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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bedane, Tarikwa Tesfa Kebede, Kidistie Bizuneh Mohapatra, Sudhir Kumar Das, Tapan Kumar Tripathy, Asis Kumar Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Agricultural Transformation Institute Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

Given maize’s significant role as a staple crop, it becomes imperative to carry out precise crop yield prediction to ensure food security. This research employs machine learning algorithms to analyze historical data related to maize crop yields, with the aim of developing a predictive model. The outcome of this study can serve as a valuable tool for pre-harvest planning and measuring crop production per unit of land area. The dataset utilized in this research spans from 2010 to 2021 and was sourced from Ethiopia’s Central Statistical Agency. Three machine learning regression algorithms—Logistic Regression, XGBoost, and Random Forest—were employed to design the predictive model. After data preparation and preprocessing, a total of 436,358 records were used for model development. The models were further trained with reduced features using principal component analysis (PCA). The experimental results demonstrate that the Random Forest algorithm, trained with a reduced number of features through PCA, yielded superior performance, with a lower error rate and a high R2 score of 0.98. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Credibility Assessment Based Byzantine-Resilient Decentralized Learning

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IEEE Transactions on Dependable and Secure Computing 2022年 1-12页

作者： Hou, Jian Wang, Fangyuan Wei, Chunling Huang, Hongyun Hu, Yong Gui, Ning School of Information Science & Technology Zhejiang Sci-Tech University China Beijing Institute of Control Engineering China Center of Multi-Media Big Data of Library Zhejiang Sci-Tech University China School of Computer Science and Engineering Central South University China

Decentralized deep learning has made significant success since it avoids the single point of failure in centralized solutions. However, the system might deviate from the correct model due to Byzantine attacks. Existing Byzantine-resilient defense models are mainly of a one-step evaluation fashion, making them vulnerable to rigorous topology and sophisticated cyber-attacks due to lack of historical evaluations. This paper proposes a credibility assessment based parameter aggregation rule (CA-PAR) that evaluates each neighboring node by its long-term performance. For each node and its neighbors, two concepts, immediate reward and history information based credibility are firstly proposed to describe the immediate reliability at current iteration and the comprehensive assessment of the reliability respectively. Thereafter, all the received parameters are aggregated in linear combination, in which the adjacent weight is determined by credibility value. Finally, the influences of suspicious nodes can gradually be reduced and eliminated. Experimental results in MNIST and CIFAR-10 datasets indicate the algorithm’s tolerance for five state-of-the-art attack methods against an arbitrary number of faulty nodes. Compared with the previous defense models, the proposed algorithm in this paper outperforms in topology constraints, training accuracy and computation cost. IEEE

关键词： Topology

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Deep learning for multi-data fusion in gravitational wave search 23

Deep learning for multi-data fusion in gravitational wave se...

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Proceedings of the 2nd International Conference on Signal Processing, computer Networks and Communications

作者： Chunliang Ma Jun Ye Wei Wang Guansheng He School of Information Engineering Jiangxi University of Science and Technology China School of Computer Science and Technology Fudan University China

ISBN: (纸本)9798400716430

Deep learning for gravitational wave detection is a research hotspot in recent years. Compared with the traditional matched filter method, the gravitational wave detection method based on deep learning has the advantage of efficiency. However, deep learning-based methods face the problem of low robustness and physical explanability. The future parallel operation of new ground detectors will prompt the research of data analysis methods for multi-detector fusion. Fusing multi-detector data can improve the robustness of in-depth learning for gravitational wave detection. The Hanford and Livingston models are trained using published data from the Advanced Laser Gravitational Wave Observatory and synthetic data from the SEOBNRv4 method, and four fusion methods from Hanford and Livingston are studied. The test results of the test set show that the arithmetic average fusion can achieve the best detection results at low false alarm probability. The detection results of one month data in August 2017 show that the mean method has the strongest false alarm suppression ability compared with other data fusion methods with the same detection probability.

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Exploiting Mode-Switching between Aerial-RIS and Active Radio in UAV-Assisted Wireless Networks 25

Exploiting Mode-Switching between Aerial-RIS and Active Radi...

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25th IEEE Wireless Communications and Networking Conference, WCNC 2024

作者： Zhao, Songhan Long, Yusi Gu, Bo Luong, Nguyen Cong Lyu, Bin Gong, Shimin School Of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus China Guangdong Provincial Key Laboratory Of Fire Science And Intelligent Emergency Technology China Phenikaa University Faculty Of Computer Science Viet Nam School Of Communications And Information Engineering Nanjing University Of Posts And Telecommunications China

ISBN: (纸本)9798350303582

In this paper, we employ dual-mode unmanned aerial vehicles (UAVs) equipped with both the active radio frequency (RF) module and aerial reconfigurable intelligent surface (ARIS) to assist ground users (GUs) for both the downlink energy transfer and uplink data transmission in a wireless-powered network. To maximize the GUs' minimum throughput, we propose a collaborative mode switching scheme for the dual-mode UAVs to dynamically switch between the active RF and passive ARIS modes according to the time-varying channel conditions. Besides, we jointly optimize the GUs' transmission control, the UAVs' beamforming, and the trajectory planning strategies. This optimization problem is intractable directly due to the non-convexity in both the objective and constraints. We design an iterative algorithm to first decompose the original problem into several subproblems, and then solve each subproblem individually by approximate optimization methods. Numerical results verify that the UAVs' collaborative mode switching along with their trajectories efficiently improves the transmission performance compared to the benchmarks in which both UAVs are operating in one fixed mode. © 2024 IEEE.

关键词： Antennas

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The Pre Assumptions to be Made for the Design of Underwater System

The Pre Assumptions to be Made for the Design of Underwater ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Mamtha, G.N. Pandey, Pratik Alam, Intekhab Department of Electronics and Communication Engineering Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

With significant bandwidth advantages over acoustics and radio-frequency technologies, submerged wireless optical communication (UWOC) has become a fascinating topic with special appeal for a variety of underwater applications. The possibility of attaining large data rates-tens of gigabits per second across considerable distances-has been shown by recent advancements, such as the establishment of testbeds and pre-aligned UWOC lines. This has opened the door for the construction of a worldwide interconnected 'Internet of Underwater Things' (IoUT). One of the suggested remedies is the implementation of a non-line-of-sight communication mode based on light scattering, which provides a different strategy from conventional line-of-sight techniques. This broad view, which takes into account both problems and solutions, emphasizes how important a role UWOC might play in promoting underwater communication and exploration. © 2023 IEEE.

关键词： Internet Of Underwater Things IoUT Light Scattering Non-Line-Of-Sight Communication Optical Communication Modes Optical Waveguide and High-Speed Data Transmission Underwater Wireless Optical Communication UWOC

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A Scalable Solution for Pneumonia Diagnosis: Transfer Learning for Chest X-ray Analysis 7

A Scalable Solution for Pneumonia Diagnosis: Transfer Learni...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Haque, Rezaul Sultana, Shayma Prasad, Chandan Hasan, Sayed Fardin, Hasib Sakib, Abdullah Al Malik, Neeru Kaushik, Keshav East West University Department of Cse Dhaka Bangladesh International American University Department of Management Information System United States Pimpri Chinchwad University Department of Computer Science and Engineering Maharashtra Pune India Westcliff University Department of Engineering Management United States Westcliff University Department of Information Technology United States Pimpri Chinchwad University School of Engineering & Technology Maharashtra Pune India Amity University Amity School of Engineering and Technology Punjab India

ISBN: (纸本)9798350350067

The high death rate and rapid spread of pneumonia make it a serious global health concern. The precision of traditional diagnostic techniques is often compromised, particularly in resource-limited settings. Existing studies have predominantly focused on either binary or multi-class classification, often relying on limited datasets, which affects the generalizability and robustness of models. Additionally, the interpretability and transparency of deep learning models remain challenging in clinical environments. Addressing these challenges is crucial for advancing diagnostic methodologies to combat pneumonia. This study explores the use of transfer learning to improve the reliability of pneumonia classification using chest X-ray images. An automated classification system was developed, utilizing a combination of image preprocessing techniques, feature extraction with GLRLM, and various machine learning and transfer learning algorithms. Two datasets were employed: the GWCMCx dataset with 5856 chest X-rays and another dataset comprising 9207 chest X-rays. The system was designed for both binary and multi-class classification scenarios. CapsuleNet emerged as the most effective model, achieving an accuracy of 99.02% in binary classification and 90.43% in multi-class classification, significantly outperforming previous studies. Additionally, a user-friendly web application was developed to facilitate the integration of the classification system into clinical workflows, accelerating clinical decision-making. This study demonstrates the potential of transfer learning to automate and improve pneumonia diagnosis, offering a scalable solution that can alleviate radiologists' workload and improve patient outcomes. © 2024 IEEE.

关键词： Transfer learning

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The application of image Style Conversion Network in the design of Zhuang brocade pattern derivation 3

The application of image Style Conversion Network in the des...

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3rd International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Meng, Kailin Shi, Zhuo Zhang, Mengxue Xiong, Biao Su, Qianli School of Art and Design Guilin University of Electronic Technology Guangxi Guilin541004 China School of Computer Science and Information Security School of Software Engineering Guangxi Guilin541004 China

ISBN: (纸本)9781510671485

Object: With the help of style conversion Network, the design of the Zhuang brocade pattern is derived to explore the new direction of inheritance and development of the Zhuang brocade pattern in the current era. Methods: The cultural connotation and design value of Zhuang brocade patterns are analyzed, and the selected Zhuang brocade patterns are used as content maps and different style maps to conduct style conversion Network experiments. The experimental results are analyzed and evaluated to find the optimal conversion solution. Investigate the effect of the conversed Zhuang brocade pattern in the derivative design. Result: By using style conversion Network, different styles of patterns can be generated quickly and can produce good derivative design effects in many products, thus achieving the purpose of integrating Zhuang brocade patterns with modern products. Conclusion: The addition of the style conversion Network algorithm makes the style simulation process significantly reduced, saving time for subsequent derivative designs and providing the possibility of mass production. The designs produced by the style conversion Network retain the original pattern characteristics of Zhuang brocade, with both auspicious symbolic meaning and national spirit, and realize the interaction between traditional crafts and the art field, in which the cultural heritage can also be shown, promoting Zhuang culture and providing new possibilities for the modern development of Zhuang brocade patterns. © 2023 SPIE.

关键词： Product design

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ICLTR: A Input-split Inverters and C-elements based Low-Cost Latch with Triple-Node-Upset Recovery

ICLTR: A Input-split Inverters and C-elements based Low-Cost...

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International Test Conference in Asia (ITC-Asia)

作者： Jiajia Zhang Zhenmin Li Gaoyang Shan Jie Song Xing Guo Xiaoqing Wen School of Computer Science and Technology Anhui University Hefei China School of Microelectronics Hefei University of Technology Hefei China Department of Software and Computer Engineering Ajou University Suwon Korea Department of Computer Science and Networks Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9798331540333

ISBN: (纸本)9798331540340

As the semiconductor technology continues to advance, integrated circuits (ICs) are becoming increasingly sensitive to soft errors, e.g., double-node upsets (DNUs) and triplenode upsets (TNUs), induced by harsh radiation. In this paper, a low-cost latch design, namely ICLTR, using input-split inverters (ISIs) and C-elements to provide complete TNU recovery, is proposed. ICLTR consists of seven ISIs, seven 2-input C-elements and a clock-gated inverter, and all these elements are interlocked. Simulation results show the complete TNU recovery for ICLTR. The simulation results also show that ICLTR can save 59.5% of the transmission delay, 36.1% of the power consumption and 81.6% of the delay-area-power product (DAPP) on average when compared with the same type of TNU recovery latch designs.

关键词： Integrated circuits Latches Sensitivity Power demand Radiation hardening (electronics) Simulation Reliability engineering

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An Improved DVB-S2 Phase Coarse Synchronization Algorithm and Its FPGA Implementation

An Improved DVB-S2 Phase Coarse Synchronization Algorithm an...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Xianyu Zhang Jing Zhang Zixuan Huang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Institude of Space Integrated Ground Network Hefei China

This paper proposes an improved phase coarse synchronization algorithm, which can be applied to the DVB-S2 system. The improved algorithm can handle the uncorrected residual frequency offset from the frequency synchronization module. Compared with the original algorithm, the residual frequency offset is nearly doubled threshold, and reduces the complexity of FPGA implementation and saves hardware resource overhead. The scheme is implemented on Xilinx Virtex-7 VC709 field programmable gate array, and simulation verifies that the scheme can achieve bit error performance below 10 -7 within the supported residual frequency offset range.

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Graph-Based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction 1st

Graph-Based Exercise- and Knowledge-Aware Learning Network ...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Liu, Mengfan Shao, Pengyang Zhang, Kun Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783030930455

Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students’ knowledge level and provide personalized teaching strategies for them. Researchers have made plenty of efforts on this task. They either leverage educational psychology methods to predict students’ scores according to the learned knowledge proficiency, or make full use of Collaborative Filtering (CF) models to represent latent factors of students and exercises. However, most of these methods either neglect the exercise-specific characteristics (e.g., exercise materials), or cannot fully explore the high-order interactions between students, exercises, as well as knowledge concepts. To this end, we propose a Graph-based Exercise- and Knowledge-Aware Learning Network for accurate student score prediction. Specifically, we learn students’ mastery of exercises and knowledge concepts respectively to model the two-fold effects of exercises and knowledge concepts. Then, to model the high-order interactions, we apply graph convolution techniques in the prediction process. Extensive experiments on two real-world datasets prove the effectiveness of our proposed Graph-EKLN. © 2021, Springer Nature Switzerland AG.

关键词： Data mining

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