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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Akhila, T.S. Relin Francis Raj, J. Kani Jesintha, D. Santhana Krishnan, R. Vinoth Kumar, V. Sundaravadivel, P. Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331523923

This research proposes a hybrid deep learning model combining Long Short-Term Memory (LSTM) networks and Transformers to enhance decision-making in international trading. The model leverages advanced techniques such as RoBERTa for text tokenization, FinBERT for sentiment analysis, and XLM-Roberta for multilingual language processing. LSTM is employed to analyze sequential numerical data like historical prices and trading volumes, providing accurate price and volatility forecasts. The Transformer component processes textual data to extract sentiment classifications and event-driven market impacts, facilitating a comprehensive understanding of market conditions. A fully connected fusion layer integrates insights from both components, enabling the model to classify market conditions holistically. The optimization process utilizes the Adam optimizer with learning rate scheduling to ensure efficient and effective training. Loss functions like Mean Squared Error (MSE) and Cross-Entropy Loss are employed to optimize numerical predictions and categorical outcomes, respectively. By combining temporal analysis with sentiment-driven insights, this hybrid model offers improved price forecasting, advanced multilingual sentiment analysis, and event-driven market condition predictions. The proposed approach aims to provide traders with actionable insights, enabling more informed decision-making in the highly dynamic and multilingual environment of international trading. © 2025 IEEE.

关键词： Costs

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Hybrid Threshold Speech Enhancement Scheme Using TEO and Wavelet Coefficients 2

Hybrid Threshold Speech Enhancement Scheme Using TEO and Wav...

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2nd International Conference on Electrical, electronics, Information and Communication Technologies, ICEEICT 2023

作者： Latha, R. Suhas, A.R. Pradeep Kumar, B.P. Ibrahim, M.Mohammed Sathiyapriya, V. HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Sona College of Technology Department of Computer Science and Engineering Salem India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350397635

Speech Enhancement (SE) aims to improve the quality of degraded speech while maintaining its intelligibility. The Wavelet Transform (WT) has become a powerful tool of signal analysis thereby widely used in signal detection and signal denoising. In this paper, we propose an effective means of SE by a hybrid threshold scheme using WT. The proposed methodology looks into both falling the noise and preserving edges of the speech signal unlike the conventional Hybrid Threshold (HT) and Soft Threshold (ST) in the wavelet domain. The threshold value in the wavelet domain is maintained constant for all sub-bands of the signal which reduces denoising efficiency. A novel speech augmentation technique built on the wavelet onsets and time adaption of introduced by calculating wavelet coefficients of the Teager Energy. Performance analysis of speech enhancement techniques using Wavelet coefficients and Teager Energy Operator (TEO) with hybrid threshold method is done. The experiment is carried out for speech data with various values of SNR vacillating from -10 to +10 db with Additive White Gaussian Noise (AWGN). © 2023 IEEE.

关键词： Speech enhancement

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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CDR-CNN: Convolutional Neural Networks for Alzheimer's Disease Severity Prediction with Clinical Dementia Rating 5

CDR-CNN: Convolutional Neural Networks for Alzheimer's Disea...

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Venkatesan, Chandran Sumithra, M.G. Balan, Elakkiya Saravanan, S. Sri Krishna College of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Sri Venkateswara College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Dayananda Sagar Academy of Technology & Management Department of Computer Science Engineering in Data Science Bengaluru India

ISBN: (纸本)9798331504403

Precisely identifying the severity of Alzheimer's using MRI is crucial for enhancing diagnostic accuracy and improving patient care. This study presents a novel convolutional neural network designed to classify four classes of Alzheimer: none, very mild, mild, and moderate. The dataset, comprising 80,000 MRI scans from 461 participants, tackles the issue of substantial class skew, with a predominance of scans in the 'none' category. The proposed method deviates from conventional approaches by preserving ordinal information in predictions, as opposed to employing one-hot encoding for categorical classification. This study examines the effectiveness of ordinal classification relative to categorical classification, explores strategies for addressing class imbalance, and assesses the performance of various pre-trained model bases. This research aims to enhance diagnostic accuracy for AD, providing valuable insights for the medical, neuroscientific, and data science communities working with MRI and other medical imaging data. © 2024 IEEE.

关键词： Convolutional neural networks

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Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks 4

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Ahmed, Marzia Mohamad, Ahmad Johari Hassan, Md Maruf Bhuiyan, Touhid Department of Software Engineering Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Malaysia Southeast University Department of Computer Science and Engineering Dhaka Bangladesh School of IT VA United States

ISBN: (纸本)9798331518882

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN's hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model's potential for accurate real-time air quality forecasting, offering a robust, sustainable solution. © 2025 IEEE.

关键词： Air Quality GBO Metaheuristic Neural Network Time series forecasting

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Smart Detection of Passive Smokers using Machine Learning 8

Smart Detection of Passive Smokers using Machine Learning

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Shah, Pratham Parekh, Astha Bajaj, Divyansh Swain, Debabrata Kumar, Manish Bhilare, Amol Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar India Pandit Deendayal Energy University Department of Electronics and Communication Engineering Gandhinagar India Vishwakarma Institute of Technology Computer Engineering Department Pune India

ISBN: (纸本)9798350391770

Smoking is a destructive and addictive practice of inhaling burning plant material. It produces nicotine in the bloodstream that creates a negative impact on the bones, hormones, DNA, eyes, and the immune system. This has become the source of a great concern, especially amongst the young generation. Machine Learning is used to find a pattern of biological attributes to identify possible smokers. The process of teaching a machine to do intelligent classification from a sizable archive of historical data is known as machine learning. The aim of this paper is to develop a learning model that can detect passive smokers through features like eyesight, weight, fasting blood sugar, cholesterol and more. On this dataset, a number of data preprocessing operations are carried out to build a reliable model which include checking for null values , encoding the data , balancing the dataset and scaling it and selection of highly correlated features. A variety of supervised learning models are employed in the model training process to create a robust machine learning model. From the set of models applied, K-Nearest Neighbour (KNN) (84 %) and Random Forest (RF) (86 %) gave the highest accuracy. However, implementing Random Forest with Hyperparameter tuning using GridSearchCV resulted in a higher accuracy (87 %). © 2024 IEEE.

关键词： Decision trees

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Deep learning:Applications,architectures,models,tools,and frameworks:A comprehensive survey

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CAAI Transactions on Intelligence technology 2023年第3期8卷 581-606页

作者： Mehdi Gheisari Fereshteh Ebrahimzadeh Mohamadtaghi Rahimi Mahdieh Moazzamigodarzi Yang Liu Pijush Kanti Dutta Pramanik Mohammad Ali Heravi Abolfazl Mehbodniya Mustafa Ghaderzadeh Mohammad Reza Feylizadeh Saeed Kosari School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)ShenzhenChina Department of Cognitive Computing Institute of Computer Science and EngineeringSaveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science Islamic Azad UniversityTehranIran Computer Engineering Department Bu‐Ali Sina UniversityHamedanIran Department of Mathematics and Statistics Iran University of Science and TechnologyTehranIran Department of Mathematics and Statistics University of Northern British ColumbiaBCCanada Peng Cheng Laboratory ShenzhenChina Deprtment of Technology Jodhpur Institute of Engineering&TechnologyJodhpurIndia Department of Computer Engineering Sadjad University of TechnologyMashhadIran Department of Electronics and Communications Engineering Kuwait College of Science and TechnologyDoha DistrictKuwait Department of Artificial Intelligence Smart University of Medical SciencesTehranIran Department of Industrial Engineering Shiraz BranchIslamic Azad UniversityShirazIran Institute of Computing Science and Technology Guangzhou UniversityGuangzhouChina

Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new *** using DL,the extraction of advanced data representations and knowledge can be made *** effective DL techniques help to find more hidden *** learning has a promising future due to its great performance and *** need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively.A survey on DL ways,advantages,drawbacks,architectures,and methods to have a straightforward and clear understanding of it from different views is explained in the ***,the existing related methods are compared with each other,and the application of DL is described in some applications,such as medical image analysis,handwriting recognition,and so on.

关键词： data mining data privacy deep learning

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Exploring Learning-based Approaches for Enhancing IoT Networking Through Big Data Processing 15

Exploring Learning-based Approaches for Enhancing IoT Networ...

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

作者： Pandey, Arvind Kumar Devrani, Rekha Sunil, M.P. Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

In current years, the internet modern day (Iota) has a visible fantastic boom. With the advancement of modern Iota technologies, new possibilities emerge for better and greater green networking and the usage of records analytics. This paper explores modern-day-based techniques so that it will leverage huge statistics processing for improving Iota networks. We have become aware of diverse approaches for community optimization, such as gadget cutting-edge algorithms, deep learning models, and different heuristics-primarily based strategies. We also define the advantages and downsides of contemporary techniques and discuss their capacity programs for Iota-like networks. We further analyze the capacity of those strategies in huge data processing and compare their overall performance and scalability. The paper concludes with a discussion of implications to the networking network and the way these methods can be implemented for higher performance in today's Iota networks. © 2024 IEEE.

关键词： Analytics Data Internet Iot Networks Performance Statistics Techniques

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A Multi-Modal Smart Switching Based Image Transmission Using Semantic Communication 17

A Multi-Modal Smart Switching Based Image Transmission Using...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Tiwari, Sanjana Tripathi, Sharda Joshi, Sandeep Bhatt, Priyanshu Natarajan, Balasubramaniam Birla Institute of Technology & Science Pilani Department of Electrical & Electronics Engineering 333031 India Kansas State University Department of Electrical & Computer Engineering 66506 United States

ISBN: (纸本)9798331531195

The conventional paradigm of communication primarily concentrates on the transmission of raw data, often disregarding its contextual meaning. However, to tackle the exponential growth in data demands along with the limited availability of transmission bandwidth, there is an increasing need to transition from Shannon's classical information-theoretic communication to a more advanced framework centered on semantics. This work presents a multi-modal semantic-based communication method for the transmission of high-definition images aimed at optimizing the transmitted data volume while maintaining a high throughput and mean intersection over union score. To this end, two architectural models are explored: a denser ResNet-based and a lightweight U-Net-based. Depending on the required QoS and resource availability, the raw image is either semantically segmented to obtain a fine-grained, pixel-level classification of the image or represented as label semantics, which provides only a higher-level, object-based, or region-based classification prior to its transmission. The experimental results show that such an adaptive semantic image processing approach leads to around 63% reduction in the transmitted data volume without compromising on the quality of image reconstruction. © 2025 IEEE.

关键词： Semantic Segmentation

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Next-Gen Manhole Monitoring: Autoencoder-Assisted Anomaly Detection 3

Next-Gen Manhole Monitoring: Autoencoder-Assisted Anomaly De...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Krishnan, R. Santhana Gopikumar, S. Muthu, A. Essaki Raj, J. Relin Francis Kumari, D. Abitha Malar, P. Stella Rose Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Civil Engineering Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical AndTechnical Sciences Department of Electronics and Communication Engineering Chennai India Rmk Engineering College Department of Computer Science and Engineering Thiruvallur India J.P. College of Engineering Department of Electronics and Communication Engineering Thenkasi India

ISBN: (纸本)9798350375190

In various regions, the maintenance of manholes is imperative, given the potential consequences for public health and safety. Neglecting this responsibility can result in severe outcomes, including loss of lives and the spread of diseases within the community. To address these challenges, an advanced anomaly detection system is proposed, leveraging deep learning with Autoencoders. Notably, the entire system operates on solar power, aligning with a commitment to environmentally sustainable practices. At the core of the system's functionality is the Autoencoder, a deep learning model to differentiate complex manhole conditions. The Autoencoder is trained on normative sensor data, capturing nuanced patterns inherent in routine manhole states. This training enables the Autoencoder to perform real-time anomaly detection by swiftly identifying deviations such as the presence of noxious gases, irregular sewage levels, or unauthorized attempts at access. When anomalies are detected, signaling potential hazards, the Autoencoder triggers alert messages disseminated to municipal authorities via a cloud server. This proactive approach empowers timely intervention, particularly critical in scenarios involving toxic gases, ensuring the safeguarding of maintenance personnel. Additionally, the system integrates an NFC reader, streamlining authorized access for designated personnel while upholding robust security measures. In essence, the incorporation of Autoencoders into the system furnishes an intelligent anomaly detection infrastructure for manhole surveillance. By continually adapting to normative patterns, the Autoencoder enhances the system's capacity to identify and promptly communicate potential risks, thereby improving the overall safety and efficiency of manhole maintenance practices. © 2024 IEEE.

关键词： Internet of things

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