model, taking into account the previously interpreted sentences.Results and discussion. The problems that arise during the operation of the natural language interpretation operator are analyzed using examples of text translation and utterance algebra. The source of problems is the dependence of the interpretation of sentences on the already accumulated results of interpretation. The features of the interpretation of negation and double negation in the language are analyzed. In particular, the negation of a sentence affects the interpretation of previous sentences, and double negation usually denotes a single negation with an indication of its scope. It is shown that even from the point of view of classical logic, linguistic negation is not unconditional, and the operation of concatenation is not commutative and associative. General rules of text interpretation in the form of step-by-step mapping of sentence elements into a linguistic model are formulated.Conlcusion. From the considered examples of the implementation of the interpretation operator, it follows that the negation of a sentence requires a change in the meaning of the operation of attributing sentences in the text. For this reason, the negative particle ”not” in the language is actually a label for changing the interpretation rule. The double negation rule in sentence logic does not hold, so sentences containing double negations are likely to contain information about the scope of the sentence negation in the text. Based on the analysis, the contours of the interpretation operator for the linguistic model are indicated.

Automatic medical image interpretation: State of the art and future directions

Automatic Natural language interpretation of medical images is an emerging field of Artificial Intelligence (AI). The task combines two fields of AI; computer vision and natural language processing. This is a challenging task that goes beyond object detection, segmentation, and classification because it also requires the understanding of the relationship between different objects of an image and the actions performed by these objects as visual representations. Image interpretation is helpful in many tasks like helping visually impaired persons, information retrieval, early childhood learning, producing human like natural interaction between robots, and many more applications. Recently this work fascinated researchers to use the same approach by using more complex biomedical images. It has been applied from generating single sentence captions to multi sentence paragraph descriptions. Medical image captioning can assist and speed up the diagnosis process of medical professionals and generated report can be used for many further tasks. This is a comprehensive review of recent years’ research of medical image captioning published in different international conferences and journals. Their common parameters are extracted to compare their methods, performance, strengths, limitations, and our recommendations are discussed. Further publicly available datasets and evaluation measures used for deep-learning based captioning of medical images are also discussed.

Towards an optimal design of natural human interaction mechanisms for a service robot with ancillary way-finding capabilities in industrial environments

ABSTRACT In addition to production-oriented robots, service robots with social skills can also perform a role in industrial environments, providing on-demand ancillary services that support production activities. In this paper, a robot that provides way-finding services within an industrial facility (e.g., finding a person or place) and is able to naturally interact with workers is presented. So as to give a more natural dimension to the robot’s verbal interaction ability and achieve acceptance from human workers, the research in this paper is directed towards the improvement of its semantic natural language interpreter, with the aim of making it able to automatically learn from new interactions. A user study is also reported, in order to assess both the capabilities of the interpreter and the performance of the robot’s communication and way-finding abilities, paying special attention to user experience (UX), which may help identify possible design problems in further research stages.

Concepts of Organization of Information-intellectual Technologies and Intellectual Support of Socio-economic Processes.

The article is devoted to the analysis of the problem of creating artificial intelligence systems that allow modelingat the logical and hardware levels of economic solutions described by mathematical operations on natural language,and which are elements of k-valued structural organization of information and intellectual technologies. It describesthe concept of organizing information and intellectual technologies, as well as intellectual support of socio-economicprocesses, in particular, financial and credit enterprises. In the article, the intellectual activity of people is investigated inits external (problem solving, understanding and interpretation of natural language, visual information, etc.) and internal(accumulation, transfer and use of knowledge) forms.

Statistical Representations of a Dashboard to Monitor Educational Videogames in Natural Language

This paper explains how Natural Language (NL) processing by computers, through smart programs as a way of Machine Learning (ML), can represent large sets of quantitative data as written statements. The study recognized the need to improve the implemented web platform using a dashboard in which we collected a set of extensive data to measure assessment factors of using children’s educational games. In this case, applying NL is a strategy to give assessments, build, and display more precise written statements to enhance the understanding of children’s gaming behavior.We propose the development of a new tool to assess the use of written explanations rather than a statistical representation of feedback information for the comprehension of parents and teachers with a lack of primary level knowledge in statistics. Applying fuzzy logic theory, we present verbatim explanations of children’s behavior playing educational videogames as NL interpretation instead of statistical representations. An educational series of digital game applications for mobile devices, identified as MIDI (Spanish acronym of “Interactive Didactic Multimedia for Children”) linked to a dashboard in the cloud, is evaluated using the dashboard metrics. MIDI games tested in local primary schools helps to evaluate the results of using the proposed tool. The guiding results allow analyzing the degrees of playability and usability factors obtained from the data produced when children play a MIDI game. The results obtained are presented in a comprehensive guiding evaluation report applying NL for parents and teachers. These guiding evaluations are useful to enhance children’s learning understanding related to the school curricula applied to ludic digital games.

Twitter Sentiment Recognition using Support Vector Machine

In this we explore the effectiveness of language features to identify Twitter messages ' feelings. We assess the utility of existing lexical tools as well as capturing features of informal and innovative language knowledge used in micro blogging. We take a supervised approach to the problem, but to create training data, we use existing hash tags in the Twitter data. We Using three separate Twitter messaging companies in our experiments. We use the hash tagged data set (HASH) for development and training, which we compile from the Edinburgh Twitter corpus, and the emoticon data set (EMOT) from the I Sieve Corporation (ISIEVE) for evaluation. Twitter contains huge amount of data . This data may be of different types such as structured data or unstructured data. So by using this data and Appling pre processing techniques we can be able to read the comments from the users. And also the comments will be classified into three categories. They are positive negative and also the neutral comments.Today they use the processing of natural language, information, and text interpretation to derive and classify text feeling into pos itive, negative, and neutral categories. We can also examine the utility of language features to identify Twitter mess ages ' feelings. In addition, state-of - the-art approaches take into consideration only the tweet to be classified when classifying the feeling; they ignore its context (i.e. related tweets).Since tweets are usually short and more ambiguous, however, it is sometimes not enough to consider only the current tweet for classification of sentiments.Informal and innovative microblogging language. We take a sup ervised approach to the problem, but to create training data, we use existing hashtags in the Twitter data.This paper also contrasts sentiment analysis approaches in evaluating political views using Naïve Bayes supervised machine learning algorithm which performs in better analysis compared to other techniques Paper

Semantic reasoning in service robots using expert systems

This paper presents the semantic-reasoning module of VIRBOT, our proposed architecture for service robots. We show that by combining symbolic AI with digital-signal processing techniques this module achieves competitive performance. Our system translates a voice command into an unambiguous representation that helps an inference engine, built around an expert system, to perform action and motion planning. First, in the natural-language interpretation process, the system generates two outputs: (1) conceptual dependence, expressing the linguistic meaning of the statement, and (2) verbal confirmation, a paraphrase in natural language that is repeated to the user to confirm that the command has been correctly understood. Then, a conceptual-dependency interpreter extracts semantic role structures from the input sentence and looks for such structures in a set of known interpretation patterns. We evaluate this approach in a series of skill-specific semantic-reasoning experiments. Finally, we demonstrate our system in the general-purpose service robot test of the RoboCup-at-Home international competition, where incomplete information is given to a robot and the robot must recognize and request the missing information, and we compare our results with a series of baselines from the competition where our proposal performed best.

Parallel computing method of deep belief networks and its application to traffic flow prediction

Deep belief networks (DBNs) with outstanding advantages of learning input data features have attained particular attention and are applied widely in image processing, speech recognition, natural language interpretation, disease diagnosis, among others. However, owing to large data, the training processes of DBNs are time-consuming and may not satisfy the requirements of real-time application systems. In this study, a single dataset is decomposed into multiple subdatasets that are distributed to multiple computing nodes. Multiple computing nodes learn the features of their own subdatasets. On the precondition of the remaining features where one computing node learns from the total dataset, the single dataset learning models and algorithms are extended to the cases where multiple computing nodes learn multiple subdatasets in a parallel manner. Learning models and algorithms are proposed for the parallel computing of DBN learning processes. A master–slave parallel computing structure is designed, where the slave computing nodes learn the features of their respective subdatasets and transmit them to the master computing node. The master computing node is critical in synthesizing the learned features from the respective slave computing nodes. The broadcast, synchronization, and synthesis are repeated until all features of subdatasets have been learned. The proposed parallel computing method is applied to traffic flow prediction using practical traffic flow data. Our experimental results verify the effectiveness of the parallel computing method of DBN learning processes in terms of decreasing pre-training and fine-tuning times and maintaining the prominent feature learning abilities.

Computing with words using intuitionistic fuzzy logic programming

Computing with words is the terminology to indicate a set of numbers and words.It is the base for natural language processing and computational theory of perceptions.It is the art to combine both human and machine perception and find a solution for the real world problems left unsolved due to improper mechanism.Animal voice interpreter, lie detector, driving a vehicle in heavy traffic, and natural language interpreter are the applications need to be automated for the next generation.The computational theory is a group of perceptions used to express propositions in a natural language.The concept of the research is to utilize intutionistic fuzzy logic to interpret perceptions to solve vague problems.The output of the research shows that the performance of proposed method is better than the existing methods.

An Artifical Management Platform Based on Deep Learning Using Cloud Computing for Smart Cities

Nowadays, deep learning is commonly used in many areas such as natural language processing, data mining, image processing and interpretation. The use of technology in city management for the purposes of effective resource management, improving the quality of service and reducing costs have led to smart city concept. The data produced by automation systems as well as internet-connected objects such as sensor, camera and mobile device are also used for smart city management. It is difficult to analyze such a big sized data by processing with conventional methods and to use them in decision-making mechanisms. In this study, deep learning based data mining was performed on big data obtained from different types of sources for smart city management and an approach to ensure that the results can be analyzed was proposed.

Gesture phase segmentation using support vector machines

An interaction between humans or between a human and a machine will be more effective if it is supported by gestures. In different levels of complexity, the communication system used in human interaction includes the use of gesture. In natural conversation, for instance, speakers use gestures for both to enhance the expressiveness of their speech and to support their own linguistic reasoning. The audience absorbs the content being transmitted also based on the speakers’ gesticulation. Thus, an analysis of gestures should add value to the purpose of the interaction. One of the concerns in the analysis of gestures is the problem arising from the segmentation of phases of a gesture (rest position, preparation, stroke, hold and retraction), which, from the standpoint of Gesture Theory, may reveal information on prosody and semantics of what is being said in a discourse. Finding an automation solution to this problem involves enabling the development of theoretical and application areas that are based on the analysis of human behavior and on the interpretation and generation of natural language. In this study, the problem of gesture phase segmentation is modeled as a problem of classification, and then support vector machine is employed to design a model able to learn the patterns of gesture that are inherent to each phase. This work presents two main highlights. The first is to address the limitations of the segmentation approach through the study of its performance in different scenarios that represent the complexity of analyzing patterns of human behavior. In this study, we reached an F-score around 0.9 for rest position and around 0.8 for stroke and preparation as segmentation results in the best cases. Moreover, it was possible to investigate how classification models are influenced by human behavior. The second highlight refers to the conduction of an analysis by considering the standpoint of specialists concerned with gesture phase segmentation in the area of Linguistics and Psycholinguistics, through which we obtained impressive results. Thus, in regard to the suitability of our approach, it is a feasible means of supporting the development of the Gesture Theory as well as the Computational Linguistics and Human Machine Interaction fields.