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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Jain, Shraddha Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology BHU Varanasi India

ISBN: (纸本)9789819743582

Electroencephalography (EEG) signals are often utilized to study cognitive processes and brain diseases. The non-stationary and non-linear nature of EEG signals makes their analysis difficult. A deep learning framework that suggested classifying seizures based on scalp EEG signals and automating cognitive tasks. We use a pre-processing module based on the Hilbert–Huang Transform (HHT) and Variational Mode Decomposition (VMD) to extract features from raw EEG data. We present an approach that combines deep learning with HHT and VMD for rapid and precise seizure detection. Our method detects seizures with an astounding 98.40% accuracy. Our suggested techniques have great potential for quantifying brain wave patterns and advancing neuroscience research, even outside of classification applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Variational mode decomposition

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Detecting Inattentive and Aggressive Driver Behavior Using Deep Learning: Recent Advances, Challenges with Performance Evaluation 4th

Detecting Inattentive and Aggressive Driver Behavior Using D...

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Debbarma, Tina Pal, Tannistha Debbarma, Nikhil Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (纸本)9789819751990

Road safety faces a grave threat from the prevalence of aggressive and inattentive driving behaviors, contributing significantly to the global rise in traffic accidents and fatalities. Identifying, tracking, and proactively addressing instances of aggressive and inattentive driving are pivotal steps toward mitigating accidents and enhancing road safety. This research project seeks to explore the potential of deep learning methods for analyzing human driving behavior, with a specific focus on identifying inattentive and aggressive driving behaviors. The ultimate goal is to develop a robust and reliable system capable of accurately recognizing and categorizing such behaviors. To achieve this objective, we harness the power of deep learning algorithm. This paper also discusses the identification and categorization of aggressive and distracted driving behaviors among human drivers, using deep learning methodologies which offer substantial promise for elevating traffic safety. The literature review uncovered several noteworthy techniques, including CNN, MTCNN, transformer-based action detection systems, and fusion models inspired by densely connected convolution networks. Comparative analysis revealed improvements in accuracy and precision through the utilization of these techniques. However, the pursuit of larger and more diverse datasets, real-time implementation capabilities, and robustness in various environmental conditions remain essential requirements. Despite these challenges, deep learning-based systems offer the potential for automatic and precise analysis of intricate driving patterns, pivotal in accident prevention and overall road safety enhancement. Future research should focus on surmounting current limitations and advancing the development of more intricate deep learning models for accurate recognition and categorization of human driving behaviors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural Computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Robust autonomous driving control using deep hybrid-learning network under rainy/snown conditions

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Multimedia Tools and Applications 2024年第41期83卷 89281-89295页

作者： Lee, Chao-Yang Khanum, Abida Sung, Tien-Wen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical Engineering National Cheng Kung University No.1 University Road Tainan City701 Taiwan College of Computer Science and Mathematics Fujian University of Technology No3 Xueyuan Road Fuzhou350011 China

The study introduces a groundbreaking two-stage deep hybrid learning architecture, Robust Autonomous Driving Control (RADC), designed to address the formidable challenge of ensuring safe and efficient autonomous driving in adverse weather conditions, including heavy rain and snow, in complex scenarios. In the first stage, our proposal utilizes an encoder as a variational autoencoder (VAE) model. This encoder leverages the VAE to extract feature information from the perceptual data surrounding the environment. Moving on to the second stage, we build a decoder as an Inception-Bidirectional Long Short-Term Memory (IBL) model. This decoder combines the VAE latent features obtained in stage one with additional control vehicle information tasks, including steering, speed, etc. Our approach involves predicting driving behavior along a predetermined route, allowing autonomous vehicles to stay centered on the road, simulating diverse driving scenarios, and achieving significant reductions in route-following time. This framework utilizes deep hybrid learning methods and harnesses Nvidia GPU capabilities to evaluate the effectiveness of InceptionNet, ResNet-50, MobileNet, DenseNet, and VGG16 convolutional neural networks. It enhances vehicle route following with improved metrics such as reduced inference time, heightened accuracy, and minimized lane changes in training. The RADC demonstrates exceptional performance, notably achieving a mean square error of 0.0464, 0.0346, and a 6-millisecond inference time in challenging weather like hard rain and snow. The proposed model is validated through comprehensive experiments in the Airsim environment, showcasing notable interpretability, generalization, and robustness capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision making

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Adaptive Dispersion Estimation for Linearized Electronic Dispersion Compensation in IM-DD Systems

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China Communications 2025年第4期22卷 55-64页

作者： Huo Jiahao Tao Jianlong Zhang Xiaoying Zhu Jin Qin Peng Wei Huangfu School of Computer and Communication Engineering University of Science and TechnologyBeijing 100083China

An adaptive dispersion estimation(ADE)is proposed to compensate dispersion and estimate the transfer function of the fiber channel with GerchbergSaxton(G-S)algorithm,using the stochastic gradient descent(SGD)method in the intensity-modulation and direct-detection(IM-DD)system,improving the tolerance of the algorithm to chromatic dispersion(CD).In order to address the divergence arising from the perturbation in the amplitude of the received signal caused by the filtering effect of the non-ideal channels,a channel-compensation equalizer(CCE)derived from the back-to-back(BTB)scenario is employed at the transmitter to make the amplitude of the received signal depicting the CD effect more *** simulation results demonstrate the essentiality of CCE for the convergence and performance improvement of the G-S *** show that it supports 112Gb/s four-level pulse amplitude modulation(PAM4)over 100 km standard single-mode fiber(SSMF)transmission under the 7%forward error correction(FEC)threshold of ***,ADE improves the tolerance to wavelength drift from about 4 nm to 42 nm,and there is a better tolerance for fiber distance perturbation,indicating the G-S algorithm and its derived algorithms with the ADE scheme exhibit superior robustness to the perturbation in the system.

关键词： chromatic dispersion digital signal process intensity modulation and direct detection

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The Evolution and Impact of Price Comparison Websites: A Technological and Economic Analysis 4

The Evolution and Impact of Price Comparison Websites: A Tec...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Patidar, Mukesh Vekariya, Daxa Pradhan, Preeti Gupta, Kartik Patidar, Praveen Kumar Khan, Pirmohammad Parul University Parul Institute of Engineering and Technology Department of Computer Science & Engineering Gujarat Vadodara India Madhya Pradesh Indore India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Madhya Pradesh Indore India Parul University Parul Institute of Technology Department of Computer Science & Engineering Gujarat Vadodara India

ISBN: (纸本)9798331518981

Price comparison websites (PCWs) have become integral to the online shopping experience, enabling consumers to make informed decisions by comparing prices, features, and reviews from multiple retailers. These platforms not only benefit consumers by saving time and money but also influence market competition by increasing pricing transparency and driving businesses to optimize their strategies. This research explores the evolution of PCWs, their technological infrastructure, and their business models. Moreover, it examines their impact on consumer behavior, the challenges they pose for retailers, and the future trends shaping the industry. Through an analysis of current practices, technological innovations, and economic effects, this work highlights the growing importance of PCWs in the global e-commerce ecosystem. © 2024 IEEE.

关键词： Marketplaces

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Cloud Security Analysis using Blockchain technology 2

Cloud Security Analysis using Blockchain Technology

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Meenakshi, K. Bharathi, B. Thangaraj, S. John Justin Sivasubramanian, S. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Vadapalani India R.M.K Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Cloud computing is now widely employed across a variety of IT-related fields. It has become an essential technology to address infrastructure and data service requirements with minimal effort, low-cost, and with a high degree of scalability. Although the use of cloud computing has increased significantly, information security concerns have not yet been fully resolved. There are still certain information security concerns that must be resolved in order for cloud computing to advance. In terms of integrity, authenticity, and secrecy, in particular, Blockchain has developed into an essential security instrument. The application of Blockchain in cloud computing security is further examined in this article after a review of the different security concerns in blockchain and cloud computing. The main challenge in the cloud computing is cloud computing security. Because data in the cloud must be transported via the Internet, data security becomes a top priority. It is necessary to maintain the primary data protection methods, such as integrity, accountability, privacies, access controls, authentications, and authorizations. Blockchain is a technological advancement that enhances cloud computing. Blockchain resolves cloud computing security problems. This study seeks to analyze and contrast various cloud environment security challenges with blockchain security difficulties. © 2023 IEEE.

关键词： Blockchain

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Data-Driven Approaches to Weather Prediction With Machine Learning 2

Data-Driven Approaches to Weather Prediction With Machine Le...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Kumar, Naween Chodisetti, Muralidhar Kumarsai, Tatini Garg, Vaasu School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798331530389

Weather forecasting joins among the most critical undertakings in agriculture, disaster management, transportation, and many other sectors. Though traditional forecasting techniques are commonly employed, they tend not to model the non-linear behavior of the atmospheric systems correctly. Thus, this study aims at improving the accuracy and efficiency of weather prediction through machine learning techniques. Considering a dataset composed of daily meteorology descriptive variables such as average temperature, humidity, wind speed, etc. and a barometric pressure, the research analyzes the effectiveness of the following machine learning algorithms as Linear Regression, Random Forest, Decision Tree and Support Vector Machine. Preprocessing of the data set included missing value treatment, feature enhancement and time-series modelling. Also, temporal analysis included season, trends and other variations which are time dependent. Models were further assessed by performance indices such as the Area Under the Curve, F1 Score and Accuracy. Results obtained suggest that complex weather patterns are best captured using more complex models such as neural networks and ensemble methods in comparison to simple models. As this study shows, machine learning may assist in improving the weather forecast by making it more accurate and more efficient. In addition, such use of these models can enhance the management of weather-sensitive sectors. The next steps will include the use of actual data collected in real-time and taking into consideration deep learning models to increase the prediction accuracy and scale, and also the ability to do multistep forecasting. © 2025 IEEE.

关键词： Wind forecasting

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Detection and Taxonomy of Aircraft using Synthetic Aperture Radar Imaging 2

Detection and Taxonomy of Aircraft using Synthetic Aperture ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Sairam, D.V. Tamilselvi, M. Devi, S Gayathri Bhuvaneswari, R. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350317558

Improvements in aircraft detection are necessary to improve surveillance. This work investigates the exact detection and classification of airplanes using YOLOv8 in conjunction with Synthetic Aperture Radar (SAR) photos. SAR imaging performs well in dimly lit and unfavorable environments. The objective is to use YOLOv8's object detection to identify aircraft kinds and categories in SAR pictures. A variety of SAR datasets are gathered, preprocessed, and trained on the model to allow for a thorough assessment of YOLOv8's performance in differentiating between different types of aircraft. Measures that assess model performance across aircraft classes include mean average precision (mAP), recall, and loss. The results demonstrate the effectiveness of YOLOv8 and show promising results in terms of speed and precision. This work drives progress in aerial surveillance by highlighting the robustness of SAR when combined with cutting edge deep learning for aircraft taxonomy. The achieved 98.5% mean average precision (mAP) attests to exceptional performance in aircraft detection and categorization from SAR images, reinforcing the utility of this integrated approach in bolstering security systems. © 2024 IEEE.

关键词： Aerospace Surveillance Aircraft Detection Aircraft Taxonomy Classification Deep Learning Imaging Techniques Machine Learning Synthetic Aperture Radar (SAR)

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Demonstration of an Application Layer Omni-frequency IoT Protocol 17

Demonstration of an Application Layer Omni-frequency IoT Pro...

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

作者： Gupta, Nayancy Peddamalla, Lokesh Mendu, Veman Sai Prabhath Banda, Gourinath Indian Institute of Technology Indore Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331531195

The advent of the Internet of Things (IoT) has transformed the way devices communicate, with an ever-increasing need for seamless interoperability and energy-efficient communication. This paper presents a unified omni-frequency protocol designed to operate independently of specific radio-wave protocols, allowing efficient and secure communication between users, devices (referred to as "things"), and cloud servers. The proposed protocol operates at the Application Layer and introduces two key components: the original equipment manufacturer (OEM) Server, which centrally manages user and device data, and the OEM Host, which verifies device credentials. This system enables efficient Internet Protocol(IP)-based and non-IP based communication, improving energy management and distribution of computational tasks to the edge. An Android application was developed to serve as the user interface, supporting multiple communication methods, including IP, Bluetooth classic version 5.0, Near-Field Communication (NFC) and ad hoc networking. The versatility of this unified approach optimizes the interactions, data processing, and energy utilization of IoT in various practical environments. © 2025 IEEE.

关键词： Network layers

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