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Artículos de revistas sobre el tema "AI Generated Text Detection"

Autor: Grafiati

Publicado: 28 de diciembre de 2024

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1

Bhattacharjee, Amrita, and Huan Liu. "Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?" ACM SIGKDD Explorations Newsletter 25, no. 2 (2024): 14–21. http://dx.doi.org/10.1145/3655103.3655106.

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Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale. Although detection methods for such AI-generated text exist already, we investigate ChatGPT's performance as a detector on such AI-generated text, inspired by works that use ChatGPT as a data labeler or annotator. We evaluate the zeroshot performance of ChatGPT in the task of human-written vs. AI-generated text detection, and perform experiments on publicly available datasets. We empirically investigate if ChatGPT is symmetrically effective in detecting AI

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2

Wang, Yu. "Survey for Detecting AI-generated Content." Advances in Engineering Technology Research 11, no. 1 (2024): 643. http://dx.doi.org/10.56028/aetr.11.1.643.2024.

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In large language models (LLMs) field, the rapid advancements have significantly improved text generation, which has blured the distinction between AI-generated and human-written texts. These developments have sparked concerns about potential risks, such as disseminating fake information or engaging in academic cheating. As the responsible use of LLMs becomes imperative, the detection of AI-generated content has become a crucial task. Most existing surveys on AI-generated text (AIGT) Detection have analysed the detection approaches from a computational perspective, with less attention to lingu

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3

A, Nykonenko. "How Text Transformations Affect AI Detection." Artificial Intelligence 29, AI.2024.29(4) (2024): 233–41. https://doi.org/10.15407/jai2024.04.233.

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This study addresses the critical issue of AI writing detection, which currently plays a key role in deterring technology misuse and proposes a foundation for the controllable and conscious use of AI. The ability to differentiate between human-written and AI-generated text is crucial for the practical application of any policies or guidelines. Current detection tools are unable to interpret their decisions in a way that is understandable to humans or provide any human-readable evidence or proof for their decisions. We assume that there should be a traceable footprint in LLM-generated texts tha

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4

Singh, Dr Viomesh, Bhavesh Agone, Aryan More, Aryan Mengawade, Atharva Deshmukh, and Atharva Badgujar. "SAVANA- A Robust Framework for Deepfake Video Detection and Hybrid Double Paraphrasing with Probabilistic Analysis Approach for AI Text Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 2074–83. http://dx.doi.org/10.22214/ijraset.2024.65526.

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Abstract: As the generative AI has advanced with a great speed, the need to detect AI-generated content, including text and deepfake media, also increased. This research work proposes a hybrid detection method that includes double paraphrasing-based consistency checks, coupled with probabilistic content analysis through natural language processing and machine learning algorithms for text and advanced deepfake detection techniques for media. Our system hybridizes the double paraphrasing framework of SAVANA with probabilistic analysis toward high accuracy on AI-text detection in forms such as DO

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5

Vismay Vora, Et al. "A Multimodal Approach for Detecting AI Generated Content using BERT and CNN." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 691–701. http://dx.doi.org/10.17762/ijritcc.v11i9.8861.

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With the advent of Generative AI technologies like LLMs and image generators, there will be an unprecedented rise in synthetic information which requires detection. While deepfake content can be identified by considering biological cues, this article proposes a technique for the detection of AI generated text using vocabulary, syntactic, semantic and stylistic features of the input data and detecting AI generated images through the use of a CNN model. The performance of these models is also evaluated and benchmarked with other comparative models. The ML Olympiad Competition dataset from Kaggle

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6

Subramaniam, Raghav. "Identifying Text Classification Failures in Multilingual AI-Generated Content." International Journal of Artificial Intelligence & Applications 14, no. 5 (2023): 57–63. http://dx.doi.org/10.5121/ijaia.2023.14505.

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With the rising popularity of generative AI tools, the nature of apparent classification failures by AI content detection softwares, especially between different languages, must be further observed. This paper aims to do this through testing OpenAI’s “AI Text Classifier” on a set of human and AI-generated texts inEnglish, German, Arabic, Hindi, Chinese, and Swahili. Given the unreliability of existing tools for detection of AIgenerated text, it is notable that specific types of classification failures often persist in slightly different ways when various languages are observed: misclassificati

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Sushma D S, Pooja C N, Varsha H S, Yasir Hussain, and P Yashash. "Detection and Classification of ChatGPT Generated Contents Using Deep Transformer Models." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 05 (2024): 1404–7. http://dx.doi.org/10.47392/irjaeh.2024.0193.

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AI advancements, particularly in neural networks, have brought about groundbreaking tools like text generators and chatbots. While these technologies offer tremendous benefits, they also pose serious risks such as privacy breaches, spread of misinformation, and challenges to academic integrity. Previous efforts to distinguish between human and AI-generated text have been limited, especially with models like ChatGPT. To tackle this, we created a dataset containing both human and ChatGPT-generated text, using it to train and test various machine and deep learning models. Your results, particular

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Alshammari, Hamed, and Khaled Elleithy. "Toward Robust Arabic AI-Generated Text Detection: Tackling Diacritics Challenges." Information 15, no. 7 (2024): 419. http://dx.doi.org/10.3390/info15070419.

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Current AI detection systems often struggle to distinguish between Arabic human-written text (HWT) and AI-generated text (AIGT) due to the small marks present above and below the Arabic text called diacritics. This study introduces robust Arabic text detection models using Transformer-based pre-trained models, specifically AraELECTRA, AraBERT, XLM-R, and mBERT. Our primary goal is to detect AIGTs in essays and overcome the challenges posed by the diacritics that usually appear in Arabic religious texts. We created several novel datasets with diacritized and non-diacritized texts comprising up

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9

Jeremie Busio Legaspi, Roan Joyce Ohoy Licuben, Emmanuel Alegado Legaspi, and Joven Aguinaldo Tolentino. "Comparing ai detectors: evaluating performance and efficiency." International Journal of Science and Research Archive 12, no. 2 (2024): 833–38. http://dx.doi.org/10.30574/ijsra.2024.12.2.1276.

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The widespread utilization of AI tools such as ChatGPT has become increasingly prevalent among learners, posing a threat to academic integrity. This study seeks to evaluate capability and efficiency of AI detection tools in distinguishing between human-authored and AI-generated works. Three-paragraph works on “AutoCAD and Architecture” were generated through ChatGPT, and three human-written works were subjected to evaluation. AI detection tools such as GPTZero, Copyleaks and Writer AI were used to evaluate these paragraphs. Parameters such as “Human/Human Text/Human Generated Text” and “AI/AI

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Kim, Min-Gyu, and Heather Desaire. "Detecting the Use of ChatGPT in University Newspapers by Analyzing Stylistic Differences with Machine Learning." Information 15, no. 6 (2024): 307. http://dx.doi.org/10.3390/info15060307.

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Large language models (LLMs) have the ability to generate text by stringing together words from their extensive training data. The leading AI text generation tool built on LLMs, ChatGPT, has quickly grown a vast user base since its release, but the domains in which it is being heavily leveraged are not yet known to the public. To understand how generative AI is reshaping print media and the extent to which it is being implemented already, methods to distinguish human-generated text from that generated by AI are required. Since college students have been early adopters of ChatGPT, we sought to

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11

Wang, Hao, Jianwei Li, and Zhengyu Li. "AI-generated text detection and classification based on BERT deep learning algorithm." Theoretical and Natural Science 39, no. 1 (2024): None. http://dx.doi.org/10.54254/2753-8818/39/20240625.

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With the rapid development and wide application of deep learning technology, AI-generated text detection plays an increasingly important role in various fields. In this study, we developed an efficient AI-generated text detection model based on the BERT algorithm, which provides new ideas and methods for solving related problems. In the data preprocessing stage, a series of steps were taken to process the text, including operations such as converting to lowercase, word splitting, removing stop words, stemming extraction, removing digits, and eliminating redundant spaces, to ensure data quality

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Corizzo, Roberto, and Sebastian Leal-Arenas. "One-Class Learning for AI-Generated Essay Detection." Applied Sciences 13, no. 13 (2023): 7901. http://dx.doi.org/10.3390/app13137901.

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Detection of AI-generated content is a crucially important task considering the increasing attention towards AI tools, such as ChatGPT, and the raised concerns with regard to academic integrity. Existing text classification approaches, including neural-network-based and feature-based methods, are mostly tailored for English data, and they are typically limited to a supervised learning setting. Although one-class learning methods are more suitable for classification tasks, their effectiveness in essay detection is still unknown. In this paper, this gap is explored by adopting linguistic feature

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13

Zeng, Zijie, Lele Sha, Yuheng Li, Kaixun Yang, Dragan Gašević, and Guangliang Chen. "Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 22502–10. http://dx.doi.org/10.1609/aaai.v38i20.30258.

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The recent large language models (LLMs), e.g., ChatGPT, have been able to generate human-like and fluent responses when provided with specific instructions. While admitting the convenience brought by technological advancement, educators also have concerns that students might leverage LLMs to complete their writing assignments and pass them off as their original work. Although many AI content detection studies have been conducted as a result of such concerns, most of these prior studies modeled AI content detection as a classification problem, assuming that a text is either entirely human-writt

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14

Krawczyk, Natalia, Barbara Probierz, and Jan Kozak. "Towards AI-Generated Essay Classification Using Numerical Text Representation." Applied Sciences 14, no. 21 (2024): 9795. http://dx.doi.org/10.3390/app14219795.

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The detection of essays written by AI compared to those authored by students is increasingly becoming a significant issue in educational settings. This research examines various numerical text representation techniques to improve the classification of these essays. Utilizing a diverse dataset, we undertook several preprocessing steps, including data cleaning, tokenization, and lemmatization. Our system analyzes different text representation methods such as Bag of Words, TF-IDF, and fastText embeddings in conjunction with multiple classifiers. Our experiments showed that TF-IDF weights paired w

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Howard, Frederick Matthew, Anran Li, Mark Riffon, Elizabeth Garrett-Mayer, and Alexander T. Pearson. "Artificial intelligence (AI) content detection in ASCO scientific abstracts from 2021 to 2023." Journal of Clinical Oncology 42, no. 16_suppl (2024): 1565. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.1565.

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1565 Background: Generative AI models such as OpenAI’s ChatGPT have been broadly utilized throughout the medical literature. Previous studies have found that AI can generate scientific abstracts which can be difficult to distinguish from the work of human authors. There is a pressing need to characterize utilization of AI in scientific writing to guide policy. Methods: In collaboration with ASCO's Center for Research and Analytics, we extracted text from all scientific abstracts submitted to ASCO 2021 – 2023 Annual Meetings. Likelihood of AI content was evaluated by four AI detectors: GPTZero,

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Xu, Zhenyu, and Victor S. Sheng. "Detecting AI-Generated Code Assignments Using Perplexity of Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23155–62. http://dx.doi.org/10.1609/aaai.v38i21.30361.

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Large language models like ChatGPT can generate human-like code, posing challenges for programming education as students may be tempted to misuse them on assignments. However, there are currently no robust detectors designed specifically to identify AI-generated code. This is an issue that needs to be addressed to maintain academic integrity while allowing proper utilization of language models. Previous work has explored different approaches to detect AI-generated text, including watermarks, feature analysis, and fine-tuning language models. In this paper, we address the challenge of determini

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Alshammari, Hamed, Ahmed El-Sayed, and Khaled Elleithy. "AI-Generated Text Detector for Arabic Language Using Encoder-Based Transformer Architecture." Big Data and Cognitive Computing 8, no. 3 (2024): 32. http://dx.doi.org/10.3390/bdcc8030032.

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The effectiveness of existing AI detectors is notably hampered when processing Arabic texts. This study introduces a novel AI text classifier designed specifically for Arabic, tackling the distinct challenges inherent in processing this language. A particular focus is placed on accurately recognizing human-written texts (HWTs), an area where existing AI detectors have demonstrated significant limitations. To achieve this goal, this paper utilized and fine-tuned two Transformer-based models, AraELECTRA and XLM-R, by training them on two distinct datasets: a large dataset comprising 43,958 examp

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Kim, Hong Jin, Jae Hyuk Yang, Dong-Gune Chang, et al. "Assessing the Reproducibility of the Structured Abstracts Generated by ChatGPT and Bard Compared to Human-Written Abstracts in the Field of Spine Surgery: Comparative Analysis." Journal of Medical Internet Research 26 (June 26, 2024): e52001. http://dx.doi.org/10.2196/52001.

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Background Due to recent advances in artificial intelligence (AI), language model applications can generate logical text output that is difficult to distinguish from human writing. ChatGPT (OpenAI) and Bard (subsequently rebranded as “Gemini”; Google AI) were developed using distinct approaches, but little has been studied about the difference in their capability to generate the abstract. The use of AI to write scientific abstracts in the field of spine surgery is the center of much debate and controversy. Objective The objective of this study is to assess the reproducibility of the structured

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Wani, Mudasir Ahmad, Mohammed ElAffendi, and Kashish Ara Shakil. "AI-Generated Spam Review Detection Framework with Deep Learning Algorithms and Natural Language Processing." Computers 13, no. 10 (2024): 264. http://dx.doi.org/10.3390/computers13100264.

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Spam reviews pose a significant challenge to the integrity of online platforms, misleading consumers and undermining the credibility of genuine feedback. This paper introduces an innovative AI-generated spam review detection framework that leverages Deep Learning algorithms and Natural Language Processing (NLP) techniques to identify and mitigate spam reviews effectively. Our framework utilizes multiple Deep Learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), to capture intricate p

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Al Karkouri, Adnane, Fadoua Ghanimi, and Salmane Bourekkadi. "Automatic Detection of Generated Texts and Energy: Exploring the Relationship." E3S Web of Conferences 412 (2023): 01101. http://dx.doi.org/10.1051/e3sconf/202341201101.

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The proliferation of artificial intelligence (AI) and natural language processing (NLP) technologies has enabled the generation of realistic and coherent texts, but it also raises concerns regarding the potential misuse of these technologies for generating misleading or malicious content. Automatic detection of generated texts is crucial in addressing this issue. This article provides a comprehensive examination of the relationship between the detection of generated texts and energy consumption, delving into the techniques, challenges, and opportunities for developing energyefficient algorithm

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Al Karkouri, Adnane, Fadoua Ghanimi, and Salmane Bourekkadi. "Unveiling the Environmental Implications of Automatic Text Generation and the Role of Detection Systems." E3S Web of Conferences 412 (2023): 01102. http://dx.doi.org/10.1051/e3sconf/202341201102.

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The emergence of artificial intelligence (AI) and natural language processing (NLP) technologies has led to the proliferation of automated systems capable of generating text. While these advancements have enhanced various fields, such as language translation and content generation, they have also given rise to concerns regarding the potential misuse of generated texts, particularly in the context of environmental preservation. This scientific article investigates the intricate relationship between automatic detection of generated texts and the environment. We examine the impact of generated te

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Lyu, Siwei. "Wrestling with the deepfakes: Detection and beyond." Open Access Government 43, no. 1 (2024): 272–73. http://dx.doi.org/10.56367/oag-043-11545.

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Wrestling with the deepfakes: Detection and beyond Siwei Lyu, SUNY Empire Innovation Professor from the University at Buffalo, State University of New York, delves into detection and beyond in the realm of DeepFakes, starting with a look at what they are. Since 2017, the term “DeepFake” has become widely known, often appearing in news and media. It combines “deep learning” (a type of artificial intelligence [AI] model) and “fake” to describe synthetic media – like text, audio, images, and videos – created with advanced AI technologies. The concept gained notoriety when a Reddit user began shar

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Fu, Yu, Deyi Xiong, and Yue Dong. "Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 18003–11. http://dx.doi.org/10.1609/aaai.v38i16.29756.

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To mitigate potential risks associated with language models (LMs), recent AI detection research proposes incorporating watermarks into machine-generated text through random vocabulary restrictions and utilizing this information for detection. In this paper, we show that watermarking algorithms designed for LMs cannot be seamlessly applied to conditional text generation (CTG) tasks without a notable decline in downstream task performance. To address this issue, we introduce a simple yet effective semantic-aware watermarking algorithm that considers the characteristics of conditional text genera

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Gupta, Varun, and Chetna Gupta. "Navigating the Landscape of AI-Generated Text Detection: Issues and Solutions for Upholding Academic Integrity." Computer 57, no. 11 (2024): 118–23. http://dx.doi.org/10.1109/mc.2024.3445068.

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Fariello, Serena, Giuseppe Fenza, Flavia Forte, Mariacristina Gallo, and Martina Marotta. "Distinguishing Human From Machine: A Review of Advances and Challenges in AI-Generated Text Detection." International Journal of Interactive Multimedia and Artificial Intelligence In press, In press (2024): 1. https://doi.org/10.9781/ijimai.2024.12.002.

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Gosling, Samuel David, Kate Ybarra, and Sara K. Angulo. "A widely used Generative-AI detector yields zero false positives." Aloma: Revista de Psicologia, Ciències de l'Educació i de l'Esport 42, no. 2 (2024): 31–43. https://doi.org/10.51698/aloma.2024.42.2.31-43.

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The widespread availability of generative-AI using Large Language Models (LLMs) has provided the means for students and others to easily cheat on written assignments – that is, students can use AI to generate text and then submit that work as their own. A variety of technical solutions have been developed to detect such cheating. However, concerns have been raised about the dangers of falsely identifying real students’ responses as having been generated by AI. Here we evaluate a Generative AI detector that comes as an option with Turnitin, a widely used plagiarism-detection platform already in

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Carrillo, Irene, Cesar Fernandez, M. Asuncion Vicente, et al. "Detecting and Reducing Gender Bias in Spanish Texts Generated with ChatGPT and Mistral Chatbots: The Lovelace Project." Proceedings of The Global Conference on Women’s Studies 3, no. 1 (2024): 29–42. http://dx.doi.org/10.33422/womensconf.v3i1.466.

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Current Artificial Intelligence (AI) systems can effortlessly and instantaneously generate text, images, songs, and videos. This capability will lead us to a future where a significant portion of available information will be partially or wholly generated by AI. In this context, it is crucial to ensure that AI-generated texts and images do not perpetuate or exacerbate existing gender biases. We examined the behavior of two common AI chatbots, ChatGPT and Mistral, when generating text in Spanish, both in terms of language inclusiveness and perpetuation of traditional male/female roles. Our anal

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Garib, Ali, and Tina A. Coffelt. "DETECTing the anomalies: Exploring implications of qualitative research in identifying AI-generated text for AI-assisted composition instruction." Computers and Composition 73 (September 2024): 102869. http://dx.doi.org/10.1016/j.compcom.2024.102869.

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He, Zhaokai, Ruolong Mao, and Yu Liu. "Predictive model on detecting ChatGPT responses against human responses." Applied and Computational Engineering 44, no. 1 (2024): 18–25. http://dx.doi.org/10.54254/2755-2721/44/20230078.

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The paper investigates the critical differences between AI-generated text and human responses in terms of linguistic patterns, structure, and content. The research makes use of datasets from HC3, collected in 2023. Our results are that ChatGPT with GPT-3.5 is more likely to use words like conjunctions and combinations of words in conversations compared to humans systematically. Our model has high accuracy in identifying AI-generated answers.

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Zaman, Asim, Baozhang Ren, and Xiang Liu. "Artificial Intelligence-Aided Automated Detection of Railroad Trespassing." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7 (2019): 25–37. http://dx.doi.org/10.1177/0361198119846468.

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Trespassing is the leading cause of rail-related deaths and has been on the rise for the past 10 years. Detection of unsafe trespassing of railroad tracks is critical for understanding and preventing fatalities. Witnessing these events has become possible with the widespread deployment of large volumes of surveillance video data in the railroad industry. This potential source of information requires immense labor to monitor in real time. To address this challenge this paper describes an artificial intelligence (AI) framework for the automatic detection of trespassing events in real time. This

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Thanathamathee, Putthiporn, Siriporn Sawangarreerak, Siripinyo Chantamunee, and Dinna Nina Mohd Nizam. "SHAP-Instance Weighted and Anchor Explainable AI: Enhancing XGBoost for Financial Fraud Detection." Emerging Science Journal 8, no. 6 (2024): 2404–30. https://doi.org/10.28991/esj-2024-08-06-016.

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This research aims to enhance financial fraud detection by integrating SHAP-Instance Weighting and Anchor Explainable AI with XGBoost, addressing challenges of class imbalance and model interpretability. The study extends SHAP values beyond feature importance to instance weighting, assigning higher weights to more influential instances. This focuses model learning on critical samples. It combines this with Anchor Explainable AI to generate interpretable if-then rules explaining model decisions. The approach is applied to a dataset of financial statements from the listed companies on the Stock

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Ramalakshmi, S., and G. Asha. "Exploring Generative AI: Models, Applications, and Challenges in Data Synthesis." Asian Journal of Research in Computer Science 17, no. 12 (2024): 123–36. https://doi.org/10.9734/ajrcos/2024/v17i12533.

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Generative AI has emerged as a transformative field within artificial intelligence, enabling the creation of new data that mimics real-world information and expands the boundaries of what machines can autonomously generate. This study discuss the various models of generative AI, focusing on Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Auto-Regressive models, each offering distinct approaches and strengths in data generation. VAEs excel in learning latent representations, making them ideal for applications like anomaly detection and data imputation. GANs, renowne

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Jadhav, Anurag. "Twitter Sentiment Analysis on Chatgpt Tweets." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1310–14. http://dx.doi.org/10.22214/ijraset.2023.56738.

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Abstract: This study investigates multilingual sentiment analysis within tweets on ChatGPT, an AI conversational model, employing Support Vector Machines (SVM) and BERT, an advanced language model. It aims to detect and classify emotions, including emoji identification, embedded within diverse messages across multiple languages on Twitter. By leveraging SVM's text classification and BERT's contextual understanding in various languages, the research delves into preprocessing techniques and feature engineering for sentiment analysis, encompassing multilingual and emoji detection. Furthermore, it

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Kirthiga, Mrs N., Miriyala Vamsi Krishna, Venkata Naveen Vadlamudi, Makani Venkata Sai Kiran, and Dudekula Hussain. "Sign Language Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1328–34. http://dx.doi.org/10.22214/ijraset.2024.58630.

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Abstract: Millions of citizens worldwide suffer from deaf and hard of hearing (DHH), a communication impairment that makes speaking difficult and necessitates the use of sign language. This communication gap frequently hampers access to education and opportunities for employment. Although AI-driven technologies have been studied to tackle this problem, no research has specifically looked into the intelligent and automatic translation of American sign gestures to text in low-resource languages (LRL), such as Nigerian languages. We suggest a unique end-to-end system for translating the American

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Baron, Philip. "Are AI detection and plagiarism similarity scores worthwhile in the age of ChatGPT and other Generative AI?" Scholarship of Teaching and Learning in the South 8, no. 2 (2024): 151–79. http://dx.doi.org/10.36615/sotls.v8i2.411.

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Recent advancements in chatbots have provided students and academics with a new mode of how knowledge can be sourced and composed. Within a very short space of time, students and academics have flocked to use ChatGPT and other Generative Artificial Intelligence (GAI) platforms owing to their capable responses. Additionally, apart from the generative chatbots (such as ChatGPT and Gemini), AI writing tools for paraphrasing, summarising, and co-writing have also become capable and increasingly prevalent to such a degree that the public is spoilt for choice. Having conducted tests on popular chatb

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Surianarayanan, Chellammal, John Jeyasekaran Lawrence, Pethuru Raj Chelliah, Edmond Prakash, and Chaminda Hewage. "Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review." Sensors 23, no. 6 (2023): 3062. http://dx.doi.org/10.3390/s23063062.

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Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neur

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Audi Albtoush, Et al. "ChatGPT: Revolutionizing User Interactions with Advanced Natural Language Processing." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 3354–60. http://dx.doi.org/10.17762/ijritcc.v11i9.9541.

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Significant advancements in artificial intelligence (AI) have unexpectedly improved the standard of living globally. One recent development garnering attention is ChatGPT, a natural language processing (NLP) model created by OpenAI. ChatGPT combines OpenAI's GPT-2 language model with supervised and reinforcement learning techniques, leveraging the extensive language patterns in the GPT-3 corpus. This enables natural text-based interactions between users and AI systems, making it suitable for customer service applications and the creation of voice and text-based virtual assistants. ChatGPT offe

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Ali, Miss Aliya Anam Shoukat. "AI-Natural Language Processing (NLP)." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 135–40. http://dx.doi.org/10.22214/ijraset.2021.37293.

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Natural Language Processing (NLP) could be a branch of Artificial Intelligence (AI) that allows machines to know the human language. Its goal is to form systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification. Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It's spread its applications in various fields like computational linguistics, email spam detection, information extraction, summarization, medical, and question answering etc. The goal of th

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Turgeon, D. Kim, Lena Krammes, Hiba-Tun-Noor Mahmood, et al. "A novel, noninvasive, multimodal screening test for the early detection of precancerous lesions and colorectal cancers using an artificial intelligence–based algorithm." Journal of Clinical Oncology 42, no. 16_suppl (2024): 3627. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.3627.

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3627 Background: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related mortality worldwide, and its incidence is increasing in younger populations. Detection of early-stage CRC and its precursor lesions, such as advanced adenomas (AA), is crucial for successful treatment and reduces CRC-related mortality. Although non-invasive methods for early detection are available and increase screening compliance, their performance with respect to detection of advanced adenomas and early-stage cancers is limited. Here, we describe a novel and non-invasive stool-based approach combini

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Patil, Prof Shital. "Air Handwriting using AI and ML." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33918.

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Air-writing refers to virtually writing linguistic characters through hand gestures in three dimensional space with six degrees of freedom. In this paper a generic video camera dependent convolutional neural network (CNN) based air-writing framework has been proposed. Gestures are performed using a marker of fixed color in front of a generic video camera followed by color based segmentation to identify the marker and track the trajectory of marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acq

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Loh, Peter K. K., Aloysius Z. Y. Lee, and Vivek Balachandran. "Towards a Hybrid Security Framework for Phishing Awareness Education and Defense." Future Internet 16, no. 3 (2024): 86. http://dx.doi.org/10.3390/fi16030086.

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The rise in generative Artificial Intelligence (AI) has led to the development of more sophisticated phishing email attacks, as well as an increase in research on using AI to aid the detection of these advanced attacks. Successful phishing email attacks severely impact businesses, as employees are usually the vulnerable targets. Defense against such attacks, therefore, requires realizing defense along both technological and human vectors. Security hardening research work along the technological vector is few and focuses mainly on the use of machine learning and natural language processing to d

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Banat, Maysaa. "Investigating the Linguistic Fingerprint of GPT-4o in Arabic-to-English Translation Using Stylometry." Journal of Translation and Language Studies 5, no. 3 (2024): 65–83. http://dx.doi.org/10.48185/jtls.v5i3.1343.

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This study explores the linguistic and stylistic characteristics of machine-generated texts, focusing on the output of GPT-4o. Using various natural language processing (NLP) techniques, including word frequency and stopword count analysis, readability and sentence structure metrics, lexical diversity measures, syntactic frequency analysis, and named entity recognition (NER), the research aims to uncover the stylometric fingerprints present in machine-generated content. The results reveal that GPT-4ogenerated texts exhibit moderate lexical diversity and syntactic complexity, with certain chapt

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Wang, Fan, Afeng Wang, Minghao Pan, et al. "Recognizing Large‐Scale AIGC on Search Engine Websites Based on Knowledge Integration and Feature Pyramid Network." Proceedings of the Association for Information Science and Technology 61, no. 1 (2024): 679–84. http://dx.doi.org/10.1002/pra2.1079.

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ABSTRACTThe proliferation of Artificial Intelligence Generated Content (AIGC) poses significant challenges to user experience and information accuracy, especially on search engine websites(Guo et al., 2023). The current solution is to identify AIGC by machine learning algorithms or publicly available AI detection tools, whereas, machine learning(Wang & Wang, 2022) algorithms degrade in accuracy as more data is available and tools such as GPTZero perform poorly in the task of AIGC detection on social media. In this paper, we propose an EPCNN model to identify AIGC on search engine websites,

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Carvalho, Floran, Julien Henriet, Francoise Greffier, Marie-Laure Betbeder, and Dana Leon-Henri. "Deep learning for the detection of acquired and non-acquired skills in students' algorithmic assessments." Journal of Education and e-Learning Research 10, no. 2 (2023): 111–18. http://dx.doi.org/10.20448/jeelr.v10i2.4449.

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This research is part of the Artificial Intelligence Virtual Trainer (AI-VT) project which aims to create a system that can identify the user's skills from a text by means of machine learning. AI-VT is a case-based reasoning learning support system can generate customized exercise lists that are specially adapted to user needs. To attain this outcome, the relevance of the first proposed exercise must be optimized to assist the system in creating personalized user profiles. To solve this problem, this project was designed to include a preliminary testing phase. As a generic tool, AI-VT was desi

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Shaik Vadla, Mahammad Khalid, Mahima Agumbe Suresh, and Vimal K. Viswanathan. "Enhancing Product Design through AI-Driven Sentiment Analysis of Amazon Reviews Using BERT." Algorithms 17, no. 2 (2024): 59. http://dx.doi.org/10.3390/a17020059.

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Understanding customer emotions and preferences is paramount for success in the dynamic product design landscape. This paper presents a study to develop a prediction pipeline to detect the aspect and perform sentiment analysis on review data. The pre-trained Bidirectional Encoder Representation from Transformers (BERT) model and the Text-to-Text Transfer Transformer (T5) are deployed to predict customer emotions. These models were trained on synthetically generated and manually labeled datasets to detect the specific features from review data, then sentiment analysis was performed to classify

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Rahnemoonfar, Maryam, Jimmy Johnson, and John Paden. "AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network." Sensors 19, no. 24 (2019): 5479. http://dx.doi.org/10.3390/s19245479.

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Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance

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Allen, Laura, Wenqian Xu, Mariko Nishikitani, Vaishnavi Atul Patil, and Dana Bradley. "AGE BIAS IN ARTIFICIAL INTELLIGENCE (AI): A VISUAL PROPERTIES ANALYSIS OF AI IMAGES OF OLDER VERSUS YOUNGER PEOPLE." Innovation in Aging 7, Supplement_1 (2023): 986. http://dx.doi.org/10.1093/geroni/igad104.3168.

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Abstract Concerns about age-related biases in Artificial Intelligence (AI)’s design, development, and data generation raise alarms for potential social inequalities. AI art-generators like MidJourney, known for creating distinctive and artistic images, have gained attention recently. However, the critical examination of their potential biases remains limited. Our study aims to investigate age-related bias in MidJourney by comparing how older and younger individuals are visually depicted. Using 19 keywords (as text prompts) tied to domains of potential social exclusion for older adults, we auto

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Bagate, Rupali Amit, and Ramadass Suguna. "Sarcasm Detection on Text for Political Domain— An Explainable Approach." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 2s (2022): 255–68. http://dx.doi.org/10.17762/ijritcc.v10i2s.5942.

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In the era of social media, a large volume of data is generated by applications such as the industrial internet of things, IoT, Facebook, Twitter, and individual usage. Artificial intelligence and big data tools plays an important role in devising mechanisms for handling this vast volume of data as per the required usage of data to form important information from this unstructured data. When the data is publicly available on the internet and social media, it is imperative to treat the data carefully to respect the sentiments of the individuals. In this paper, the authors have attempted to solv

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Pingua, Bhagyajit, Deepak Murmu, Meenakshi Kandpal, et al. "Mitigating adversarial manipulation in LLMs: a prompt-based approach to counter Jailbreak attacks (Prompt-G)." PeerJ Computer Science 10 (October 22, 2024): e2374. http://dx.doi.org/10.7717/peerj-cs.2374.

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Large language models (LLMs) have become transformative tools in areas like text generation, natural language processing, and conversational AI. However, their widespread use introduces security risks, such as jailbreak attacks, which exploit LLM’s vulnerabilities to manipulate outputs or extract sensitive information. Malicious actors can use LLMs to spread misinformation, manipulate public opinion, and promote harmful ideologies, raising ethical concerns. Balancing safety and accuracy require carefully weighing potential risks against benefits. Prompt Guarding (Prompt-G) addresses these chal

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"An Empirical Study of AI-Generated Text Detection Tools." Advances in Machine Learning & Artificial Intelligence 4, no. 2 (2023). http://dx.doi.org/10.33140/amlai.04.02.03.

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Since ChatGPT has emerged as a major AIGC model, providing high-quality responses across a wide range of applications (including software development and maintenance), it has attracted much interest from many individuals. ChatGPT has great promise, but there are serious problems that might arise from its misuse, especially in the realms of education and public safety. Several AIGC detectors are available, and they have all been tested on genuine text. However, more study is needed to see how effective they are for multi-domain ChatGPT material. This study aims to fill this need by creating a m

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