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Intelligent Identification of Landslide Based on Deep Semi-supervised learning 5

Intelligent Identification of Landslide Based on Deep Semi-s...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Xie, Dalan Yang, Ronghao Qiao, Yichun Zhang, Jinbo College of Earth Sciences Chengdu University of Technology Chengdu610059 China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Landslides are a frequent and widespread geological disaster. Because a large number of deadly landslide disasters are newly added every year, efficient landslide identification is of great significance in applications such as earthquake prevention and disaster reduction. Visual interpretation not only consumes a lot of manpower and material resources, but also relies heavily on the experience of professionals;while deep learning-based methods often require a large amount of labeled sample data to improve model performance. In order to solve the problem of time-consuming and labor-intensive labeling of landslide sample data, this paper uses a semi-supervised semantic segmentation method of U2PL [1] to automatically identify landslides in optical remote sensing images;Unlabeled samples are used to avoid the performance degradation of traditional supervised learning when there are insufficient training samples;deep learning is used to capture more features hidden in landslide images, and semi-supervised learning is used to use a small number of labeled samples to reduce a lot of manual labeling work. the results show that with the addition of unlabeled data, the Iou of landslide identification has been significantly improved. Even with a certain number of labeled samples, gradually increasing the unlabeled sample data, the Iou of the model landslide identification has increased by 4.93%, and the overall mIou has increased by 2.51 %. At the same time, the method is superior to the existing Yolov5 algorithm in both efficiency and accuracy. © 2022 IEEE.

关键词： Landslides

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data-driven simulation of reservoir dynamics based on erroneous inputs from fast supply streamline data 5

Data-driven simulation of reservoir dynamics based on errone...

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5th international conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Saberali, Behzad Zhang, Kai Shandong Qingdao266580 China

ISBN: (纸本)9781510668621

this work introduced an AI-based simulation method as an innovative solution to address the limitations of conventional numerical reservoir simulation methods. the primary motivations behind this research were the time-consuming nature of finite difference (FD) simulation and the considerably lower accuracy of streamline (SL) simulation. We adopted a novel approach to overcome these challenges by incorporating erroneous yet rapidly supplied streamline data as inputs to our model. this unique input selection allowed us to leverage the direct training strategy in a deep learning-based network. Furthermore, by integrating FD grid data as training outputs, we improved the model’s accuracy and expanded its functionalities. the developed proxy model demonstrates remarkable performance, surpassing the FD simulator’s computational efficiency while outperforming the SL simulator’s accuracy. through validation of the benchmark Egg model, our research confirmed the potential of this AI-based proxy model as a reliable, accurate, and efficient alternative in dynamic reservoir modeling. © 2023 SPIE.

关键词： pattern recognition

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Sarcasm Detection Using machine learning and Deep learning 5th

Sarcasm Detection Using Machine Learning and Deep Learning

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5th international conference on Recent Trends in machine learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Kshirsagar, Vanita Ganesh Yadav, Sunil Kumar Karande, Nikhil Chaudhari, Bhushan Amity School of Engineering and Technology Amity University Rajasthan Jaipur India Dr. D. Y Patil Institute of Technology Pune Pimpri India SISA Infotech Pvt. Ltd Bangalore India Dr D Y Patil Medical College Pune Pimpri India Dr D Y Patil Vidyapeeth Pune India

ISBN: (纸本)9789819788644

Combining deep learning algorithms, mental health assessments, and sarcasm recognition creates an intriguing synergy for deciphering emotional states in textual data. Utilizing cutting-edge technologies to predict mental health presents prospects for early identification and intervention. the primary factors impacted by people’s snarky remarks or sarcastic statements, unfavorable tweets, and stress at work. It either directly or indirectly contributes to a person’s descent into depression. In India, individuals find it difficult to acknowledge that they are depressed or experiencing mental health issues. they also find it difficult to discuss or disclose their problems with others or to talk about their mental health. It is imperative to identify a person’s depression early on. New approaches must be used to diagnose and track symptoms of depression on an everyday basis. In this work, we have employed a novel approach to depression prediction, utilizing sophisticated machine learning algorithms and deep data analysis methods. As compared to the machine learning (ML) algorithm Deep learning algorithms provide better accuracy. the suggested neural network approach extends the capabilities of a pre-trained transformer-based architecture through the strategic integration of convolutional neural network (CNN) and long short-term memory (LSTM) components. In this, we have used Natural Language Processing (NLP) techniques to analyze large amounts of sarcastic utterances in order to identify the sarcastic terms that have a detrimental impact on people’s mental health. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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DDoS Attack Detection Model using machine learning Algorithm in Next Generation Firewall 5

DDoS Attack Detection Model using Machine Learning Algorithm...

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5th international conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Patel, Manthan Amritha, P.P. Sudheer, Vinay B. Sethumadhavan, M. TIFAC - CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

Next generation firewall is taking major part to secure network environment in the industry. this device will monitor all the traffic which is coming inside the network or going outside of the network. With all these security devices attackers can still perform various kind of attacks on the network. DDoS attack is one of the hardest attack to identify which will send packets to the network and which will look like normal traffic but it will act as a DDoS traffic. In this paper we used binary decision tree, XGBoost and support vector machine to identify DDoS attack traffic pattern from the different features in the packet header. data will be fetched from the packet header and among them standard deviation of the packet bytes and packet flows are the features considered. We have applied this data on the trained dataset. Algorithm will predict whether the traffic coming inside the network is trusted or not. Out of the three algorithms Binary Decision tree algorithm is giving 99 percentage of accuracy and will predict the data as fast as possible. Here priority is to filter DDos attacks of any security level in the line speed of the NIDS or any other appliances. this method of DDoS attack detection will add extra layer of security in the next generation firewall which will make firewall more robust. © 2024 the Authors. Published by Elsevier B.V.

关键词： Denial-of-service attack

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Research on Dynamic Modeling Algorithm of College Students' learning Motivation Factors based on SPSS and Artificial Intelligence Testing machine 5

Research on Dynamic Modeling Algorithm of College Students' ...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Yang, Suyun Shaoyang Univ Shaoyang 422000 Hunan Peoples R China

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Research on the dynamic modeling algorithm of college students' learning motivation factors based on SPSS and artificial intelligence testing machine is conducted in this paper. In the designed statistical model, according to the results of the two-sample ANOVA, that is, whether the population variances of the two samples are equal, the t-test of the assumptions of equal variance and heteroscedasticity is selected to infer whether the population means of the two samples are equal. It reflects the variation degree of another variable after controlling the value of one variable. When both variables are normal continuous variables and there is a linear relationship between them on the scatter diagram, it can be considered that there is a linear correlation trend between them. With this designed novel model, the SPSS and artificial intelligence testing machine is designed. through the various testing, the general performance is validated.

关键词： SPSS analysis artificial intelligence dynamic modeling motivation factors testing machine data mining

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ICMVA 2022 - 5th international conference on machine Vision and Applications

ICMVA 2022 - 5th International Conference on Machine Vision ...

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5th international conference on machine Vision and Applications, ICMVA 2022

ISBN: (纸本)9781450395670

the proceedings contain 19 papers. the topics discussed include: towards face representation learning conditioned on the soft biometrics;a semantic segmentation approach for road defect detection and quantification;depth and thermal images in face detection â€" a detailed comparison between image modalities;the study of emotional brain to detect emotions using brain EEG signals and improving accuracy of emotion detection system using feature selection techniques;transfer learning based precise pose estimation with insufficient data;analysis and example implementation of data visualization technology;simultaneous integration of multimodal interfaces for generating structured and reliable robotic task configurations;optimizing train-test data for person re-identification in real-world applications;and development of a powerful facial recognition system through an API using ESP32-Cam and Amazon Rekognition service as tools offered by industry 5.0.

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Diagnosing Bacteria Samples Using data mining: Review study 5

Diagnosing Bacteria Samples Using Data Mining: Review study

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5th international conference on Engineering Technology and its Applications, IICETA 2022

作者： Badr, Ahmed Adnan Abbas, thekra Aboksour, Mohammed Fadhel Al-Mustansiryia University Dept. of Computer Science Baghdad Iraq Al-Mustansiryia University Dept. of Microbiology Baghdad Iraq

ISBN: (纸本)9781665472159

Bacteria are implicated in a lot of biological and chemical activities, some of which are dangerous and others beneficial. Bacterial samples go through several stages before identification. Some of these stages are done visually with a microscope to detect the bacteria's shape and color of the gram stain, while others include exposing these samples to chemical and organic substances. Researchers have developed intelligence computer systems capable of diagnosing and classifying bacteria in order to minimize the amount of human labor and increase diagnosis accuracy. this paper will provide a detailed look at previous studies that tried to find solutions to the problem of diagnosing and classifying bacteria samples using artificial intelligence techniques such as deep learning, machine learning and data mining, as well as analyzing the results of these studies and clarifying the challenges of building comprehensive systems capable of performing this task. © 2022 IEEE.

关键词： Bacteria

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Exploration of transfer learning capability of multilingual models for text classification 23

Exploration of transfer learning capability of multilingual ...

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5th international conference on pattern recognition and Intelligent Systems, PRIS 2023

作者： Bhargava, Maddineni Vijayan, Karthika Anand, Oshin Raina, Gaurav Indian Institute of Technology Madras Tamil Nadu Chennai India Sahaj AI Bangalore Karnataka India

ISBN: (纸本)9781450399968

the use of multilingual models for natural language processing is becoming increasingly popular in industrial and business applications, particularly in multilingual societies. In this study, we investigate the transfer learning capabilities of multilingual language models like mBERT and XLM-R across several Indian languages. We study the performance characteristics of a classifier model with mBERT/XLM-R as the front-end, which is trained only in one language for two tasks: text categorization of news articles and sentiment analysis of product reviews. News articles, on the same event but in different languages, are representative of what may be termed as inherently parallel' data;i.e. data that exhibits similar content across multiple languages, though not necessarily in parallel sentences. Other examples of such data would be customer inquiries/reviews about the same product, social media activity pertaining to the same topic, etcetera. After training in one language, we study the performance characteristics of this classifier model when applied to other languages. Our experiments reveal that by exploiting the inherently parallel nature of the data, XLM-R performs remarkably well when adapted for any Indian language dataset. Further, our study reveals the importance of simultaneously fine-tuning multilingual models with in-domain data from one language in order to express their cross-lingual and domain transfer learning abilities together. © 2023 ACM.

关键词： Sentiment analysis

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Revolutionizing Requirements Elicitation: Deep learning-Based Classification of Functional and Non-Functional Requirements

Revolutionizing Requirements Elicitation: Deep Learning-Base...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Grace Hanna Nathan Boyar Nathan Garay Mina Maleki Dept. of Electrical & Computer Engineering & Computer Science University of Detroit Mercy Detroit MI United States

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

the requirements elicitation phase in the software development life cycle (SDLC) is both critical and challenging, especially in the context of big data and rapid technological advancement. Traditional approaches like workshops and proto-typing, while useful, often struggle to keep pace with the massive data volumes and rapidly changing user demands characteristic of modern technology. this paper introduces a data-driven approach that utilizes deep learning (DL) and natural language processing (NLP) to enhance the requirements elicitation process by extracting requirements and classifying them into functional and non-functional categories. Our research involves a deep neural network (DNN) trained on a large dataset of transcriptions from client/user stories. this DNN can identify whether a specific text represents a functional requirement, a non-functional requirement, or neither. Our approach shows a marked improvement over previous methods, with a 33% increase in accuracy and an 18% increase in the F1 score. these results indicate the capability for deep learning techniques to play a vital role in elicitation.

关键词： Deep learning Accuracy conferences Artificial neural networks Big data Natural language processing Real-time systems pattern recognition Software development management

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Algorithmic Approaches to Classify Autism Spectrum Disorders: A Research Perspective

Algorithmic Approaches to Classify Autism Spectrum Disorders...

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13th international conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th international conference on Emerging data and Industry 4.0, EDI40 2022

作者： Jacob, Shomona Gracia Bait Ali Sulaiman, Majdi Mohammed Bennet, Bensujin Faculty University of Technology and Applied Sciences Nizwa611 Oman Assistant Academic Dean University of Technology and Applied Sciences Salalah211 Oman Head of Section University of Technology and Applied Sciences Nizwa611 Oman

Autism Spectrum Disorder (ASD) is a neurodevelopmental disability that exhibits sluggish progress in vocal development, restricted interest in normal activity and repetitive disoriented behavior. this syndrome, has gained a lot of attention due to its prevalence among children across all countries and from different economic backgrounds. However, ASD detection and treatment yet remains in its infancy due to the lack of awareness among parents, limited screening of proper developmental milestones and a dearth of diagnostic tools to classify this syndrome with convincing accuracy. Recent studies report that scalable biomarkers for early detection have made little progress in research due to the erraticism of this disorder. Moreover, the study on developing tools or applications for parents, teachers, and healthcare workers to identify children who exhibit any form of autism is still a work in progress. the research work undertaken in this paper presents an analysis of supervised machine learning algorithms on mining interesting details that link the diverse nature of ASD and the possibility of computationally detecting markers for the syndrome. the preliminary findings on the performance of traditional machine learning algorithms in ASD classification is reported with the possibility of integrating deep learning architectures for ASD detection and therapy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Autism disorder data mining machine learning pattern discovery Supervised learning

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