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SSRN 2024年

作者： Mangi, Fawad Ali Su, Guoxin Zhang, Minjie Liu, Li School of Computing and Information Technology University of Wollongong Wollongong2522 Australia Department of Computer Systems Engineering Mehran University of Engineering and Technology Sindh Jamshoro76062 Pakistan School of Big Data and Software Engineering Chongqing University Chongqing400030 China

Process mining is a rapidly growing research field that uses data science techniques to analyse business processes and extract insights about their performance, efficiency, and compliance. The process mining approach begins with process discovery, which involves creating process models based on event logs;and depending on the type of models discovered, the correctness of models needs to be guaranteed. Verification is paramount to ensure the accuracy and reliability of discovered process models in process mining, the current research lacks to model the stochastic nature of process mining models. This paper introduces a novel technique for verification of process mining models, leveraging continuous time Markov chain (CTMC) model of probabilistic model checking technique. The proposed method aims to address the inherent uncertainties and dynamic nature of real-world processes, providing a more robust and comprehensive verification framework compared to traditional methods. The technique employs CTMC to represent the stochastic behavior of processes, allowing for the incorporation of time and probability in the analysis of process models. This approach enables the evaluation of process models under various conditions and scenarios, considering the probabilistic aspects and temporal characteristics inherent in business processes. The paper presents a detailed methodology of the proposed technique, illustrating its application through a case study of loan application process performed in a financial institution, and empirical evaluations. © 2024, The Authors. All rights reserved.

关键词： Model checking

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A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

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computers, Materials & Continua 2022年第1期70卷 1617-1630页

作者： Khadija Manzoor Fiaz Majeed Ansar Siddique Talha Meraj Hafiz Tayyab Rauf Mohammed A.El-Meligy Mohamed Sharaf Abd Elatty E.Abd Elgawad Department of Information Technology University of GujratGujrat50700Pakistan Department of Software Engineering University of GujratGujrat50700Pakistan Department of Computer Science COMSATS University Islamabad-Wah CampusWah Cantt47040Pakistan Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke-on-TrentUK Advanced Manufacturing Institute King Saud UniversityRiyadh11421Saudi Arabia Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Skin diseases effectively influence all parts of *** and accurate detection of skin cancer is necessary to avoid significant *** manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color ***,an automated system to identify these skin diseases is *** studies on skin disease classification using different techniques have been ***,previous techniques failed to identify multi-class skin disease images due to their similar *** the proposed study,a computer-aided framework for automatic skin disease detection is *** the proposed research,we collected and normalized the datasets from two databases(ISIC archive,Mendeley)based on six Basal Cell Carcinoma(BCC),Actinic Keratosis(AK),Seborrheic Keratosis(SK),Nevus(N),Squamous Cell Carcinoma(SCC),and Melanoma(M)common skin ***,segmentation is performed using deep Convolutional Neural Networks(CNN).Furthermore,three types of features are extracted from segmented skin lesions:ABCD rule,GLCM,and in-depth *** transfer learning is used for deep feature extraction,while a support vector machine(SVM)is used for *** results show that SVM outperformed other studies in terms of accuracy,as AK disease achieved 100%accuracy,BCC 92.7%,M 95.1%,N 97.8%,SK 93.1%,SCC 91.4%with a global accuracy of 95.4%.

关键词： AlexNet CNN GLCM SVMs skin disease

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RDG-TE: Link reliability-aware DRL-GNN-based traffic engineering in SDN

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Expert systems with Applications 2025年 265卷

作者： Farhan, Muhammad Shah, Nadir Wang, Lei Muntean, Gabriel-Miro Song, Houbing Herbert School of Software Dalian University of Technology Dalian China Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Key Lab of Ubiquitous Network and Service Software of Liaoning Province China School of Electronic Engineering Dublin City University Dublin Ireland Department of Information Systems University of Maryland Baltimore United States

The existing advanced machine learning approaches based on Graph Neural Networks (GNN) for efficient traffic engineering (TE) in software Defined Networking (SDN) overlook consideration of link reliability values. Link reliability is a very important parameter, directly linked to end-to-end delay for data packet transmission, and can be used to improve the delivery performance. This research article proposes two versions of RDG-TE, a novel model that integrates Deep Reinforcement Learning (DRL) and GNN in order to solve efficiently network TE problems by considering link reliability in both model training and reward computation. The proposed model enables improved performance by more accurately predicting the SDN network behavior in case of link failure. Testing results show that, in comparison to the closest state-of-art approach, our proposed approach reduces the number of disturbed flows by 24.19% and the hop count by 35.38% while also increases the reliable route prediction accuracy by 40.17%. © 2024 Elsevier Ltd

关键词： Deep reinforcement learning

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Learning Transferable Negative Prompts for Out-of-Distribution Detection

Learning Transferable Negative Prompts for Out-of-Distributi...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Tianqi Li Guansong Pang Xiao Bai Wenjun Miao Jin Zheng State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Jiangxi Research Institute Beihang University China School of Computing and Information Systems Singapore Management University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, named ‘NegPrompt’, to learn a set of negative prompts, each representing a negative connotation of a given class label, for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only, without any reliance on external out-lier data. Further, current methods assume the availability of samples of all ID classes, rendering them ineffective in open-vocabulary learning scenarios where the inference stage can contain novel ID classes not present during training. In contrast, our learned negative prompts are transferable to novel class labels. Experiments on various ImageNet benchmarks show that NegPrompt surpasses state-of-the-art prompt-learning-based OOD detection methods and maintains a consistent lead in hard OOD detection in closed- and open-vocabulary classification scenarios. Code is available at https://***/mala-lab/negprompt.

关键词： Training Learning systems computer vision Codes Computational modeling Semantics Lead

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M3bunet:Mobile Mean Max Unet for Pancreas Segmentation on Ct-Scans

SSRN

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SSRN 2024年

作者： Juwita, Juwita Hassan, Ghulam Mubashar Akhtar, Naveed Datta, Amitava Department of Computer Science and Software Engineering The University of Western Australia Australia School of Computing and Information Systems The University of Melbourne Australia Department of Informatics Syiah Kuala University Banda Aceh Indonesia

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on NIH and MSD pancreas datasets. Our approach demonstrates performance improvement, achieving an average DSC value of up to 89.53±1.82 and IOU score of up to 81.16±0.03 for the NIH, and 88.60 ±1.48 DSC and 79.90±2.19 IOU for the MSD. © 2024, The Authors. All rights reserved.

关键词： computerized tomography

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From Data to Diagnosis: Investigating Approaches in Mental Illness Detection

From Data to Diagnosis: Investigating Approaches in Mental I...

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Intelligent Methods, systems, and Applications (IMSA)

作者： Reem Kadry Montasser Sherif A. Mazen Iman M. A. Helal Software Engineering Department Faculty of Computer Science October University for Modern Sciences and Arts Giza Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Mental illness is a considerable global public health problem, impacting both individual well-being and society's health. The growing popularity of social media and the increase of other data sources led to more research interest in detecting mental illness with a particular focus on utilizing and fusing emotional information. Artificial intelligence techniques like natural language processing and others, have shown encouraging improvements in recognizing complex relationships in a wide range of textual data, including clinical notes, social media posts, and interviews. In this paper, we assess the different branches of artificial intelligence based on previous research in mental illness detection, discussing methods, trends, challenges, and future research directions.

关键词： Deep learning Machine learning algorithms Accuracy Social networking (online) Soft sensors Mental health Feature extraction

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SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection

arXiv

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arXiv 2024年

作者： Yuan, Shuai Qin, Hanlin Yan, Xiang Akhtar, Naveed Mian, Ajmal The School of Optoelectronic Engineering Xidian University Xi’an710071 China The School of Computing and Information Systems Faculty of Engineering and IT The University of Melbourne ParkvilleVIC3052 Australia The Department of Computer Science and Software Engineering The University of Western Australia Perth6009 Australia

This is the pre-acceptance version, to read the final version please go to IEEE TRANSACTION ON GEOSCIENCE AND REMOTE SENSING on IEEE Xplore. Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. However, largely overlooking effective global information modeling, existing techniques struggle when the target has high similarities with the background. We present a Spatial-channel Cross Transformer Network (SCTransNet) that leverages spatial-channel cross transformer blocks (SCTBs) on top of long-range skip connections to address the aforementioned challenge. In the proposed SCTBs, the outputs of all encoders are interacted with cross transformer to generate mixed features, which are redistributed to all decoders to effectively reinforce semantic differences between the target and clutter at full levels. Specifically, SCTB contains the following two key elements: (a) spatial-embedded single-head channel-cross attention (SSCA) for exchanging local spatial features and full-level global channel information to eliminate ambiguity among the encoders and facilitate high-level semantic associations of the images, and (b) a complementary feed-forward network (CFN) for enhancing the feature discriminability via a multi-scale strategy and cross-spatial-channel information interaction to promote beneficial information transfer. Our SCTransNet effectively encodes the semantic differences between targets and backgrounds to boost its internal representation for detecting small infrared targets accurately. Extensive experiments on three public datasets, NUDT-SIRST, NUAA-SIRST, and IRSTD-1K, demonstrate that the proposed SCTransNet outperforms existing IRSTD methods. Our code will be made public at https://***/xdFai/SCTransNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

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Journal of computer Science & Technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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A Sequential-based Deep Learning Model for Dry Beans Classification

A Sequential-based Deep Learning Model for Dry Beans Classif...

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Smart Computing and Application (ICSCA), International Conference on

作者： R. Sujatha Jyotir Moy Chatterjee A. Rohith Rabie A. Ramadan Software and Systems Engineering Vellore Institute of Technology Vellore India Information Technology Lord Buddha Education Foundation Kathmandu Nepal Computer Engineering Cairo University Giza Egypt College of Computer Science and Engineering Hail University Hail Saudi Arabia

People take various types of cereals every day in their regular meals, but most people do not know their importance while consuming them. Each cereal has its benefit. One such cereal content is dry beans. Dry beans are a variety of beans that are produced in pods. These dry beans can be cooked and eaten, and it has numerous proteins and vitamins and are highly beneficial for our health. It also provides excellent immunity. Each bean has its characteristics, which can be identified with its unique features. These can be oval or kidney-shaped or even without a proper shape. According to their shape and features, they are classified separately. These nutrient-rich beans have to be classified based on their shapes. Human eyes sometimes may oversee or misclassify these tiny cereals for classification. This work involves the classification of 7 such dry bean varieties utilizing a deep learning context. The dataset utilized in this paper includes 13611 dry bean data for seven different UCI Machine learning repositories. In this work, we have taken seven different categorical labels: the dry bean varieties. The classification is done using deep learning techniques, and here we have utilized the Keras Sequential Algorithm for the classification. It is a supervised learning concept of machine learning used to predict two or more categorical labels. With the help of the deep learning approach, these dry beans are classified and obtained an accuracy of 94.88%.

关键词： Deep learning Proteins Industries Machine learning algorithms Shape Supervised learning Prediction algorithms

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AI-Driven Cybersecurity and Functional Framework for Remote Laboratories: A software engineering Perspective

AI-Driven Cybersecurity and Functional Framework for Remote ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nesreen Otoum Enrique Arias Jesús Jover Alfaro Department of Software Engineering University of Petra Amman Jordan Department of Computer Systems University of Castilla-La Mancha Albacete Spain Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Remote laboratories have reshaped the way engineering courses provide hands-on experimentation by enabling students to control physical lab equipment via online interfaces. nevertheless, their ease, However, these platforms face challenges such as security vulnerabilities, performance instability, and limited user support. This paper introduces a layered framework that integrates artificial intelligence (AI) into a secure, modular design. Using conventional software engineering techniques, our approach improves cybersecurity through intelligent threat detection, automated resource balancing, and context-aware user interaction. We provide a comprehensive explanation of the framework's architecture, which methodically addresses critical requirements. This includes a proactive cybersecurity apparatus that adapts to emergent threats, as well as real-time device control and data acquisition. Ultimately, this work aims to assist educators and institutions build or modernize remote lab environments that offer robust protection and seamless learning outcomes, even under rapidly shifting demands.

关键词： Remote laboratories computer architecture Threat assessment Real-time systems Resource management Reliability computer security Protection Standards software engineering

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