Search results for: Kazakh speech dataset

Authors: Vo Ngoc Phu, Phan Thi Tuoi

Abstract:

We have explored different methods of improving the accuracy of sentiment classification. The sentiment orientation of a document can be positive (+), negative (-), or neutral (0). We combine five dictionaries from [2, 3, 4, 5, 6] into the new one with 21137 entries. The new dictionary has many verbs, adverbs, phrases and idioms, that are not in five ones before. The paper shows that our proposed method based on the combination of Term-Counting method and Enhanced Contextual Valence Shifters method has improved the accuracy of sentiment classification. The combined method has accuracy 68.984% on the testing dataset, and 69.224% on the training dataset. All of these methods are implemented to classify the reviews based on our new dictionary and the Internet Movie data set.

Keywords: sentiment classification, sentiment orientation, valence shifters, contextual, valence shifters, term counting

Procedia PDF Downloads 504

Authors: B. S. Premalatha, Kausalya Sahani

Abstract:

Background: Glomus jugulare tumors arise within the jugular foramen and are commonly seen in females particularly on the left side. Surgical excision of the tumor may cause lower cranial nerve deficits. Cranial nerve involvement produces hoarseness of voice, slurred speech, and dysphagia along with other physical symptoms, thereby affecting the quality of life of individuals. Though oncological clearance is mainly emphasized on while treating these individuals, little importance is given to their communication, voice and swallowing problems, which play a crucial part in daily functioning. Objective: To examine the functions of voice, speech and swallowing outcomes of the subjects, following excision of glomus jugulare tumor. Methods: Two female subjects aged 56 and 62 years had come with a complaint of change in voice, inability to swallow and reduced clarity of speech following surgery for left glomus jugulare tumor were participants of the study. Their surgical information revealed multiple cranial nerve palsies involving the left facial, left superior and recurrent branches of the vagus nerve, left pharyngeal, left soft palate, left hypoglossal and vestibular nerves. Functional outcomes of voice, speech and swallowing were evaluated by perceptual and objective assessment procedures. Assessment included the examination of oral structures and functions, dysarthria by Frenchey dysarthria assessment, cranial nerve functions and swallowing functions. MDVP and Dr. Speech software were used to evaluate acoustic parameters of voice and quality of voice respectively. Results: The study revealed that both the subjects, subsequent to excision of glomus jugulare tumor, showed a varied picture of affected oral structure and functions, articulation, voice and swallowing functions. The cranial nerve assessment showed impairment of the vagus, hypoglossal, facial and glossopharyngeal nerves. Voice examination indicated vocal cord paralysis associated with breathy quality of voice, weak voluntary cough, reduced pitch and loudness range, and poor respiratory support. Perturbation parameters as jitter, shimmer were affected along with s/z ratio indicative of voice fold pathology. Reduced MPD(Maximum Phonation Duration) of vowels indicated that disturbed coordination between respiratory and laryngeal systems. Hypernasality was found to be a prominent feature which reduced speech intelligibility. Imprecise articulation was seen in both the subjects as the hypoglossal nerve was affected following surgery. Injury to vagus, hypoglossal, gloss pharyngeal and facial nerves disturbed the function of swallowing. All the phases of swallow were affected. Aspiration was observed before and during the swallow, confirming the oropharyngeal dysphagia. All the subsystems were affected as per Frenchey Dysarthria Assessment signifying the diagnosis of flaccid dysarthria. Conclusion: There is an observable communication and swallowing difficulty seen following excision of glomus jugulare tumor. Even with complete resection, extensive rehabilitation may be necessary due to significant lower cranial nerve dysfunction. The finding of the present study stresses the need for involvement of as speech and swallowing therapist for pre-operative counseling and assessment of functional outcomes.

Keywords: functional outcome, glomus jugulare tumor excision, multiple cranial nerve impairment, speech and swallowing

Procedia PDF Downloads 252

Authors: Rosa Figueroa, Christopher Flores

Abstract:

Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

Procedia PDF Downloads 297

Authors: Mohamed Shawki Elamir, Heinrich Gotzig, Raoul Zoellner, Patrick Maeder

Abstract:

In this work, a methodology is presented for identifying the optimal digitization parameters for the analog signal of ultrasonic sensors. These digitization parameters are the resolution of the analog to digital conversion and the sampling rate. This is accomplished through the derivation of characteristic curves based on Fano inequality and the calculation of the mutual information content over a given dataset. The mutual information is calculated between the examples in the dataset and the corresponding variation in the feature that needs to be estimated. The optimal parameters are identified in a manner that ensures optimal estimation performance while preventing inefficiency in using unnecessarily powerful analog to digital converters.

Keywords: analog to digital conversion, digitization, sampling rate, ultrasonic

Procedia PDF Downloads 207

Authors: Paul Lam, Kevin Wong, Chi Him Chan

Abstract:

Constant practicing and accurate, immediate feedback are the keys to improving students’ speaking skills. However, traditional oral examination often fails to provide such opportunities to students. The traditional, face-to-face oral assessment is often time consuming – attending the oral needs of one student often leads to the negligence of others. Hence, teachers can only provide limited opportunities and feedback to students. Moreover, students’ incentive to practice is also reduced by their anxiety and shyness in speaking the new language. A mobile app was developed to use artificial intelligence (AI) to provide immediate feedback to students’ speaking performance as an attempt to solve the above-mentioned problems. Firstly, it was thought that online exercises would greatly increase the learning opportunities of students as they can now practice more without the needs of teachers’ presence. Secondly, the automatic feedback provided by the AI would enhance students’ motivation to practice as there is an instant evaluation of their performance. Lastly, students should feel less anxious and shy compared to directly practicing oral in front of teachers. Technically, the program made use of speech-to-text functions to generate feedback to students. To be specific, the software analyzes students’ oral input through certain speech-to-text AI engine and then cleans up the results further to the point that can be compared with the targeted text. The mobile app has invited English teachers for the pilot use and asked for their feedback. Preliminary trials indicated that the approach has limitations. Many of the users’ pronunciation were automatically corrected by the speech recognition function as wise guessing is already integrated into many of such systems. Nevertheless, teachers have confidence that the app can be further improved for accuracy. It has the potential to significantly improve oral drilling by giving students more chances to practice. Moreover, they believe that the success of this mobile app confirms the potential to extend the AI-assisted assessment to other language skills, such as writing, reading, and listening.

Keywords: artificial Intelligence, mobile learning, oral assessment, oral practice, speech-to-text function

Procedia PDF Downloads 103

Authors: Tamer Mesallam, Medhat Yousef, Ayna Almasaad

Abstract:

BACKGROUND: Cochlear implantation (CI) in children with additional disabilities can be a fundamental and supportive intervention. Although, there may be some positive impacts of CI on children with multiple disabilities such as better outcomes of communication skills, development, and quality of life, the families of those children complain from the post-implant habilitation efforts that considered as a burden. OBJECTIVE: To investigate the outcomes of CI children with different co-disabilities through using the Meaningful Auditory Integration Scale (MAIS) and the Meaningful Use of Speech Scale (MUSS) as outcome measurement tools. METHODS: The study sample comprised 25 hearing-impaired children with co-disability who received cochlear implantation. Age and gender-matched control group of 25 cochlear-implanted children without any other disability has been also included. The participants' auditory skills and speech outcomes were assessed using MAIS and MUSS tests. RESULTS: There was a statistically significant difference in the different outcomes measure between the two groups. However, the outcomes of some multiple disabilities subgroups were comparable to the control group. Around 40% of the participants with co-disabilities experienced advancement in their methods of communication from behavior to oral mode. CONCLUSION: Cochlear-implanted children with multiple disabilities showed variable degrees of auditory and speech outcomes. The degree of benefits depends on the type of the co-disability. Long-term follow-up is recommended for those children.

Keywords: children with disabilities, Cochlear implants, hearing impairment, language development

Procedia PDF Downloads 119

Authors: Kriuk Boris, Kriuk Fedor

Abstract:

This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications.

Keywords: siamese networks, semantic textual similarity, similarity functions, STS benchmark dataset, threshold selection

Procedia PDF Downloads 37

Authors: Ahmad FAROOQ, Xinfeng Zhang, Fahad Sabah, Raheem Sarwar

Abstract:

In Traditional Chinese Medicine, tongue diagnosis is a vital inspection tool (TCM). In this study, we explore the potential of machine learning in tongue diagnosis. It begins with the cataloguing of the various classifications and characteristics of the human tongue. We infer 24 kinds of tongues from the material and coating of the tongue, and we identify 21 attributes of the tongue. The next step is to apply machine learning methods to the tongue dataset. We use the Weka machine learning platform to conduct the experiment for performance analysis. The 457 instances of the tongue dataset are used to test the performance of five different machine learning methods, including SVM, Random Forests, Decision Trees, and Naive Bayes. Based on accuracy and Area under the ROC Curve, the Support Vector Machine algorithm was shown to be the most effective for tongue diagnosis (AUC).

Keywords: medical imaging, image retrieval, machine learning, tongue

Procedia PDF Downloads 81

Authors: M. Bilal Ishfaq, Adnan N. Qureshi

Abstract:

COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

Procedia PDF Downloads 75

Authors: Hozaifa Zaki, Ghada Soliman

Abstract:

In this paper, we introduced an approach for Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder (ARABEX) with bidirectional LSTM. This approach is used for translating the Arabic dot-matrix expiration dates into their corresponding filled-in dates. A custom lightweight Convolutional Recurrent Neural Network (CRNN) model is then employed to extract the expiration dates. Due to the lack of available dataset images for the Arabic dot-matrix expiration date, we generated synthetic images by creating an Arabic dot-matrix True Type Font (TTF) matrix to address this limitation. Our model was trained on a realistic synthetic dataset of 3287 images, covering the period from 2019 to 2027, represented in the format of yyyy/mm/dd. We then trained our custom CRNN model using the generated synthetic images to assess the performance of our model (ARABEX) by extracting expiration dates from the translated images. Our proposed approach achieved an accuracy of 99.4% on the test dataset of 658 images, while also achieving a Structural Similarity Index (SSIM) of 0.46 for image translation on our dataset. The ARABEX approach demonstrates its ability to be applied to various downstream learning tasks, including image translation and reconstruction. Moreover, this pipeline (ARABEX+CRNN) can be seamlessly integrated into automated sorting systems to extract expiry dates and sort products accordingly during the manufacturing stage. By eliminating the need for manual entry of expiration dates, which can be time-consuming and inefficient for merchants, our approach offers significant results in terms of efficiency and accuracy for Arabic dot-matrix expiration date recognition.

Keywords: computer vision, deep learning, image processing, character recognition

Procedia PDF Downloads 82

Authors: Marc Arthur Bien Aimé, Julio da Silveira Moreira

Abstract:

This study examines the political speech in Haiti over the course of the decade 2011-2021, focusing on the speeches of the presidents Michel J. Martelly and Jovenel Moïse and their impacts on their awareness collective. In using a qualitative approach, we have analyzed the speech of the president pronounced in response to the political instability of countries, as well as interviews with a group of 20 Haitians living in Port- Au-Prince. Our results put in evidence their complex relationship between politics, awareness collective, and the influence of the powers imperialists. We show that the situation in Haiti's disastrous social and political situation is driven by personal political interests and the absence of a state political project. Moreover, the speeches of the president’s analysis are meaningless, transforming concepts such as social progress and justice in simple words. This political rhetoric contributes to the domination symbolic of the population of Haitian. This study is also linked to the theme “Constitutions, processes democratic and critical of the state in Latin America,” emphasizing the importance of analysis of political speech to understand the complexities of the democratic process and criticism of the State in their Latin American region. We suggest future research to deepen our understanding of these political dynamics and their impact on public policies and developments of the constitutions throughout Latin America.

Keywords: political discourse, conscience collective, inequality social, democratic processes, constitutions, Haiti

Procedia PDF Downloads 61

Authors: M. Leonora D. Guerrero

Abstract:

The study attempted to uncover the most and least frequent phonological variation evident in the speech patterns of grade 1 teachers in select public elementary schools in the Philippines. It also determined the lectal description of the participants based on Tayao’s consonant charts for American and Philippine English. Descriptive method was utilized. A total of 24 grade 1 teachers participated in the study. The instrument used was word list. Each column in the word list is represented by words with the target consonant phonemes: labiodental fricatives f/ and /v/ and lingua-alveolar fricative /z/. These phonemes were in the initial, medial, and final positions, respectively. Findings of the study revealed that the most frequent variation happened when the participants read words with /z/ in the final position while the least frequent variation happened when the participants read words with /z/ in the initial position. The study likewise proved that the grade 1 teachers exhibited the segmental features of both the mesolect and basilect. Based on these results, it is suggested that teachers of English in the Philippines must aspire to manifest the features of the mesolect, if not, the acrolect since it is expected of the academicians not to be displaying the phonological features of the acrolects since this variety is only used by the 'uneducated.' This is especially so with grade 1 teachers who are often mimicked by their students who classify their speech as the 'standard.'

Keywords: consonant phonemes, lectal description, Philippine English, phonological variation

Procedia PDF Downloads 213

Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

Abstract:

Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs.

Keywords: emotion recognition, speech, deep learning, human-robot interaction, neural networks

Procedia PDF Downloads 170

Authors: Dina Mahmoud Hammouri

Abstract:

The study investigates the Jordanian EFL learners’ pragmatic competence through their production of the speech acts of responding to requests, making suggestions, making threats and expressing farewells. The sample of the study consists of 130 Jordanian EFL learners and native speakers. 2600 responses were collected through a Discourse Completion Test (DCT). The findings of the study revealed that the tested students showed similarities and differences in performing the strategies of four speech acts. Differences in the students’ performances led to pragmatic failure instances. The pragmatic failure committed by students refers to a lack of linguistic competence (i.e., pragmalinguistic failure), sociocultural differences and pragmatic transfer (i.e., sociopragmatic failure). EFL learners employed many mechanisms to maintain their communicative competence; the analysis of the test on speech acts showed learners’ tendency towards using particular strategies, resorting to modify strategies and relating them to their grammatical competence, prefabrication, performing long forms, buffing and transfer. The results were also suggestive of the learners’ lack of pragmalinguistic and sociopragmatic knowledge. The implications of this study are for language teachers to teach interlanguage pragmatics explicitly in EFL contexts to draw learners’ attention to both pragmalinguistic and sociopragmatic features, pay more attention to these areas and allocate more time and practice to solve learners’ problems in these areas. The implication of this study is also for pedagogical material designers to provide sufficient and well-organized pragmatic input.

Keywords: pragmatic failure, Jordanian EFL learner, sociopragmatic competence, pragmalinguistic competence

Procedia PDF Downloads 80

Authors: P. Shrivastava, A. Shukla, K. Verma, S. Rungta

Abstract:

Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset.

Keywords: parkinson, gait, feature selection, bat algorithm

Procedia PDF Downloads 545

Authors: Anochao Phetcharat

Abstract:

This research aims to study problems of translation basic knowledge, particularly from English into Thai. The researcher used 38 2nd-year non-English speaking students of Suratthani Rajabhat University as samples. The samples were required to translate an A4-sized article from English into Thai assigned as a part of BEN0202 Translation for Business, a requirement subject for Business English Department, which was also taught by the researcher. After completion of the translation, numerous problems were found and the research grouped them into 4 major types. The normally occurred problems in English-Thai translation works are the lack of knowledge in terms of parts of speech, word-by-word translation employment, misspellings as well as the poor knowledge in English language structure. However, this research is currently under the process of data analysis and shall be completed by the beginning of August. The researcher, nevertheless, predicts that all the above-mentioned problems, will support the researcher’s hypothesizes, that are; 1) the lack of knowledge in terms of parts of speech causes the mistranslation problem; 2) employing word-by-word translation technique hugely results in the mistranslation problem; 3) misspellings yields the mistranslation problem; and 4) the poor knowledge in English language structure also brings about translation errors. The research also predicts that, of all the aforementioned problems, the following ones are found the most, respectively: the poor knowledge in English language structure, word-by-word translation employment, the lack of knowledge in terms of parts of speech, and misspellings.

Keywords: problem, student, Thai, translation

Procedia PDF Downloads 436

Authors: Qisheng Hu, Jianglei Han, Yue Yang, My Hoa Ha

Abstract:

When a company is considering purchasing software for commercial use, contract risk assessment is critical to identify risks to mitigate the potential adverse business impact, e.g., security, financial and regulatory risks. Contract risk assessment requires reviewers with specialized knowledge and time to evaluate the legal documents manually. Specifically, validating contracts for a software vendor requires the following steps: manual screening, interpreting legal documents, and extracting risk-prone segments. To automate the process, we proposed a framework to assist legal contract document risk identification, leveraging pre-trained deep learning models and natural language processing techniques. Given a set of pre-defined risk evaluation problems, our framework utilizes the pre-trained transformer-based models for question-answering to identify risk-prone sections in a contract. Furthermore, the question-answering model encodes the concatenated question-contract text and predicts the start and end position for clause extraction. Due to the limited labelled dataset for training, we leveraged transfer learning by fine-tuning the models with the CUAD dataset to enhance the model. On a dataset comprising 287 contract documents and 2000 labelled samples, our best model achieved an F1 score of 0.687.

Keywords: contract risk assessment, NLP, transfer learning, question answering

Procedia PDF Downloads 129

Authors: Laura Mclean

Abstract:

A recurring point of interest in computational phylogenetic analysis of Indo-European family trees is the inference of a Germano-Italo-Celtic clade in some versions of the trees produced. The presence of this clade in the models is intriguing as there is little evidence for innovations shared among Germanic, Italic, and Celtic, the evidence generally used in the traditional method to construct a subgroup. One source of this unexpected outcome could be the input to the models. The datasets in the various models used so far, for the most part, take as their basis the Swadesh list, a list compiled by Morris Swadesh and then revised several times, containing up to 207 words that he believed were resistant to change among languages. The judgments made by Swadesh for this list, however, were subjective and based on his intuition rather than rigorous analysis. Some scholars used the Swadesh 200 list as the basis for their Indo-European dataset and made cognacy judgements for each of the words on the list. Another dataset is largely based on the Swadesh 207 list as well although the authors include additional lexical and non-lexical data, and they implement ‘split coding’ to deal with cases of polymorphic characters. A different team of scholars uses a different dataset, IECoR, which combines several different lists, one of which is the Swadesh 200 list. In fact, the Swadesh list is used in some form in every study surveyed and each dataset has three words that, when they are coded as cognates, seemingly contribute to the inference of a Germano-Italo-Celtic clade which could happen due to these clades sharing three words among only themselves. These three words are ‘fish’, ‘flower’, and ‘man’ (in the case of ‘man’, one dataset includes Lithuanian in the cognacy coding and removes the word ‘man’ from the screened data). This collection of cognates shared among Germanic, Italic, and Celtic that were deemed important enough to be included on the Swadesh list, without the ability to account for possible reasons for shared cognates that are not shared innovations, gives an impression of affinity between the Germanic, Celtic, and Italic branches without adequate methodological support. However, by changing how cognacy is defined (ie. root cognates, borrowings vs inherited cognates etc.), we will be able to identify whether these three cognates are significant enough to infer a clade for Germanic, Celtic, and Italic. This paper examines the question of what definition of cognacy should be used for phylogenetic datasets by examining the Germano-Italo-Celtic clade as a case study and offers insights into the reconstruction of a Germano-Italo-Celtic clade.

Keywords: historical, computational, Italo-Celtic, Germanic

Procedia PDF Downloads 50

Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

Abstract:

Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

Procedia PDF Downloads 88

Authors: Yassine Jamoussi, Ameni Youssfi, Henda Ben Ghezala

Abstract:

With the growing interest in social networking, the interaction of social actors evolved to a source of knowledge in which it becomes possible to perform context aware-reasoning. The information extraction from social networking especially Twitter and Facebook is one of the problems in this area. To extract text from social networking, we need several lexical features and large scale word clustering. We attempt to expand existing tokenizer and to develop our own tagger in order to support the incorrect words currently in existence in Facebook and Twitter. Our goal in this work is to benefit from the lexical features developed for Twitter and online conversational text in previous works, and to develop an extraction model for constructing a huge knowledge based on actions

Keywords: social networking, information extraction, part-of-speech tagging, natural language processing

Procedia PDF Downloads 305

Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

Abstract:

Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

Procedia PDF Downloads 234

Authors: Konysbek Aksaule

Abstract:

The purpose of this work is to explore new and effective ways of teaching English to students who are studying a foreign language since the timeliness of the problem disclosed in this article is due to the high level of English proficiency that potential specialists must have due to high competition in the context of global globalization. The article presents an analysis of the feasibility and effectiveness of using an authentic feature film in teaching English to students. The methodological basis of the study includes an assessment of the level of students' proficiency in a foreign language, the stage of evaluating the film, and the method of selecting the film for certain categories of students. The study also contains a list of practical tasks that can be applied in the process of viewing and perception of an original feature film in a foreign language, and which are aimed at developing language skills such as speaking and listening. The results of this study proved that teaching English to students through watching an original film is one of the most effective methods because it improves speech perception, speech reproduction ability, and also expands the vocabulary of students and makes their speech fluent. In addition, learning English through watching foreign films has a huge impact on the cultural views and knowledge of students about the country of the language being studied and the world in general. Thus, this study demonstrates the high potential of using authentic feature film in English lessons for pedagogical science and methods of teaching English in general.

Keywords: university, education, students, foreign language, feature film

Procedia PDF Downloads 148

Authors: Mei-Hui Liu

Abstract:

This study examines the effectiveness of a blended professional development program on preservice EFL (English as a foreign language) teachers’ learning to teach speech acts with the advent of Information and Communication Technology, researchers and scholars underscore the significance of integrating online and face-to-face learning opportunities in the teacher education field. Yet, a paucity of evidence has been documented to investigate the extent to which such a blended professional learning model may impact real classroom practice and student learning outcome. This yearlong project involves various stakeholders, including 25 preservice teachers, 5 English professionals, and 45 secondary school students. Multiple data sources collected are surveys, interviews, reflection journals, online discussion messages, artifacts, and discourse completion tests. Relying on the theoretical lenses of Community of Inquiry, data analysis depicts the nature and process of preservice teachers’ professional development in this blended learning community, which triggers and fosters both face-to-face and synchronous/asynchronous online interactions among preservice teachers and English professionals (i.e., university faculty and in-service teachers). Also included is the student learning outcome after preservice teachers put what they learn from the support community into instructional practice. Pedagogical implications and research suggestions are further provided based on the research findings and limitations.

Keywords: blended professional development, preservice EFL teachers, speech act instruction, student learning outcome

Procedia PDF Downloads 225

Authors: Daurenbek Kuleimenov, Elmira Otar

Abstract:

Market institutions extension within transit societies contributes to constituting the new type of middle class and households livelihood strategies. The middle class households as an example of prosperity in many cases encourage the ordinary ones to do the same economic actions. Therefore, practices of using market institutions by middle class households in transit societies, which are mostly characterized by huge influence of traditional attitudes, can carry habitual features for the whole society. Market institutions consumption habit of the middle class households makes them trendsetters of economic habits of other households while adapting to the market economy. Moreover different social-economic positions of households lead them to different consuming results such as worsening or improving household economy due to indebtedness.

Keywords: middle class, households, market institutions, transition

Procedia PDF Downloads 372

Authors: Lokman Gozcu, Sule Deniz Gozcu

Abstract:

This study aims to analyze the skills of speaking in the acquisition of Turkish as a foreign language. One of the most important things for the individual who learns a foreign language is to be successful in the oral communication (speaking) skills and to interact in an understandable way. Speech skill requires much more time and effort than other language skills. In this direction, it is necessary to make an analysis of these oral communication skills, which is important in Turkish language acquisition as a foreign language and to draw out a road map according to the result. The aim of this study is to determine the competence and attitudes of speaking competence according to the individuals who learn Turkish as a foreign language and to be considered as speaking skill elements; Grammar, emphasis, intonation, body language, speed, ranking, accuracy, fluency, pronunciation, etc. and the results and suggestions based on these determinations. A mixed method has been chosen for data collection and analysis. A Likert scale (for competence and attitude) was applied to 190 individuals who were interviewed face-to-face (for speech skills) with a semi-structured interview form about 22 participants randomly selected. In addition, the observation form related to the 22 participants interviewed were completed by the researcher during the interview, and after the completion of the collection of all the voice recordings, analyses of voice recordings with the speech skills evaluation scale was made. The results of the research revealed that the speech skills of the individuals who learned Turkish as a foreign language have various perspectives. According to the results, the most inadequate aspects of the participants' ability to speak in Turkish include vocabulary, using humorous elements while speaking Turkish, being able to include items such as idioms and proverbs while speaking Turkish, Turkish fluency respectively. In addition, the participants were found not to feel comfortable while speaking Turkish, to feel ridiculous and to be nervous while speaking in formal settings. There are conclusions and suggestions for the situations that arise after the have been analyses made.

Keywords: learning Turkish as a foreign language, proficiency criteria, phonetic (modalities), speaking skills

Procedia PDF Downloads 241

Authors: Akemi Nasu, Keiko Matsumoto

Abstract:

Rehabilitation for cancer requires setting up individual goals for each patient and an approach that properly fits the stage of cancer when putting into practice. In order to cope with the daily changes in the patients' condition, the establishment of a good cooperative relationship between the nurses and the physiotherapists, occupational therapists, and speech-language-hearing therapists (therapists) becomes essential. This study will focus on the present situation of the cooperation between nurses and therapists, especially the speech-language-hearing therapists, and aim to elucidate what develops there. A semi-structured interview was conducted targeted at a physical therapist having practical experience in working in collaboration with nurses. The contents of the interview were transcribed and converted to data, and the data was encoded and categorized with sequentially increasing degrees of abstraction to conduct a qualitative explorative factor analysis of the data. When providing ethical explanations, particular care was taken to ensure that participants would not be subjected to any disadvantages as a result of participating in the study. In addition, they were also informed that their privacy would be ensured and that they have the right to decline to participate in the study. In addition, they were also informed that the results of the study would be announced publicly at an applicable nursing academic conference. This study has been approved following application to the ethical committee of the university with which the researchers are affiliated. The survey participant is a female speech-language-hearing therapist in her forties. As a result of the analysis, 6 categories were extracted consisting of 'measures to address appetite and aspiration pneumonia prevention', 'limitation of the care a therapist alone could provide', 'the all-inclusive patient- supportive care provided by nurses', 'expand the beneficial cooperation with nurses', 'providing education for nurses on the swallowing function utilizing videofluoroscopic examination of swallowing', 'enhancement of communication including conferences'. In order to improve the team performance, and for the teamwork competency necessary for the provision of safer care, mutual support is essential. As for the cooperation between nurses and therapists, this survey indicates that the maturing of the cooperation between professionals in order to improve nursing professionals' knowledge and enhance communication will lead to an improvement in the quality of the rehabilitation for cancer.

Keywords: cancer rehabilitation, nurses, speech-language-hearing therapists, interprofessional work

Procedia PDF Downloads 133

Authors: Nami Takase

Abstract:

Computer-mediated-communication allows people to connect and interact with each other as if they were sharing the same space. The current study examined the effects of using video letters (VLs) on the development of second language speaking skills of Common European Framework of Reference for Languages (CEFR) A1 and CEFR B2 level learners of English as a foreign language. Two groups were formed to measure the impact of VLs. The experimental and control groups were given the same topic, and both groups worked with a native English-speaking university student from the United States of America. Students in the experimental group exchanged VLs, and students in the control group used video conferencing. Pre- and post-tests were conducted to examine the effects of each practice mode. The transcribed speech-text data showed that the VL group had improved speech accuracy scores, while the video conferencing group had increased sentence complexity scores. The use of VLs may be more effective for beginner-level learners because they are able to notice their own errors and replay videos to better understand the native speaker’s speech at their own pace. Both the VL and video conferencing groups provided positive feedback regarding their interactions with native speakers. The results showed how different types of computer-mediated communication impacts different areas of language learning and speaking practice and how each of these types of online communication tool is suited to different teaching objectives.

Keywords: computer-assisted-language-learning, computer-mediated-communication, english as a foreign language, speaking

Procedia PDF Downloads 99

Authors: Premvadee Na Nakornpanom

Abstract:

Language mixing in spontaneous speech has been widely discussed, but not in virtual situations; especially in context of the third language learning students. Thus, this study was an attempt to explore the characteristics of the mixing of Japanese, English and Thai in a mobile chat room by students with their background of Japanese, English, and Thai. The result found that Insertion of Thai and English content words was a very common linguistic phenomenon embedded in the utterances. As chatting is to be ‘relational’ or ‘interactional’, it affected the style of lexical choices to be speech-like, more personal and emotional-related. A Japanese sentence-final question particle“か”(ka) was added to the end of the sentence based on Thai grammar rule. Moreover, some unique characteristics were created. The non-verbal cues were represented in personal, Thai styles by inserting textual representations of images or feelings available on the websites into streams of conversations.

Keywords: code-mixing, Japanese, English, Thai, line chat

Procedia PDF Downloads 652

Authors: Richard O’Riordan, Saritha Unnikrishnan

Abstract:

Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

Procedia PDF Downloads 104

Authors: Haiqiao Du, Qian Wang, Shuwei Wang, Jianan Li

Abstract:

Purpose: The aim is to evaluate the effect of cochlear implantation (CI) in patients with unilateral hearing loss, with a view to providing data support for the selection of therapeutic interventions for patients with single-sided deafness (SSD)/asymmetric hearing loss (AHL) and the broadening of the indications for CI. Methods: The study subjects were patients with unilateral hearing loss who underwent cochlear implantation surgery in our hospital in August 2022 and were willing to cooperate with the test and were divided into 2 groups: SSD group and AHL group. The enrolled patients were followed up for hearing level, tinnitus changes, speech recognition ability, sound source localization ability, and quality of life at five-time points: preoperatively, and 1, 3, 6, and 12 months after postoperative start-up. Results: As of June 30, 2024, a total of nine patients completed follow-up, including four in the SSD group and five in the AHL group. The mean postoperative hearing aid thresholds on the CI side were 31.56 dB HL and 34.75 dB HL in the two groups, respectively. Of the four patients with preoperative tinnitus symptoms (three patients in the SSD group and one patient in the AHL group), all showed a degree of reduction in Tinnitus Handicap Inventory (THI) scores, except for one patient who showed no change. In both the SSD and AHL groups, the sound source localization results (expressed as RMS error values, with smaller values indicating better ability) were 66.87° and 77.41° preoperatively and 29.34° and 54.60° 12 months after postoperative start-up, respectively, which showed that the ability to localize the sound source improved significantly with longer implantation time. The level of speech recognition was assessed by 3 test methods: speech recognition rate of monosyllabic words in a quiet environment and speech recognition rate of different sound source directions at 0° and 90° (implantation side) in a noisy environment. The results of the 3 tests were 99.0%, 72.0%, and 36.0% in the preoperative SSD group and 96.0%, 83.6%, and 73.8% in the AHL group, respectively, whereas they fluctuated in the postoperative period 3 months after start-up, and stabilized at 12 months after start-up to 99.0%, 100.0%, and 100.0% in the SSD group and 99.5%, 96.0%, and 99.0%. Quality of life was subjectively evaluated by three tests: the Speech Spatial Quality of Sound Auditory Scale (SSQ-12), the Quality-of-Life Bilateral Listening Questionnaire (QLBHE), and the Nijmegen Cochlear Implantation Inventory (NCIQ). The results of the SSQ-12 (with a 10-point score out of 10) showed that the scores of preoperative and postoperative 12 months after start-up were 6.35 and 6.46 in the SSD group, while they were 5.61 and 9.83 in the AHL group. The QLBHE scores (100 points out of 100) were 61.0 and 76.0 in the SSD group and 53.4 and 63.7 in the AHL group for the preoperative versus the postoperative 12 months after start-up. Conclusion: Patients with unilateral hearing loss can benefit from cochlear implantation: CI implantation is effective in compensating for the hearing on the affected side and reduces the accompanying tinnitus symptoms; there is a significant improvement in sound source localization and speech recognition in the presence of noise; and the quality of life is improved.

Keywords: single-sided deafness, asymmetric hearing loss, cochlear implant, unilateral hearing loss

Procedia PDF Downloads 14