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Discovering the Location by Intelligent Chatbot Support using NLP 6

Discovering the Location by Intelligent Chatbot Support usin...

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6th international conference on Computing, Communication, Control and Automation, ICCUBEA 2022

作者： Palliyamakkal, Jayashree J. Ghotkar, Archana Department of Computer Engineering Pune India

ISBN: (纸本)9781665484510

Discovering the person location through a chatbot within the geographical virtual barrier boundary i.e., called Geofence that allows users to detect when someone enters or departs a location. Geofence main motive is for finding the person in a specific area rather than the entire map area. The polygonal geofence is a type of geofence that helps us to cover precisely the boundaries of a building that we want to cover. The coordinates of a building, i.e., the Latitude and Longitude, can be used to generate a polygonal geofence that covers only that area of the building. Although a circular geofence can be utilized, it will cover an undesirable area, which is why a polygonal geofence is preferable. With the assist of GPS/Wi-Fi technology to track a person's location, geofence will cover the necessary geographical virtual barrier borders, and a chatbot will provide the person's location anytime humans ask it. GPS can send location data to the chatbot whether the person is inside the virtual boundary;otherwise, it will indicate that the person is not in that zone. Similarly, Wi-Fi will assist in tracking the location of people inside the building per floor. The chatbot will assist in determining a person's location so that we can determine exactly where the person is standing. The chatbot is a text-based input system that allows us to communicate as if we were chatting with a human. natural language understanding and natural language Generation are used in natural language processing the key objective is that any student who desires to meet with a specific teacher to rectify any doubts or submit work can employ this intelligent chatbot to discover the teacher's current position. © 2022 ieee.

关键词： Location

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A Research On The New Generation Artificial Intelligence Technology Generative Pretraining Transformer 3 3

A Research On The New Generation Artificial Intelligence Tec...

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3rd international Informatics and Software engineering conference, IISEC 2022

作者： Aydin, Nazif Ayhan Erdem, O. Gazi University Graduate School of Natural and Applied Sciences Department of Computer Engineering Ankara Turkey Gazi University Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

In the digitalizing world, Artificial Intelligence (AI) paves the way for the automation of routine work done by humans and makes life easier. Recently, there is no area where AI and its applications are not used in daily life, from health to education, from transportation to energy, and from agriculture to tourism. AI applications are making rapid progress in the direction of important and current developments, especially in natural language processing (NLP) and Deep Learning (DL). A concrete example of progress in these areas is the GPT-3 (Generative Pre-trained Transformer 3) language model. AI-assisted GPT-3 technology is the DL model that is effectively used in many NLP fields, which can produce long and consistent content similar to the texts written by people using pre-trained algorithms. The GPT-3 architecture has reached a level that can compete with people in many areas by producing optimum solutions for all kinds of inputs by using the Transformer-based language model, which is an attention-based deep learning technique. This article aims to convey the efficiency, structure and potential of the Transformer-assisted GPT-3 model, which is one of the most up-to-date NLP technologies, to the reader. Since the number of Turkish studies in the field of GPT-3 AI is quite limited, it is considered that this study will contribute to the literature in terms of both quantity and quality. In addition, the performance parameters of the model were examined by making a customized fine-tuned sample application in the beta version of the GPT-3 model. © 2022 ieee.

关键词： Deep learning

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AI and Corporate Sustainability: Exploring the Environmental and Social Impacts of AI Integration

AI and Corporate Sustainability: Exploring the Environmental...

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knowledge engineering and Communication Systems (ICKECS), international conference on

作者： Ilkhom Nosirov Ilmidin Yormatov Nilufar Yuldasheva Feruza Avulchayeva Department of Management Fergana Polytechnic Institute Fergana Uzbekistan

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

The integration of Artificial Intelligence (AI) within corporate sectors has ushered in a transformative era for sustainability practices, redefining environmental and social governance (ESG) strategies. This comprehensive survey explores the multifaceted impacts of AI on corporate sustainability, examining both the potential benefits and challenges. By leveraging diverse AI technologies such as machine learning, natural language processing, and robotics, companies can enhance their sustainability efforts through improved efficiency, reduced waste, and innovative solutions to complex environmental problems. However, the deployment of AI also presents significant challenges, including energy consumption, ethical considerations, and the socio-economic implications of automation. Through a detailed analysis of current technologies, this paper employs mathematical expressions to quantify the environmental benefits of AI integration, including algorithms that optimize resource use and reduce carbon footprints. Comparative results highlight the effectiveness of various AI applications in achieving sustainability goals, supported by graphs and statistical analysis. Moreover, the study delves into case studies of successful AI implementation in sectors such as energy, manufacturing, and waste management, providing a holistic view of AI’s role in promoting corporate sustainability. By balancing the technological advancements with ethical and environmental considerations, this survey underscores the critical need for responsible AI integration in the pursuit of a more sustainable and equitable corporate future.

关键词： Waste management Surveys Ethics Machine learning natural language processing Artificial intelligence Sustainable development

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An Efficient and Precise Method to Detect Fake News using Machine Learning Algorithms 2

An Efficient and Precise Method to Detect Fake News using Ma...

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2nd ieee Mysore Sub Section international conference, MysuruCon 2022

作者： Priya, H.P. Sultana, Shabana The National Institute of Engineering Department of Cse Mysuru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

social media has become a part of life now a days which plays an important role to provide the entertainment as well as a news most of the news which has been posted through any of the social media could be a fake news and such news will easily spread throughout the globe instantly and people might not aware of it. The opinions of the people can be shaped in bias or inversely for government or for any organization. Therefore, to recognize the moto and real meaning of any posts in social media there is a necessary to find a system which is efficient in detecting false information among the posts in social media. Fake news is misleading or false information which spreads globally and the trending area of interest on detecting falsification which has the limitations of few collections of resources. This Fake News can be detected by using various techniques of ML and AI concepts which is the purpose of this proposed work of this paper is to present a system which has efficiency to expect whether a set of data or a bit of information is fake or real on the basis of its content, by using the concepts of natural language processing (NLP) perspective and with the results of practical examinations of the system. To detect the fake news in the news collected from the post we use 14 attributes (experience for users, overall posts, and news type How Often, Word Count (+ and -), Location, and Time) to divide the single piece of news subjecting into various dimensions of attributes content found in the news by the Artificial Intelligence approaches and Machine Learning for the analysis and to categorize the news in which the system analyzes the various methods foe classification and suggestions for an active natural language processing method to identify FAKE NEWS. Visual Studio 2010 or its higher version and SQL server as a software tool with that by using C# language and additionally with the resources and the concept of 14 attributes the objective of detecting fake news can be

关键词： Machine learning

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Comparative Analysis of Transformer based Models for Question Answering 2

Comparative Analysis of Transformer based Models for Questio...

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2nd international conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Rawat, Anchal Singh Samant, Surender Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9781665474160

There have been numerous significant developments in natural language processing (NLP) in past few years. Now language models can learn improved representations of the natural language due to the Transformer and the self-attention mechanism. On numerous NLP benchmarks, these attention-based models have produced excellent, cutting-edge results. The rising popularity of transfer learning is one of the contributing causes. This paper presents a comparison between some of the transformers models like BERT, ALBERT, RoBERTa, XLNET, DistilBERT, Electra, Pegasus etc. and the application of some of these models for Question-Answering (QA) system using SQUAD2 dataset. There are a lot of machine learning algorithms that which helps in solving question answering task using NLP. Previously "Bag of Words"was most common method for answering questions that were presumed by the developers. But this was a very time-consuming process for developers to frame each pair of question answer. This was very useful for chatbots but it was not able to answer question for large database. Nowadays with the help of transformers libraries, HuggingFace developed a BERT Question Answering model which scans a specified document provided by user and tries to answer the questions relating to that document. It can quickly answer from a large document. © 2022 ieee.

关键词： Learning algorithms

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Developing natural language processing Algorithms to Fact-Check Speech or Text

Developing Natural Language Processing Algorithms to Fact-Ch...

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3rd international conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Joshi, Himanshu Sanjay Taherdoost, Hamed College of Business Westcliff University CA United States University Canada West Vancouver Canada GUS Institute Global University Systems London United Kingdom

natural language processing (NLP) is pivotal in the fight against misinformation, offering tools to process and analyze both text and speech. This paper presents the development of NLP algorithms specifically designed for fact-checking, leveraging the capabilities of Question-Answering (QA) systems and knowledge graphs. We conduct a comprehensive analysis of existing QA systems, propose enhancements for fact-checking, and validate our approach through comparative studies. Our findings contribute to the ongoing efforts to improve the reliability of information in the digital age. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Question answering

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An Offensive language Identification Based on Deep Semantic Feature Fusion 8

An Offensive Language Identification Based on Deep Semantic ...

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8th ieee international conference on Computer and Communications, ICCC 2022

作者： Li, Xiang Zeng, Zhi Wu, Mingmin Huang, Zhongqiang Sha, Ying Shi, Lei College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China

ISBN: (纸本)9781665450515

Various forms of social interactions are often char-acterized by toxic or offensive words that can be collectively referred to as offensive languages, which has become a unique linguistic phenomenon in social media platforms. How to detect and identify these offensive languages in social media platforms has become one of the important research in the field of natural language processing. Existing methods utilize machine learning algorithms or text representation models based on deep learning to learn the features of offensive languages and identify them, which have achieved good performances. However, traditional machine learning-based methods mainly rely on keyword identi-fication and blocking, deep learning-based methods do not ade-quately explore the fused deep semantic features of the content by combining word-level embeddings and sentence-level deep semantic feature representations of sentences, which cannot ef-fectively identify offensive languages that do not contain common offensive words but indicate offensive meanings. In this research, we propose a novel offensive language identification model based on deep semantic feature fusion, which uses the pre-trained model Bert to obtain word-level embedding representations of offensive languages, and then integrates the RCNN that combines with the attention mechanism to extract the fused deep semantic feature representations of offensive languages, and label encoder and offensive predictor to improve the identification accuracy and generalization ability of the model so that the performances of the model do not rely on the offensive language lexicon entirely and can identify offensive languages that do not contain common offensive words but indicate offensive meanings. Experimental results on Wikipedia and Twitter comment datasets show that our proposed model can better understand the context and discover potential offensive meanings, and outperforms existing methods. © 2022 ieee.

关键词： Embeddings

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Archimedes Optimization with Enhanced Deep Learning based Recommendation System for Drug Supply Chain Management 2

Archimedes Optimization with Enhanced Deep Learning based Re...

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2nd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Rathor, Ketan Chandre, Shanker Thillaivanan, Alagu Naga Raju, M. Sikka, Vinit Singh, Kamlesh GyanSys Inc.Mumbai Maharashtra India Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal India Shadan College of Engineering and Technology Peerancheru Hyderabad India University Gitam School of Technology Department of Computer Science and Engineering Karnstaka Bengaluru India Manav Rachna International Institute of Research and Studies Haryana Faridabad India Graphic Era Hill University Dehradun Campus Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350348002

Recently,pharmaceutical corporations are confronting difficulties while tracking their products in the supply chain process, allowing counterfeiters to include their fake medicines into market. Counterfeit drugs were examined as a great challenge for pharmaceutical sector worldwide. Sentiment analysis can be used to analyse customer reviews of drugs to determine overall sentiment towards the drug. Positive reviews can indicate that a drug is effective and well-tolerated, while negative reviews may indicate potential side effects or lack of effectiveness. However, it's important to note that sentiment analysis is a subfield of natural language processing which uses statistical and machine learning techniques to identify and extract subjective information from source materials. Therefore, this article introduces an Archimedes Optimization with Enhanced Deep Learning based Recommendation System (AOAEDL-RS) for Drug Supply Chain Management. The proposed AOAEDL-RS technique majorly examines the drug reviews for the recommendation of drugs. It follows a three stage process: preprocessing, classification, and parameter tuning. Firstly, the AOAEDL-RS technique performs preprocessing and word2vec embedding processes. Secondly, the context based BiLSTM-CNN (CBLSTM-CNN) model is applied for drug review classification and classification. Thirdly, the AOAEDL-RS technique uses AOA for the optimal hyperparameter tuning of CBLSTM-CNN method. The result analysis of the AOAEDL-RS technique is tested on drug reviews dataset and the outcomes show the improved outcomes of the AOAEDL-RS method. © 2023 ieee.

关键词： Recommender systems

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A Smart Homes through natural language processing using Internet of Things

A Smart Homes through Natural Language Processing using Inte...

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2022 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Alanya-Beltran, Joel Singh, Rajesh Sameer Valderrama-Zapata, Carlos Goyal, Sonali Batra, Neera Universidad Tecnológica Del Perú Electronic Department Peru Uttaranchal University Uttaranchal Institute of Technology India ISBAT University Faculty of Information & Communication Technology Kampala Uganda Universidad San Ignacio de Loyola Peru MMEC MMDU Department of Computer Science and Engineering

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

The IoT is mainly regarded as the key innovation that allows us to utilize online services for integrating so several devices, assistance in having received the guidance, and empower in continuing to act within it in an efficient manner. So several devices are linked to the web that also enable in aiding the participants to utilize the device pretty nearly. This research is concentrated on identifying the critical factors that will help build smart homes by means of The internet of Things and language processing. The two main factors taken into consideration are voice understanding integration with devices and remote arduino microcontroller *** and primary resources are utilized to gather data for the research, and a five - point likert evaluation is applied to quantitatively apply that data. For the purpose of interpreting the route diagram using the structural equations model, the authors use sophisticated data analysis methods that analyse moment structures. © 2022 ieee.

关键词： Internet of things

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Transfer Learning Approach to Multilabel Biomedical Literature Classification using Transformer Models

Transfer Learning Approach to Multilabel Biomedical Literatu...

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international conference for Convergence of Technology (I2CT)

作者： Pahalage Dona Thushari Sakina Niazi Shweta Meena Department of Software Engineering Delhi Technological University New Delhi India

The recently developed transformer models have a significant influence on natural language processing (NLP) research. Transformer-based models have attained innovative results on numerous NLP benchmarks in linguistics. Researchers have also investigated transformer models for a variety of applications in the biomedical field. To facilitate information classification, this work aims to thoroughly investigate four state-of-the-art transformer-based models (BERT, Roberta, SciBERT, and ClinicalBERT). Large language model training, however, requires a lot of time and processing resources. As a result, pre-trained language models like BERT are advantageous because they (1) offer cutting-edge performance, (2) The knowledge gained can be used to do several tasks, such as categorization, and (3) free up practitioners from having to gather enough resources (including the hardware, data and time) to train models. However, pre-trained models possibly will perform poorly in some domains since they are generic. Here, we inspect the case of multilabel classification for biomedical Literature, which is a domain that has received relatively minor attention in the literature assessing pre-trained language models. Based on our study, using SciBERT as the baseline model gave the best results compared to the rest of the models with an 89.35% micro F1 score. In the context of classifying Biomedical Literature, domain-specific pre-trained models tend to perform better compared to generic models.

关键词： Training Biological system modeling Bit error rate Transfer learning Text categorization Transformers natural language processing

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