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41st International Symposium on Automation and Robotics in Construction, ISARC 2024

作者： Chang, Woei-Chyi Gonzalez Garcia, Nestor F. Esmaeili, Behzad Hasanzadeh, Sogand Lyles School of Civil Engineering Purdue University United States Department of Systems Engineering and Computing Universidad de los Andes Colombia School of Industrial Engineering Purdue University United States

ISBN: (纸本)9780645832211

Establishing proper trust between human workers and robots is crucial for ensuring safe and effective human-robot interaction in various industries, including construction. An accurate trust prediction facilitates timely feedback and interventions, helping workers calibrate their trust levels. While machine-learning modeling personalization (i.e., tailoring models to individual characteristics) has garnered attention in the literature, the conventional approach of developing a personalized model for each individual is impractical in labor-intensive industries like construction. Such an approach compromises efficiency and leads to an accuracy-efficiency tradeoff. To address this gap, this study aims to investigate the tradeoff inherent in model personalization and identify a cost-effective solution to enhance trust prediction accuracy without compromising efficiency. The results suggested that a partial model personalization method can effectively balance this tradeoff. Moreover, the proposed feature-based partial personalization approach enables a cost-effective trust prediction model development for the construction industry, demonstrating its broader applicability to other worker-related predictions in other settings. This study provides insights into the strategies to improve trust prediction accuracy while maintaining the efficiency of model development by considering the distinctiveness of the future construction industry. © 2024 ISARC. All Rights Reserved.

关键词： Efficiency

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A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator 22

A Hybrid Multi-Objective Teaching Learning-Based Optimizatio...

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6th International Conference on Intelligent systems, Metaheuristics and Swarm Intelligence, ISMSI 2022

作者： Tahernezhad-Javazm, Farajollah Rankin, Debbie Coyle, Damien School Of Computing Engineering And Intelligent Systems Ulster University United Kingdom School Of Computing Engineering And Intelligent Systems Ulster University United Kingdom

ISBN: (纸本)9781450396288

Hybrid multi-objective evolutionary algorithms have recently become a hot topic in the domain of metaheuristics. Introducing new algorithms that inherit other algorithms' operators and structures can improve the performance of the algorithm. Here, we proposed a hybrid multi-objective algorithm based on the operators of the genetic algorithm (GA) and teaching learning-based optimization (TLBO) and the structures of reference point-based (from NSGA-III) and R2 indicators methods. The new algorithm (R2-HMTLBO) improves diversity and convergence by using NSGA-III and R2-based TLBO, respectively. Also, to enhance the algorithm performance, an elite archive is proposed. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems and compared to four state-of-the-art algorithms. IGD metric is applied for comparison, and the results reveal that the proposed R2-HMTLBO outperforms MOEA/D, MOMBI-II, and MOEA/IGD-NS significantly in 16/19 tests, 14/19 tests and 13/19 tests, respectively. Furthermore, R2-HMTLBO obtained considerably better results compared to all other algorithms in 4 test problems, although it does not outperform NSGA-III on a number of tests. © 2022 Owner/Author.

关键词： Genetic algorithms

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Strong Co-Location Pattern Mining Incorporating Multi-Path and Distance Decay Effects 7

Strong Co-Location Pattern Mining Incorporating Multi-Path a...

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7th International Conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： He, Yonggui Yang, Peizhong Wang, Lizhen Chen, Hongmei Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Kunming China

ISBN: (纸本)9798331529314

Spatial co-location pattern mining (SCPM) is a sub-field of data mining, which aims to discover the subset of spatial features whose instances are frequently located in proximate areas. SCPM has broad prospects in many applications, such as ecology, public health, smart cities, etc. In recent years, improving and applying SCPM technology with the constraints of road networks has emerged as a prominent research focus. However, existing studies solely focus on the shortest path between instances when assessing the proximity relationships, neglecting other proximity paths, and thus may overlook co-location patterns with strong associations. To address this issue, this paper introduces a novel metric called Strong Proximity Score, which integrates both multi-path proximity and distance decay effects to measure the strength of proximity relationships. Additionally, the Minimum First Search (MFS) algorithm is presented, which utilizes the strategy of minimum instance pair search to accelerate the calculation of Strong Proximity Score. Extensive experiments conducted on real datasets of points of interest demonstrate the superiority of the method based on Strong Proximity Score over traditional SCPM methods and confirm the efficiency of MFS algorithm. © 2024 IEEE.

关键词： Smart city

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Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations

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Computer Modeling in engineering & Sciences 2023年第1期134卷 221-239页

作者： Tianyuan Jia Xiangyong Chen Xiurong Yao Feng Zhao Jianlong Qiu School of Automation and Electrical Engineering and Key Laboratory of Complex Systems and Intelligent Computing in Universities of ShandongLinyi UniversityLinyi276005China The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and School of AutomationChina University of GeosciencesWuhan430074China

Fixed-time synchronization(FTS)of delayed memristor-based neural networks(MNNs)with discontinuous activations is studied in this *** continuous and discontinuous activations are considered *** the mixed delays which are closer to reality are taken into the ***,two kinds of control schemes are proposed,including feedback and adaptive control *** on some lemmas,mathematical inequalities and the designed controllers,a few synchronization criteria are ***,the upper bound of settling time(ST)which is independent of the initial values is ***,the feasibility of our theory is attested by simulation examples.

关键词： Memristor-based neural networks fixed-time synchronization mixed delays discontinuous activations adaptive control

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Chlorophyll-based Saturable Absorber for Q-switched Pulse Generation in Er-Doped Fiber Laser Cavity 10

Chlorophyll-based Saturable Absorber for Q-switched Pulse Ge...

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10th IEEE International Conference on Photonics, ICP 2024

作者： Tarmizi, S.Z.A. Ahmad Mansor, M. Abidin, N.H. Zainol Mahdi, M.A. Mazlan, M.F. Computer and Communication Systems Engineering Universiti Putra Malaysia Selangor UPM Serdang 43400 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor 47500 Malaysia

ISBN: (纸本)9798350383164

In this paper, we present experimental results on the use of all-organic Chlorophyll-based saturable absorber (Chl-SA) extracted from spinach for the generation of Q- switched pulse. The Chl-SA was fabricated by depositing Chl in powder form onto a fiber ferrule. The Chl-SA generated Q- switched pulse at 1559-1560 nm when employed in a 980 nm laser diode pumped erbium-doped fiber laser (EDFL) cavity. Pulse generation was achieved from 45 mW pump power up to 70 mW pump powers where the repetition rate increased while pulse duration shortened with increasing pump power. The highest repetition rate of 15.15 kHz and shortest pulse width of 32 µs were obtained. This highlights the promising potential of chlorophyll as a standalone material and its capacity to potentially boost the generation of pulse lasers when combined with other materials. ©2024 IEEE.

关键词： Pulse repetition rate

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EnsembleCAM: Unified Visualization for Explainable Cervical Cancer Identification

EnsembleCAM: Unified Visualization for Explainable Cervical ...

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2024 International Research Conference on Smart computing and systems engineering, SCSE 2024

作者： Sritharan, Niruthikka Gnanavel, Nishaanthini Inparaj, Prathushan Meedeniya, Dulani Yogarajah, Pratheepan University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka Ulster University School of Computing Engineering and Intelligent Systems Londonderry United Kingdom

ISBN: (纸本)9798350375688

Cervical cancer, which is ranked fourth among cancers affecting women, is highly treatable when detected early through the pap smear test. Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), analyze pap smear images, yet their Black-Box nature raises transparency concerns in medical diagnostics. This paper introduces a solution named EnsembleCAM to enhance interpretability by unifying visual explanations through the combination of diverse Class Activation Maps (CAMs). Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques, develop an XceptionNet based binary classification model with an accuracy of 89% and apply GradCAM, GradCAM++, Score-CAM, Eigen-CAM and LayerCAM on this classifier. Then, the novel EnsembleCAM is constructed taking the median of activation maps from the five individual CAM methods. The analysis of activation maps of each CAM method and EnsembleCAM confirmed that in activation maps of EnsembleCAM, higher activation values were more concentrated around the nucleus which is the most important region in indicating cervical malignancy. The evaluation using pixel flipping performance metric also proved that the EnsembleCAM effectively recognises regions vital to the model's decision-making through its steepest drop in the mean prediction score when the pixels in the region contributing most to the model's decision were flipped. © 2024 IEEE.

关键词： Visualization

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An Efficient Technique for Feature Selection in Learning to Rank Using NSGA-II

An Efficient Technique for Feature Selection in Learning to ...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Amala, K.J. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Feature Selection (FS) is essential for optimizing Learning to Rank (LTR) models by determining the vital choice of attributes from a complex dataset. This study examines feature selection strategies within the framework of learning to rank, specifically emphasizing a multi-objective evolutionary algorithm utilizing the listwise approach. Conventional ranking algorithms frequently experience issues with superfluous or unnecessary features, undermining their efficacy. The suggested method employs a combination of instance selection, evolutionary optimization, and ensemble techniques to choose feature subsets. The methodology seeks to reduce the feature count while enhancing ranking precision, assessed via the Normalized Discounted Cumulative Gain (NDCG) metric. Experiments were performed using LETOR 4.0 dataset (MQ2007-list and MQ2008-list), evaluating proposed method against established listwise ranking algorithms like ListMLE, ListNet, RankCosine and p-ListMLE. The findings indicate that, although there are performance discrepancies relative to current algorithms, the suggested method attains substantial feature reduction up to 60 percent while maintaining competitive ranking accuracy. © 2024 IEEE.

关键词： Optimization algorithms

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Partial label learning via label-specific features enhancement and semi-disambiguation

Partial label learning via label-specific features enhanceme...

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2024 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Hu, Jinhua Chai, Jing Sun, Xianran School of information Science and Engineering Yunnan University Kunming650504 China Yunnan Key Laboratory of Intelligent Systems and Computing China

ISBN: (纸本)9781510682597

Partial Label Learning (PLL) represents a common multi-class weakly supervised learning challenge, where each instance is linked with a set of possible labels, only one of which is the ground-truth label, and it remains unknown during the training. A common strategy for solving this multi-class problems is to decompose it into a series of binary classification problems based on certain coding rules (e.g., OvO, ECOC), and then process each binary classification problem separately using the original features. To address the deficiency of discriminative information in the original features, a feature enhancement mechanism is proposed to introduce label-specific features to improve the discriminative performance of features. Firstly, label-specific features are generated for each decomposed binary classification problem, and then fused with the original features. The fused features are used to design corresponding binary classifiers. Additionally, due to the presence of a large number of false positive labels in the training examples, a semi-disambiguation strategy can be employed to remove some false positive labels, thereby "slimming down" the candidate label set as a preprocessing step to reduce its ambiguity. The proposed partial label learning algorithm based on features enhancement and semi-disambiguation proves its effectiveness on synthetic and real-world data sets through extensive experiments. © 2024 SPIE.

关键词： Contrastive Learning

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Interpretable Cervical Cell Classification: A Comparative Analysis 4

Interpretable Cervical Cell Classification: A Comparative An...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Gnanavel, Nishaanthini Inparaj, Prathushan Sritharan, Niruthikka Meedeniya, Dulani Yogarajah, Pratheepan University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (纸本)9798350384864

Cervical cancer is a significant global health issue, and traditional screening methods like Pap smears are labor-intensive and may miss some cases. Automation is needed, but it faces challenges in terms of interpretability and data availability. To address this, the paper proposes using Explainable Artificial Intelligence (XAI) techniques like GradCAM, GradCAM++, and LRP to improve the transparency and interpretability of a cervical cell classification model, making it a novel contribution to enhancing the trustworthiness of automated cervical cancer detection. Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques and develop a binary classification model, achieving a 91.94% accuracy with VGG16. The qualitative analysis of XAI methods confirmed that the model relied on nucleus and cytoplasm features, key indicators of malignancy. The least mean image entropy of 2.4849 and steep prediction confidence drop with perturbations quantitatively proved Layer-wise Relevance Propagation (LRP) to be the most effective XAI technique for cervical cell classification. © 2024 IEEE.

关键词： Artificial intelligence

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Enhancing Skin Cancer Detection Through Automated Classification Using Convolutional Neural Networks 1

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7th International Conference on Intelligent computing and Optimization, ICO 2023

作者： Hossain, Md Jakir Bhuiyan, M. D. Wahidul Islam Raisa, Sadia Afrin Tomal, Minhazul Amin Jennifer, Sanjeda Sara Reza, Ahmed Wasif Siddique, Nazmul Department of Computer Science and Engineering East West University Dhaka Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Skin cancer presents a significant public health concern, necessitating early detection and intervention to mitigate life-threatening consequences. Despite its prevalence, skin cancer is not always given the attention it deserves. In this paper, we propose an automated procedure for distinguishing skin cancers using several neural network algorithms. We used the MobileNet architecture whose accuracy is not very high and this is why we have also used other algorithms such as Sequential CNN (Convolution Neural Network), VGG16, and Xception to improve the accuracy of our model. By comparing these algorithms, we found that the Sequential CNN performed best with an accuracy of 94%. The proposed model utilizes the Sequential CNN algorithm to achieve a high level of accuracy, which is of utmost importance for the early detection of skin cancer. The high accuracy of the proposed model makes it a promising tool for detecting skin cancer early. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

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