State of Art for Semantic Analysis of Natural Language Processing

Maulud, Dastan Hussen; Zeebaree, Subhi R. M.; Jacksi, Karwan; Sadeeq, Mohammed A. M.; Sharif, Karzan Hussein

doi:10.48161/qaj.v1n2a44

Cited by 60 publications

(29 citation statements)

References 34 publications

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“…Early analysis indicates that a linear perceptron may not be a general classifier and, on the other side, can be a network with a hidden layer of unbounded length with a nonpolynomial activation feature. There are multiple applications of DL including natural language processing, 21 computer vision, 22 speech recognition, and cooperative communication. [23][24][25] A detailed investigation of DL is given in Liu et al 26 Figure 1 gives the Gray box representation of the DNN.…”

Section: Research Methods 21 DL Basicsmentioning

confidence: 99%

See 1 more Smart Citation

Impact of the learning rate and batch size on NOMA system using LSTM-based deep neural network

Shankar¹,

Sarojini²,

Mehraj

et al. 2021

Journal of Defense Modeling & Simulation

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In this work, the deep learning (DL)-based fifth-generation (5G) non-orthogonal multiple access (NOMA) detector is investigated over the independent and identically distributed (i.i.d.) Nakagami- m fading channel conditions. The end-to-end system performance comparisons are given between the DL NOMA detector with the existing conventional successive interference cancelation (SIC)-based NOMA detector and from results, it has been proved that the DL NOMA detector performance is better than the convention SIC NOMA detector. In our analysis, the long-short term memory (LSTM) recurrent neural network (RNN) is employed, and the results are compared with the minimum mean square estimation (MMSE) and least square estimation (LS) detector’s performance considering all practical conditions such as multipath fading and nonlinear clipping distortion. It has been shown that with the increase in the relay to destination (RD) channel gain, the bit error rate (BER) improves. Also, with the increase in fading parameter m, the BER performance improves. The simulation curves demonstrate that when the clipping ratio (CR) is unity, the performance of the DL-based detector significantly improves as compared to the MMSE and LS detector for the signal-to-noise ratio (SNR) values greater than 15 dB and it proves that the DL technique is more robust to the nonlinear clipping distortion.

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Section: Research Methods 21 DL Basicsmentioning

confidence: 99%

“…Given time lags of unknown duration, LSTM is well suited to classify, process, and predict time series. LSTM has an advantage over alternative RNNs, hidden Markov models, 21 and other sequence learning methods due to its relative insensitivity to gap length. RNN has a structure that is very similar to that of a hidden Markov model.…”

Section: Introductionmentioning

confidence: 99%

Impact of the learning rate and batch size on NOMA system using LSTM-based deep neural network

Shankar¹,

Sarojini²,

Mehraj

et al. 2021

Journal of Defense Modeling & Simulation

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“…Maulud et al [18] compared recent NLP techniques and conclude that advanced semantic methods have good accuracy.…”

Section: B the Semantic Analysismentioning

confidence: 99%

Text Analyzing Tool for Simplifying the Syllabus Creation Process

Sodhi¹,

Choudhary²

2023

IJIET

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In curriculum development, syllabus creation is an important activity. The need for a syllabus repository and data extraction is essential for creating a new syllabus. Faculties do this job manually by adding topics from their intelligence as well as analyzing syllabuses of the same course available on the search engine. Because it is a time-consuming job, this research aims to propose and implement a text-analyzing tool. The methodology is used, first to create a syllabus repository of computer science courses, do data extraction & normalization, analyzes the contents of different syllabuses, and suggests the contents to the faculties. This tool is experimented on the sample for creating the syllabus of C Programming. The result of the tool is that it suggests the topics that can be included in the syllabus to the faculties. The time and effort required to create a syllabus are reduced by using this tool. In the future weightage to the syllabuses can be given for classification that will give more accurate results while analyzing syllabuses. This tool can be improved by using machine learning algorithms for creating syllabus repositories and data extraction. Semantic comparison can give more accurate results by creating a specific model for computer terminologies.

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“…Moreover, the ndings of the "human" approach could be validated. Such ML algorithms have been extensively used in the literature for accurate sentiment analysis (Maulud et al, 2021;Trappey et al, 2020).…”

Section: Cross-mapping Of the 17 Sdgs To The European Green Deal Poli...mentioning

confidence: 99%

Integrating the 17 SDGs into the European Green Deal, through Strategic and Financial Approaches

Plataniotis

Koundouri²,

Alamanos

et al. 2023

Preprint

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The European Green Deal (EGD) is the growth strategy for Europe, covering multiple domains, and aiming to an equitable, carbon neutral European Union by 2050. The UN Agenda 2030, with its 17 Sustainable Development Goals (SDGs) set the bases for a global sustainability transition. However, the integration of the SDGs into the EGD is an overlooked issue in the literature, although it is particularly important, given Europe’s slow progress to achieve the sustainability targets. In this paper, 22 central policies and strategies published during 2020–21 to support the EGD's implementation are assessed on how they align with Agenda’s 2030 aspirations, using novel text-mining methodologies: one human-based and one machine-learning-based. The results outline an alignment of EGD policies to the main SDGs themes relevant to Food, Land, Oceans, Energy, but also a strong indication that the progress towards sustainability passes through "Peace, Justice, and Strong Institutions" (SDG16) and international "Partnerships for the Goals" (SDG17). We further explain the underlying policy mechanisms of the established ‘necessary transformations’ to build a sustainable Europe, along with the relevance of valuing the natural capital and integrating it into future investment and financial decisions.

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State of Art for Semantic Analysis of Natural Language Processing

Cited by 60 publications

References 34 publications

Impact of the learning rate and batch size on NOMA system using LSTM-based deep neural network

Impact of the learning rate and batch size on NOMA system using LSTM-based deep neural network

Text Analyzing Tool for Simplifying the Syllabus Creation Process

Integrating the 17 SDGs into the European Green Deal, through Strategic and Financial Approaches

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