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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： P. Suthanthiradevi G Revathy K. Rejini Muthu Lakshmi.V Department of Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Department of Computer science and engineering Vels Institute of science Technology and Advanced studies Chennai Department of Computer Science Engineering Amrita college of Engineering and Technology Nagercoil Department of Computer Science and Engineering St. Joseph's College of Engineering OMR Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Biological system modeling Large language models Text detection Writing Transformers Natural language processing

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Assessing the Efficiency of AI for Multivariate data Analysis 3

Assessing the Efficiency of AI for Multivariate Data Analysi...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Jambi Ratna Raja Chaturvedi, Mukesh Anandhasilambarasan, D. Ashok, V. Nawadkar, Ashwini R. Va, Manoj Genba Sopanrao Moze College of Engineering Department of Computer Engineering Maharashtra Pune India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India

ISBN: (纸本)9798350319125

This study aimed to evaluate the efficiency of synthetic Intelligence (AI) for multivariate records analysis. Multivariate statistics evaluation is a complicated venture that historically requires considerable manual labor and information in information science and machine mastering. AI can offer an extra green and accurate method to the venture. Consequently, this study employed a ramification of AI-based total methods to identify patterns and generate insights from the dataset. Traditional and ML algorithms were used to generate models based on the records. The models have been evaluated based on their metrics - accuracy, precision, and keep in mind. In the end, the effects from the models were analyzed to identify trends and institutions among the variables. The findings concluded that AI may want to provide a robust and accurate technique to perform multivariate records analysis. However, further research is needed to develop novel tactics to utilize AI for multivariate statistics evaluation with increased accuracy and precision. © 2023 IEEE.

关键词： considerable multivariate ramification statistics evaluation synthetic

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R.S. Ramya Keren Lois Daniel S. Rajkumar Sruthi S Madhavan Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision.

关键词： Training Analytical models Adaptation models Statistical analysis Atmospheric modeling Computational modeling Decision making Predictive models Autonomous aerial vehicles Safety

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Identifying Inaccurate Stories: Leveraging High Knowledge and Big data 15

Identifying Inaccurate Stories: Leveraging High Knowledge an...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Khan, Mohammad Ahmar Verma, Pushpendra Kumar Ghaldiyal, Shivangi Balaji, N.V. Francis, G. Arun Patil, Swati Bula Department of Management Information System College of Commerce & Business Administration Dhofar University Country Sultanate of Oman Oman School of Computer Science and Applications IIMT University Meerut India Department of Computer Applications Invertis University Uttar Pradesh Bareilly India Department of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India IT Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350370249

Research on fake news has gained significant traction in a number of fields, We describe how the issue is tackled from a natural language processing standpoint in this research with the aim of developing identify false material in news. Collecting high-quality data, such as examples of false and actual news stories on a diverse range of subjects, is the key obstacle in this field of study. We examine the available before introducing the site as our lab's gift to the . We provide the whole text of the news that have already been applied based on human evaluation of the articles' authenticity. In order to clarify the gaps and causes of imbalance in the datasets that are now accessible and to direct ongoing attempts, we also conduct an appearance-based experiment. We urge the public to gather more information and make it accessible for study. ©2024 IEEE.

关键词： data collection

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Deep Learning Applications for Wind Energy Forecasting in Smart Grids 10

Deep Learning Applications for Wind Energy Forecasting in Sm...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Wadood, Hamid Haris, Muhammad Hassan, Aqib Malik, Muhammad Osama Yousaf, Hamza Ullah, Kaleem University of Alabama at Birmingham Department of Computer Science Alabama United States Institute for Insight - J Mack Robinson College of Business Georgia State University AtlantaGA United States School of Computing and Mathematical Sciences University of Greenwich London United Kingdom University of Tulsa Department of Business Analytics Oklahoma United States Stetson-Hatcher School of Business Mercer University GA30341 United States University of Engineering and Technology Department of Energy System Engineering Peshawar Pakistan

ISBN: (纸本)9798331532895

Driven by the pressing need for sustainable and reliable energy sources, this paper critically evaluates deep learning's role in advancing wind energy forecasting, highlighting its potential to address current energy demands through improved prediction accuracy. Due to the inherent unpredictability of wind, conventional forecasting models often fall short;however, deep learning models offer enhanced reliability by effectively identifying complex, embedded patterns within wind data. The paper examines techniques such as time-series recurrent neural networks, restricted Boltzmann machines, convolutional neural networks, and autoencoders, each targeting distinct challenges in wind forecasting to strengthen grid integration and ensure energy reliability. Future directions-including hybrid model approaches and model interpretability-are discussed, underscoring deep learning's promise as a powerful tool for optimizing wind energy utilization and supporting a more sustainable, low-carbon energy infrastructure. © 2024 IEEE.

关键词： Wind forecasting

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Automated Plant Leaf Classification using Ensemble Transfer Learning in CNN model

Automated Plant Leaf Classification using Ensemble Transfer ...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Yuvalatha, S. Keerthika, J. Prabhavathy, S. Banupriya, M. Priyadharshini, R. Bannari Amman Institute of Technology Department of Computer Science and Business Systems Sathyamangalam India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Electronics and Communication Engineering Coimbatore India Hindustan Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Ranganathar Institute of Engineering Department of Computer Science and Engineeeirng Coimbatore India

ISBN: (纸本)9781665453424

In the agriculture production, one of the biggest challenges is detecting the plant leaf disease in their early stages which significantly affects the farmer's earnings. In this paper various transfer learning CNN model of mobile NetV2, ResNet, InceptionV3, VGG16, Densenet are used to categories the potato leaf disease into healthy, Early blight, Late blight. data fragmentation strategies are applied to expand the number of images for the collected dataset to train the model. The mobileNetV2 achieve the accuracy of 86.8%. In this work the ensemble transfer learning for CNN model is proposed to identify the leaf disease. For the classification, majority voting ensemble techniques are used and obtained the accuracy of 94.8%. © 2022 IEEE.

关键词： Learning systems

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IoT-Enabled Smart Manhole Management system for Real-time Status, Water Level, and Gas Detection

IoT-Enabled Smart Manhole Management System for Real-time St...

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Imran, Md Asif Masfequier Rahman Swapno, S.M. Chhabra, Gunjan Kaushik, Keshav Anzum Mahi, Md. Arif Islam, Md Babul Haque, Rezaul Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Graphic Era Hill University Department of Cse Uttarakhand Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Modeling Electronic and System Engineering Unical Dept. of Computer Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Modeling Electronic and System Engineering Unical Department of Computer Rende Italy

ISBN: (纸本)9798350375237

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring system improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city. © 2024 IEEE.

关键词： Maintenance

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Evaluation of Machine Learning Algorithms for Text Classification Tasks 3

Evaluation of Machine Learning Algorithms for Text Classific...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Aravindan, M.K. Raajini, X. Mercilin Ingle, Yashwant S. Jangid, Pradeep Kumar Katyal, Ashu Naval, Preeti Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

Assessing machine-learning algorithms for texttype responsibilities is critical to advancing the world of herbal language processing (NLP). As using applications that use NLP grows, so does the want to pick the maximum accurate algorithms out of a pool of to-be-had solutions. This paper reviews the current United States of America research concerning assessing system reading algorithms for textual content-type obligations. Numerous evaluation metrics, consisting of accuracy, precision, consider, and f-degree, are discussed, in addition to the strategies used to examine algorithms and determine the foremost solutions. The diverse challenges confronted in this venture are highlighted, giving readers an outline of the challenges related to assessing the tool and getting to know algorithms for text-kind responsibilities. © 2023 IEEE.

关键词： Natural language processing systems

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Deep Learning Approaches for Enhancing Content Retrieval 3

Deep Learning Approaches for Enhancing Content Retrieval

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Khurana, Saniya Singh, Shweta Ranganathaswamy, M.K. Renuka, G. Diwakar, Mandar P. Jain, Sandeep Kumar Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350319125

Deep learning strategies for content material retrieval have been explored and updated to cope with the want for accurate search effects. This machine up to date is used to date apprehend and examine text-up-to-date content material from sources, up-to-date webpages, or virtual up-todate motion pictures. As the quantity and complexity of online content will increase, so up-to-date have the want for higher up-to-date up to date those resources. Recent advancements in deep gaining knowledge have enabled better content retrieval structures that are up to date, updated correctly, and speedy to understand statistics from one-of-a-kind sources. Such structures include natural language processing (NLP), sentiment analysis, entity reputation, and up-to-date updated reputation. Those deep studying technologies have been used to decorate content material retrieval by enhancing search effects' accuracy, robotically updated extracting applicable statistics, categorizing information, and identifying patterns. Additionally, deep-getting, updated systems are up-to-date and quick to examine large volumes of data, generating and relevant consequences than up-to-date methods. In summary, deep up-to-date techniques can beautify the retrieval system of content material efficiently, enhancing the accuracy and pace of seek effects. © 2023 IEEE.

关键词： categorizing comprehend effectively learning techniques

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Detection of DDOS Attacks in Cloud Environment Using Deep Learning

Detection of DDOS Attacks in Cloud Environment Using Deep Le...

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IEEE International Conference on system, Computation, Automation and Networking (ICSCAN)

作者： S. Shanmuganathan R. Charumathi V. Vijayakumari C. Gayathri T. Ragupathi K. Madhan Department of CSE Mailam Engineering College mailam India Department of CSE Er.Perumal Manimekalai College of Engineering Hosur Department of CSE The Kavery Engineering College Tamilnadu Department of CSE Government College of Technology Coimbatore Department of Data Science and Business System SRM Institute of Science and Technology Kattankulathur Tamilnadu

The rapid evolution of cloud computing and the increasing reliance on Software -Defined Networking (SDN) have introduced new challenges in ensuring the security and availability of cloud based services. Distributed Denial of Service (DDoS) attacks, in particular, pose a significant threat to the stability and performance of cloud infrastructures. As network traffic patterns continue to increase and evolve, it is crucial to extract pertinent features that aid in identifying attacks and improving the accuracy of detection. Therefore, effective detection mechanisms are crucial to identify and mitigate DDoS attacks in SDN-based cloud environments. This study presents a novel approach that combines Decision Tree with Long Short-Term Memory (LSTM) for effective DDoS detection in cloud infrastructures. The model is evaluated using the 2019 dataset. The simulation findings validateits efficacy and show that it improves upon existing works in terms of accuracy.

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