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Unleashing the Power of Dynamic Mode Decomposition and Deep learning for Rainfall Prediction in North-East India 1st

Unleashing the Power of Dynamic Mode Decomposition and De...

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1st international conference on Computation of Artificial Intelligence and Machine learning, ICCAIML 2024

作者： Chowdary, Paleti Nikhil Pingali, Sathvika Unnikrishnan, Pranav Sanjeev, Rohan Sowmya, V. Gopalakrishnan, E.A. Dhanya, M. Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9783031714801

Accurate rainfall forecasting is crucial for effective disaster preparedness and mitigation in the North-East region of India, which is prone to extreme weather events such as floods and landslides. In this study, we investigated the use of two data-driven methods, dynamic mode decomposition (DMD) and long-short-term memory (LSTM), for rainfall forecasting using daily rainfall data collected from the India Meteorological Department in northeast region over a period of 122 years. We conducted a comparative analysis of these methods to determine their relative effectiveness in predicting rainfall patterns. Using historical rainfall data from multiple weather stations, we trained and validated our models to forecast future rainfall patterns. Our results indicate that both DMD and LSTM are effective in forecasting rainfall, with LSTM outperforming DMD in terms of accuracy, revealing that LSTM has the ability to capture complex nonlinear relationships in the data, making it a powerful tool for rainfall forecasting. The study reveals that the DMD method achieved Mean Squared Error (MSE) values ranging from 150.44 mm to 263.34 mm and Mean Absolute Error (MAE) values from 91.34 mm to 154.61 mm. In contrast, the Deep learning (DL) approach, utilizing LSTM, demonstrated a normalized MAE value of 0.35 and a normalized RMSE value of 0.534. Our findings suggest that data-driven methods such as DMD and deep learning approaches like LSTM can significantly improve rainfall forecasting accuracy in the North-East region of India, helping to mitigate the impact of extreme weather events and enhance the region’s resilience to climate change. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dynamic mode decomposition

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Sentiment Analysis of Twitter Posts on 5G Technology Using ML 1st

Sentiment Analysis of Twitter Posts on 5G Technology Using M...

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1st international conference on computing and Emerging Technologies, ICCET 2023

作者： Faryal, Mehak Khan, Muhammad Farhan Rezaei, Saeid Sohail, Muhammad Salim, Kinza Khan, Muhammad Imran Iqbal, Adeel University of Engineering and Technology Taxila Pakistan Confirm Centre for Smart Manufacturing University College Cork Cork Ireland Riphah International University Islamabad Pakistan Hospital Islamabad Pakistan Insight Centre for Data Analytics University College Cork Cork Ireland Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad Pakistan

ISBN: (纸本)9783031776168

In recent times, Twitter has emerged as a fascinating platform for conducting sentiment analysis and opinion mining due to its massive text corpus. Numerous users express their views on various trending topics and extensively use hashtags. This study aims to analyze and classify the sentiments of Twitter users regarding 5G technology using hashtags such as #5G and related ones. The study aims to understand users’ perceptions of 5G in terms of its mobility, reach, and impact on health. The emotions expressed about 5G are classified into positive, negative, and neutral categories using machine learning (ML) algorithms such as Support Vector Machine (SVM), Logistic Regression (LR), Multinomial Naive Bayes (MNB), and Random Forest, along with sentiment analysis libraries like Sci-kit and NLTK. The resulting classification model shows improved performance, evaluated using metrics such as accuracy, recall, and F1-score. Using SVM on a self-extracted dataset named "5G Myths," an accuracy of 83.09% is achieved, while using LR, MNB, and Random Forest results in an accuracy of 80%, 75%, and 57%, respectively. The study demonstrates that it is feasible to identify the critical factors and information that shape public opinion about the acceptance or rejection of 5G technology on Twitter. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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Improved Early–Modern Japanese Printed Character Recognition Rate with Generated Characters 30th

Improved Early–Modern Japanese Printed Character Recognitio...

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30th international conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied computing, CSCE 2024

作者： Koiso, Norie Takemoto, Yuki Ishikawa, Yu Takata, Masami Nara Women’s University Nara Japan College of Information Science and Engineering Ritsumeikan University Osaka Japan Kyoto Women’s University Kyoto Japan

ISBN: (纸本)9783031856372

The National Diet Library’s digital collection contains about 400, 000 valuable books from the Meiji period to the early Showa period. The books are stored as image data and have not been converted into text. Therefore, the use of information is limited. There are manual and automatic methods of texting Early–modern Japanese printed book, but manual methods cost a fortune. OCR is used for automation, but Early–modern Japanese printed book’s characteristics reduce recognition rates. Therefore, it is necessary to develop a character recognition method specific to Early–modern Japanese printed book. Collecting Early–modern Japanese printed character is also manual, and it is difficult to collect many characters evenly. In this paper, we propose a method to improve Early–modern Japanese printed character recognition accuracy using images generated from modern characters. CycleGAN is used to generate images of modern characters from modern characters. The generated image is incorporated into train data to create a character recognition model. The experiment showed that the recognition rate was improved by using the generated image in train data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep Metric learning Early-modern Japanese printed character recognition

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SSSDAD: Structured State Space Diffusion Anomaly Detection in Industrial Time Series Data 20th

SSSDAD: Structured State Space Diffusion Anomaly Detection i...

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20th international conference on Data Science, ICDATA 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied computing, CSCE 2024

作者： Hirt, Manuel Meier, Daniel Jazdi, Nasser Luy, Johann-Friedrich Kasneci, Enkelejda Daimler Truck Group Stuttgart70327 Germany TUM School of Computation Information and Technology Technical University of Munich Munich80333 Germany Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart70569 Germany

ISBN: (纸本)9783031858550

Detecting anomalies in industrial time series data is crucial for maintaining complex systems, avoiding costly downtime, and ensuring safety. Conventional approaches often require assistance in accurately identifying outliers, particularly in dynamic and noisy industrial environments. This paper proposes a novel framework, called Structured State Space Diffusion Anomaly Detection (SSSDAD), that utilizes Denoising Diffusion Models (DDMs) for robust detection of outliers in industrial time series data. Our approach combines deep generative models with the temporal modeling capabilities of Structured State Space Models to capture the variability and complex temporal dependencies in industrial processes. The DDM is trained on the underlying dynamics of normal operation, allowing our model to distinguish between regular fluctuations and anomalous events in industrial systems. In addition, we present a customized detection mechanism based on reconstruction error that uses the learned representations of the DDM to measure deviations from the expected behavior. Our approach performs better in identifying anomalies than traditional methods and has been proven effective in various industrial datasets, including a real-world dataset of an electric truck. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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EDSFINet:Efficient Double stages feature interaction network for Remote Sensing Image Change Detection 3

EDSFINet:Efficient Double stages feature interaction network...

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3rd international conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Wei, Gang Miao, Yuqi Wang, Zhicheng Xia, Jiaxuan CAD Research Center Tongji University 4800 Cao'an Road Shanghai 201804 China School of Computing and Artificial Intelligence Southwest Jiaotong University 999 Xi'an Road Chengdu 611756 China

ISBN: (纸本)9781510690219

Accurate remote sensing change detection contributes to more rational protection of the environment. How to perform more efficient and sufficient feature interaction and feature fusion between two corresponding high-resolution time series images has always been a problem in this area. Most existing methods use a one-stage network to perform feature interaction between two time series images, which often leads to insufficient and inefficient feature interaction. Therefore, this article proposes an efficient double-stage feature interaction network (EDSFINet) aimed at achieving more comprehensive and efficient feature interaction. This is the first exploration of using a two-stage feature interaction framework to accomplish this task, marking an innovative attempt in this field. For the interaction of shallow features, we designed a shallow feature interaction module (SFIM), which is mainly based on spatial and channel attention for image interaction, and can more effectively extract low-level information of the image, including edges, textures, and color distribution. For the interaction of deep features, we designed a deep feature interaction module (DFIM), which is mainly based on Transformer attention for image interaction, which can more effectively capture global features and long-distance dependencies. To achieve more efficient feature fusion, we designed a fusion feature enhancement module, which is implemented using the attention of the Transformer with linear computational complexity and can perform feature fusion enhancement more efficiently. To alleviate the class imbalance challenge, we adopted multi-scale supervised training during the training process to better utilize the multi-scale information of the images. According to experimental results, the F1 scores of EDSFINet on the LEVIR-CD is 91.36%, and the IoU indicators is 84.09%, which are better than the existing methods. © 2025 SPIE.

关键词： Change detection Deep learning Feature interaction Remote sensing

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Real-Time Automated Pothole Detection and Localization with Deep learning and Geolocation Integration for Improved Road Safety and Maintenance 4th

Real-Time Automated Pothole Detection and Localization with ...

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4th international conference on Advanced Network Technologies and Intelligent computing, ANTIC 2024

作者： Rao, Abhishek S. H, Karthik Pai B. S, Shyvalanai B. Nayak, Trupthi D. R, Vaishnavi Thanisha Karnataka Nitte574110 India

ISBN: (纸本)9783031837920

Real-time identification of road damage, particularly potholes, is essential for enhancing road safety and minimizing vehicle damage. Traditional road inspection methods are often labor-intensive and inefficient. To overcome these limitations, we propose an automated system that leverages deep learning to detect potholes in real-time from video input. This system can detect potholes under varying lighting conditions, specifically the YOLO (You Only Look Once) model, utilizing advanced computer vision techniques, including daylight, darkness, and vehicle headlights. The system aims to improve road monitoring by automatically identifying potholes and notifying authorities of their locations. By integrating geolocation services, the system pinpoints pothole locations using latitude and longitude coordinates and sends real-time alerts via a Telegram bot. Additionally, image enhancement techniques are employed to optimize detection performance in low-light conditions. The system has demonstrated high accuracy in detecting large potholes and identifying multiple potholes within a single frame. Through automated pothole detection and location sharing, this solution has the potential to significantly enhance road maintenance efficiency, thereby improving road safety and reducing accidents caused by road damage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Highway accidents

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HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in Industrial Control Systems Using MLPNN and Attention Mechanisms 14th

HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in ...

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14th international conference on Applications and Techniques in Information Security, ATIS 2024

作者： Abinash, S. Sriram, V. S. Shankar Krithivasan, Kannan Priyanga, S. Department of Computer Science and Engineering Srinivasa Ramanujan Centre SASTRA Deemed University Tamilnadu Thanjavur India School of Computing SASTRA Deemed University Tamilnadu Thanjavur India

ISBN: (纸本)9789819797424

Integrating Industrial Control Systems (ICS) with Internet of Things (IoT) technologies has amplified the vulnerability of ICS to a larger range of cyber-attacks, posing significant risks to Critical Infrastructures (CI). Recent cyber-attacks on oil and gas sectors and water treatment plants highlight this potential threat. Current attack detection methods rely on unified machine learning techniques, which pose data privacy and transfer challenges. To address these issues, the self-attention-based learning method has become a popular and effective solution for detecting attacks in ICS. This paper presents a novel Hypergraph Attention-based Multilayer Perceptron Neural Network (HATT-MLPNN) for detecting cyber-attacks in ICS environments. The hypergraph-based attention layer helps to optimise the Multilayer Perceptron Neural Network (MLPNN) weights for different feature sets. Integrating hypergraph attention mechanisms into an MLPNN has significantly increased the ability to capture and leverage complex feature interactions in ICS datasets. The proposed model is evaluated on iTrust’s Secure Water Treatment (SWaT) and Mississippi’s Gas Pipeline dataset and experimental evaluations reveal that the training of the proposed attack detection model is faster when trained on labeled data. The model is consistently outperformed with recall and F1- scores on both datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity and Lightweight Models with Knowledge Distillation

Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity...

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international Communication Systems and Networks and Workshops, COMSNETS

作者： Avi Deb Raha Mrityunjoy Gain Sudip Kumar Saha Md Shohanur Rahman Apurba Adhikary Rameswar Debnath Anupam Kumar Bairagi Sujit Biswas Computer Science and Engineering Discipline Khulna University Khulna Bangladesh AI Innovation Lab Khulna Bangladesh United International University Dhaka Bangladesh Dept. of Mechatronics Engineering KUET Khulna Bangladesh Dept. of ICE Noakhali Science and Technology University Noakhali Bangladesh Computer Science Department City University of London UK

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Monkeypox, a novel zoonotic disease akin to smallpox, necessitates prompt and accurate diagnosis for effective treatment. Conventional diagnostic techniques, such as Polymerase Chain Reaction (PCR), offer high precision but require specialized equipment and trained personnel, rendering them impractical in resource-limited settings. Existing deep learning approaches for monkeypox diagnosis have predominantly relied on single, resource-intensive models, prioritizing accuracy over deployment feasibility across diverse computing platforms. In this study, we present a resource-conscious model deployment strategy that balances diagnostic accuracy with computational efficiency, enabling precise diagnosis in both resource-rich environments, such as hospitals, and resource-constrained contexts. We utilize a pretrained ConvNeXt-B model, trained on the extensive ImageNet-22K dataset, for deployment in resource-abundant scenarios, and a SqueezeNet model, optimized for resourcelimited devices using the ImageNet-1K dataset. To enhance the performance of the lightweight SqueezeNet model without increasing computational complexity, we apply Knowledge Distillation. Experimental results demonstrate that the ConvNeXt-B model achieves an accuracy of 95.75%, which is 3.47% higher than the previous studies. Similarly, the Knowledge Distillation-enhanced SqueezeNet model attains an accuracy of 91.89%, representing a 2.32% improvement over the baseline. This dualmodel approach ensures that accurate monkeypox diagnostics are accessible across a wide range of computational environments, thereby supporting more effective outbreak management and contributing to improved public health outcomes.

关键词： Accuracy Hospitals Computational modeling Telemedicine Rendering (computer graphics) Skin Security Polymers Public healthcare Diseases

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Analysis of Tracking Algorithms for Multi-person Tracking 3rd

Analysis of Tracking Algorithms for Multi-person Tracking

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3rd international conference on computing, Communication and learning, CoCoLe 2024

作者： Anne, M. Evany Brindha, M. Department of Computer Science and Engineering National Institute of Technology TamilNadu Tiruchirappalli620015 India

ISBN: (纸本)9783031790409

Tracking humans using computer vision techniques is important in many surveillance applications. Apart from accuracy and speed, how tracking handles diverse settings such as normal and occluded scenarios also play a critical role. Detection-based tracking is discussed in this study. In this paper, the Multiple Object Tracker is implemented using the tracking-by-detection methodology. Detection is accomplished using the YOLOV8 model, and tracking is performed by various algorithms such as Centroid tracking, Kernelized Correlation Filter (KCF) tracking, CamShift tracking, and Channel and Spatial Reliability Tracker (CSRT) tracking. After performing the tracking by the different tracking algorithms, their performances are analyzed based on different videos. Moreover, in order to keep track of the number of people in the video, unique IDs are assigned to each person based on the IOU distance within the present and previous frames of the video. Besides that, in many edge cases such as occlusion scenarios, the system should be able to maintain the object identity efficiently to keep the count of the number of persons present in the video. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object detection

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Forecasting Sustainability: A Study of Demand Prediction in Circular Economics 1st

Forecasting Sustainability: A Study of Demand Prediction in ...

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1st international Research conference on computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Harshavardhini, C. Sarayu, K. Roshan, C.R. Sangeetha, S.K.B. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus Chennai India SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9783031823824

The study explores the potential of AI enhanced demand prediction for recycled and upcycled products, aiming to bridge the gap between sustainable production and market adoption. We propose a study utilizing machine learning algorithms to analyze historical sales data, market trends, and consumer sentiment, thereby forecasting demand for eco-friendly products with unprecedented accuracy. Leveraging business analytics and social media influence data, we propose a novel framework to: (1) compare consumer preferences and purchase patterns for recycled and upcycled products against their traditional counterpart (2) identify key factors driving demand for sustainable products, (3) compare demand patterns between ARIMA and LSTM models, and (4) analyze the impact of social media influence on consumer preferences. By predicting future demand with enhanced accuracy, this framework aims to empower businesses to optimize production, pricing, and marketing strategies, ultimately enabling a more sustainable and circular economy. The study holds significant implications for both businesses and consumers seeking to minimize environmental impact while catering to evolving market trends. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Circular economy

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