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An Automatic Lung Nodule Classification System Based on Hybrid Transfer Learning Approach

SN computer science 2022年第4期3卷 1-10页

作者： Saikia, Trishna Kumar, Ranjeet Kumar, Dilip Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jamshedpur 831014 India School of Electronics Engineering Vellore Institute of Technology Chennai Campus Chennai 600127 India

Lung cancers are not only affecting the lung but also impair the respiratory mechanism. Worldwide, it becomes one of the high causes of mortality in humans. Several computer aided diagnosis (CAD) systems have been designed in recent years for diagnosis of several diseases. Early detection of lung cancer has become very important which can enhance survival chances among humans. The average rates of survival people with lung-cancer can rise from 14 to 49 percent if the diagnosed is taking place in time. Compare to X-ray, computed tomography (CT) is more operative as it involves multiple imaging approaches to support each other. In this study, we have used lung-patient CT scan images to classify the lung nodule into four classes, which are small-cell-carcinoma, adenocarcinoma, squamous-cell-carcinoma and large-cell-carcinoma. This study has proposed VGG networks integrated with support vector machine and random forest to create a hybrid algorithm that further helps in reducing computation complexity of classification. The lung nodules are classified using these hybrids algorithms with highest accuracy 98.70% and compared the results with existing methods. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Computed tomography Convolution neural network Random forest Support vector machine

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Robust Reward Placement under Uncertainty

arXiv

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arXiv 2024年

作者： Petsinis, Petros Zhang, Kaichen Pavlogiannis, Andreas Zhou, Jingbo Karras, Panagiotis Department of Computer Science Aarhus University Denmark Artificial Intelligence Thrust Hong Kong University of Science and Technology Guangzhou China Business Intelligence Lab Baidu Research China Department of Computer Science University of Copenhagen Denmark

We consider a problem of placing generators of rewards to be collected by randomly moving agents in a network. In many settings, the precise mobility pattern may be one of several possible, based on parameters outside our control, such as weather conditions. The placement should be robust to this uncertainty, to gain a competent total reward across possible networks. To study such scenarios, we introduce the Robust Reward Placement problem (RRP). Agents move randomly by a Markovian Mobility Model with a predetermined set of locations whose connectivity is chosen adversarially from a known set Π of candidates. We aim to select a set of reward states within a budget that maximizes the minimum ratio, among all candidates in Π, of the collected total reward over the optimal collectable reward under the same candidate. We prove that RRP is NP-hard and inapproximable, and develop Ψ-Saturate, a pseudo-polynomial time algorithm that achieves an ϵ-additive approximation by exceeding the budget constraint by a factor that scales as O(ln |Π|/ϵ). In addition, we present several heuristics, most prominently one inspired by a dynamic programming algorithm for the max-min 0-1 KNAPSACK problem. We corroborate our theoretical analysis with an experimental evaluation on synthetic and real data. © 2024, CC BY.

关键词： Dynamic programming

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Few Clean Instances Help Denoising Distant Supervision 29

Few Clean Instances Help Denoising Distant Supervision

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29th International Conference on Computational Linguistics, COLING 2022

作者： Liu, Yufang Huang, Ziyin Wang, Yijun Sun, Changzhi Lan, Man Wu, Yuanbin Mou, Xiaofeng Wang, Ding School of Computer Science and Technology East China Normal University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Bytedance AI Lab China AI Innovation Center Midea Group China

Existing distantly supervised relation extractors usually rely on noisy data for both model training and evaluation, which may lead to garbage-in-garbage-out systems. To alleviate the problem, we study whether a small clean dataset could help improve the quality of distantly supervised models. We show that besides getting a more convincing evaluation of models, a small clean dataset also helps us to build more robust denoising models. Specifically, we propose a new criterion for clean instance selection based on influence functions. It collects sample-level evidence for recognizing good instances (which is more informative than loss-level evidence). We also propose a teacher-student mechanism for controlling purity of intermediate results when bootstrapping the clean set. The whole approach is model-agnostic and demonstrates strong performances on both denoising real (NYT) and synthetic noisy datasets. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Computational linguistics

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Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN (Extended Abstract) 39

Anomaly Detection in Quasi-Periodic Time Series based on Aut...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Liu, Fan Zhou, Xingshe Cao, Jinli Wang, Zhu Wang, Tianben Wang, Hua Zhang, Yanchun Nanjing Research Institute of Electronics Technology Nanjing China School of Computer Science Northwestern Polytechnical University Xi'an China Department of Computer Science and Computer Engineering La Trobe University Melbourne Australia Northwest A&f University Yangling China College of Engineering and Science Victoria University Melbourne Australia Guangzhou University Guangzhou China New Network Dept. Pengcheng Lab Shenzhen China

ISBN: (纸本)9798350322279

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose an automatic QTS anomaly detection framework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to yield a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3% accuracy, TCQSA exceeds two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs. © 2023 IEEE.

关键词： Anomaly detection

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Guest Editorial: Ubiquitous Intelligence for Internet of Vehicles

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IEEE Internet of Things Magazine 2023年第3期6卷 52-54页

作者： Peng, Haixia Cheng, Nan Ye, Qiang Fang, He Yan, Shen Choo, Kim-Kwang Raymond Duong, Trung Q. California State University Long Beach Department of Computer Engineering and Computer Science Long BeachCA United States School of Telecommunications Engineering Xidian University State Key Lab. of ISN Shaanxi China Memorial University of Newfoundland Department of Computer Science NL Canada Soochow University China Intelligent Automotive Solution BU Huawei Technologies Co. Ltd Policy Standard & Patent Dept. China The University of Texas at San Antonio Cloud Technology Endowed United States Royal Academy of Engineering Queen's University Belfast United Kingdom

As one of the prominent networking paradigms in the realm of the Internet of Things (IoT), the Internet of Vehicles (IoV) facilitates seamless information dissemination and task processing among vehicles equipped with onboard sensing, communication, computing, and storage capabilities. With the advancements in artificial intelligence (AI) techniques, there is a vision to achieve pervasive intelligence within the IoV ecosystem. Various network entities, including connected vehicles, wireless base stations, edge/cloud servers, and aerial/space-assisted devices (such as drones and satellites), are expected to interact efficiently to perceive, reason, and make intelligent decisions based on contextual awareness. These advancements aim to enhance networking and computing effectiveness. The realization of an intelligent, safe, and ubiquitous IoV heavily relies on highly responsive task computing, adaptive networking, and efficient resource control to meet the increasingly diverse requirements of vehicular applications.

关键词： Special issues and sections Internet of Vehicles Intelligent systems Ubiquitous computing

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GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Reducing Data Redundancy

GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Red...

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International Conference on Distributed Computing Systems

作者： Xiaohai Dai Yifan Zhou Jiang Xiao Feng Cheng Xia Xie Hai Jin Bo Li 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 Technology Huazhong University of Science and Technology China School of Computer Science and Technology Hainan University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

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Theoretically Achieving Continuous Representation of Oriented Bounding Boxes

Theoretically Achieving Continuous Representation of Oriente...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zi-Kai Xiao Guo-Ye Yang Xue Yang Tai-Jiang Mu Junchi Yan Shi-Min Hu Department of Computer Science and Technology BNRist Tsinghua University Department of CSE & MoE Key Lab of AI Shanghai Jiao Tong University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Considerable efforts have been devoted to Oriented Ob-ject Detection (OOD). However, one lasting issue regarding the discontinuity in Oriented Bounding Box (OBB) rep-resentation remains unresolved, which is an inherent bot-tleneck for extant OOD methods. This paper endeavors to completely solve this issue in a theoretically guaranteed manner and puts an end to the ad-hoc efforts in this di-rection. Prior studies typically can only address one of the two cases of discontinuity: rotation and aspect ratio, and often inadvertently introduce decoding discontinuity, e.g. Decoding Incompleteness (DI) and Decoding Ambi-guity (DA) as discussed in literature. Specifically, we pro-pose a novel representation method called Continuous OBB (COBB), which can be readily integrated into existing de-tectors e.g. Faster-RCNN as a plugin. It can theoreti-cally ensure continuity in bounding box regression which to our best knowledge, has not been achieved in literature for rectangle-based object representation. For fairness and transparency of experiments, we have developed a modu-larized benchmark based on the open-source deep learning framework Jittor's detection toolbox JDetfor OOD evaluation. On the popular DOTA dataset, by integrating Faster-RCNN as the same baseline model, our new method out-performs the peer method Gliding Vertex by 1.13% mAP 50 (relative improvement 1.54%), and 2.46% mAP 75 (relative improvement 5.91%), without any tricks.

关键词： Deep learning computer vision Computational modeling Detectors Benchmark testing Decoding Sensors

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Gain and Impedance Bandwidth Enhancement of a Loop Antenna for X-Band Applications

Gain and Impedance Bandwidth Enhancement of a Loop Antenna f...

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Sagiru Gaya Mohamed Saeed AlShehhi Khaled Al-Wahedi Mohammed A. Abou-Khousa Department of Electrical Engineering and Computer Science Khalifa University of Science and Technology Abu Dhabi UAE smart sensing lab (SSL) Khalifa University of Science (KU)

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This paper presents the new design for a rectangular loop antenna (RLA) with enhanced impedance bandwidth and realized gain. The newly proposed RLA includes a truncated rectangular loop and two parasitic elements positioned on either side of the loop, strategically employed to enhance bandwidth. Additionally, to concentrate radiated power, a metallic sheet is positioned beneath the antenna at a distance of 3.35 mm. Through simulations, the study illustrates that the suggested design achieves a notable 60% improvement in bandwidth and a gain enhancement of approximately 6.6 dBi. The antenna operates within the frequency range of 10.2 GHz to 11.7 GHz, achieving a maximum gain of 11 dBi at a resonance frequency of 11 GHz. Given its performance in the X-band frequency range, the proposed RLA emerges as a promising candidate for applications such as synthetic aperture radar (SAR) imaging.

关键词： Antenna measurements Resonant frequency Loop antennas Bandwidth Resonance Radar polarimetry Frequency measurement

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Application of Generative Graph Models in Biological Network Regeneration: A Selective Review and Qualitative Analysis

Application of Generative Graph Models in Biological Network...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Binon Teji Swarup Roy Pietro Hiram Guzzi Dinabandhu Bhandari Department of Computer Applications Network Reconstruction & Analysis (NETRA) Lab Sikkim University Gangtok India School of Computer Science and Biomedical Engineering University of Catanzaro Italy Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Biological networks are essential for understanding the complex cellular mechanisms of living organisms. Simulating biological networks allows researchers to model and understand complex cellular processes without the need for extensive and costly experiments. The ability to generate synthetic graphs that closely resemble these real-world, complex biological processes is a vital research area. Graph generation in the context of biological networks is particularly significant because it can lead to insights into cellular functions, disease mechanisms, and therapeutic targets. Recent advances in deep learning, particularly in graph generative models, have opened new avenues for applications in the biological domain. These advancements have the potential to revolutionize our understanding of biological systems. However, despite the development of numerous effective graph generation models, there has been limited work assessing the qualitative aspects of the generated graphs within the context of biological networks. Addressing this gap is crucial for ensuring that synthetic graphs are not only structurally accurate but also biologically *** various graph generation models are available, their application to biological network recreation has been limited. In this paper, we focus on graph generation, specifically edge and node-independent models for biological networks. We assess four candidate models across four gene expression networks. Our systematic assessment examines the models’ qualitative aspects, including graph structural properties, generation diversity, and computational efficiency. Our findings highlight the strengths and limitations of current models, offering insights to guide the development of more robust graph generation techniques that accurately replicate biological network characteristics.

关键词： Analytical models Accuracy Systematics Reviews Biological system modeling Computational modeling Neural networks Predictive models Biology Organisms

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RJoules: An Energy Measurement Tool for R 23

RJoules: An Energy Measurement Tool for R

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Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering

作者： Rajrupa Chattaraj Sridhar Chimalakonda Research in Intelligent Systems and Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

ISBN: (纸本)9798350329964

With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose "RJoules", a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. The demonstration of the tool is available at https://***/yMKFuvAM-DE and related artifacts at https://***/RJoules/.

关键词： energy measurement

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