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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Muhammad, Dost Rafiullah Bendechache, Malika ADAPT Centre School of Computer Science University of Galway Ireland Department of Medical Laboratory Technology Riphah International University islamabad Pakistan

ISBN: (纸本)9781837242672

Genitourinary malignancies significantly impact global health and present substantial physical and economic challenges. To improve diagnostics, there has been a growing reliance on machine learning and deep learning algorithms in clinical settings but it is slow to gain wide acceptance due to the complex nature of these "black-box" models. In this paper, we introduced an explainable deep-learning framework for the diagnosis of Genitourinary Cancers utilizing laboratory data to enhance trust and transparency for healthcare staff and patients. Firstly, we trained various approaches namely Deep Neural Network (DNN), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbour (KNN) and Naive Bayes (NB) on patient's data and evaluated their performance through standard metrics. The experimental results shows that our proposed DNN performed better than other state-of-the-art achieving an accuracy of 90.64%, precision of 90.58%, recall of 90.70% and F1-score of 90.55%. Secondly, to improve the transparency and explainability of the DNN, we integrated explainable AI approaches including Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) to empower healthcare staff to make more precise and informed decisions in clinical practice. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Diseases

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ROBUSTIFYING AND BOOSTING TRAINING-FREE NEURAL ARCHITECTURE SEARCH 12

ROBUSTIFYING AND BOOSTING TRAINING-FREE NEURAL ARCHITECTURE ...

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12th International Conference on Learning Representations, ICLR 2024

作者： He, Zhenfeng Shu, Yao Dai, Zhongxiang Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore Singapore China Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States

Neural architecture search (NAS) has become a key component of AutoML and a standard tool to automate the design of deep neural networks. Recently, training-free NAS as an emerging paradigm has successfully reduced the search costs of standard training-based NAS by estimating the true architecture performance with only training-free metrics. Nevertheless, the estimation ability of these metrics typically varies across different tasks, making it challenging to achieve robust and consistently good search performance on diverse tasks with only a single training-free metric. Meanwhile, the estimation gap between training-free metrics and the true architecture performances limits training-free NAS to achieve superior performance. To address these challenges, we propose the robustifying and boosting training-free NAS (RoBoT) algorithm which (a) employs the optimized combination of existing training-free metrics explored from Bayesian optimization to develop a robust and consistently better-performing metric on diverse tasks, and (b) applies greedy search, i.e., the exploitation, on the newly developed metric to bridge the aforementioned gap and consequently to boost the search performance of standard training-free NAS further. Remarkably, the expected performance of our RoBoT can be theoretically guaranteed, which improves over the existing training-free NAS under mild conditions with additional interesting insights. Our extensive experiments on various NAS benchmark tasks yield substantial empirical evidence to support our theoretical results. Our code has been made publicly available at https://***/hzf1174/RoBoT. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Deep neural networks

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RandoMix: a mixed sample data augmentation method with multiple mixed modes

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Multimedia Tools and Applications 2025年第8期84卷 4343-4359页

作者： Liu, Xiaoliang Shen, Furao Zhao, Jian Nie, Changhai National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Artificial Intelligence Nanjing University Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing China

Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains. In this study, we introduce a novel mixed-sample data augmentation method called RandoMix. RandoMix is specifically designed to simultaneously address robustness and diversity challenges. It leverages a combination of linear and mask mixed modes, introducing flexibility in candidate selection and weight adjustments. We evaluate the effectiveness of RandoMix on diverse datasets, including CIFAR-10/100, Tiny-ImageNet, ImageNet, and Google Speech Commands. Our results demonstrate its superior erformance compared to existing techniques such as Mixup, CutMix, Fmix, and ResizeMix. Notably, RandoMix excels in enhancing model robustness against adversarial noise, natural noise, and sample occlusion. The comprehensive experimental results and insights into parameter tuning underscore the potential of RandoMix as a versatile and effective data augmentation method. Moreover, it seamlessly integrates into the training pipeline. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data augmentation Diversity Mixed-sample RandoMix Robustness

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Enhancing Big Data Security Through Anomaly Detection 8

Enhancing Big Data Security Through Anomaly Detection

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Vakkund, Sharan Kumar, Sachin Rao, Shaifali Anusha Hegde, H. Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798350350593

Securing the massive and fast-moving data streams typical in Big Data environments presents unique challenges that traditional static security measures simply can't handle. To effectively protect these data flows, we need methods that can analyze traffic in real-time and respond swiftly to potential threats. Anomaly detection is one such method, offering an automated way to identify unusual or suspicious activities within Big Data systems. In this study, we explore several widely-used anomaly detection algorithms, evaluating their effectiveness in identifying anomalies within large datasets. Specifically, we will assess these algorithms using the UNSW-NB15 Dataset, aiming to pinpoint which algorithm, or combination of algorithms, is best suited for the demands of Big Data security. © 2024 IEEE.

关键词： Adversarial machine learning

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Secured Digital and QR Code Payments: A Comparative Analysis of Payment Models

Secured Digital and QR Code Payments: A Comparative Analysis...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Bhavna Galhotra Shalini Bhaskar Bajaj Aman Jatain Vivek Jaglan Computer science dept Amity University JIMS Computer science dept Amity University Department of Computer Science & Technology Manav Rachna University Research Graphics Era University Dehradun

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The internet's explosive expansion has paved the way for a wide range of smartphone applications that use QR codes and digital wallets to facilitate online payments. Security is a major concern for web-based applications since they are being delivered over the Internet at a rapid pace. Due to unlawful access to many accounts, which causes problems with end-to-end security, the security of these applications has grown to be a top concern for both customers and business owners. This work has addressed the several models that are accessible for security applications, also provided a comparative analysis of all the models, taking into account their security privacy, adaptability, accuracy reliability, vulnerabilities and suitability for digital wallets and online payment systems through QR codes.

关键词： Adaptation models Analytical models Privacy Biological system modeling QR codes Logic gates Tokenization Mobile applications Reliability Standards

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SEED-VII: A Multimodal Dataset of Six Basic Emotions with Continuous Labels for Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 969-985页

作者： Jiang, Wei-Bang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai200240 China

Recognizing emotions from physiological signals is a topic that has garnered widespread interest, and research continues to develop novel techniques for perceiving emotions. However, the emergence of deep learning has highlighted the need for comprehensive and high-quality emotional datasets that enable the accurate decoding of human emotions. To systematically explore human emotions, we develop a multimodal dataset consisting of six basic (happiness, sadness, fear, disgust, surprise, and anger) emotions and the neutral emotion, named SEED-VII. This multimodal dataset includes electroencephalography (EEG) and eye movement signals. The seven emotions in SEED-VII are elicited by 80 different videos and fully investigated with continuous labels that indicate the intensity levels of the corresponding emotions. Additionally, we propose a novel Multimodal Adaptive Emotion Transformer (MAET), that can flexibly process both unimodal and multimodal inputs. Adversarial training is utilized in the MAET to mitigate subject discrepancies, which enhances domain generalization. Our extensive experiments, encompassing both subject-dependent and cross-subject conditions, demonstrate the superior performance of the MAET in terms of handling various inputs. Continuous labels are used to filter the data with high emotional intensity, and this strategy is proven to be effective for attaining improved emotion recognition performance. Furthermore, complementary properties between the EEG signals and eye movements and stable neural patterns of the seven emotions are observed. © 2010-2012 IEEE.

关键词： Eye movements

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Modified honey bees inspired optimization method for transportation problem

Advances in Transportation Studies

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Advances in Transportation Studies 2023年 59卷 71-86页

作者： Nemmich, M.A. Boukhari, N. Boudali, A. Debbat, F. Department of Computer Science University of Sciences and Technology of Oran Mohamed Boudiaf Oran Algeria Department of Mathematics University Djillali liabes of Sidi Bel Abbes Sidi Bel Abbes Algeria Department of Electronics Laboratory of coding and security of information University of Sciences and Technology of Oran Mohamed Boudiaf Oran Algeria Department of Computer Science University Mustapha Stambouli of Mascara Mascara Algeria

The Capacitated vehicle routing problem (CVRP) is a complex transportation issue that involves designing a set of routes for a group of homogenous vehicles to serve a set of customers at shortest distance travelled. It belongs to the class of NP-hard problems with a high computational complexity, and then it is difficult to solve this problem with traditional optimization methods when the problem size is large. Consequently, in this study, an improved swarm optimization metaheuristic approach has been proposed based on intelligent foraging behavior of honey bees with several specifically designed local search operators and features to solve this problem. The effectiveness of the proposed approach was illustrated on the standard benchmark problems. The results revealed that the proposed approach has fast convergence rate and high computational accuracy. Overall, our experiments show that our approach could be a very efficient approach for solving the CVRP and its experimental results are competitive in terms of the quality of the solutions reported in other well-known methods. © 2023, Aracne Editrice. All rights reserved.

关键词： Computational complexity

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Joint Optimization of Wireless Charging Station Location and Operation Scheduling for Electric Buses under Uncertain Demand

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IEEE Access 2025年 13卷 91765-91776页

作者： Li, Jiacheng Noto, Masato Zhang, Yang Guo, Jia Kanagawa University Department of Applied Systems and Mathematics Yokohama Japan Kanagawa University Department of Computer Science Yokohama Japan Hubei Key Laboratory of Digital Finance Innovation Hubei University of Economics Wuhan China Hosei University Graduate School of Computer and Information Sciences Tokyo Japan Hubei University of Economics Japan School of Information Engineering Hubei Wuhan430205 China

In the context of electric bus route operations, the introduction of wireless charging facilities significantly impacts the operational scheduling of bus fleets. This study focuses on a single bus route and proposes a joint optimization model for determining departure frequencies and charging station locations under uncertain demand conditions. The model aims to minimize both user travel costs and transit operation costs by jointly optimizing departure schedules across different time periods, electric bus assignments, and wireless charging station placement. An adaptive genetic algorithm (AGA) is developed to solve the model efficiently. A case study based on a real-world bus route in China demonstrates that the proposed strategy not only improves system performance but also yields tangible benefits—reducing the required number of electric buses by 29.4%, shortening departure intervals by 12.5%, and lowering daily operational costs by 10.5%. © 2013 IEEE.

关键词： Genetic algorithms

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A novel method of cybersecurity knowledge graph construction and storage optimization based on cyber range 9

A novel method of cybersecurity knowledge graph construction...

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9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Luo, Cui Wang, Haiyan Yuan, Huaping Gu, Zhaoqian Hu, Ning Peng Cheng Laboratory New Network Research Department Shenzhen518000 China School of Computer Science and Technology Shenzhen518000 China

ISBN: (纸本)9798350391367

With the increasing importance of cyber defense exercises, cyber ranges have become a crucial platform for simulating real network environments for security technology evaluations and cultivating cybersecurity talent. To enhance the real-time detection and accuracy of cyber attacks in cyber range scenarios, this paper addresses the performance bottlenecks, complexity in data storage, and scalability limitations encountered by existing technologies in handling large-scale cybersecurity knowledge. We propose an innovative cybersecurity knowledge storage scheme designed to overcome these challenges and improve the efficiency and effectiveness of cybersecurity operations within simulated network environments. By defining cybersecurity entities and relationships, optimizing attack template matching algorithms, and leveraging graph database technology, achieves efficient cybersecurity information mining and data analysis. Experimental results confirm that the approach supports sub-second attack template matching in cyber range attack detection scenarios, significantly enhancing the detection capability to network security events. Additionally, this approach delves into the development and application of cyber ranges, as well as the evolution of network security knowledge storage technology, providing an efficient, accurate, and user-friendly solution for cybersecurity. © 2024 IEEE.

关键词： Cyber attacks

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Mobile Robot Path Planning Based on A-Star Algorithm and Artificial Potential Field Method for Autonomous Navigation 12

Mobile Robot Path Planning Based on A-Star Algorithm and Art...

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2024 12th International Conference on Systems and Control, ICSC 2024

作者： Louda, Souhaib Karkar, Nora Seghir, Fateh Refoufi, Salim Ferhat Abbas University of Setif 1 LSI Laboratory Department of Electronics Setif Algeria Ferhat Abbas University of Setif 1 LSI Laboratory Department of Computer Science Setif Algeria

ISBN: (纸本)9798331531812

This paper presents a hybrid path-planning algorithm that combines both global and local planning techniques. An offline global path planning approach based on the A -Star graph search algorithm is used to find the optimal path from the start to the goal. However, A -Star has some limitations, such as a lack of smoothness, lower safety margins, and an inability to adapt to dynamic environments. To address these issues, we propose integrating an online local path planning method based on the Artificial Potential Field (APF) for real-time obstacle avoidance. Simulation results demonstrate the effectiveness of the hybrid approach, guiding the mobile robot along the optimal global path while ensuring the locally gener-ated trajectory is smooth enough for execution by the controller. The results also show improved navigation performance, with enhanced efficiency and safety due to the hybrid algorithm. © 2024 IEEE.

关键词： Mobile robots

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