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Automation and Computation (AUTOCOM), International Conference on

作者： Jayaprakash B J. Premkumar Neeraj Das Ankit Punia Subrat Kumar Nayak Varun Ojha School of Sciences Computer Science & IT JAIN (Deemed to be University) Bangalore Karnataka India Department of Biomedical Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Wireless body area networks are an attractive technology for measuring health data in medical applications. Several challenges exist in designing and implementing such reliable, energy-efficient communication protocols for these networks. This new strategy solution strategy is based on a hybrid communication protocol that simultaneously obtains the positive aspects of centralized and distributed structures. By centralizing network maintenance and control functions such as health monitoring of nodes and transmission control, the proposed protocol supports reliable data transmission with minimal energy consumption in WBANs. This reduces the overhead and collisions, giving the network better reliability. Unlike other protocols mentioned where the data flow from the sensor node to the central node is at least distributed, in our case, a few nodes send their data at a time to the Central Node. It enables low-power communication since nodes only need to wake up during their allotted transmission time slot. Through simulations, we demonstrate that the new protocol surpasses existing protocols in reliability and energy consumption performance. It can also help to minimize the energy consumption of WBAN nodes, which makes it an appropriate candidate for long-time medical monitoring applications. All in all, this work helps to pave the way for the efficient use of WBANs in a wide range of medical applications that require robustness and low-energy communications.

关键词： Wireless communication Biomedical equipment Wireless sensor networks Energy consumption Protocols Medical services Body area networks Reliability engineering Energy efficiency Reliability

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Enhancing Weather Forecast Accuracy Using Deep Learning Models 15th

Enhancing Weather Forecast Accuracy Using Deep Learning Mode...

引用

15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Kumar, Naween Dash, Yajnaseni Abraham, Ajith Ahmad, Shaik Irfan Saifullah, Shaik School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810824

The weather has a significant impact on a wide range of businesses and daily activities, including transportation, agriculture, and disaster relief. Planning for risk mitigation, allocating resources, and making every decision depends on accurate and precise weather pattern forecasts. In recent years, deep learning algorithms have become effective tools in improving the accuracy of weather forecasting. The model uses neural networks to uncover complex patterns from weather data. This study presents the use of deep learning models to predict climate change. This article highlights the shortcomings of traditional methods and the benefits of using deep learning, laying the foundation for our study of how deep learning can improve the accuracy of weather forecasting. Recurrent neural networks (RNN) and Convolutional neural networks (CNN) were used to predict weather patterns. The results indicate that RNN produces accurate results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CNN Deep learning Machine learning RNN Weather forecasting

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A GNN-Based Learning Approach for Energy Optimization in Relay-Assisted IoT Networks

A GNN-Based Learning Approach for Energy Optimization in Rel...

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IEEE Conference on Wireless Communications and Networking

作者： Huapeng Yang Han Ji Zhangqin Huang Xiping Wu Beijing Engineering Research Center for IoT Software and Systems College of Computer Science Beijing University of Technology Beijing China School of Electrical and Electronic Engineering University College Dublin Dublin Ireland The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Minimizing energy consumption is critical for the long-range (LoRa) Internet of Things (IoT) networks, to extend the battery lifetime of end devices (EDs) while reducing the maintenance cost. To overcome the excessive computational complexity required by the traditional optimization methods, learning approaches such as deep neural network (DNN) and reinforcement learning (RL) have been researched in wireless networks, where star topologies are widely employed. In contrast, LoRa usually deploys relays to assist the connection between the EDs and the gateway (GW), leading to a much more complex network topology. Consequently, DNN and RL would become less effective in LoRa, since those methods are difficult to learn the complex network topology constructed by LoRa. In this paper, we propose a learning method based on graph neural network (GNN), which is known for its prominent ability to capture and represent intricate graph-structural dependencies, to tackle the energy optimization problem for relay-assisted LoRa. Specifically, the multi-hop LoRa network is modeled as a directed graph, with channel state information (CSI) defined as node features, while the spreading factor and transmission power are deemed labels. A hierarchical message aggregation mechanism is proposed to effectively capture the multi-hop structural dependencies, followed by the process of inductive learning. Results show that against conventional optimization algorithms, the proposed method can achieve near-optimal energy consumption with a gap of 10% to 16%, while reducing the runtime by about six orders of magnitude. Compared to DNN, the GNN-based model can provide an energy saving of up to 32%, at a similar level of inference time.

关键词： Energy consumption Runtime Wireless networks LoRa Artificial neural networks Spread spectrum communication Complex networks Graph neural networks Topology Internet of Things

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SE-GNN: Seed Expanded-Aware Graph Neural Network with Iterative Optimization for Semi-supervised Entity Alignment

arXiv

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arXiv 2025年

作者： Meng, Tao Shan, Shuo Shao, Hongen Shou, Yuntao Ai, Wei Li, Keqin School of Computer and Information Engineering Central South University of Forestry and Technology Hunan Changsha410004 China Department of Computer Science State University of New York New PaltzNY12561 United States

Entity alignment aims to use pre-aligned seed pairs to find other equivalent entities from different knowledge graphs (KGs) and is widely used in graph fusion-related fields. However, as the scale of KGs increases, manually annotating pre-aligned seed pairs becomes difficult. Existing research utilizes entity embeddings obtained by aggregating single structural information to identify potential seed pairs, thus reducing the reliance on pre-aligned seed pairs. However, due to the structural heterogeneity of KGs, the quality of potential seed pairs obtained using only a single structural information is not ideal. In addition, although existing research improves the quality of potential seed pairs through semi-supervised iteration, they underestimate the impact of embedding distortion produced by noisy seed pairs on the alignment effect. In order to solve the above problems, we propose a seed expanded-aware graph neural network with iterative optimization for semi-supervised entity alignment, named SE-GNN. First, we utilize the semantic attributes and structural features of entities, combined with a conditional filtering mechanism, to obtain high-quality initial potential seed pairs. Next, we designed a local and global awareness mechanism. It introduces initial potential seed pairs and combines local and global information to obtain a more comprehensive entity embedding representation, which alleviates the impact of KGs structural heterogeneity and lays the foundation for the optimization of initial potential seed pairs. Then, we designed the threshold nearest neighbor embedding correction strategy. It combines the similarity threshold and the bidirectional nearest neighbor method as a filtering mechanism to select iterative potential seed pairs and also uses an embedding correction strategy to eliminate the embedding distortion. Finally, we will reach the optimized potential seeds after iterative rounds to input local and global sensing mechanisms, obtain the final e

关键词： Knowledge graph

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A Deep Transfer Learning Model for Classifying Alzheimer's Disease Using MRI Images

A Deep Transfer Learning Model for Classifying Alzheimer's D...

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Computing and Information technology (ICCIT-1441), 2020 International Conference on

作者： Anitha Thavasimuthu P. Jesu Jayarain Narmatha C. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Faculty of Computers and Information Technology University of Tabuk Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Alzheimer's disease (AD) affects the cells of the human brain and leads to the development of dementia. The progressive brain cells degeneration results in the inability to carry out everyday duties. The therapy for this condition is currently insufficiently developed. Nevertheless, detecting the AD at an early stage can help control its transmission. To detect AD at an early stage using brain Magnetic Resonance Imaging (MRI), it is necessary to create an automated system that can identify and categorize individuals with dementia. In this research, a deep learning (DL) model is developed for the classification of AD based on MRI pictures. The research model was created by applying a pre-trained transfer learning model known as ResNeXt-101. The OASIS-3 dataset of MRI images is initially gathered and utilized for both training the model and evaluating its performance. The dataset comprises 2842 MRI pictures pertaining to AD and its different phases. As part of the preprocessing stage, all the images are resized and cropped to a uniform size of 224×224 pixels. Furthermore, they are enhanced utilizing the contrast enhancement technique. The ResU-Net segmentation method is utilized following the preprocessing phase. The ResNeXt-101 model is employed for the purpose of classification the segmented images. The MRI pictures are divided into training and testing sets, with 75% allotted to train the model and 25% allocated to evaluate the model. The classification is conducted using a binary approach, categorizing the data as either the presence or absence of AD. The research model's performance was evaluated using metrics such as accuracy, specificity, sensitivity, f1-score, and precision. The research model achieved a training accuracy of 99.67%, sensitivity of 99.54%, precision of 99.72%, specificity of 99.41%, and a f1-score of 99.60%. The model achieved an accuracy of 99.38%, a sensitivity of 98.29%, a precision of 99.40%, a specificity of 99.24%, and a f1-score of 99.36

关键词： Training Performance evaluation Image segmentation Sensitivity Accuracy Magnetic resonance imaging Computational modeling Transfer learning Alzheimer's disease Testing

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Modeling of Dengue Fever Outbreaks Using Machine Learning Techniques 15th

Modeling of Dengue Fever Outbreaks Using Machine Learning Te...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Singh, Divyanshi Harshita Srivastava, Ananya Dash, Yajnaseni Abraham, Ajith School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810794

Dengue is a life-threatening disease that has been increasing at an unprecedented rate in India as well as around the globe. The disease is affected by various factors such as population, urbanization, as well as climate change. Dynamic transmission of dengue fever is majorly influenced by climate change. Rising temperatures, altered rainfall patterns, and changing ecosystems contribute to the spread and severity of the disease. Random Forest, Long Short-Term Memory (LSTM), k-Nearest Neighbors (KNN), and LightGBM, are used here to understand and predict dengue fever dynamics, aiding in developing effective prevention and control strategies. This study focuses on the importance of interdisciplinary approaches, integrating artificial intelligence, climatology, epidemiology, ecology, and public health, to lessen the impact of climate change on dengue fever transmission and safeguard global health security. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial Intelligence Climatology Dengue Healthcare KNN LightGBM LSTM Machine Learning Public health Random Forest

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Grating pitch comparator traceable to the Cr atom transition frequency

引用

Measurement: Journal of the International Measurement Confederation 2025年 242卷

作者： Lin, Zichao Deng, Xiao Wu, Yize Feng, Jingtong Zhu, Hongyu Yang, Yaao Yu, Jing Xue, Dongbai Wang, Jianbo Shi, Yushu Jin, Tao Zhang, Wentao Dun, Xiong Cheng, Xinbin Li, Tongbao MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai Frontiers Science Center of Digital Optics Institute of Precision Optical Engineering School of Physics Science and Engineering Tongji University Shanghai200092 China National Institute of Metrology China Beijing100029 China College of metrology and measurement engineering China Jiliang University Hangzhou310018 China Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin541004 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Calibration of the grating pitch typically requires metrological instruments or diffraction techniques. The traceability and stability demands for laser wavelength in these techniques present substantial challenges for on-site grating calibration. This paper introduces a novel traceability approach to grating pitch calibration using a grating pitch comparator that utilizes the transition frequency of the chromium. The chromium transition frequency is converted into a chromium grating through atom lithography, establishing a self-traceable length standard dCr = 212.7787 ± 0.0049 nm. Calibration of the test grating's pitch is achieved by comparing the phase shift caused by the simultaneous movement of the chromium grating and the test grating. By using the dCr, this approach combines the high-accuracy of fundamental physical constants with the environmental stability of grating interferometers, eliminating the need for measuring laser wavelength, air refraction, diffraction angles. It significantly enhances the precision and makes more straightforward and portable, crucial for facilitating traceable on-site grating measurements. © 2024

关键词： Comparators (optical)

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Quality Scores Compression of Genomic Sequencing Data: A Comprehensive Review and Performance Evaluation

IEEE Transactions on Computational Biology and Bioinformatic...

引用

IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 817-831页

作者： Yuansheng Liu Tao Tang Zexuan Zhu Xiangxiang Zeng Quan Zou Keqin Li College of Information Science and Engineering Hunan University Changsha Hunan China School of Mordern Posts Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China National Supercomputing Center Changsha Hunan China Department of Computer Science State University of NY New Paltz NY USA

Advanced sequencing technologies have profoundly revolutionized biology and produced vast amounts of raw sequencing data during the past decades. The enormous amount of sequencing data proposed significant challenges of data storage and transmission. Compressing a big file into a small file is an encouraging method to tackle these challenges. Howver, it has been found that traditional text data compression algorithms are not well-suited for handing the vast sequencing datasets. Therefore, several algorithms are designed specifically for the efficient compression of sequencing data. Recently, considerable research has been devoted to compressing quality scores stored in the FASTQ format file, resulting in substantial advances in compression performance. Despite these advances, there has been no systematic review and evaluation of these algorithms or software. In this review, we aim to conduct a broad review of the existing quality score compression algorithms. We mainly discuss those algorithms from two categories, i.e., lossless and lossy compression. Additionally, we benchmark the compression performance of 12 tools using 14 real datasets. We anticipate that our review will provide practical guidance for others seeking to design an appropriate algorithm for compressing quality scores.

关键词： Sequential analysis Genomics Bioinformatics Compression algorithms Data compression Image coding Electronic mail Data mining Computational biology Training

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HuangmeiSinger: A Dataset and A Branchformer-Diffusion Model for Huangmei Opera Synthesis 25

HuangmeiSinger: A Dataset and A Branchformer-Diffusion Model...

引用

Proceedings of the 4th International Conference on computer, Artificial Intelligence and Control engineering

作者： Yufeng Qiu Guofu Zhang Zhaopin Su Yang Zhou Xiaoyi Bian School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China College of Humanities and Social Sciences Anhui Agricultural University Hefei Anhui China

ISBN: (纸本)9798400712647

Singing voice synthesis has been extensively used in metaverse, music creation and entertainment, and cultural preservation and inheritance. However, the synthesis of traditional operas, such as Huangmei opera, has been limited due to the lack of professionally annotated high-quality datasets and appropriate deep learning models. In this work, we develop a singing voice dataset and propose an acoustic model tailored for the unique singing style for Huangmei opera. More specifically, we first propose a data annotation method, effectively addressing the challenges posed by the numerous arias in this art form. Next, we construct our Huangmei opera singing voice dataset with the detailed musical score information, where each singing recording is captured at a high sampling rate of 44.1 kHz. Subsequently, we incorporate the Branchformer encoder and the pitch diffusion module to handle the complex and diverse melodies characteristic of Huangmei opera. Finally, extensive subjective and objective experiments demonstrate the effectiveness of the proposed dataset and model. Audio samples, the dataset, and the codes are available at https://***/***/opera/.

关键词： Chinese operas

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CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization

arXiv

引用

arXiv 2025年

作者： Xu, Junhao Zhang, Yanan Cai, Zhi Huang, Di State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Multi-agent collaborative perception enhances perceptual capabilities by utilizing information from multiple agents and is considered a fundamental solution to the problem of weak single-vehicle perception in autonomous driving. However, existing collaborative perception methods face a dilemma between communication efficiency and perception accuracy. To address this issue, we propose a novel communication-efficient collaborative perception framework based on supply-demand awareness and intermediate-late hybridization, dubbed as CoSDH. By modeling the supply-demand relationship between agents, the framework refines the selection of collaboration regions, reducing unnecessary communication cost while maintaining accuracy. In addition, we innovatively introduce the intermediate-late hybrid collaboration mode, where late-stage collaboration compensates for the performance degradation in collaborative perception under low communication bandwidth. Extensive experiments on multiple datasets, including both simulated and real-world scenarios, demonstrate that CoSDH achieves state-of-the-art detection accuracy and optimal bandwidth tradeoffs, delivering superior detection precision under real communication bandwidths, thus proving its effectiveness and practical applicability. The code will be released at https://***/Xu2729/CoSDH. Copyright © 2025, The Authors. All rights reserved.

关键词： Bandwidth

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