Computer Language Research Articles

Machine Language Approach for Modeling and Predicting Rainfall in Different Zones of Kerala

The forecasting of rainfall is said to be the most difficult of other hydrological processes due to sudden changes in atmospheric processes. The rainfall directly and indirectly influences agriculture and allied sectors. Sudden changes in rainfall or an uneven distribution of rainfall can lead to crop loss. In order to avoid such problems and take the necessary precautions, it is mandatory to forecast the rainfall using various models with maximum precision. In this study, rainfall for northern, central and southern Kerala, India, was predicted using an artificial neural network (ANN) with a multi-layer perceptron (MLP) feed-forward neural network and an extreme learning machine (ELM) neural network. The monthly rainfall data was collected for a period of 39 years (1982–2020) from the regional agricultural research stations (RARS), Pilicode and Pattambi, for the northern and central zones of Kerala, respectively, whereas for the southern zone of Kerala, data was collected from RARS, Vellayani, for a period of 36 years (1985–2020). For the rainfall data collected from three different zones of Kerala, the MLP and ELM were applied. The comparison and validation of MLP and ELM models was done based on the error values of mean square error (MSE), root mean square error (RMSE), and mean absolute error(MAE). The results indicated that for three different zones in Kerala, ANN with MLP showed better performance in forecasting rainfall compared to the ELM model. The best-selected model was also used for forecasting the next 5 years rainfall in each zone of Kerala.

Conversational Artificial Intelligence: A Catalyst for Rethinking Assessment in Higher Education

Conversational Artificial Intelligence has disrupted higher education by fundamentally altering its landscape. Fuelled by natural language processing and machine learning this technology has gained widespread adoption particularly since the release of ChatGPT in November 2022. As universities embrace digital transformation, assessment practices must evolve to align with the capabilities of Artificial Intelligence-driven chatbots and virtual assistants. This paper explores how conversational artificial intelligence impacts higher education, in particular, student assessment. A fundamental shift in assessment and evaluation of student competencies is necessary to not only consider knowledge retention but also critical thinking, communication, and adaptability skills. A review of the literature was conducted to understand how assignments should change due to the emergence of this disruptive technology. Conversational Artificial Intelligence and its application within the higher education context is uncertain, with disparate practices—in terms of ethical consideration and understanding—across the sector. A case study was conducted in which MSc Management students undertaking a specific module were tasked to use three Artificial Intelligence tools in their report writing of a business, to verify the sources and content provided by the Artificial Intelligence tool, and to critically evaluate the process as well as the output received for each prompt. The paper proposes a collaborative approach to navigate the ethical implementation and utilization of conversational Artificial Intelligence in higher education, advocating for the co-creation of guidelines through forums like Knowledge Cafés, stressing the need to rethink student assignments and its assessment and the adoption of artificial intelligence technologies by students for assignments.

Artificial Intelligence (Neural Network) in the Diagnosis of Benign Skin Tumors in Pediatric Patients

Relevance: ten years ago, artificial intelligence (AI), particularly neural networks (NN), as a diagnostic option in practice seemed a distant prospect. Today, the use of AI is becoming an increasingly popular and daily improving approach in all aspects of clinical and fundamental medicine. Purpose: design and learning of a NN to recognize four types of benign melanocytic skin tumors, integration into a mobile app to apply in practice. Material and methods: сlinical and dermatoscopic analysis of skin tumors was carried out in 600 children. In 65 cases the tumors were removed. Histological types were dermal nevus – 43% (n=28), complex nevus - 33.8% (n=22), pyogenic granuloma - 10.8% (n=7), Spitz-nevus - 6.2% (n=4), blue nevus - 3.1% (n=2), melanoma - 3.1% (n=2). Seven patients with pyogenic granulomas and two patients with melanoma were excluded. The test set included 56 dermatoscopic images. Due to the small number of images augmentation was performed. The database has been increased from 600 images to 1800. NN is written in the machine language Python. The machine learning framework was TensorFlow 2.0. The network architecture is based on the pre-trained model “EfficientNet B7”. This model uses the “supervised learning” paradigm. Each element of the sample had a class affiliation. Results: an accuracy of 83% was achieved after a period of learning on the test set. Mathematical metrics calculated in the Scikit-learn library. Sensitivity was 100% (blue nevus), 73% (complex nevus), 93% (dermal nevus), 75% (Spitz-nevus), and specificity were 98%; 94%; 82%; 98%, respectively. AI was integrated into the mobile app “KIDS NEVI”. Conclusion: AI as an auxiliary method for the skin tumors diagnosis in children and adolescents has demonstrated high potential and great opportunities. Dermatoscopic analysis of a skin tumor and a mobile app are able to provide “double control”, quick and correct clinical diagnosis and determination treatment tactics.

Spam Review Detection Using Weighted Swarm Support Vector Machines and Pre-Trained Word Embedding for Multiple Languages

Before making a purchase, many find internet reviews to be a vital source of information. On top of that, companies may learn a lot about their products and services via these reviews. Having faith in these reviews was especially important during the COVID-19 pandemic, when many stayed inside and read reviews at a dizzying pace. The pandemic altered the atmosphere and people's preferences in addition to increasing the number of evaluations. Spam reviewers keep an eye on these changes and try to improve their sneaky techniques. In order to deceive customers or harm competitors, reviews that are deemed spam may include inaccurate, misleading, or dishonest information. Consequently, this work introduces a WSVM plus an HHO to identify spam reviews. The HHO is similar to an algorithm in that it optimises hyperparameters and uses feature weights. Using English, Spanish, and Arabic language corpora as datasets, the multilingual difficulty in spam reviews has been tackled. Ngram-3, TFIDF, whereas One-hot encoding are three methods for representing words, while pre-trained word incorporation (BERT) is another one that has been used. Each of the four such studies has shed light on and provided a solution to a different facet. From start to finish, the proposed technique beat rival cutting-edge algorithms in every test. For the Multi dataset, the WSVM-HHO achieved a success rate of 84.270 percent; for the English information set, 89.565 percent; for the Spanish information set, 71.913 percent; and for the Arabic dataset, 88.565 percent. Furthermore, we have extensively researched the review environment before to and during the COVID-19 event. To further enhance detection performance, it has been designed to merge its existing textual attributes with statistical information to build a new dataset.

Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning.

Rapid identification of individuals developing a psychotic spectrum disorder (PSD) is crucial because untreated psychosis is associated with poor outcomes and decreased treatment response. Lack of recognition of early psychotic symptoms often delays diagnosis, further worsening these outcomes. The proposed study is a cross-sectional, retrospective analysis of electronic health record data including clinician documentation and patient-clinician secure messages for patients aged 15-29 years with ≥ 1 primary care encounter between 2017 and 2019 within 2 Kaiser Permanente regions. Patients with new-onset PSD will be distinguished from those without a diagnosis if they have ≥ 1 PSD diagnosis within 12 months following the primary care encounter. The prediction model will be trained using a trisourced natural language processing feature extraction design and validated both within each region separately and in a modified combined sample. This proposed model leverages the strengths of the large volume of patient-specific data from an integrated electronic health record with natural language processing to identify patients at elevated chance of developing a PSD. This project carries the potential to reduce the duration of untreated psychosis and thereby improve long-term patient outcomes.

Inheriting the glory of PāṆinian grammar into computer science

the integration of Pāṇinian grammar principles into computer science represents a significant advancement in computational linguistics and natural language processing. Pāṇini, an ancient Indian grammarian, formulated an elaborate system of grammar known as Aṣṭādhyāyī around the 4th century BCE, which has been revered for its systematic and concise rules governing Sanskrit language structure. In recent years, there has been a growing interest in adapting Pāṇinian principles to enhance computational frameworks for understanding human languages. This paper explores the theoretical foundations of Pāṇinian grammar and its applicability in the context of modern computational linguistics. The integration of Paninian grammar into computer science enhances the accuracy and efficiency of natural language processing system and contributes to preservation and dissemination of ancient linguistic knowledge. Through this the researchers aim to bridge gap between traditional linguistic scholarship and modern computational methodologies. By embracing the systematic and rigorous framework laid out by Pāṇini millennia ago, researchers are poised to achieve new breakthroughs in understanding and processing human language through innovative computational techniques. The journey from ancient Sanskrit grammar to innovative computer science exemplifies a harmonious blend of tradition and innovation in the quest for linguistic knowledge and technological advancement. Keywords - Pāṇini, Aṣṭādhyāyī, Computational linguistics, Machine translation, semantic analysis, syntactic rules.

ASSESSMENT OF THE FINANCIAL RESULT OF PUBLIC-PRIVATE PARTNERSHIP PROJECT IMPLEMENTATION USING THE EXAMPLE OF A CONCESSION AGREEMENT

The article is aimed at developing a methodological approach to improving the efficiency of assessing the financial results of implementing Ukrainian and European business projects. In this context, the object of study is a specific individual partnership and cooperation project. The research has established that one of the key informational aspects of such a partnership is the assessment and forecasting of the financial result. The goal of the research is to present the methodological approach to assessing financial performance for an individual project in the most detailed form possible. The article presents an approach based on modern simulation modelling methods, systems analysis methodology, and the method of composite seasonal indices. All modelling processes were conducted using MATLAB software. MATLAB is a high-level programming language and interactive environment for numerical computation, visualization, and programming. This program is widely used for modelling and data processing in fields such as engineering, scientific research, and mathematics. The research result is presented as an approach to the mathematical modelling of assessing the financial performance of partnership projects of Ukraine with representatives of EU business. It is necessary to emphasize that the presented research has limitations related to changes in Ukraine's hyperdynamic external environment provoked by war. Forecasts may vary depending on the development of military actions. Therefore, the prospects for further research should be directed towards improving the informational provision in the system of partnership and cooperation with EU countries to increase their own investment security.

A Detecting Fake News Using Machine Learning: A Comparative Study of Techniques

Many people get their news via the Internet and social media platforms, and given the rapid growth of these platforms, fake news may now spread easily and quickly. False information that aims to mislead and harm society and the individual is known as fake news. By deliberately spreading false information, the media distort public opinion and threaten the social order by leading people to believe things that are not true. With the massive expansion of social media networks, the spread of fake news has increased dramatically. Although interesting, it poses some difficulties due to limited resources (such as datasets and published research). This paper presents diverse machine-learning techniques to identify fabricated news by analyzing the textual content. Several techniques were used, including SVM, RF, logistic regression, Naive Bayes, Gradient Boosting, AdaBoost, KNN, DT, and XGBoost. Based on comparing the results, who got the best result with an accuracy rate of 0.9967 and the lowest loss of 0.003 The study includes a variety of methodologies, such as natural language processing (NLP), machine learning, and data mining, which have been found to improve the efficiency of text processing to increase accuracy and can save time and effort by automatically identifying fake news, especially in light of the massive amount from materials available on the Internet.

The Impact of Artificial Intelligence in Educational System

The way we teach and learn could be completely transformed by artificial intelligence (AI), which could make the process more efficient, personalized, and interesting. Artificial intelligence (AI) in education is the application of AI technologies, like natural language processing and machine learning, to improve the educational process. Through the use of algorithms, teachers are able to customize learning for each student by analyzing data, finding trends, and making predictions. Artificial intelligence (AI) and its potential impact on education have gained widespread attention because of ChatGPT's impressive performance on standardized academic assessments. For the development and implementation of AI-driven technologies in schools, colleges, and universities to be sustainable, a thorough knowledge of their effects on the current educational system is needed. The application of AI in education has a lot of potential advantages. One of the biggest benefits of AI in education is personalized learning, which allows students to learn at their own pace and in a fashion that best fits their learning preferences. This can improve student results. Chatbots, automated grading and evaluation, and intelligent tutoring systems can boost productivity, free up teachers' time, and deliver more precise and consistent feedback. Some of the challenges that need to be resolved are potential bias, cost, lack of confidence, and privacy and security concerns. AI can improve data analysis, empowering teachers to make facts-based decisions. There are some impacts of artificial intelligence in educational system that are covered in this review. These are the applications of artificial intelligence in automated assessment, intelligent tutoring systems, personalized learning, and collaborative teacher-student learning.

Sentiment analysis and social media analytics in brand management: Techniques, trends, and implications

In the contemporary digital landscape, sentiment analysis and social media analytics have emerged as pivotal tools in brand management. This research explores the influence of sentiment analysis on understanding consumer perceptions and the effectiveness of social media analytics in shaping brand strategies. Through a comprehensive review of existing literature and case studies, the study highlights the critical role these techniques play in assessing consumer sentiment and optimizing brand engagement. Sentiment analysis, utilizing natural language processing (NLP) and machine learning algorithms, provides insights into consumer attitudes and emotional responses. It enables brands to refine their marketing strategies based on real-time feedback and improve brand perception. Concurrently, social media analytics offers valuable data on consumer interactions across various platforms, revealing trends and patterns that inform strategic decisions. The integration of sentiment analysis with social media analytics offers a holistic view of consumer behavior, though it presents challenges such as data accuracy, privacy issues, and the dynamic nature of social media. The research identifies key findings, including the impact of sentiment analysis on brand strategy and the role of social media analytics in enhancing brand visibility and engagement. Recommendations for brand managers include leveraging sentiment insights for strategic adjustments, adopting advanced analytics tools, and addressing privacy concerns. The study also suggests areas for future research, including the exploration of advanced sentiment analysis techniques, integration with emerging technologies, and the development of best practices for ethical data use. Overall, this research underscores the importance of sentiment analysis and social media analytics in modern brand management, providing actionable insights for optimizing brand strategies and improving consumer engagement.

From rule-based models to deep learning transformers architectures for natural language processing and sign language translation systems: survey, taxonomy and performance evaluation

With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translation system, from sign to gloss to text and vice-versa. There has been a wealth of research on machine translations and related reviews. However, there are few works on sign language machine translation considering the particularity of the language being continuous and dynamic. This paper aims to address this void, providing a retrospective analysis of the temporal evolution of sign language machine translation algorithms and a taxonomy of the Transformers architectures, the most used approach in language translation. We also present the requirements of a real-time Quality-of-Service sign language machine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation systems.

Formation of a heterogeneous group of UAVS with a reasonable number of false and real drones

The subject of scientific research is the joint use of false and real unmanned aerial vehicles (UAVs) as part of a heterogeneous group of UAVs to perform military tasks. This article aimsto determine the appropriate number of false and real drones (UAVs) as part of a heterogeneous group of UAVs to ensure that a certain number of real UAVs can fly to targets with the aim of further reliable target destruction. The scientific task is to develop a methodology for determining the appropriate number of false drones in a heterogeneous group of UAVs, considering the diversity of UAVs included in the UAV group. To achieve the goals of scientificresearch, partial scientific tasks were solved. The joint use of false drones as part of a UAV group to defeat targets with a given degree of damage was formalized. The formalization was carried out taking into account two possible cases of use: a) when the enemy has a sufficient number of means to destroy the entire group of UAVs; b) when the enemy has an insufficient number of means to destroy the entire UAV group. A mathematical model for determining the optimal composition of false and real drones (UAVs) as parts of a heterogeneous group of UAVs has been developed, which will allow to fulfill the task of defeating enemy targets with the desired degree of reliability. A program code has been developed that simplifies the mathematical calculations in the presented mathematical model and allows it to be used in the process of making an appropriate military decision. An algorithm to find the numbers of real and false UAVs in a heterogeneous group of UAVs is proposed. The obtained formulas and algorithms were verified by computer simulation using the Monte Carlo method. Methods. The mathematical model is based on combinatorial methods of probability theory. Programming for calculating analytical formulas and computer modeling of the Monte Carlo method was carried out based on the R computer language. The following results were obtained. A multifunctional algorithm is presented: on one hand, its application makes it possible to determine the optimal number of false UAVs in a heterogeneous group of UAVs to ensure that the required number of real UAVs reach the target, and on the other hand, to determine the predicted loss level of real UAVs in a heterogeneous group of UAVs when using a certain number of false drones.Conclusions. The availability of the developed mathematical model, algorithm, and program code makes it possible to predict the possible results of the combat use of heterogeneous groups of UAVs based on the initial parameters and to substantiate recommendations for a possible composition of such groups.

A deaf-centred art-science approach to community engagement with sign language technologies

Sign Language Machine Translation (SLMT) is an emerging technology that will primarily impact deaf and hard of hearing communities. Historically, these technologies have been developed by hearing scientists, without input from deaf researchers or community representatives. SignON was a three-year project exploring the application of SLMT, and using a co-creation approach which was led by the European Union of the Deaf. Here we describe an art-science method to engage deaf, hard of hearing, and hearing audiences with SLMT through theatre and performance. We also reflect on the insights, perspectives, and feedback shared by audience members.

Investigation of bias in the automated assessment of school violence

ObjectivesNatural language processing and machine learning have the potential to lead to biased predictions. We designed a novel Automated RIsk Assessment (ARIA) machine learning algorithm that assesses risk of violence and aggression in adolescents using natural language processing of transcribed student interviews. This work evaluated the possible sources of bias in the study design and the algorithm, tested how much of a prediction was explained by demographic covariates, and investigated the misclassifications based on demographic variables. MethodsWe recruited students 10–18 years of age and enrolled in middle or high schools in Ohio, Kentucky, Indiana, and Tennessee. The reference standard outcome was determined by a forensic psychiatrist as either a “high” or “low” risk level. ARIA used L2-regularized logistic regression to predict a risk level for each student using contextual and semantic features. We conducted three analyses: a PROBAST analysis of risk in study design; analysis of demographic variables as covariates; and a prediction analysis. Covariates were included in the linear regression analyses and comprised of race, sex, ethnicity, household education, annual household income, age at the time of visit, and utilization of public assistance. ResultsWe recruited 412 students from 204 schools. ARIA performed with an AUC of 0.92, sensitivity of 71%, NPV of 77%, and specificity of 95%. Of these, 387 students with complete demographic information were included in the analysis. Individual linear regressions resulted in a coefficient of determination less than 0.08 across all demographic variables. When using all demographic variables to predict ARIA’s risk assessment score, the multiple linear regression model resulted in a coefficient of determination of 0.189. ARIA performed with a lower False Negative Rate (FNR) of 15.2% (CI [0 – 40]) for the Black subgroup and 12.7%, CI [0 – 41.4] for Other races, compared to an FNR of 26.1% (CI [14.1 – 41.8]) in the White subgroup. ConclusionsBias assessment is needed to address shortcomings within machine learning. In our work, student race, ethnicity, sex, use of public assistance, and annual household income did not explain ARIA’s risk assessment score of students. ARIA will continue to be evaluated regularly with increased subject recruitment.

Using machine learning to develop customer insights from user-generated content

Uncovering customer insights (CI) is indispensable for contemporary marketing strategies. The widespread availability of user-generated content (UGC) presents a unique opportunity for firms to gain a nuanced understanding of their customers. However, the size and complexity of UGC datasets pose significant challenges for traditional market research methods, limiting their effectiveness in this context. To address this challenge, this study leverages natural language processing (NLP) and machine learning (ML) techniques to extract nuanced insights from UGC. By integrating sentiment analysis and topic modeling algorithms, we analyzed a dataset of approximately four million X posts (formerly tweets) encompassing 20 global brands across industries. The findings reveal primary brand-related emotions and identify the top 10 keywords indicative of brand-related sentiment. Using FedEx as a case study, we identify five prominent areas of customer concern: parcel tracking, small business services, the firm's comparative performance, package delivery dynamics, and customer service. Overall, this study offers a roadmap for academics to navigate the complex landscape of generating CI from UGC datasets. It thus raises pertinent practical implications, including boosting customer service, refining marketing strategies, and better understanding customer needs and preferences, thereby contributing to more effective, more responsive business strategies.

Smart Voice Based Medical Chatbot for Disease Prediction

The global shortage of medical resources has intensified in recent years, exacerbated by a growing and aging population, particularly following the pandemic. However, advancements in robotics and artificial intelligence have empowered healthcare professionals to address these challenges. Intelligent Speech Technology (IST) is one such innovation that has helped doctors and patients enhance medical efficiency and reduce cumbersome tasks. Despite this, complex hospital environments with background noise and variations in patient pronunciation have limited IST's broader application. Yet, as machine learning technologies rapidly advance intelligent speech recognition capabilities, these obstacles are expected to be overcome in the near future. This project focuses on the concept of an AI-based voice- assisted chatbot for medical disease prediction. Deployed on a Raspberry Pi as a standalone hardware device, it utilizes natural language processing (NLP) and machine learning (ML) techniques to detect diseases by interacting with users through a series of questions. A groundbreaking innovation, the Speech-Assisted Chatbot for Medical Infectious Disease Prediction, promises to transform healthcare delivery. By leveraging AI-driven algorithms and natural language processing, it facilitates quick diagnosis and prescription generation through spoken interactions with patients. This simplifies and accelerates procedures, reduces error risks, improves patient care standards, saves valuable time for healthcare professionals, and enhances service availability, all contributing to better outcomes and more efficient healthcare processes.. Key Words: chatbot, NLP, Machine learning, Medical diagnosis, Speech, Artificial Intelligence etc.

Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.

Peptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has substantially increased, allowing the application of more complex computational models to study the relations between the peptide composition and function. This work introduces AMP-Detector, a sequence-based classification model for the detection of peptides' functional biological activity, focusing on accelerating the discovery and de novo design of potential antimicrobial peptides (AMPs). AMP-Detector introduces a novel sequence-based pipeline to train binary classification models, integrating protein language models and machine learning algorithms. This pipeline produced 21 models targeting antimicrobial, antiviral, and antibacterial activity, achieving average precision exceeding 83%. Benchmark analyses revealed that our models outperformed existing methods for AMPs and delivered comparable results for other biological activity types. Utilizing the Peptide Atlas, we applied AMP-Detector to discover over 190,000 potential AMPs and demonstrated that it is an integrative approach with generative learning to aid in de novo design, resulting in over 500 novel AMPs. The combination of our methodology, robust models, and a generative design strategy offers a significant advancement in peptide-based drug discovery and represents a pivotal tool for therapeutic applications.

Language in Brains, Minds, and Machines.

It has long been argued that only humans could produce and understand language. But now, for the first time, artificial language models (LMs) achieve this feat. Here we survey the new purchase LMs are providing on the question of how language is implemented in the brain. We discuss why, a priori, LMs might be expected to share similarities with the human language system. We then summarize evidence that LMs represent linguistic information similarly enough to humans to enable relatively accurate brain encoding and decoding during language processing. Finally, we examine which LM properties-their architecture, task performance, or training-are critical for capturing human neural responses to language and review studies using LMs as in silico model organisms for testing hypotheses about language. These ongoing investigations bring us closer to understanding the representations and processes that underlie our ability to comprehend sentences and express thoughts in language.

Text Classification Based on Natural Language Processing and Machine Learning in Multi-Label Corpus

The rapid development of the Internet has led to a geometric expansion of text information resources online. Among them, corpus, as the basic data source of natural language processing based on statistical language model, its construction and application have become a hot issue in current language processing research. After consulting a large number of relevant literature and materials, it was found that many researchers have provided new ideas for multi label corpus text classification methods. However, this article adds its own understanding and takes this as the direction and basis. In the introduction, the research significance of text classification was introduced, and then academic research and analysis were carried out on the two key sentences of corpus text classification and natural language processing in multi-tag corpus text classification. This article then utilizes an algorithm model to provide a theoretical basis for the study of multi-label corpus text classification methods; At the end of this article, a simulation comparative experiment is conducted, and the experiment is summarized and discussed; In the Enterprise L corpus, the difference in recall rates before and after the use of Entrance 1 was 5.5%, the difference in recall rates before and after the use of Entrance 2 was 7.8%, the difference in recall rates before and after the use of Entrance 3 was 3.3%, and the difference in recall rates before and after the use of Entrance 4 was 4.5%. At the same time, with the continuous research of natural language processing and machine learning, the research on text classification methods of multi tag corpus is also facing new opportunities and challenges.

Natural Language Processing Pretraining Language Model for Computer Intelligent Recognition Technology

Computer intelligent recognition technology refers to the use of computer vision, Natural Language Processing (NLP), machine learning and other technologies to enable computers to recognize, analyze, understand and answer human language and behavior. The common applications of computer intelligent recognition technology include image recognition, NLP, face recognition, target tracking, and other fields. NLP is a field of computer science, which involves the interaction between computers and natural languages. NLP technology can be used to process, analyze and generate natural language data, such as text, voice and image. Common NLP technology applications include language translation, emotion analysis, text classification, speech recognition and question answering system. Language model is a machine learning model, which uses a large number of text data for training to learn language patterns and relationships in text data. Although the language model has made great progress in the past few years, it still faces some challenges, including: poor semantic understanding, confusion in multilingual processing, slow language processing and other shortcomings. Therefore, in order to optimize these shortcomings, this article would study the pre-training language model based on NLP technology, which aimed at using NLP technology to optimize and improve the performance of the language model, thus optimizing the computer intelligent recognition technology. The model had a higher language understanding ability and more accurate prediction ability. In addition, the model could learn language rules and structures by using a large number of corpus, so as to better understand natural language. Through experiments, it could be known that the data size and total computing time of the traditional Generative Pretrained Transformer-2 (GPT-2) language model were 10 GB and 97 hours respectively. The data size and total computing time of BERT (Bidirectional Encoder Representations from Transformer) were 12 GB and 86 hours respectively. The data size and total computing time of the pre-training language model based on NLP were 18 GB and 71 hours respectively. Obviously, the pre-training language model based on NLP had a larger data size and shorter computing time. The experimental results showed that the NLP technology could better optimize the language model and effectively improve its various capabilities. This article opened up a new development direction for computer intelligent recognition technology and provided excellent technical support for the development of language models.