Gotowe bibliografie tematyczne / Indic language- Term detection

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Gotowa bibliografia na temat „Indic language- Term detection”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Indic language- Term detection"

Harika, S., T. Yamini, T. Nagasaikamesh, S. H. Basha, S. Santosh Kumar i Mrs S. Sri DurgaKameswari. "Alzheimers Disease Detection Using Different Machine Learning Algorithms". International Journal for Research in Applied Science and Engineering Technology 10, nr 10 (31.10.2022): 62–66. http://dx.doi.org/10.22214/ijraset.2022.46937.

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Abstract: Alzheimer’s disease is the most common form of dementia affecting the brain’s parts. A broad term used to describe illnesses and conditions that causes a deterioration in memory, language, and other cognitive abilities severe enough to interface with daily life is “dementia”. According to estimates, this disease affects 6.2 million Americans and 5 million people in India aged 65and older. In 2019, the most recent year for which data are available, official death certificates reported 121,499 deaths from AD, making Alzheimer’s the “sixth leading cause of death in the country and the fifth leading cause of death for people 65 and older”. In this paper, we suggest several machine Learning algorithms like Decision trees, SVM, Logistic regression, and Naive Bayes identify AD at an early stage. The Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Investigations (OASIS) provide data sets white used to detect the disease in its early stage. The datasets consist of longitudinal MRI data (age, gender, mini mental status, CDR) By taking into account many factors in each method, such as precision, F1 Score, Recall, and specificity are calculated. The results obtained 93.7% of maximum accuracy for the Decision Tree Algorithm.

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Mongia, Anoushka. "DEVELOPING AN EFFECTIVE MACHINE LEARNING ALGORITHM SYSTEM IN THE EARLY DETECTION AND DIAGNOSIS OF ALZHEIMER’S DISEASE". International Journal of Research in Medical Sciences and Technology 11, nr 01 (2021): 222–29. http://dx.doi.org/10.37648/ijrmst.v11i01.022.

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A broad term used to describe diseases and conditions that cause deterioration in memory, language, and other mental capacities sufficiently extreme to communicate with day-to-day existence is "dementia". Alzheimer's disease is the most well-known type of Dementia influencing the mind's parts. As per range, this disorder influences 6.2 million Americans and 5 million individuals in India matured 65 and more seasoned. In 2019, the latest year for which information is accessible, official passing declarations revealed 121,499 deaths from Promotion, Alzheimer's, the "6th driving reason for death in the nation". In this paper, we propose AI calculations like Decision trees (DT), SVM, Linear regression, and Naive Bayes determines Promotion at the beginning phase. The Alzheimer's Sickness Neuroimaging Drive (ADNI) and the Open Access Series of Imaging Examinations give informational collections used to identify the disease in its beginning phase. The datasets comprise longitudinal X-ray information (age, orientation, small-scale mental status, CDR). By taking into; account many variables in every strategy, for example, accuracy, F1 Score, Review, and explicitness are determined. The outcomes acquired 93.7% of the greatest precision for the DT Calculation.

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Chitransh, Apar, i Birinderjit Singh Kalyan. "ARM Microcontroller Based Wireless Industrial Automation System". Indian Journal of Microprocessors and Microcontroller 1, nr 2 (10.09.2021): 8–11. http://dx.doi.org/10.35940/ijmm.b1705.091221.

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In modern era most of the work being completed by the new and advanced various technology. Most of the industries are being run on the robotics technologies. But in INDIA most of the company are running in various technologies as like embedded system, plc, Arduino for sensing the alcohol detection and gas detection system and most important thing microcontroller. In this paper we discuss the ARM microcontroller based wireless industrial automation system. This automation consists the coordinator module and sensor module. In which one module is connected with the monitoring computer that is called the coordinator module and for connecting with the monitor of the computer it is also called the centralized unit. And the sensor module is an ARM microcontroller for a monitoring and controlling the whole plant. The coordinator unit main work is to collects the all type of data from the sensor module and provide that information to the IP network. For better communication between these two modules, we use the best technology is ZIGBEE technology. Its main work to preset the changing and control the plant various parameter. ARM microcontroller using the embedded c language coding. This paper we do the deeply study about the ARM microcontroller and about the wireless industrial automation system and the term of ZIGBEE technology.

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Prusty, Sashikanta, Sujit Kumar Dash, Srikanta Patnaik i Sushree Gayatri Priyadarsini Prusty. "IMPACT OF COVID-19 ON BREAST CANCER SCREENING PROGRAM (BCSP) IN INDIA". Indian Journal of Computer Science and Engineering 14, nr 3 (20.06.2023): 416–28. http://dx.doi.org/10.21817/indjcse/2023/v14i2/231403132.

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In the past three years, covid-19 viruses have spread rapidly worldwide, while low and middle-income countries were affected mostly so far. Emergency limits were imposed due to the rapid infection and significant mortality rates. Only emergency medical treatments are available during these shutdowns and lockdowns in India. All non-emergency treatments, such as Breast Cancer Screening Program (BCSP), have been temporarily halted due to the huge number of deaths caused by coronavirus. However, the ability of BC screening programs to improve survival rates while lowering mortality rates has been well demonstrated. Suspension may result in poorer outcomes for patients with BC. In this regard, early detection and treatment are critical for increased survival and long-term quality of life. Thus, we have taken breast cancer patients' data for the last six years i.e. from 2016 to 2021 in India to properly evaluate and analyze for our research. Assessing recent results for various features from, modeled evaluations can aid pandemic responses. Besides that, we proposed a novel method that implements the EDA technique to graphically represent BC patients' data. This experiment was done using Python programming language on Jupyter 6.4.3 platform. We found the sudden rise of BC patients from lakhs to millions in 2019. This signifies the deadly coronavirus has greatly affected people during the pandemic days when people are more serious about this virus rather than screening their breasts.

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Sadanandam, Manchala. "HMM Based Language Identification from Speech Utterances of Popular Indic Languages Using Spectral and Prosodic Features". Traitement du Signal 38, nr 2 (30.04.2021): 521–28. http://dx.doi.org/10.18280/ts.380232.

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Language identification system (LID) is a system which automatically recognises the languages of short-term duration of unknown utterance of human beings. It recognises the discriminate features and reveals the language of utterance that belongs to. In this paper, we consider concatenated feature vectors of Mel Frequency Cepstral Coefficients (MFCC) and Pitch for designing LID. We design a reference model one for each language using 14-dimensional feature vectors using Hidden Markov model (HMM) then evaluate against all reference models of listed languages. The likelihood value of test sample feature vectors given in the evaluation is considered to decide the language of unknown utterance of test speech sample. In this paper we consider seven Indian languages for the experimental set up and the performance of system is evaluated. The average performance of the system is 89.31% and 90.63% for three states and four states HMM for 3sec test speech utterances respectively and also it is also observed that the system gives significant results with 3sec test speech for four state HMM even though we follow simple procedure.

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Allassonnière-Tang, Marc, i Marcin Kilarski. "Functions of gender and numeral classifiers in Nepali". Poznan Studies in Contemporary Linguistics 56, nr 1 (26.03.2020): 113–68. http://dx.doi.org/10.1515/psicl-2020-0004.

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AbstractWe examine the complex nominal classification system in Nepali (Indo-European, Indic), a language spoken at the intersection of the Indo-European and Sino-Tibetan language families, which are usually associated with prototypical examples of grammatical gender and numeral classifiers, respectively. In a typologically rare pattern, Nepali possesses two gender systems based on the human/non-human and masculine/feminine oppositions, in addition to which it has also developed an inventory of at least ten numeral classifiers as a result of contact with neighbouring Sino-Tibetan languages. Based on an analysis of the lexical and discourse functions of the three systems, we show that their functional contribution involves a largely complementary distribution of workload with respect to individual functions as well as the type of categorized nouns and referents. The study thus contributes to the ongoing discussions concerning the typology and functions of nominal classification as well as the effects of long-term language contact on language structure.

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Bera, Abhijit, Mrinal Kanti Ghose i Dibyendu Kumar Pal. "Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)". International Journal of System Dynamics Applications 10, nr 4 (październik 2021): 1–12. http://dx.doi.org/10.4018/ijsda.20211001.oa16.

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Multilingual Sentiment analysis plays an important role in a country like India with many languages as the style of expression varies in different languages. The Indian people speak in total 22 different languages and with the help of Google Indic keyboard people can express their sentiments i.e reviews about anything in the social media in their native language from individual smart phones. It has been found that machine learning approach has overcome the limitations of other approaches. In this paper, a detailed study has been carried out based on Natural Language Processing (NLP) using Simple Neural Network (SNN) ,Convolutional Neural Network(CNN), and Long Short Term Memory (LSTM)Neural Network followed by another amalgamated model adding a CNN layer on top of the LSTM without worrying about versatility of multilingualism. Around 4000 samples of reviews in English, Hindi and in Bengali languages are considered to generate outputs for the above models and analyzed. The experimental results on these realistic reviews are found to be effective for further research work.

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Torbati, Amir Hossein Harati Nejad, i Joseph Picone. "Predicting search term reliability for spoken term detection systems". International Journal of Speech Technology 17, nr 1 (6.06.2013): 1–9. http://dx.doi.org/10.1007/s10772-013-9197-1.

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Rozprawy doktorskie na temat "Indic language- Term detection"

Wallace, Roy Geoffrey. "Fast and accurate phonetic spoken term detection". Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/39610/1/Roy_Wallace_Thesis.pdf.

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For the first time in human history, large volumes of spoken audio are being broadcast, made available on the internet, archived, and monitored for surveillance every day. New technologies are urgently required to unlock these vast and powerful stores of information. Spoken Term Detection (STD) systems provide access to speech collections by detecting individual occurrences of specified search terms. The aim of this work is to develop improved STD solutions based on phonetic indexing. In particular, this work aims to develop phonetic STD systems for applications that require open-vocabulary search, fast indexing and search speeds, and accurate term detection. Within this scope, novel contributions are made within two research themes, that is, accommodating phone recognition errors and, secondly, modelling uncertainty with probabilistic scores. A state-of-the-art Dynamic Match Lattice Spotting (DMLS) system is used to address the problem of accommodating phone recognition errors with approximate phone sequence matching. Extensive experimentation on the use of DMLS is carried out and a number of novel enhancements are developed that provide for faster indexing, faster search, and improved accuracy. Firstly, a novel comparison of methods for deriving a phone error cost model is presented to improve STD accuracy, resulting in up to a 33% improvement in the Figure of Merit. A method is also presented for drastically increasing the speed of DMLS search by at least an order of magnitude with no loss in search accuracy. An investigation is then presented of the effects of increasing indexing speed for DMLS, by using simpler modelling during phone decoding, with results highlighting the trade-off between indexing speed, search speed and search accuracy. The Figure of Merit is further improved by up to 25% using a novel proposal to utilise word-level language modelling during DMLS indexing. Analysis shows that this use of language modelling can, however, be unhelpful or even disadvantageous for terms with a very low language model probability. The DMLS approach to STD involves generating an index of phone sequences using phone recognition. An alternative approach to phonetic STD is also investigated that instead indexes probabilistic acoustic scores in the form of a posterior-feature matrix. A state-of-the-art system is described and its use for STD is explored through several experiments on spontaneous conversational telephone speech. A novel technique and framework is proposed for discriminatively training such a system to directly maximise the Figure of Merit. This results in a 13% improvement in the Figure of Merit on held-out data. The framework is also found to be particularly useful for index compression in conjunction with the proposed optimisation technique, providing for a substantial index compression factor in addition to an overall gain in the Figure of Merit. These contributions significantly advance the state-of-the-art in phonetic STD, by improving the utility of such systems in a wide range of applications.

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Comuni, Federica. "A natural language processing solution to probable Alzheimer’s disease detection in conversation transcripts". Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-19889.

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This study proposes an accuracy comparison of two of the best performing machine learning algorithms in natural language processing, the Bayesian Network and the Long Short-Term Memory (LSTM) Recurrent Neural Network, in detecting Alzheimer’s disease symptoms in conversation transcripts. Because of the current global rise of life expectancy, the number of seniors affected by Alzheimer’s disease worldwide is increasing each year. Early detection is important to ensure that affected seniors take measures to relieve symptoms when possible or prepare plans before further cognitive decline occurs. Literature shows that natural language processing can be a valid tool for early diagnosis of the disease. This study found that mild dementia and possible Alzheimer’s can be detected in conversation transcripts with promising results, and that the LSTM is particularly accurate in said detection, reaching an accuracy of 86.5% on the chosen dataset. The Bayesian Network classified with an accuracy of 72.1%. The study confirms the effectiveness of a natural language processing approach to detecting Alzheimer’s disease.

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Fancellu, Federico. "Computational models for multilingual negation scope detection". Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33038.

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Negation is a common property of languages, in that there are few languages, if any, that lack means to revert the truth-value of a statement. A challenge to cross-lingual studies of negation lies in the fact that languages encode and use it in different ways. Although this variation has been extensively researched in linguistics, little has been done in automated language processing. In particular, we lack computational models of processing negation that can be generalized across language. We even lack knowledge of what the development of such models would require. These models however exist and can be built by means of existing cross-lingual resources, even when annotated data for a language other than English is not available. This thesis shows this in the context of detecting string-level negation scope, i.e. the set of tokens in a sentence whose meaning is affected by a negation marker (e.g. 'not'). Our contribution has two parts. First, we investigate the scenario where annotated training data is available. We show that Bi-directional Long Short Term Memory (BiLSTM) networks are state-of-the-art models whose features can be generalized across language. We also show that these models suffer from genre effects and that for most of the corpora we have experimented with, high performance is simply an artifact of the annotation styles, where negation scope is often a span of text delimited by punctuation. Second, we investigate the scenario where annotated data is available in only one language, experimenting with model transfer. To test our approach, we first build NEGPAR, a parallel corpus annotated for negation, where pre-existing annotations on English sentences have been edited and extended to Chinese translations. We then show that transferring a model for negation scope detection across languages is possible by means of structured neural models where negation scope is detected on top of a cross-linguistically consistent representation, Universal Dependencies. On the other hand, we found cross-lingual lexical information only to help very little with performance. Finally, error analysis shows that performance is better when a negation marker is in the same dependency substructure as its scope and that some of the phenomena related to negation scope requiring lexical knowledge are still not captured correctly. In the conclusions, we tie up the contributions of this thesis and we point future work towards representing negation scope across languages at the level of logical form as well.

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Tahmasebi, Nina N. [Verfasser]. "Models and algorithms for automatic detection of language evolution : towards finding and interpreting of content in long-term archives / Nina N. Tahmasebi". Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2013. http://d-nb.info/1046025635/34.

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Abbs, Brandon Robert. "The temporal dynamics of auditory memory for static and dynamic sounds". Diss., University of Iowa, 2008. http://ir.uiowa.edu/etd/4.

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Fapšo, Michal. "Vyhledávání výrazů v řeči pomocí mluvených příkladů". Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-261237.

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Tato práce se zabývá vyhledáváním výrazů v řeči pomocí mluvených příkladů (QbE STD). Výrazy jsou zadávány v mluvené podobě a jsou vyhledány v množině řečových nahrávek, výstupem vyhledávání je seznam detekcí s jejich skóre a časováním. V práci popisujeme, analyzujeme a srovnáváme tři různé přístupy ke QbE STD v jazykově závislých a jazykově nezávislých podmínkách, s jedním a pěti příklady na dotaz. Pro naše experimenty jsme použili česká, maďarská, anglická a arabská (levantská) data, a pro každý z těchto jazyků jsme natrénovali 3-stavový fonémový rozpoznávač. To nám dalo 16 možných kombinací jazyka pro vyhodnocení a jazyka na kterém byl natrénovaný rozpoznávač. Čtyři kombinace byly tedy závislé na jazyce (language-dependent) a 12 bylo jazykově nezávislých (language-independent). Všechny QbE systémy byly vyhodnoceny na stejných datech a stejných fonémových posteriorních příznacích, pomocí metrik: nesdružené Figure-of-Merit (non pooled FOM) a námi navrhnuté nesdružené Figure-of-Merit se simulací normalizace přes promluvy (utterrance-normalized non-pooled Figure-of-Merit). Ty nám poskytly relevantní údaje pro porovnání těchto QbE přístupů a pro získání lepšího vhledu do jejich chování. QbE přístupy použité v této práci jsou: sekvenční statistické modelování (GMM/HMM), srovnávání vzorů v příznacích (DTW) a srovnávání grafů hypotéz (WFST). Abychom porovnali výsledky QbE přístupů s běžnými STD systémy vyhledávajícími textové výrazy, vyhodnotili jsme jazykově závislé konfigurace také s akustickým detektorem klíčových slov (AKWS) a systémem pro vyhledávání fonémových řetězců v grafech hypotéz (WFSTlat). Jádrem této práce je vývoj, analýza a zlepšení systému WFST QbE STD, který po zlepšení dosahuje podobných výsledků jako DTW systém v jazykově závislých podmínkách.

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Popli, Abhimanyu. "Framework for query-by-example and text based spoken term detection in multilingual and mixlingual speech". Thesis, 2018. http://localhost:8080/iit/handle/2074/7640.

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Części książek na temat "Indic language- Term detection"

Jain, Arpan, Amandeep Singh i Anupam Shukla. "Vowel Onset Point Detection in Hindi Language Using Long Short-Term Memory". W Advances in Intelligent Systems and Computing, 505–15. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3329-3_47.

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Kat, Bora. "Natural Language Processing for the Turkish Academic Texts in the Engineering Field: Key-Term Extraction, Similarity Detection, Subject/Topic Assignment". W IFIP Advances in Information and Communication Technology, 411–24. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34107-6_33.

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Desamsetti, Sankar, Satya Hemalatha Juttuka, Yamini Mahitha Posina, S. Rama Sree i B. S. Kiruthika Devi. "Artificial Intelligence Based Fake News Detection Techniques". W Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde221284.

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Fake news on social media platforms is increasing rapidly, so many people are becoming victims of this news without their interference. It is a big challenge for us to detect who is spreading fake news. Fake news spreads faster nowadays than in the past due to the widespread use of the internet. This research paper is a study of techniques based on artificial intelligence, such as neural networks, natural language processing, and machine learning algorithms that work together. The learning models surveyed are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Recurrent Neural Network (RNN) methods. Natural language processing methods contain the tokenization model, and machine learning includes Term Frequency-Inverse Document Frequency (TFIDF) and unsupervised algorithms. The algorithms are compared and their effectiveness in detecting fake news is investigated, along with the advantages and disadvantages of the respective techniques.

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Kapočiūtė-Dzikienė, Jurgita. "Intent Detection-Based Lithuanian Chatbot Created via Automatic DNN Hyper-Parameter Optimization". W Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200608.

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In this paper, we tackle an intent detection problem for the Lithuanian language with the real supervised data. Our main focus is on the enhancement of the Natural Language Understanding (NLU) module, responsible for the comprehension of user’s questions. The NLU model is trained with a properly selected word vectorization type and Deep Neural Network (DNN) classifier. During our experiments, we have experimentally investigated fastText and BERT embeddings. Besides, we have automatically optimized different architectures and hyper-parameters of the following DNN approaches: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Convolutional Neural Network (CNN). The highest accuracy=∼0.715 (∼0.675 and ∼0.625 over random and majority baselines, respectively) was achieved with the CNN classifier applied on a top of BERT embeddings. The detailed error analysis revealed that prediction accuracies degrade for the least covered intents and due to intent ambiguities; therefore, in the future, we are planning to make necessary adjustments to boost the intent detection accuracy for the Lithuanian language even more.

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Do, Nguyet Quang, Ali Selamat, Kok Cheng Lim i Ondrej Krejcar. "Malicious URL Detection with Distributed Representation and Deep Learning". W Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220248.

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There exist numerous solutions to detect malicious URLs based on Natural Language Processing and machine learning technologies. However, there is a lack of comparative analysis among approaches using distributed representation and deep learning. To solve this problem, this paper performs a comparative study on phishing URL detection based on text embedding and deep learning algorithms. Specifically, character-level and word-level embedding were combined to learn the feature representations from the webpage URLs. In addition, three deep learning models, including Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM), were constructed for effective classification of phishing websites. Several experiments were conducted and various evaluation metrics were used to assess the performance of these deep learning models. The findings obtained from the experiments indicated that the combination of the character-level and word-level embedding approach produced better results than the individual text representation methods. Also, the CNN-based model outperformed the other two deep learning algorithms in terms of both detection accuracy and execution time.

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N, Ramesh. "Natural Language Processing and Text Analytics: Techniques and Applications". W Cutting-Edge Technologies in Innovations in Computer Science and Engineering. San International Scientific Publications, 2023. http://dx.doi.org/10.59646/csebookc2/004.

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The combination of NLP and Text Analytics provides a powerful toolset for businesses to gain insights from their text data. This includes analyzing customer feedback, social media posts, news articles, and other text-based sources to understand customer sentiment, identify emerging trends, and gain competitive insights. However, there are challenges in the field of NLP and Text Analytics, such as understanding the context and ambiguity of natural language, dealing with noisy and unstructured data, and ensuring data privacy and security. These challenges require the development of advanced NLP and Text Analytics techniques and algorithms to address them. Text Analytics, on the other hand, is a broader term that refers to the process of extracting insights and meaning from unstructured text data. It involves the use of NLP techniques, as well as statistical and machine learning methods, to identify patterns, trends, and relationships in text data. Text Analytics can be applied to a wide range of applications, including social media analysis, customer feedback analysis, market research, and fraud detection.

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Hamou, Reda Mohamed, Abdelmalek Amine i Moulay Tahar. "The Impact of the Mode of Data Representation for the Result Quality of the Detection and Filtering of Spam". W Ontologies and Big Data Considerations for Effective Intelligence, 150–68. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2058-0.ch004.

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Spam is now of phenomenal proportions since it represents a high percentage of total emails exchanged on the Internet. In the fight against spam, we are using this article to develop a hybrid algorithm based primarily on the probabilistic model in this case, Naïve Bayes, for weighting the terms of the matrix term -category and second place used an algorithm of unsupervised learning (K-means) to filter two classes, namely spam and ham (legitimate email). To determine the sensitive parameters that make up the classifications we are interested in studying the content of the messages by using a representation of messages using the n-gram words and characters independent of languages (because a message may be received in any language) to later decide what representation to use to get a good classification. We have chosen several metrics as evaluation to validate our results.

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Karim, Tajbia, i Zainal Rasyid Mahayuddin. "Real-Time Bangla Sign Words Interpretation Uttered at Medical Emergencies". W 5G, Artificial Intelligence, and Next Generation Internet of Things, 143–60. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8634-4.ch006.

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Sign language is a medium of communication using gestures which is developed for people who suffer from auditory or verbal impairment. But sign language is usually not widely familiar to common people. For this reason, a sign language interpreter is essential for translating sign word representation to regular text or voice. Though there are some research works on American Sign Language interpretation, but it is inadequate in number for other languages. This chapter illustrates real time interpretation of medical emergency relevant Bangla sign words using machine learning. Considering medical emergency situations, a real time word level interpreter is designed, and the performance is evaluated. The designed deep neural network uses Mediapipe Holistic network for feature detection from human body in transitional movement and Long short term memory (LSTM) to predict the image sequences of each sign action. The designed system provides 99% model training accuracy and 92.22% test accuracy in real time detection.

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Kostka, Ilka, i Miriam Eisenstein Ebsworth. "Using Turnitin to Support Students' Understanding of Textual Borrowing in Academic Writing". W Scholarly Ethics and Publishing, 269–97. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8057-7.ch013.

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Concerns about plagiarism are salient for the academic writing of second language (L2) writers of English, who face several challenges while learning academic discourse and proper citation conventions. Effective instruction is crucial in helping them learn to avoid plagiarism and borrow from sources appropriately. In this chapter, the authors present a case study of an English as a Second Language (ESL) composition class at a Midwestern university in the United States. This study is framed by a social view of learning that draws from Lave and Wenger's (1991) notion of a community of practice. Data included weekly classroom observations, interviews at the beginning, middle, and end of the 10-week academic term, surveys, and student participants' online blogs. Findings illustrate how Turnitin, an Internet-based matched-text detection program, was used to support academic writing instruction and help socialize learners into an American academic discourse community.

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Streszczenia konferencji na temat "Indic language- Term detection"

Das, Mithun, Somnath Banerjee i Animesh Mukherjee. "Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages". W HT '22: 33rd ACM Conference on Hypertext and Social Media. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3511095.3531277.

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Mullick, Ankan. "Exploring Multilingual Intent Dynamics and Applications". W Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/818.

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Multilingual Intent Detection and explore its different characteristics are major field of study for last few years. But, detection of intention dynamics from text or voice, especially in the Indian multilingual contexts, is a challenging task. So, my first research question is on intent detection and then I work on the application in Indian Multilingual Healthcare scenario. Speech dialogue systems are designed by a pre-defined set of intents to perform user specified tasks. Newer intentions may surface over time that call for retraining. However, the newer intents may not be explicitly announced and need to be inferred dynamically. Hence, here are two crucial jobs: (a) recognizing newly emergent intents; and (b) annotating the data of the new intents in order to effectively retrain the underlying classifier. The tasks become specially challenging when a large number of new intents emerge simultaneously and there is a limited budget of manual annotation. We develop MNID (Multiple Novel Intent Detection), a cluster based framework that can identify multiple novel intents while optimized human annotation cost. Empirical findings on numerous benchmark datasets (of varying sizes) show that MNID surpasses the baseline approaches in terms of accuracy and F1-score by wisely allocating the budget for annotation. We apply intent detection approach on different domains in Indian multilingual scenarios - healthcare, finance etc. The creation of advanced NLU healthcare systems is threatened by the lack of data and technology constraints for resource-poor languages in developing nations like India. We evaluate the current state of several cutting-edge language models used in the healthcare with the goal of detecting query intents and corresponding entities. We conduct comprehensive trials on a number of models different realistic contexts, and we investigate the practical relevance depending on budget and the availability of data on English.

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Li, Wei, Ji Wu i Ping Lv. "High performance Chinese Spoken Term Detection based on term expansion". W 2010 7th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2010. http://dx.doi.org/10.1109/iscslp.2010.5684852.

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Motlicek, Petr, i Fabio Valente. "Application of out-of-language detection to spoken term detection". W 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495038.

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Ohno, Teppei, i Tomoyosi Akiba. "Incorporating syllable duration into line-detection-based spoken term detection". W 2012 IEEE Spoken Language Technology Workshop (SLT 2012). IEEE, 2012. http://dx.doi.org/10.1109/slt.2012.6424223.

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Pahwa, Bhavish. "BpHigh@TamilNLP-ACL2022: Effects of Data Augmentation on Indic-Transformer based classifier for Abusive Comments Detection in Tamil". W Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.dravidianlangtech-1.22.

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Kanda, Naoyuki, Ryu Takeda i Yasunari Obuchi. "Using rhythmic features for Japanese spoken term detection". W 2012 IEEE Spoken Language Technology Workshop (SLT 2012). IEEE, 2012. http://dx.doi.org/10.1109/slt.2012.6424217.

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Pham, Van Tung, Nancy F. Chen, Sunil Sivadas, Haihua Xu, I.-Fan Chen, Chongjia Ni, Eng Siong Chng i Haizhou Li. "System and keyword dependent fusion for spoken term detection". W 2014 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2014. http://dx.doi.org/10.1109/slt.2014.7078613.

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Lee, Shi-wook, Kazuyo Tanaka i Yoshiaki Itoh. "Effective combination of heterogeneous subword-based spoken term detection systems". W 2014 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2014. http://dx.doi.org/10.1109/slt.2014.7078614.

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Wintrode, Jonathan, i Sanjeev Khudanpur. "Combining local and broad topic context to improve term detection". W 2014 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2014. http://dx.doi.org/10.1109/slt.2014.7078615.

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