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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Agalayil, Helen Terese Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid evolution of smartphone technology and the diverse range of available models have made selecting a cost-effective mobile phone a complex decision for consumers. Although brand, internal memory, camera quality, battery life, processor speed are all important determinants of purchase decisions, it is frequently unclear how these factors relate to the price. This work explores the application of machine learning algorithms to predict mobile phone prices and their price category classification based on these characteristics, offering insights to aid consumer decision-making. Five classifiers and five regressors were compared using accuracy and time complexity as evaluation metrics and k-NN was found to be the best classifier for the price class classification and LightGBM was found to be the best regressor for price prediction. © 2025 IEEE.

关键词： Smartphones

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Exploring red-edge spectral indices for mangrove canopy height mapping using random forest regression with Sentinel-2 and spaceborne GEDI LiDAR data 5

Exploring red-edge spectral indices for mangrove canopy heig...

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Land Surface and Cryosphere Remote Sensing V 2024

作者： Jamaluddin, Ilham Chen, Ying-Nong Ayudyanti, Amalia Gita Fan, Kuo-Chin Department of Computer Science and Information Engineering National Central University Taiwan Center for Space and Remote Sensing Research National Central University Taiwan

ISBN: (数字)9781510682696

ISBN: (纸本)9781510682689

Mangroves are coastal vegetation and considered as one of the blue carbon ecosystems. Canopy height is a critical parameter in understanding these ecosystems and related to biomass information. Remote sensing and machine learning techniques are increasingly utilized for large-scale mangrove canopy height mapping. Sentinel-2 is an optical satellite with various bands, including red-edge (RE) bands that are related to vegetation biophysical information. This study investigates the potential of red-edge spectral indices for mangrove canopy height mapping using Random Forest Regression (RFR). The study area is located around Charlotte Harbor Preserve State Park, Florida, USA in 2020. Sentinel-2 data was used to produce several red-edge spectral indices including NDVI, NDVIRE1, NDVIRE2, NDVIRE3, CIRE1, CIRE2, CIRE3, and CIVI which served as inputs for the RFR model. Spaceborne GEDI LiDAR rh98 canopy height data was used here to produce the target data. The dataset was divided into 80% training and 20% testing subsets. Our results indicate that CIVI utilizing red-edge 1 and 2 is the most important feature of RFR for mangrove canopy height estimation. The mean absolute error (MAE) and the root mean squared error (RMSE) based on the testing dataset were 1.662 m and 2.291 m, respectively. To further validate the results, we introduced an independent testing dataset using a canopy height model from the airborne LiDAR data located near the study area. We used the trained RFR model to predict the mangrove canopy height in the testing area and found the MAE and RMSE from the independent testing dataset were 2.511 m and 2.812 m, respectively. Based on the results, most of the red-edge spectral indices have a better performance than the spectral index without red-edge bands, and the use of several red-edge spectral indices provides promising results for mangrove canopy height mapping. © 2025 SPIE.

关键词： Mean square error

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A Traceability System Model of Agricultural Products based on Blockchain 24

A Traceability System Model of Agricultural Products based o...

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8th International Conference on Computing and Data Analysis, ICCDA 2024

作者： Bo, Li Luo, Xuegang Lv, Junrui Xue, Yan School of Information and Engineering Sichuan Tourism University Sichuan Chengdu China School of Mathematics and Computer Science Panzhihua University Sichuan Panzhihua China

ISBN: (纸本)9798400710445

People’s life is closely related to the quality and safety of agricultural products. Traditional agricultural product traceability technology uses centralized management, which has many drawbacks, particularly easy manipulation, and unreliable centralization. This paper proposes a traceability model based on blockchain that consists of the agricultural product blockchain model, distributed storage model DSB, agricultural product traceability model, practical Byzantine consensus algorithm PBFT, and smart contract to realize the safe and distributed traceability of agricultural products. The results showed that the blockchain-based method of agricultural product traceability is accurate, practical, and efficient. The model has a success rate of 100%, a minimum average time delay of 20326 ms, a maximum throughput of traceability of 4702.03 b/s, and distinct curvilinear characteristics in the performance parameters as the number of nodes is raised. The study’s conclusions provide a useful design framework and implementation method for blockchain-based agricultural product traceability technology, Serve as a guide for research and development on related technologies, and have reference value. © 2024 Copyright held by the owner/author(s).

关键词： Blockchain

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Augmenting Emotion Features in Irony Detection with Large Language Modeling 25th

Augmenting Emotion Features in Irony Detection with Large ...

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25th Workshop on Chinese Lexical Semantics, CLSW 2024

作者： Lin, Yucheng Xia, Yuhan Long, Yunfei School of Computer Science and Electronic Engineering University of Essex Wivenhoe Park ColchesterCO4 3SQ United Kingdom

ISBN: (纸本)9789819635115

This study introduces a novel method for irony detection, applying Large Language Models (LLMs) with prompt-based learning to facilitate emotion-centric text augmentation. Traditional irony detection techniques typically fall short due to their reliance on static linguistic features and predefined knowledge bases, often overlooking the nuanced emotional dimensions integral to irony. In contrast, our methodology augments the detection process by integrating subtle emotional cues, augmented through LLMs, into three benchmark pre-trained NLP models-BERT, T5, and GPT-2-which are widely recognized as foundational in irony detection. We assessed our method using the SemEval-2018 Task 3 dataset and observed substantial enhancements in irony detection capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Modeling languages

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Reversible data hiding scheme using prediction neural network and adaptive modulation mapping

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Multimedia Tools and Applications 2025年第9期84卷 6665-6686页

作者： Chen, Si-Sheng Chang, Chin-Chen Horng, Ji-Hwei School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fujian Fuzhou35030 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Department of Electrical Engineering National Quemoy University Kinmen89250 Taiwan

Prediction error expansion (PEE) is an attractive approach for reversible data hiding (RDH). The key issue for PEE-based RDH is to improve the prediction accuracy and design a better modulation mapping. This paper proposes a novel prediction error modulation (PEM) scheme, which comprises a prediction neural network and a modulation mapping rule generation algorithm. We use the multi-scale feature extraction (MSFE) module and the residual dense networks (RDN) to construct the prediction neural network. In the proposed RDH scheme, the cover image is divided into two parts, the reference pixel set and the cover pixel set, based on the chessboard pattern. The prediction network serves to predict the values of the cover pixel set using the reference pixel set. An optimization model is set up to adaptively determine the optimal modulation mapping for data hiding based on the specific prediction-error histogram and payload constraint. Experimental results show the superiority of the proposed prediction neural network and the optimized modulation mapping compared with related works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mapping

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Forecasting Bitcoin Price Trends: Integrated Machine Learning with Market Trends 3

Forecasting Bitcoin Price Trends: Integrated Machine Learnin...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Samir Ahamed, S. Radha, D. Kirthika Devi, V.S. Department of Computer Science And Engineering Amrita school of computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita school of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Bitcoin price prediction remains a complicated task in financial markets since price fluctuations are characterized by high variability, non-linear and dependent on different trends. The common models of forecasting can be disruptive because they do not readily accommodate the breadth of the data and the dynamics of the marketplace. The paper presents a new approach to predicting Bitcoin price shifts based on state-of-the-art data structures and both machine learning and technical analysis. The formative historical price datasets and large scale features are organized and manipulated in an efficient manner with the aid of data structures as well as the temporal and non-linear trends in prices are captured by ARIMA,Exponential smoothing and Random. Also, trends of moving averages are used to boost the model’s competency to analyze the sentiments of the market. The proposed framework was put through a test on real world Bitcoin price data and it was shown to provide better results as compared to the traditional methods with better accuracy and visualization. Besides that, our method enhances the accuracy of the forecast, while the deployment of the method allows developing a realtime Bitcoin price prediction model that can be useful for traders and investors. The integration of machine learning and technical analysis gives a powerful and reliable tool to forecast the Bitcoins price trends. © 2025 IEEE.

关键词： Prediction models

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Analysis of optimization Techniques for Dynamic Neural Networks 3

Analysis of optimization Techniques for Dynamic Neural Netwo...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mallegowda, M. Nair, Saanvi Khirwal, Sahil Animesh, Manik Mishra, Vasuman Kanavalli, Anita Ms Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Dept. of Cse Bengaluru India

ISBN: (纸本)9798331515911

Models based on dynamic neural networks such as DeBERTa and Vision Transformers ViT have disrupted the two fields. However, there is one complication - their performance requires so much computation which limits its realistic applications. The aim of this study is to propose advanced optimization techniques that can increase the efficiency of these models without compromising their performance. We adopt approaches such as DistilBERT, DeBERTa and Vision Transformer models. Then we carry out detailed empirical analysis of the consequences of these techniques on the model accuracy, time performance, throughput as well as resources consumption. The findings show enhancement models, where some methods improved the throughput by 29% and reduced the GPU usage by 61% at the same or higher accuracy. This reveals the capacity of dynamic neural networks in resource-constrained settings and enhances the overall vision of the use of such models in practice. © 2025 IEEE.

关键词： Neural network models

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Hardware Security in Evolving FinTech Landscape 5th

Hardware Security in Evolving FinTech Landscape

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Bhowmik, Biswajit Dongala, Joshitha Reddy Sudhama, Kruthika K. Antony, Reshma Tresa Girish, K.K. Maharshi Sushrut CAS Lab BRICS Laboratory Department of Computer Science and Engineering National Institute of Technology Karnataka Mangalore Surathkal575025 India

ISBN: (纸本)9789819745395

The assimilation of technology into the financial sector, often referred to as FinTech, has brought about a significant transformation. This shift has not only widened the scope of financial inclusivity but has also fundamentally reshaped the contours of financial solutions delivered. As FinTech solutions continue to empower individuals with greater control over their finances through mobile banking, digital wallets, and advanced data analytics, the security of these innovations becomes paramount. While software security has traditionally received more attention, this paper underscores the significance of hardware security, which serves as the foundational infrastructure for software security measures. It delves into the factors used to evaluate hardware security and outlines various categories of hardware attacks. A case study, focusing on point-of-sale (PoS) systems, exemplifies the importance of hardware security in FinTech. Ultimately, this research contributes to a comprehensive understanding of the evolving FinTech landscape and its implications for both financial inclusion and cybersecurity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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Emotion Recognition System Using Indian Music for Therapeutic Cause

Emotion Recognition System Using Indian Music for Therapeuti...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Das, Paromita Gupta, Somsubhra Neogi, Biswarup MAKAUT West Bengal Kolkata India School of Computer Science Swami Vivekananda University Barrackpore India JIS College of Engineering Kalyani India

ISBN: (纸本)9789819613472

The therapeutic uses of music have been appreciated and refined by researchers as its effects are vast in relaxation as well as recreation. Through its ability to regulate our emotions, music aids in returning the body to its original vibration. A song is the result of the interplay of language, melody, and singer’s voice. The listener’s emotional state is influenced by the emotions that a singer conveys through their voice, including happiness, sorrow, worry, calm, or exhaustion. Indian music is examined, and a method for information retrieval is started in order to suggest a therapeutic system by identifying and detecting Indian music. Utilising Music Information Retrieval is an effective way to examine many aspects of a piece of music. This approach, however, studies various aspects of music and creates a therapeutic reason through categorisation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal processing

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A Robust Observer Based End-Effector Trajectory Tracking Control of Collaborative Robotic Manipulators Actuated by Brushless DC Motors

A Robust Observer Based End-Effector Trajectory Tracking Con...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Saka, Irem Unver, Sukru Selim, Erman Tatlicioglu, Enver Zergeroglu, Erkan Dogan, K. Merve Ege University Department of Computer Engineering the Sivas University of Science and Technology Sivas58000 Turkey Gebze Technical University Department of Computer Engineering Gebze Technical University Gebze Kocaeli41400 Turkey Embry-Riddle Aeronautical University Aerospace Engineering Department Daytona BeachFL32114 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This study presents the design and the corresponding stability analysis of a robust observer based partial state feedback end-effector tracking controller for collaborative robot manipulators actuated via brushless DC motors. The proposed controller-observer couple compensate for the uncertainties associated with the robot link dynamics, kinematics of the manipulator and joint actuator parameters while not requiring joint velocity measurements. The overall stability and convergence of the error signals into an ultimate bound around origin is ensured via Lyapunov type arguments. Numerical simulation studies are also presented to back the theoretical findings and to illustrate the effectiveness of the proposed methodology. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： State feedback

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