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Abnormal wind speed detection and prediction: methodology and case study

Intelligent Marine Technology and Systems 2025年第1期3卷 1-10页

作者： Yang, Yuting Zhang, Cong Lam, Kin-Man Sun, Xin Xue, Yu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong China Faculty of Data Science City University of Macau Macao China College of Engineering Ocean University of China Qingdao China

Accurate wind speed prediction is crucial for conserving power resources and enhancing power utilization efficiency. However, deviations from typical wind patterns can introduce errors into predictions, potentially leading to imbalances between wind power supply and demand. Consequently, developing a model to forecast abnormal wind speeds is essential. To address this, we leverage the microcanonical multifractal formalism algorithm to detect abnormal wind speeds. In this paper, we integrate ensemble empirical mode decomposition, phase space reconstruction, and long short-term memory (LSTM) networks to predict these anomalies. Initially, wind speed data is meticulously pre-processed to generate datasets for one-hour, one-day, and non-zero wind speeds. Subsequently, LSTM networks are used to forecast abnormal wind speeds. Evaluations of our methodology across different datasets demonstrate its effectiveness, particularly excelling in one-hour forecasts.

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LossControl: Defending Membership Inference Attacks by Controlling the Loss

LossControl: Defending Membership Inference Attacks by Contr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bo Yang Hongwei Yang Renhao Lu Hui He Weizhe Zhang Haoyu He Rahul Yadav Faculty of Computing Harbin Institute of Technology Harbin China Department of Data Science and AI Faculty of Information Technology Monash University Melbourne Australia College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Machine learning models are vulnerable to membership inference attacks (MIAs), where adversaries attempt to predict whether specific samples are part of the model’s training set. Previous studies have demonstrated a strong correlation between the distinguishability of training and testing loss distributions and the model’s susceptibility to MIAs. Motivated by existing results, we propose a novel training framework called LossControl, which focuses on manipulating loss to mitigate privacy leaks. In LossControl, we first utilize Soft-label Training to replace the general learning process, which facilitates model training while improving generalization. Next, we monitor overfitting samples during the training process and prevent further loss reduction by applying our designed Loss Ascent to these samples without sacrificing model performance. Through extensive evaluations across four diverse datasets (including images, medical data, and transaction records), our method consistently outperforms defense mechanisms against state-of-the-art attacks and achieves optimal model performance in most experiments, demonstrating LossControl’s superior resilience against MIAs and its ability to strike an unparalleled balance between privacy and utility.

关键词： Training Privacy Process control Machine learning Predictive models Signal processing Speech processing Testing Resilience Overfitting

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Apple Leaf Disease Identification Using Resnet50

Apple Leaf Disease Identification Using Resnet50

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Karthigaiveni, S. Archana, R. Ravi, Vasanth Khatoon, P Salma Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India St. Joseph's Institute of technology Department of Artificial intelligence and Data science OMR Chennai India Jain Deemed to be University Faculty of Engineering and Technology Department of Computer Science and Engineering Jain Global Campus India Mlr Institute of Technology Department of CSE-DS Hyderabad India

ISBN: (纸本)9798331543617

Apple leaf disease is a primary issue limiting apple yield and quality. The standard diagnostic approach requires considerable time for illness detection, causing farmers to frequently miss the optimal period for prevention and treatment. The detect of leaf diseases through leaf imagery is a vital study area in computer vision, with the primary objective being to devise an effective method for representing images of sick leaves. The efficient diagnosis and categorization of plant diseases constitutes a significant research aim in agriculture. This paper proposes a novel deep learning, ResNet. To evaluate the efficacy of the ResNet model, a number of comparative experiments were performed with leading transfer learning methodologies. This demonstrates that the proposed method is both possible and successful. © 2024 IEEE.

关键词： CNN Diseases Leaf Resnet Transfer Learning

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Explainable Artificial Intelligence in Malignant Lymphoma Classification: Optimized DenseNet121 Deep Learning Approach with Particle Swarm Optimization and Genetic Algorithm.

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IEEE Access 2025年 13卷 98639-98655页

作者： Elwahsh, Haitham Bakhiet, Ali Alirr, Omar Ibrahim Khalifa, Tarek Alsabaan, Maazen Ibrahem, Mohamed I. El-Shafeiy, Engy Applied Science Private University Faculty of Information Technology Amman 11931 Jordan Kafrelsheikh University Faculty of Computers and Information Department of Computer Science Kafrelsheikh 33516 Egypt Badya University Faculty of Computing and Data Science Egypt American University of the Middle East College of Engineering and Technology Egaila 54200 Kuwait King Saud University College of Computer and Information Sciences Department of Computer Engineering P.O.Box 51178 Riyadh 11543 Saudi Arabia Augusta University School of Computer and Cyber Sciences Augusta 30912 GA United States University of Sadat City Faculty of Computers and Artificial Intelligence Department of Computer Science Sadat City 32897 Egypt

One of the forms of cancerous tumors that can be fatal is malignant lymphoma. Histopathological examination of lymphoma tissue images is a diagnostic technique for detecting malignant lymphomas. Differentiating lymphoma subtypes manually is challenging due to their similar morphological features. This paper introduces a cutting-edge methodology for classifying malignant lymphoma using Hematoxylin and Eosin (H&E)-stained biopsy images. It integrates feature reduction using Particle Swarm Optimization (PSO) and model optimization through a Genetic Algorithm (GA) in conjunction with DenseNet121 architecture. The DenseNet121 model, which employs four distinct freezing strategies, extracts features from the images, generating an initial 4096 features per image. These features are subsequently reduced by more than 54.4% using PSO, thereby enhancing the computational efficiency without sacrificing classification accuracy. The trained GA Deep Neural Network (DNN) model achieved a significant score of 96.77% testing accuracy with high precision, recall and F1 scores as well. The results after comparison with other existing state-of-the-art models showed the usefulness of the proposed approach in terms of performance as well as the consumption of resources for medical imaging classification. It is recommended that the combined application of PSO for feature reduction and GA for model optimization can be successfully used for improving accuracy rate of such algorithms while reducing computation time. This technique is expected to be further developed and applied in different tasks of computer aided diagnosis of malignant lymphoma in particular the tasks with requirements for explainable artificial intelligence. © 2013 IEEE.

关键词： Artificial intelligent Deep Learning Genetic Algorithm malignant lymphoma Optimized DenseNet121 Particle Swarm Optimization

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SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Wang, Guoan Ye, Jin Cheng, Junlong Li, Tianbin Chen, Zhaolin Cai, Jianfei He, Junjun Zhuang, Bohan Shanghai Artificial Intelligence Laboratory Shanghai China College of Computer Science East China Normal University Shanghai China Department of Data Science and AI Faculty of IT Monash University Australia College of Computer Science Sichuan University Chendu China

Volumetric medical image segmentation is pivotal in enhancing disease diagnosis, treatment planning, and advancing medical research. While existing volumetric foundation models for medical image segmentation, such as SAM-Med3D and SegVol, have shown remarkable performance on general organs and tumors, their ability to segment certain categories in clinical downstream tasks remains limited. Supervised Finetuning (SFT) serves as an effective way to adapt such foundation models for task-specific downstream tasks but at the cost of degrading the general knowledge previously stored in the original foundation *** address this, we propose SAM-Med3D-MoE, a novel framework that seamlessly integrates task-specific finetuned models with the foundational model, creating a unified model at minimal additional training expense for an extra gating network. This gating network, in conjunction with a selection strategy, allows the unified model to achieve comparable performance of the original models in their respective tasks — both general and specialized — without updating any parameters of *** comprehensive experiments demonstrate the efficacy of SAM-Med3D-MoE, with an average Dice performance increase from 53.2% to 56.4% on 15 specific classes. It especially gets remarkable gains of 29.6%, 8.5%, 11.2% on the spinal cord, esophagus, and right hip, respectively. Additionally, it achieves 48.9% Dice on the challenging SPPIN2023 Challenge, significantly surpassing the general expert’s performance of 32.3%. We anticipate that SAM-Med3D-MoE can serve as a new framework for adapting the foundation model to specific areas in medical image analysis. Codes and datasets will be publicly available. Copyright © 2024, The Authors. All rights reserved.

关键词： Diagnosis

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MG-Net: Learn to Customize QAOA with Circuit Depth Awareness

arXiv

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arXiv 2024年

作者： Qian, Yang Wang, Xinbiao Du, Yuxuan Luo, Yong Tao, Dacheng School of Computer Science Faculty of Engineering University of Sydney NSW2008 Australia Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling combinatorial optimization challenges. However, their practical realization confronts a dilemma: the requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. To address this dilemma, here we first analyze the convergence behavior of QAOA, uncovering the origins of this dilemma and elucidating the intricate relationship between the employed mixer Hamiltonian, the specific problem at hand, and the permissible maximum circuit depth. Harnessing this understanding, we introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians tailored to distinct tasks and circuit depths. Systematic simulations, encompassing Ising models and weighted Max-Cut instances with up to 64 qubits, substantiate our theoretical findings, highlighting MG-Net's superior performance in terms of both approximation ratio and efficiency. © 2024, CC BY.

关键词： Hamiltonians

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A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

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computer Modeling in Engineering & sciences 2024年第9期140卷 2165-2206页

作者： Qusay M.Salih Md.Arafatur Rahman Ahmad Firdaus Mohammed Rajih Jassim Hasan Kahtan Jasni Mohamad Zain Ahmed Hussein Ali Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)PekanPahang26600Malaysia Department of Medical Equipment Technology Engineering Al Salam University CollegeBaghdadIraq School of Mathematics and Computer Science University of WolverhamptonWolverhampton01902UK Department of Production Engineering and Metallurgy University of TechnologyBaghdadIraq Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARAShah AlamSelangor40450Malaysia Department Computer Science Al-Salam University CollegeBaghdadIraq

The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already *** realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ***,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G ***,this study aims to review,examine,and summarize existing studies and research activities in *** study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and ***,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of *** study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.

关键词： 6G bibliometric wireless networks review

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IndoKEPLER, IndoWiki, and IndoLAMA: A Knowledge-enhanced Language Model, dataset, and Benchmark for the Indonesian Language 7

IndoKEPLER, IndoWiki, and IndoLAMA: A Knowledge-enhanced Lan...

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7th International Workshop on Big data and Information Security, IWBIS 2022

作者： Ramli, Inigo Krisnadhi, Adila Alfa Prasojo, Radityo Eko Universitas Indonesia Faculty of Computer Science Tokopedia-UI Ai Center of Excellence Depok Indonesia Pitik Digital Indonesia Data & Ai Team Jakarta Indonesia

ISBN: (数字)9781665489508

ISBN: (纸本)9781665489508

Pretrained language models posses an ability to learn the structural representation of a natural language by processing unstructured textual data. However, the current language model design lacks the ability to learn factual knowledge from knowledge graphs. Several attempts have been made to address this issue, such as the development of KEPLER. KEPLER combines the BERT language model and TransE knowledge embedding method to achieve a language model that can incorporate knowledge graphs as training data. Unfortunately, such knowledge enhanced language model is not yet available for the Indonesian language. In this experiment, we propose IndoKEPLER: a language model trained usingWikipedia Bahasa Indonesia andWikidata. We also create a new knowledge probing benchmark named IndoLAMA to test the ability of a language model to recall factual knowledge. The benchmark is based on LAMA, which is designed to test the suitability of our language model to be used as a knowledge base. IndoLAMA tests a language model by giving cloze style question and compare the prediction of the model to the factually correct answer. This experiment shows that IndoKEPLER increases the ability of a normal DistilBERT model to recall factual knowledge by 0.8%. Moreover, the most significant increase happens when dealing with many-to-one relationships, where IndoKEPLER outperforms it's original text encoder model by 3%. © 2022 IEEE.

关键词： Knowledge graph

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Classifying Turkish Trade Registry Gazette Announcements 7

Classifying Turkish Trade Registry Gazette Announcements

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7th International Conference on computer science and Engineering, UBMK 2022

作者： Demirtas, Irem Nur Arslan, Secil Eyigit, Gulsen Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey Data Science and Artificial Intelligence Prometeia Spa Istanbul Merkez Şubesi Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Turkish Trade Registry Gazette is an important source of information in many sectors such as banking and telecommunication. Although the newspaper is publicly available, the data is hard to acquire, and announcements are offered in image format. It is possible to search for a specific announcement a company has, but there exist many other unrelated announcements in the image returned. This poses multiple challenges in the way of information extraction. Due to the structure of the documents in these images, it is hard to perform OCR directly. Moreover, even in the case where the text is extracted, the announcement boundaries must be detected to split the announcements within the page. Once the announcements are extracted, the announcement of the searched company should be matched. Since no information regarding the surrounding announcements is given as a result of the query, these announcements should also be categorized to detect any events of interest other companies may have. In this work, we address all of these problems and present a pipeline that includes image processing, OCR, announcement splitting, and document classification steps. © 2022 IEEE.

关键词： Commerce

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A Conceptual Framework for Addressing Class Imbalance in Image data: Challenges and Strategies

A Conceptual Framework for Addressing Class Imbalance in Ima...

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International IEEE Conference on Intelligent Systems, IS

作者： Saara Asif Muhammad Uzair Akmal Leonid Koval Selvine G. Mathias Simon Knollmeyer Daniel Grossmann AIMotion Technische Hochschule Ingolstadt Ingolstadt Germany Faculty of Computer Science and Data Processing Technische Hochschule Ingolstadt Ingolstadt Germany

ISBN: (数字)9798350350982

ISBN: (纸本)9798350350999

The presence of class imbalance, denoting a dis-proportionate distribution of class instances in a dataset, has emerged as a significant challenge in the era of Deep Learning (DL) where models crave abundance in data. This issue is pervasive in various real-world applications, where certain classes exhibit limited data representation. This problem is frequently encountered when dealing with image data, which exhibits an imbalanced distribution, with one class significantly outnumbering the others. Failing to address class imbalance introduces bias in machine learning and deep learning models, favoring the majority classes and leading to subpar performance for the minority classes. This research specifically delves into the recurrent problem in the context of image data, that is “class imbalance”. The research comprehensively explores the existing challenges based on data pre-processing, algorithmic techniques, hybrid methodologies, and state-of-the-art solutions.

关键词： Deep learning Accuracy Machine learning algorithms Merging Generative adversarial networks data models Intelligent systems

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