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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Mahfuri, Mahmoud Ghazal, Taher M. Mudassar, Muhammad Siddiqui, Shahan Yamin Farooq, Sajid Kanwal, Nayab Ahmad, Munir International University for Science and Technolgy Computer and Information Technolgy Department Faculty of Engineering Syria Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Skyline University College School of Information Technology University City Sharjah 1797 United Arab Emirates The University of Lahore Lahore Pakistan Minhaj University Department of Computer Science Lahore Pakistan School of Computer Science National College of Business Administration & Economics Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The successful and continuously increasing applications of machine learning (ML) and data mining (DM) in the wide range of fields such as e-businesses, public or private sectors, marketing and retails has led the ML and DM applications in other industries and health sectors. In the health sector the data is information wise too rich but not knowledge rich. It can be due to the lack of effective tools and research study in this era. In this research work, an efficient model is proposed based on the machine learning and data mining tools to predict a particular medical disease that is the heart disease. The solution for the problem which is addressed that data is not knowledge-rich, is presented in this paper. Waikato Environment and Python is used for to analyze the data, so that an automation system can be formed. Several experiments are to be implemented to compare the performance through Supervised Learning and Unsupervised Learning. Bayesian learning (Naïve Bayes), Tree's algorithm (Decision Tree), and Neural Networks (MLPNN) are applied on the same dataset. Comparatively, Decision Tree gives best accuracy when compared with Naïve Bayes, ANN performs more effective when compared with others two Bayesian algorithms (Naïve Bayes) and Tree's algorithm (Decision tree). © 2023 IEEE.

关键词： Decision trees

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Fed-AugMix: Balancing Privacy and Utility via Data Augmentation

arXiv

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arXiv 2024年

作者： Li, Haoyang Chen, Wei Zhang, Xiaojin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Software Engineering Huazhong University of Science and Technology Wuhan430074 China

Gradient leakage attacks pose a significant threat to the privacy guarantees of federated learning. While distortion-based protection mechanisms are commonly employed to mitigate this issue, they often lead to notable performance degradation. Existing methods struggle to preserve model performance while ensuring privacy. To address this challenge, we propose a novel data augmentation-based framework designed to achieve a favorable privacy-utility trade-off, with the potential to enhance model performance in certain cases. Our framework incorporates the AugMix algorithm at the client level, enabling data augmentation with controllable severity. By integrating the Jensen-Shannon divergence into the loss function, we embed the distortion introduced by AugMix into the model gradients, effectively safeguarding privacy against deep leakage attacks. Moreover, the JS divergence promotes model consistency across different augmentations of the same image, enhancing both robustness and performance. Extensive experiments on benchmark datasets demonstrate the effectiveness and stability of our method in protecting privacy. Furthermore, our approach maintains, and in some cases improves, model performance, showcasing its ability to achieve a robust privacy-utility trade-off. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Eco-Secure SCADA: Towards Machine Learning Reliability for Green Cybersecurity

Eco-Secure SCADA: Towards Machine Learning Reliability for G...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Chijioke Ahakonye, Love Allen Mukisa, Kalibbala Jonathan Nwakanma, Cosmas Ifeanyi Kim, Dong-Seong ICT Convergence Research Center Kumoh National Institute of Technology Gumi Korea Republic of Kumoh National Institute of Technology IT-Convergence Engineering Gumi Korea Republic of NSLab Co. Ltd. Gumi Korea Republic of Kumoh National Institute of Technology Gumi Korea Republic of West Virginia University Computer Science and Electrical Engineering MorgantownWV26506 United States

ISBN: (纸本)9798331507022

This study explores energy-efficient machine learning approaches for intrusion detection in SCADA systems, addressing the dual challenges of cybersecurity and sustainability. A comprehensive evaluation of models, including Decision Trees, Random Forests, and LightGBM, highlights their performance across SCADA, IIoT, and Edge IoT environments. Decision Trees achieve exceptional efficiency scores, with 83.85 in Edge IoT systems, demonstrating high accuracy with minimal energy consumption. Random Forests and LightGBM balance scalability, computational cost, and resilience, supporting robust deployment in resource-constrained environments. Integrating lightweight and high-performing models provides a roadmap for achieving eco-secure SCADA systems, advancing the synergy between green cybersecurity and machine learning reliability. © 2025 IEEE.

关键词： SCADA systems

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric Sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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Short-term solar eruptive activity prediction models based on machine learning approaches:A review

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Science China Earth Sciences 2024年第12期67卷 3727-3764页

作者： Xin HUANG Zhongrui ZHAO Yufeng ZHONG Long XU Marianna B.KORSÓS R.ERDÉLYI Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China Beijing Cellular Exploration Science and Technology Center Beijing 100190China Dipartimento di Fisica e Astronomia“Ettore Majorana” Universitàdi CataniaCatania I 95123Italy Department of Astronomy Eötvös Loránd UniversityBudapest H-1112Hungary Hungarian Solar Physics Foundation Gyula H-5700Hungary Solar Physics&Space Plasma Research Center(SP2RC) School of Mathematics and StatisticsUniversity of SheffieldSheffield S37RHUK

Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our *** short-term solar eruptive activity prediction is an active field of research in the space weather ***,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive *** the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant *** this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.

关键词： Solar flare Coronal mass ejection Solar proton event Machine learning Prediction model

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FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search

arXiv

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arXiv 2024年

作者： Tian, Bing Liu, Haikun Tang, Yuhang Xiao, Shihai Duan, Zhuohui Liao, Xiaofei Zhang, Xuecang Zhu, Junhua Zhang, Yu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd China

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services None of modern ANNS systems can address these issues simultaneously. We present FusionANNS, a high-throughput, low-latency cost-efficient, and high-accuracy ANNS system for billion scale datasets using SSDs and only one entry-level GPU The key idea of FusionANNS lies in CPU/GPU collabo rative filtering and re-ranking mechanisms, which signifi cantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We imple ment FusionANNS and compare it with the state-of-the-art SSD-based ANNS system–SPANN and GPU-accelerated in memory ANNS system–RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN;2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Performance optimization of microbial electrolysis cell (MEC) for palm oil mill effluent (POME) wastewater treatment and sustainable Bio-H2 production using response surface methodology (RSM)

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INTERnational JOURNAL OF HYDROGEN ENERGY 2022年第34期47卷 15464-15479页

作者： Chandrasekhar, Abudukeremu Kadier ab Junying Wang ab K. Wang, Junying Chandrasekhar, K. Abdeshahian, Peyman Islam, M. Amirul Ghanbari, Farshid Bajpai, Mukul Katoch, Surjit Singh Bhagawati, Prashant Basavaraj Li, Hui Kalil, Mohd Sahaid Hamid, Aidil Abdul Abu Hasan, Hassimi Ma, Peng-Cheng [a]Laboratory of Environmental Science and Technology The Xinjiang Technical Institute of Physics and Chemistry Key Laboratory of Functional Materials and Devices for Special Environments Chinese Academy of Sciences Urumqi 830011 China [b]Center of Material and Opto-electronic Research University of Chinese Academy of Sciences Beijing 100049 China [c]School of Civil and Environmental Engineering Yonsei University Seoul 03722 Republic of Korea [d]Department of Biotechnology Engineering School of Lorena University of São Paulo São Paulo 12602-810 Brazil [e]Laboratory for Quantum Semiconductors and Photon-based BioNanotechnology Department of Electrical and Computer Engineering Faculty of Engineering Université de Sherbrooke Sherbrooke Québec J1K 2R1 Canada [f]Research Center for Environmental Contaminants (RCEC) Abadan University of Medical Sciences Abadan Iran [g]Environmental Engineering Lab Civil Engineering Department National Institute of Technology Hamirpur Hamirpur Himachal Pradesh 177005 India [h]Faculty of Engineering Department of Civil Engineering Annasaheb Dange College of Engineering and Technology Ashta Maharashtra 416301 India [i]Department of Chemical and Process Engineering Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia [j]School of Biosciences and Biotechnology Faculty of Science and Technology National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia [k]Research Centre for Sustainable Process Technology Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia

Microbial electrolysis cells (MECs) are a new bio-electrochemical method for converting organic matter to hydrogen gas (H-2). Palm oil mill effluent (POME) is hazardous wastewater that is mostly formed during the crude oil extraction process in the palm oil industry. In the present study, POME was used in the MEC system for hydrogen generation as a feasible treatment technology. To enhance biohydrogen generation from POME in the MEC, an empirical model was generated using response surface methodology (RSM). A central composite design (CCD) was utilized to perform twenty experimental runs of MEC given three important variables, namely incubation temperature, initial pH, and influent dilution rate. Experimental results from CCD showed that an average value of 1.16 m(3) H-2/m(3) d for maximum hydrogen production rate (HPR) was produced. A second-order polynomial model was adjusted to the experimental results from CCD. The regression model showed that the quadratic term of all variables tested had a highly significant effect (P < 0.01) on maximum HPR as a defined response. The analysis of the empirical model revealed that the optimal conditions for maximum HPR were incubation temperature, initial pH, and influent dilution rate of 30.23 ?, 6.63, and 50.71%, respectively. Generated regression model predicted a maximum HPR of 1.1659 m(3) H-2/m(3) d could be generated under optimum conditions. Confirmation experimentation was conducted in the optimal conditions determined. Experimental results of the validation test showed that a maximum HPR of 1.1747 m(3) H-2/m(3) d was produced. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Microbial electrolysis cell (MEC) Hydrogen production Palm oil mill effluent (POME) Wastewater Optimization Response surface methodology (RSM)

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Efficient distributed algorithms for holistic aggregation functions on random regular graphs

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Science China(Information Sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph \({\cal C}{\cal O}{\cal N}{\cal G}{\cal E}{\cal S}{\cal T}\) model communication complexity round complexity

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CodecFake+: A Large-Scale Neural Audio Codec-Based Deepfake Speech Dataset

arXiv

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arXiv 2025年

作者： Chen, Xuanjun Du, Jiawei Wu, Haibin Zhang, Lin Lin, I. Ming Chiu, I. Hsiang Ren, Wenze Tseng, Yuan Tsao, Yu Jang, Jyh-Shing Roger Lee, Hung-Yi Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Brno University of Technology Brno61266 Czech Republic Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

With the rapid advancement of neural audio codecs, codec-based speech generation (CoSG) systems have become highly powerful. Unfortunately, CoSG also enables the creation of highly realistic deepfake speech, making it easier to mimic an individual’s voice and spread misinformation. We refer to this emerging deepfake speech generated by CoSG systems as CodecFake. Detecting such CodecFake is an urgent challenge, yet most existing systems primarily focus on detecting fake speech generated by traditional speech synthesis models and fail to detect CodecFake speech. In this paper, we introduce CodecFake+, a large-scale dataset designed to advance codec-based deepfake speech detection and support the anti-spoofing community. To our knowledge, CodecFake+ is the largest dataset designed for this purpose, and encompasses the most diverse range of neural audio codec architectures. The training set is generated through re-synthesis using 31 publicly available open-source neural audio codec models, while the evaluation set includes web-sourced data from 17 advanced CoSG models. We also propose a novel and comprehensive neural audio codec taxonomy that categorizes codecs by their root components: vector quantizer, auxiliary objectives, and decoder types. Our proposed dataset and taxonomy enable detailed analysis at multiple levels to discern the key factors for successful CodecFake detection. At the individual codec level, we validate the effectiveness of using codec re-synthesized speech (CoRS) as training data for large-scale CodecFake detection. At the taxonomy level, we show that detection performance is strongest when the re-synthesis model incorporates disentanglement auxiliary objectives or a frequency-domain decoder. Furthermore, from the perspective of using all the CoRS training data, we show that our proposed taxonomy can be used to select better training data. By balancing training data with respect to decoder types, we improve detection performance and outperform mod

关键词： Taxonomies

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Remote Sensing Image Classification of Sugarcane Harvest based on Tensorflow 9

Remote Sensing Image Classification of Sugarcane Harvest bas...

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9th International Conference on Digital Home, ICDH 2022

作者： Chen, Senzheng Li, Zhang Li, Ji Ye, Huichun Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China National Local Joint Engineering Research Center of Satellite Navigation and Location Service Aerospace Information Research Institute CAS Guilin541004 China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

In this paper, deep learning is mainly introduced into remote sensing image processing, and the convolutional neural network model is used to process remote sensing images, so that the remote sensing images can be classified after the crops are harvested and after harvesting, and then the unknown harvested remote sensing images are processed. classification, which is of great significance in the field of agriculture. We can roughly estimate the harvesting and unharvesting of sugarcane through remote sensing images. In this paper, the harvesting situation of sugarcane and remote sensing images are used to train the harvesting situation, and then the harvesting situation of sugarcane is predicted, so as to monitor the harvesting situation of sugarcane. Make the relevant state departments have an advance plan for the import and export of sugar. The second objective of Sustainable Development (SDGs) is to eradicate hunger, food security, improve nutrition and promote sustainable agriculture, and this paper is an experiment in response to the needs of this objective. © 2022 IEEE.

关键词： Harvesting

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