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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Roy, Kaushiki Bhattacharjee, Deboparna Banik, Debapriya Krejcar, Ondrej Sarkar, Ram Kolkata700091 India University of Engineering and Management Kolkata700160 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Computer Science and Engineering RCC Institute of Information Technology Kolkata700015 India Department of Computer Science and Engineering ICFAI University Tripura Agartala799210 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

ISBN: (纸本)9789819726103

Cervical cancer is one of the most common causes of death among women worldwide. However, this fatal disease can be treated, and the mortality rate can be decreased if detected at an early stage. The Papanicolaou test is the gold standard for screening cervical cancer patients. However, the process of manual inspection is tedious and subject to manual errors. Thus, computer-based automatic screening is considered a viable alternative. To this end, in the present work, we have developed a novel system for classifying cervical cancer images as either normal or malignant. We have used the Herlev dataset in this study, which is the standard Pap smear image benchmark dataset. The proposed framework uses multiple base deep learning frameworks, namely Vision Transformer, Xception, VGG-19, VGG-16, and ResNet-101, for classification. Further, we have introduced a novel reward-based weighing technique to decide the weights of individual classifiers, which are, in turn, used to make decisions about the final class label of an input image using the weighted average technique. The proposed framework achieves an overall accuracy, precision, recall, F1-score, and AUC of 96%, 94%, 92%, 93%, and 94%, respectively, for the binary classification task that is normal vs. malignant. An elaborate study of the performance achieved by our proposed framework on the Herlev dataset shows it to be both robust and effective for the Pap smear image classification task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Training a Lightweight CNN Model for Fine-Grained Sewer Pipe Cracks Classification Based on Knowledge Distillation 5

Training a Lightweight CNN Model for Fine-Grained Sewer Pipe...

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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Ma, Beier Zuo, Xin Shen, Jifeng Shu, Xin Huang, Shucheng Li, Yuanhao Jiangsu University of Science and Technology School of Computer Science and Engineering Zhenjiang China Jiangsu University School of Electronic and Informatics Engineering Zhenjiang China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Current sewage pipeline inspection researches focus on locating defects, with only a few studies addressing the crack fine-grained classification task. However, in engineering applications, such as Pipeline Assessment Certification Program (PACP), subcategory coding of cacks is required. Therefore, this paper studies the fine-grained classification of pipeline cracks. Algorithms based on deep learning have been widely used in the field of sewage pipeline inspection, but these models have a large number of parameters and are computationally time-consuming. Edge or embedded devices with weak GPUs cannot accept the time and storage costs of these large models. To solve the above problems, this paper introduces knowledge distillation (KD) which is one of the mainstream model compression methods. We select the large-scale network Resnet50 as the teacher model and design a lightweight network Combined MobileNetV2 & Inception (CMI) for crack detection as the student model. High-temperature distillation allows the student model to obtain dark knowledge from the pre-trained, well-performing teacher model, which is combined with ground truth (GT) for training. Experiments show that KD is effective. By adjusting the KD hyperparameters, the classification accuracy of the student model is improved by 1.41%, and the F1-score is improved by 1.12%. This demonstrates the robustness of knowledge distillation to improve the performance of small models, laying the foundation for future deployment on multi-sensor automatic detection robots. © 2022 IEEE.

关键词： computer vision

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Development and preliminary evaluation of a robotic device for mri-guided needle breast biopsy

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Journal of Medical Robotics Research 2024年第3-4期9卷

作者： Ioannides, Cleanthis Antoniou, Anastasia Zinonos, Vasiliki Damianou, Christakis Department of Interventional Radiology German Oncology Center 1 Nikis Avenue 4108 Agios Athanasios Limassol Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 30 Archbishop Kyprianou Street Limassol 3036 Cyprus

This study concerns the development and evaluation of a simple and ergonomic robotic system for Magnetic Resonance Imaging (MRI)-guided needle breast biopsy with lateral needle approach. The device comprises two piezoelectrically actuated linear motion stages intended to align a needle supporter with the target for manual needle insertion. The device demonstrated submillimeter accuracy and safe operation within a 3 T clinical MRI scanner. In phantom studies, tumor simulators of varying sizes were successfully targeted in both laboratory and MRI settings. © 2024 World Scientific Publishing Company.

关键词： accuracy agar phantom breast biopsy MRI-guided positioning device

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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computer Modeling in engineering & Sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

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Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model 46

Enhanced Driver Stress Prediction from Multiple Biosignals v...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Singh Roha, Vishal Gaber Gendy, Maggie Ezzat Pavan, Kaveti Deserno, Thomas M. Ganapathy, Nagarajan Yuce, Mehmet R. Monash University Dept. of Electrical & Computer Systems Melbourne Australia Indian Institute of Technology Hyderabad Dept. of Biomedical Engineering Hyderabad India Technical University of Braunschweig Plri Lab for Medical Informatics Brunswick Germany

ISBN: (纸本)9798350371499

In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions. © 2024 IEEE.

关键词： Photoplethysmography

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Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

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Complex System Modeling and Simulation 2023年第3期3卷 169-190页

作者： Yinan Guo Yao Huang Shirong Ge Yizhe Zhang Ersong Jiang Bin Cheng Shengxiang Yang School of Mechanical Electronic and Information Engineering China University of Mining and Technology(Beijing)Beijing 100083China Inner Mongolia Research Institute China University of Mining and Technology(Beijing)Ordos 017010China Institute of Artificial Intelligence School of Computer Science and InformaticsDe Montfort UniversityLeicesterLE19BHUK.

Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines,and the rationality of their routes plays the direct impact on operation safety and energy *** studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil,however,less works are for electric trackless rubber-tyred ***,energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving,especially the limited cruising ability of electric trackless rubber-tyred vehichles(TRVs).To address this issue,an energy consumption model of an electric trackless rubber-tyred vehicle is formulated,in which the effects from total mass,speed profiles,slope of roadways,and energy management mode are all *** that,a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance,allowable load,and endurance *** a problem-solver,an improved artificial bee colony algorithm is put *** especially,an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific *** order to assign onlookers to some promising food sources reasonably,their selection probability is adaptively *** a stagnant food source,a knowledge-driven initialization is developed to generate a feasible *** experimental results on four real-world instances indicate that improved artificial bee colony algorithm(IABC)outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.

关键词： electric trackless rubber-tyred vehicles low-carbon routing artificial bee colony algorithm

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Effective Prediction of Energy Consumption in Automated Guided Vehicles with Recurrent and Convolutional Neural Networks

Effective Prediction of Energy Consumption in Automated Guid...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Benecki, Pawel Kostrzewa, Daniel Grzesik, Piotr Shubyn, Bohdan Syu, Jia-Hao Lin, Jerry Chun-Wei Sunderam, Vaidy Mrozek, Dariusz Silesian University of Technology Department of Applied Informatics Gliwice Poland Lviv Polytechnic National University Department of Telecommunications Lviv Ukraine National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Emory University Department of Computer Science AtlantaGA30322 United States

ISBN: (纸本)9798350324457

Detection and prediction of failures in Automated Guided Vehicles (AGV) are essential for the uninterrupted operation of production plants. Anomaly detection is usually achieved by comparing expected measurement values with actual observations. Thus, it is crucial to predict telemetry signals properly. In this paper, we research the prediction of energy consumption using state-of-the-art Artificial Neural Networks architectures (SCINet) compared with other Recurrent Neural Network (RNN) approaches on the data streams acquired from CoBotAGV. We especially focus on the possibility of applying feature weighting. We show that it can improve prediction capabilities. We also investigate resource utilization in terms of time to fit the embedded AGV environment. © 2023 IEEE.

关键词： anomaly detection automated guided vehicles feature weighting Industry 4.0 time series prediction

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MobileVitV2-Based Fusion of Vision Transformers and Convolutional Neural Networks for Underwater Image Enhancement 13

MobileVitV2-Based Fusion of Vision Transformers and Convolut...

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13th International Conference on Information Science and technology, ICIST 2023

作者： Liu, Jiaji Li, Fang Luo, Xiaonan Li, Ji Wang, Dong Lan, Rushi School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China College of Mathematics and Informatics College of Software Engineering South China Agricultural University Guangzhou China

ISBN: (纸本)9798350313925

Acquisition of underwater images presents challenges stemming from inherent distortions and the degradation of color and contrast within the aquatic environment. These challenges, characterized by image quality issues, pose significant obstacles to the training of supervised deep learning models on extensive and diverse datasets, potentially constraining the overall performance of the model. High-quality underwater images play an important role in the development of Autonomous Underwater Vehicles(AUVs). In this work, we present MobileVitV2 Block Fusion Augmentor and Global-Local Feature Fusion Block with Attent-UNet, which are offered as blocks of generator. Through extensive experiments on different datasets, our proposed method achieves better visual quality, and the method can obtain better quality images in poorer underwater environments. © 2023 IEEE.

关键词： Image enhancement

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Directional Metasurface with Selective Polarization Using Antenna Elements

Directional Metasurface with Selective Polarization Using An...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： David Chatzichristodoulou Photos Vryonides Dimitra Psychogiou Symeon Nikolaou Frederick Research Center Nicosia Cyprus RF and Microwave Solutions LTD Cyprus Dept. Electrical Computer Engineering and Informatics Frederick University Nicosia Cyprus School of Engineering University College Cork Cork Ireland Tyndall National Institute Cork Ireland

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

A new concept of a directional, non-reciprocal metasurface (MSF) where the polarization of the transmitted wave can be selected at will is presented. Each cell of the proposed MSF has a circularly polarized (CP) elliptical patch on side 1 and a reconfigurable orthogonal (either x- or y-) linear polarization (LP) square patch on side 2. Any random polarization incident wave on side 1 is collected by the array of CP antennas. The signal is transmitted to the reconfigurable LP antennas on side 2 through vias. The reconfigurable LP patches have orthogonal feed from two segments which are connected with the common microstrip line through single pole double through (SPDT) switches. The appropriate switch position results in the preferred polarization of the transmitted wave. Two transistors along the transmission lines of the two antennas ensure the non-reciprocity of the directional metasurface. The proposed design operates effectively in the frequency range 3.4-3.6 GHz and it presents nearly 20 dB enhancement of the transmitted wave with the selected linear polarization in the forward direction and 50 dB isolation as compared to the propagation in the opposite direction.

关键词： Polarization Power transmission lines Patch antennas Transmitting antennas Directive antennas Metasurfaces Transistors

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"How to make them stay?"-Diverse Counterfactual Explanations of Employee Attrition

arXiv

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arXiv 2023年

作者： Artelt, André Gregoriades, Andreas Faculty of Technology Bielefeld University Bielefeld Germany Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Management Entrepreneurship and Digital Business Cyprus University of Technology Limassol Cyprus

Employee attrition is an important and complex problem that can directly affect an organisation’s competitiveness and performance. Explaining the reasons why employees leave an organisation is a key human resource management challenge due to the high costs and time required to attract and keep talented employees. Businesses therefore aim to increase employee retention rates to minimise their costs and maximise their performance. Machine learning (ML) has been applied in various aspects of human resource management including attrition prediction to provide businesses with insights on proactive measures on how to prevent talented employees from quitting. Among these ML methods, the best performance has been reported by ensemble or deep neural networks, which by nature constitute black box techniques and thus cannot be easily interpreted. To enable the understanding of these models’ reasoning several explainability frameworks have been proposed to either explain individual cases using local interpretation approaches or provide global explanations describing the overall logic of the predictive model. Counterfactual explanation methods have attracted considerable attention in recent years since they can be used to explain and recommend actions to be performed to obtain the desired outcome. However current counterfactual explanations methods focus on optimising the changes to be made on individual cases to achieve the desired outcome. In the attrition problem it is important to be able to foresee what would be the effect of an organisation’s action to a group of employees where the goal is to prevent them from leaving the company. Therefore, in this paper we propose the use of counterfactual explanations focusing on multiple attrition cases from historical data, to identify the optimum interventions that an organisation needs to make to its practices/policies to prevent or minimise attrition probability for these cases. The proposed technique is applied on an employee att

关键词： Human resource management

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