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A seven layer DNN approach for social-media multilevel image-based text classification 2nd

A seven layer DNN approach for social-media multilevel image...

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2nd international conference on Next-Generation computing and Information Systems, ICNGCIS 2023

作者： Tripathi, Veena Upadhyay, Navin Mani Sharma, Saurabh Model Institute of Engineering and Technology J&K Jammu India

ISBN: (纸本)9781032738659

In recent years, social media platforms have played a significant role in disaster management. To extract valuable insights and understand the meaning behind social media text content, using conventional machine learning methods, text mining solutions have been created. Through these techniques, messages are intended to be categorized into several themes, such counsel and caution. Nevertheless, these methods frequently focus on a single event and have trouble generalizing to cross events classifications. The effectiveness of traditional models trained on historical data for classifying social media messages from future events remains unclear. Our research focuses on the efficacy of a CNN model in classifying Twitter topics across diverse events. Three geotagged datasets from twitter gathered during the COVID-19 pandemic’s management are the subject of this study. Two conventional machine learning techniques (LR and SVM) are contrasted with the CNN model’s performance. Our experiments conclusively show that the CNN model significantly surpasses the performance of both SVM and LR models in terms of accuracy, regardless of whether the evaluation is based on individual events or across different events. This implies that the CNN model may efficiently categorize messages for impending events and pre-train on Twitter data from past occurrences, improving situational awareness. In conclusion, this study demonstrates the CNN model’s capability compared to traditional machine learning techniques for disaster management. © 2025 The Author(s).

关键词： Disasters

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A Deep learning Assisted Crop Pest Classification Model by Using Enhanced Light Gradient Boost Machine (ELGBM) Logics

A Deep Learning Assisted Crop Pest Classification Model by U...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Venkata Siva Prasad, Ch. Malarvizhi, C. Porkodi, M. Vaikash, K.G. Sunheriya, N. Al-Mousa, Mohammad R. Jeppiaar Engineering College Department of Ece Chennai India Rajalakshmi Institute of Technology Department of Ece Chennai India Sona College of Technology Department of Eee Salem India School of Mechanical Engineering Vellore Institute of Technology Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Information Technology Zarqa University Department of Cyber Security zarqa Jordan Universiry of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Many different kinds of pests and insects impede the development of vital field crops including wheat, sugarcane, and rice, and they also diminish agricultural yields. Due to their similar appearance in the early stages of crop growth, accurately classifying and identifying all varieties of crop insects is a challenging task for farmers. Automatic feature extraction and handling of complicated high-level features in image classification applications are two requirements for a new model that can solve this problem with the help of deep learning aided architectures. This study investigates the use of an Enhanced Light Gradient Boost Machine (ELGBM), a deep learning algorithm, for the purpose of crop pest classification and pesticide recommendation. The suggested model's efficacy is assessed by cross-validation with a traditional learning technique known as Convolution Neural Network (CNN). Furthermore, the system incorporates a recommendation component that proposes the most efficient pesticide options for crop protection depending on the discovered pests. The suggested methodology outperforms more traditional approaches in terms of classification accuracy, which is around 98%. The outcomes proved that the suggested ELGBM model outperformed pre-trained models in labeling different kinds of field crop insects, suggesting its potential for use in agricultural crop protection systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Harnessing Long Short Term Memory Networks for Stock Market Forecasts

Harnessing Long Short Term Memory Networks for Stock Market ...

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2024 international conference on engineering and computing, ICECT 2024

作者： Faheem, Alishba Arshad, Usama Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

Stock market trend prediction is a classical re-gression problem with effects on stock exchange. This research is presenting a detailed exploration of how Long Short Term Memory (LSTM) networks may be used for predicting stock market trends. The LSTM model was employed instead of the conventional Recurrent Neural Networks (RNNs), to learn from historical financial data. The study examines the operations of the LSTM model and the optimization of hyper parameters that lead to accurate prediction outcomes from the given dataset. Our model gave encouraging results that underlined the LSTM model's prowess in capturing intricate temporal dependencies in data. The model's result were in a low Root Mean Square Error (RMSE) score and an impressively high accuracy level that were good indicators of the model's adaptability at trend identification. Our study amplifies the need for advanced machine learning algorithms like LSTM that increase prediction accuracy while offering insights into decision-making processes for both investors and financial analysts at the same time. Further, it also hints at the potential of deep learning within the stock market. The paper concludes by discussions on all possible improvements to the existing model, future directions, and how incorporating additional data types can increase our predictive accuracy. This research primarily underscores the significant role LSTM plays in deciphering complex financial data trends accurately. © 2024 IEEE.

关键词： Long short-term memory

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Research on Beyond-Visual-Range Air Combat Decision Making Based on Improved Self-Play Deep Reinforcement learning 7

Research on Beyond-Visual-Range Air Combat Decision Making B...

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7th international conference on Machine learning and Natural Language Processing, MLNLP 2024

作者： Zhao, Liuhang Xing, Chenguang Yang, Xinghao Aviation System Engineering Institute of China Beijing China

ISBN: (纸本)9798350354973

As air combat technologies evolve, beyond-visual-range (BVR) operations have become the dominant mode of engagement. This paper addresses the autonomous decision-making challenges in BVR air combat by proposing an improved self-play deep reinforcement learning method. First, process-guiding rewards are introduced to mitigate the issue of reward sparsity commonly faced in BVR decision-making research. Second, an improved self-play approach with auxiliary strategies is proposed, enabling the main policy to identify and address its weaknesses more effectively. Simulation experiments validate the method's effectiveness, with the trained agent achieving an average win rate of 79% against rule-based expert strategy model and exhibiting behavior closely aligned with human experts. The results demonstrate that the proposed method enhances both offensive and defensive decision-making, ensuring robust performance in complex BVR scenarios. © 2024 IEEE.

关键词： Deep reinforcement learning

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DigiTeachVR: Digitally-Enhanced teaching Platform for Improving Data Science Skills and Virtual Reality Competences in Cross-Disciplinary engineering Education 14th

DigiTeachVR: Digitally-Enhanced Teaching Platform for Improv...

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14th international conference on learning Factories (CLF)

作者： Koh, Linus Stricker, Philipp Reisinger, Julia Ansari, Fazel TU Wien Res Grp Prod & Maintenance Management Theresianumgasse 27 A-1040 Vienna Austria TU Wien Dept Integrated Planning & Ind Bldg Karlspl 13 A-1040 Vienna Austria

ISBN: (纸本)9783031653995;9783031654008

Integrated factory design (IFD) requires cross-disciplinary competences obtained inter alia from areas of industrial, mechanical, civil engineering and architecture as well as operations, assets and facility management. However, traditional teaching practices in higher education could neither effectively provide solutions to cross-disciplinary competencies for IFD nor supply evolving job market demands (aka skill gaps). Information and Communication Technologies (ICT) are pivotal in this transformation, shaping smart learning environments that integrate collaborative activities and harness freely available online tools and cloud services. However, challenges persist in cross-disciplinary fields like IFD, where data science and visualization technologies like Virtual Reality (VR) are increasingly applied but often not fully integrated into traditional curricula. To address this educational gap, this paper introduces the digiTeachVR platform, which incorporates VR and data science into a collaborative teaching platform designed for IFD. Undergraduate and graduate students from diverse disciplines participate in a parametric design course that leverages industrial data science and VR within an interactive, self-paced, collaborative environment. The digiTeachVR's approach involves self-taught programming lessons followed by collaborative programming on Python. This is to develop parametric designs, subsequently transformed into VR models using Rhino 3D. A pilot study of digiTeachVR at the TU Wien indicates a significant (79%) increase in student enthusiasm, suggesting improved content delivery and enhanced learning outcomes (63%). The paper presents the approach, summarizes the key achievements and identifies the future research pathways for enhancing quality of cross-disciplinary engineering education.

关键词： Technology-enhanced learning Blended learning engineering Education Industrial Data Science Virtual Reality Integrated Factory Design

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A Convolutional Autoencoder Framework for Probabilistic Anomaly Detection on Infrastructure Systems

A Convolutional Autoencoder Framework for Probabilistic Anom...

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ASCE international conference on computing in Civil engineering (I3CE)

作者： Gu, Yueyan Jazizadeh, Farrokh Virginia Tech Dept Civil & Environm Engn Blacksburg VA 24061 USA

ISBN: (数字)9780784485248

ISBN: (纸本)9780784485248

Data-driven autonomous anomaly detection, empowered by advanced sensing and machine learning techniques, has the potential to significantly reduce costs associated with infrastructure maintenance. In this paper, we studied the impact of probabilistic anomaly detection in the context of a proposed deep learning-empowered framework for multivariate time series anomaly detection. In this framework, two-dimensional convolutional autoencoders are utilized to learn the patterns of normal data and detect unseen anomalous data points that fail to be reconstructed by the well-trained autoencoders. Kernel density estimation was utilized to generate a probability distribution function based on raw window-based anomaly predictions. Our assessments showed promising performance in a real-world case study of Class I railroad track anomaly detection. We conducted a comparative study that proved our hypothesis that probabilistic anomaly detection outperforms the deterministic approach of anomaly detection using direct autoencoder output. Furthermore, the impact of multivariate time series data processing methods, autoencoder architecture, and anomaly score thresholds was also investigated.

关键词： Deep learning Convolutional Autoencoders Anomaly Detection Railroad Track

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AI-Driven Mycology: Transfer learning for Automated Mushroom Species Identification

AI-Driven Mycology: Transfer Learning for Automated Mushroom...

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2024 IEEE international conference on computing, Applications and Systems, COMPAS 2024

作者： Ahmad, Md Hasan Rahman Sohag, Sohanur Rimon Bormon, Sajib Shishir, Hasanur Rahman Hossain Antar, Sabbir Daffodil International University Department of Computer Science and Engineering Ashulia Dhaka Bangladesh Noakhali Science and Technology University Department of Software Engineering Noakhali Bangladesh

ISBN: (纸本)9798331529765

Mushroom categorization is a difficult process since there are so many different species and they all have different aesthetic qualities. In this paper, we are to investigate the use of transfer learning techniques for precise mushroom classification. We have examined the performance of EfficientNetB0, ResNet50, InceptionV3, and MobileNet, four well-known transfer learning models. The mushroom dataset is first preprocessed to make sure that the images are properly scaled and normalized. After initializing and fine-tuning the pre-trained weights of the above-mentioned models on our mushroom dataset, we then apply transfer learning techniques. Each model is trained using a carefully chosen training set, and its performance is assessed using a different test set. Our findings reveal that all four transfer learning models successfully categorize mushrooms, achieving outstanding classification accuracies. With a 97% accuracy rate, EfficientNetB0 surpasses the other models when we compare them. Its sophisticated design, which combines compound scaling and AutoML approaches to attain the best accuracy and efficiency, is responsible for its exceptional performance. The results of this study demonstrate the value of transfer learning in tasks involving mushroom categorization. Additionally, they stress the benefit of using cutting-edge models like EfficientNetB0, which produce greater accuracy compared to conventional transfer learning architectures. The development of automated methods for mushroom identification and subsequent breakthroughs in the mycology field can be facilitated by the use of these findings by academics and professionals working on mushroom classification. © 2024 IEEE.

关键词： Adversarial machine learning

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Detection of Stroke in CT Scans Using Deep learning and Image Processing Techniques

Detection of Stroke in CT Scans Using Deep Learning and Imag...

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2024 IEEE international conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Kaur, Gagandeep Nigam, Nishesh Khamrokulovna, Akhmadaliyeva Gulnora Almusawi, Muntather Kala Priyadarshini, G. Ashwin Shenoy, M. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Fergana Public Health Medical Institute Teacher of the Department of Biomedical Engineering Fergana Uzbekistan The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Cse Nitte India

ISBN: (纸本)9798350390759

This work describes a robust paradigm for inferring strokes from CT scans using deep reinforcement learning and image analysis. The steps which are as follows: first, a large volume of high quality CT scan images will be gathered second, the pre-processing of the scan images to improve the image quality and third, an advanced CNN model will be designed for accurate stroke detection. This performance has been further enhanced by integrating data augmentation and the application of data transfer learning, where the model reaches an accuracy of 94. AUC-ROC of 0. 2% for the mice group and an AUC-ROC of 0. 2% for the rats group. 97. In conclusion, the structural and functional understanding of the model proves that the model is sensitive coupled with high specificity and was able to generalize beyond the data used in developing this model and across the different types of strokes including ischemic, hemorrhagic, and transient ischemic strokes. Comparing with benchmark models, it is evident that the proposed approach outperforms the baseline counterparts considerably. These outcomes can be concluded as the fact that computer-aided methods can help radiologists in the quick and accurate decision making with a possibility of positive changes to the disease progression. The future research areas on the proposed method are the increase in model interpretability and the incorporation of other modalities in an effort to increase the accuracy of the results. The study thus establishes the potential of Deep learning in overhauling the conventional medical diagnosis and especially in timely accurate identification and diagnosis of strokes thus enriching health care. © 2024 IEEE.

关键词： Deep reinforcement learning

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Smart Environmental Monitoring System for Air and Water Quality Index

Smart Environmental Monitoring System for Air and Water Qual...

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2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Tripathi, Ansh Dubry, Saransh Rama, P. Angayarkanni, V. Kalaivani, J. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Srmist Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies India

ISBN: (纸本)9798331529833

The quality of air and water is essential for human health, environmental sustainability, and ecosystem balance. However, pollution from industrial activities, urbanization, and agricultural practices has made effective environmental monitoring a global priority. Traditional monitoring methods, which rely on manual sampling and laboratory analysis, are often time-consuming and lack real-time capabilities. This paper presents a Smart Environmental Monitoring (SEM) system leveraging IoT and sensor technology to monitor air and water quality in real time using two key sensors: the MQ135 for Air Quality Index (AQI) and the pH sensor for Water Quality Index (WQI). By focusing on these two critical parameters, the SEM system offers a streamlined yet robust solution for assessing environmental quality. The system collects data continuously, transmits it to a cloud platform for processing, and employs machine learning algorithms to forecast future pollution trends. A user-friendly dashboard displays real-time AQI and WQI levels, historical trends, and predictive insights, empowering users to respond proactively to environmental risks. Deployment results demonstrate the SEM system's capability in detecting pollution patterns, identifying high-risk areas, and predicting pollution spikes during peak traffic and in industrial zones. Comparative analysis with traditional monitoring highlights the SEM system's superior accuracy, scalability, and predictive abilities. Although the SEM system has higher initial setup costs, its operational efficiency and long-term benefits in mitigating environmental risks make it a sustainable choice for public health and environmental management. © 2025 IEEE.

关键词： Environmental monitoring

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Charting a New Course: Encouraging Programming Class Participation and Interest Through Multimedia and Flipped Classroom Activities 14th

Charting a New Course: Encouraging Programming Class Partic...

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14th international conference on Methodologies and Intelligent Systems for Technology Enhanced learning, mis4TEL 2024

作者： Nikolov, Ivan Department of Architecture Design and Media Technology Aalborg University Aalborg9000 Denmark

ISBN: (纸本)9783031855603

The integration of new technologies and evolving learning preferences necessitates a reevaluation of teaching methods in higher education. Beyond traditional lectures and textbooks, students now seek increased engagement and interactive learning experiences. This shift is particularly crucial in engineering and computer programming courses. In this paper, we showcase the development of three project-based learning courses across different semesters in Aalborg University’s Medialogy education. These courses were strategically redesigned to leverage multimedia materials and embrace flipped classroom learning, aiming to enhance class participation and overall student engagement. We provide an overview of the advantages and challenges associated with these teaching designs, present initial results indicating improved student engagement, and discuss potential pitfalls and future directions to fortify this approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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