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arXiv 2022年

作者： Zhang, Jiankang Liu, Dong Chen, Sheng Ng, Soon Xin Maunder, Robert G. Hanzo, Lajos Department of Computing & Informatics Bournemouth University BH12 5BB United Kingdom School of Cyber Science and Technology Beihang University Beijing100191 China School of Electronics and Computer Science University of Southampton SO17 1BJ United Kingdom

Providing Internet service above the clouds is of ever-increasing interest and in this context aeronautical ad-hoc networking (AANET) constitutes a promising solution. However, the optimization of packet routing in large ad hoc networks is quite challenging. In this paper, we develop a discrete ǫ multiobjective genetic algorithm (ǫ-DMOGA) for jointly optimizing the end-to-end latency, the end-to-end spectral efficiency (SE), and the path expiration time (PET) that specifies how long the routing path can be relied on without re-optimizing the path. More specifically, a distance-based adaptive coding and modulation (ACM) scheme specifically designed for aeronautical communications is exploited for quantifying each link’s achievable SE. Furthermore, the queueing delay at each node is also incorporated into the multiple-objective optimization metric. Our ǫ-DMOGA assisted multiple-objective routing optimization is validated by real historical flight data collected over the Australian airspace on two selected representative dates. © 2022, CC BY.

关键词： Packet networks

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Detection of Three Key Phenologiccal Stages During Growth Period of Rice Using Time Series Sentinel-1 Data

Detection of Three Key Phenologiccal Stages During Growth Pe...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Wenjian Li Lingli Zhao Zhiqu Liu Hongtao Shi Jie Yang Juan M. Lopez-Sanchez School of Remote Sensing and Information Engineering Wuhan University Wuhan China Laboratory of Target Microwave Properties Deqing Academy of Satellite Applications Deqing China School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou China The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Institute for Computing Research University of Alicante Alicante Spain

Monitoring rice growth from the space has aroused the interests of researchers from all over the world. Previous researches about rice phenology focused on dividing paddy rice into different phenological intervals, without knowing the specific date when a certain phenological stage starts. In this paper, different characteristics of the start of three important phenological stages are analyzed. A set of C-band SAR images acquired by SENTINEL-1 has been used to retrieve the start of three key phenological stages (leaf development, stem elongation and inflorescence emergence) of paddy rice. A dynamic time warping method was applied for the alignment of SAR time series curves, which are constructed using backscattering coefficients of different polarimetric channels (VH, VV and VH/VV ratio). Results show that all three kinds of time series curves show great potential in detecting start of leaf development of rice and VH/VV curves perform better in recognizing start of inflorescence emergence.

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Few-shot learning based histopathological image classification of colorectal cancer

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Intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

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Understanding Mobile Phone Use in Electricity Scarce Context: Extended TAM for Sub-Saharan African Countries

Understanding Mobile Phone Use in Electricity Scarce Context...

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IST-Africa

作者： Martin Mabeifam Ujakpa Lorrain Pombili Tuutaleni Gloria Iyawa Valerianus Hashiyana Parcidio Andre Iyaloo Ndevahoma Nalina Suresh Jude O. Osakwe Information and Communication Technology Faculty International University of Management Windhoek Namibia Faculty of Informatics Namibia University of Science and Technology Windhoek Namibia School of Computing University of Namibia Windhoek Namibia

In many developing countries, rural, remote and nomadic regions seem to be off the national grid power infrastructure leading to power scarcity, which could affect the presence and user experience of electronic devices such as radio and mobile phones. Considering the above, this study examined the presence of mobile phones in a rural community and the effect of electricity scarcity in the village on mobile phone user experience. A qualitative methodology was adopted. The context was purposefully selected to include communities with scarce electricity supply. 10 participants were purposively and conveniently sampled from the village and face to face interviews conducted to collect data. Based on the data analysis, the study found that mobile phones are present in electric scarce communities and that they are used for communication, socialization and businesses. People in electricity scarce communities’ power their mobile phones through various means including power banks, solar panel systems, car batteries and generators. The effect of scarce electricity on mobile phone user experience include users missing calls and missed opportunities included job related ones. Considering the usefulness of mobile phones and power to keep it running, as found in this study, the intention of rural dwellers to own, power and use mobile phones is high and this conforms to the perceived usefulness, perceived benefit and perceived performance constructs of the extended Technological Acceptance Model in the Sub-Saharan African context. The sample size for this study was small and hence is recommended that future studies consider a larger population.

关键词： Data analysis Sociology Developing countries Mobile handsets User experience Generators Solar panels

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Traffic Classification based on Incremental Learning Algorithms for the Software-Defined Networks

Traffic Classification based on Incremental Learning Algorit...

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2022 International Conference on Frontiers of Information technology, FIT 2022

作者： Eldhai, Arwa M. Hamdan, Mosab Khan, Suleman Hamzah, Muzaffar Marsono, M.N. University of Science and Technology Faculty of Engineering Khartoum11115 Sudan Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia University of São Paulo Department of Computer Science São Paulo05508-090 Brazil University of Central Lancashire School of Psychology and Computer Science PrestonPR1 2HE United Kingdom Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor81310 Malaysia

ISBN: (纸本)9798350345933

A new era of network administration has been ushered in by recent developments in software-defined networks (SDN) and traffic classification (TC) using machine learning (ML) techniques. All network devices are centrally managed and accessible through the SDN, which can simplify the TC process. The traditional data mining/ML approach that uses TC assumes that all of the task's data is always accessible and can be viewed simultaneously without processing time and memory restrictions. Therefore, these approaches are not effective in the case of stream learning since, in more realistic settings, the data is not available all at once and has a distinct distribution. Consequently, incremental learning algorithms (ILAs) can handle online data mining. This study's primary goal is to contrast various ILA approaches to enhance SDN's TC performance. In this study, we propose four ILAs: the self- adjusting memory coupled with the k Nearest Neighbor (kNN) classifier (SAMKNNC), the very fast decision rules classifier (VFDRC), the extremely fast decision tree classifier (EFDTC), and the streaming random patches ensemble classifier (SRPC). Both real and synthetic datasets are used for validation. Experimental findings reveal that the proposed techniques perform better in SDN traffic classification since they can effectively identify drift and use less memory and time. © 2022 IEEE.

关键词： Learning algorithms

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Farmers’ Use of Mobile Devices in Developing Countries

Farmers’ Use of Mobile Devices in Developing Countries

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IST-Africa

作者： Martin Mabeifam Ujakpa Abner Kristof Augusto Domingos Valerianus Hashiyana Nalina Suresh Jude O. Osakwe Gloria Iyawa Information and Communication Technology Faculty International University of Management Windhoek Namibia School of Computing University of Namibia Windhoek Namibia Faculty of Informatics Namibia University of Science and Technology Windhoek Namibia

The introduction of mobile phone technology seems to have brought many benefits to many farmers, especially in developing countries, as it provides access relevant and reliable agricultural information to make more effective farming decisions. Many rural farmers face information asymmetry problems, which seem to emanate from pitfalls in the agriculture or farming extension services that could be solved through the use of mobile devices and technologies. To explore how farmers use mobile devices to access information to aid them in their agricultural practices, this study explored farmers use of mobile devices in the rural arears in Namibia, specifically, the Uukwiyu Uushona community in the Oshana Region for farming purposes. Applying a quantitative research method, data was collected from 140 participants using questionnaire. Thematic analyses to unearth trends were undertaken. The results show that farmers’ use of mobile devices adds value and economic benefits to their practices. The study further found that farmers use mobile devices to get up to date information on input and output market transactions, weather information, and banking services. Based on the findings that some of the information on mobile phones are complex and hence difficult to comprehend, it is recommended that mobile agricultural information service providers team up with information systems experts to develop mobile applications or USSD applications that provide agriculture information in its simplest and comprehensive form so as for most farmers if not all, with all kinds of mobile devices will to be able to access agriculture information. Having some of the mobile agricultural information in native or local languages will be helpful especially for the illiterate group of farmers. Considering that only scanty literature similar studies especially in rural Namibia exist, this study contributed to the body of knowledge of mobile device use by farmers in rural areas.

关键词： Rural areas Information services Developing countries Market research Mobile handsets Agriculture Mobile applications

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Hybrid BiGRU-CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Ullah, Kaleem Shakir, Daniyal Abid, Usama Alahmari, Saad Aslam, Sheraz Ullah, Zahid US-Pakistan Centre for Advanced Studies in Energy University of Engineering and Technology Peshawar Peshawar Pakistan Sr. Business Analyst S&P Global Islamabad Islamabad Pakistan Department of Computer Science and Information Technology School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science CTL eurocolege Limassol Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Lombardia Italy

Integrating renewable energy sources into smart grids increases supply and demand management because renewable energy sources are intermittent and variable. To overcome this type of challenge, short-term load forecasting (STLF) is essential for managing energy, demand-side flexibility, and the stability of smart grids with renewable energy integration. This paper presents a new model called BiGRU-CNN to improve the operation of STLF in smart grids. The BiGRU-CNN model integrates bidirectional gated recurrent units (BiGRUs) to model temporal dependencies and convolutional neural networks (CNNs) to extract spatial patterns from energy consumption data. The newly developed BiGRU captures past and future contexts through bidirectional processing, and the CNN component extracts high-level features to enhance the accuracy of load demand prediction. The BiGRU-CNN model is compared with two other hybrid models, CNN-LSTM and CNN-GRU, on real-world data from American electric power (AEP) and ISONE datasets. Simulation results show that the proposed BiGRU-CNN outperforms other models, with single-step forecasting yielding root mean square error (RMSE) of 121.43 (AEP) and 123.57 (ISONE), mean absolute error (MAE) of 90.95 (AEP) and 62.97 (ISONE), and mean absolute percentage error (MAPE) of 0.61% (AEP) and 0.41% (ISONE). For multi-step forecasting, the model yields RMSE of 680.02 (AEP) and 581.12 (ISONE), MAE of 481.12 (AEP) and 411.20 (ISONE), and MAPE of 3.27% (AEP) and 2.91% (ISONE). The results show that the BiGRU-CNN model can generate accurate and reliable STLF, which is useful in managing massive renewable energy-integrated smart grids. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Convolutional neural networks

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Hybrid K-Means and Firefly Algorithm-Based Load Balancer for Dynamic Task Scheduling in Fog computing for Postoperative Healthcare Systems

Hybrid K-Means and Firefly Algorithm-Based Load Balancer for...

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Advanced computing Technologies (ICoACT), International Conference on

作者： N. Jagadish Kumar Rakesh Premkumar L. Maria Michael Visuwasam Gopalakrishnan Arjunan G. Yuyaraj C. Tharun Kumar Dept of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India School of Informatics and Computing Indiana University Bloomington USA Dept. of AI & DS RMK College of Engineering and Technology Puduvoyal Chennai India Accenture Bangalore India Dept of CSE (5 years Integrated) Sri Sai Ram Engineering College Chennai Tamil Nadu India Dept. of CSBS Sri Sai Ram Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

In fog computing for postoperative healthcare systems, efficient task scheduling and load balancing are crucial for improving response time, energy efficiency, makespan, and the overall performance of the system. This manuscript introduces a novel hybrid load balancing framework that amalgamates KMeans clustering with the Firefly Algorithm (FA) to tackle the dynamic task scheduling complexities inherent in fog-based healthcare environments. The K-Means methodology is employed to categorize tasks according to their computational necessities and resource requirements, thereby establishing clusters that facilitate optimized resource distribution. The Firefly Algorithm, recognized for its proficiency in addressing optimization challenges, is harnessed to ascertain the optimal scheduling arrangement within each task cluster by adeptly modifying task allocation among fog nodes in response to light intensity, which is directly proportional to the overall performance of the system. Empirical findings indicate that the proposed mechanism markedly surpasses conventional algorithms regarding the optimization of response time, makespan, cost, and energy consumption, thereby presenting a viable solution for the management of postoperative care within healthcare systems employing fog computing infrastructure. The results substantiate that our proposed model enhances response time by 18%, makespan by 17%, energy consumption by 15%, cost by 17%, and level of load balance by 18%.

关键词： Processor scheduling Heuristic algorithms Clustering algorithms Medical services Optimal scheduling Dynamic scheduling Load management Time factors Resource management Edge computing

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Towards Unified Token Learning for Vision-Language Tracking

arXiv

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arXiv 2023年

作者： Zheng, Yaozong Zhong, Bineng Liang, Qihua Li, Guorong Ji, Rongrong Li, Xianxian The Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China The Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China The School of Computer Science and Technology Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100049 China Media Analytics and Computing Lab Department of Artificial Intelligence School of Informatics Xiamen University 361005 China

In this paper, we present a simple, flexible and effective vision-language (VL) tracking pipeline, termed MMTrack, which casts VL tracking as a token generation task. Traditional paradigms address VL tracking task indirectly with sophisticated prior designs, making them over-specialize on the features of specific architectures or mechanisms. In contrast, our proposed framework serializes language description and bounding box into a sequence of discrete tokens. In this new design paradigm, all token queries are required to perceive the desired target and directly predict spatial coordinates of the target in an auto-regressive manner. The design without other prior modules avoids multiple sub-tasks learning and hand-designed loss functions, significantly reducing the complexity of VL tracking modeling and allowing our tracker to use a simple cross-entropy loss as unified optimization objective for VL tracking task. Extensive experiments on TNL2K, LaSOT, LaSOText and OTB99-Lang benchmarks show that our approach achieves promising results, compared to other state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

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Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

arXiv

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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