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Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning

Computers, Materials & Continua 2022年第10期73卷 953-963页

作者： Muhammad Umar Nasir Muhammad Adnan Khan Muhammad Zubair Taher MGhazal Raed A.Said Hussam Al Hamadi Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13120GyeonggidoKorea Faculty of Computing Riphah International UniversityIslamabad45000Pakistan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia Canadian University Dubai00000UAE Cyber-Physical Systems Khalifa UniversityAbu Dhabi127788UAE

One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic ***,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic *** is a challenging process since it must be completed effectively and *** research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical *** the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic *** predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be *** predict genetic disease,deep and machine learning approaches are *** most productive tool model provides more precise *** simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation *** approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives.

关键词： Genetic disorder machine learning deep learning single gene inheritance gene disorder mitochondrial gene inheritance disorder

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Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng University of Oxford Department of Computer Science United Kingdom Peking University Department of Information and Computing Science School of Mathematical Science China Monash University Department of Software Systems and Cybersecurity Australia Singapore Management University School of Computing and Information Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025 IEEE.

关键词： Federated learning

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Investigating the Impacts of Digital Food Supply Chain Traceability on Sustainability: An Australian Study 34

Investigating the Impacts of Digital Food Supply Chain Trace...

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34th Australasian Conference on information systems, ACIS 2023

作者： Kurnia, Sherah Li, Mingye Liu, Libo Cui, Tingru Li, Wen Tortorella, Guilherme Luz Palmer, Martin Ali, Mazen School of Computing and Information Systems University of Melbourne Melbourne Australia Department of Mechanical Engineering University of Melbourne Melbourne Australia Department of Chemical Engineering University of Melbourne Melbourne Australia College of Information Technology University of Bahrain Bahrain

Digital food supply chain traceability (DFSCT) has the potential to address global sustainability concerns. However, limited studies investigate the use of technologies to support food supply chain traceability. Furthermore, empirical studies that show the impacts of DFSCT on sustainability are lacking. This research in progress aims to investigate the impact of DFSCT on the economic, environmental, and social benefits, and to explore how these three benefits affect each other. Through a survey of 310 organisations in the Australian food industry, our study findings show the importance of DFSCT in enhancing organisations’ sustainability performance, with empirical evidence demonstrating the positive impact of social and environmental performance on economic performance. This paper offers modest contributions to research and practice related to the role of technology in supporting traceability and sustainable supply chain management. Copyright © 2023 Kurnia, Li, Liu, Cui, Li, Tortorella, Palmer and Ali.

关键词： Sustainable development

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Triangle Matters! TopDyG: Topology-aware Transformer for Link Prediction on Dynamic Graphs 25

Triangle Matters! TopDyG: Topology-aware Transformer for Lin...

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34th ACM Web Conference, WWW 2025

作者： Zhang, Xin Cai, Fei Zheng, Jianming Pan, Zhiqiang Chen, Wanyu Chen, Honghui Chen, Chonghao National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China College of Electronic Countermeasures National University of Defense Technology Hefei China

ISBN: (纸本)9798400712746

Dynamic graph link prediction is widely utilized in the complex web of the real world, such as social networks, citation networks, recommendation systems, etc. Recent Transformer-based link prediction methods on dynamic graphs not only fail to model the fine-grained structures such as triangles with the vanilla Transformers in the graph serialization process, but also amplify the imbalanced distribution of graphs because of their over-estimation of high-degree nodes. To tackle these issues, we propose a Topology-aware Transformer on Dynamic Graph (TopDyG) for link prediction, consisting of a topology injected Transformer (Ti-Transformer) and a mutual information learning (Mi-Learning). The Ti-Transformer explores the explicit structure of serialized graphs, capturing the topological features. The Mi-Learning mines the relationship between nodes by modeling the mutual information with a prior knowledge, alleviating the over-estimation of high-degree nodes when applying the Transformer-based models for the dynamic graph link prediction task. Extensive experiments on four public datasets containing both transductive and inductive settings present the superiority of our proposal. In particular, TopDyG presents an improvement of 43.27% and 28.75% against the state-of-the-art baselines in terms of NDCG and Jaccard, respectively. The advantages are especially obvious on the high-density graphs. © 2025 Copyright held by the owner/author(s).

关键词： Recommender systems

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From Medical Consultation to Diagnosis Generation Using Deep Learning 19th

From Medical Consultation to Diagnosis Generation Using Deep...

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19th International Conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Ortiz-Perez, David Merino-Balaguer, Alejandro Rodriguez-Juan, Javier Garcia-Rodriguez, Jose Tomás, David J. Nalepa, Grzegorz Department of Computer Science and Technology University of Alicante Carr. de San Vicente del Raspeig Spain Department of Software and Computing Systems University of Alicante Carr. de San Vicente del Raspeig Spain Jagiellonian University Kraków Poland

ISBN: (纸本)9783031750090

Sanitary professionals often experience burnout due to long working hours and the stress associated with handling sensitive situations. Consequently, in recent years, significant efforts have been made to automate various tasks within their roles using deep learning methods. This study proposes an automated system for generating summaries of medical consultations, aiming to alleviate doctors’ workloads. The architecture takes audio recordings of medical consultations as input, which are then transcribed and diarized for further processing. Subsequently, the entire transcription is summarized using state-of-the-art transformer-based models. For this study, a dataset comprising fourteen consultations, along with two summaries provided by different sanitary professionals, was utilized. In order to address the limitations of the available data, a data augmentation technique was implemented which makes use of the capabilities of large language models. The proposed models achieved a result of 0.52 in the ROGUE-1 metric, demonstrating the effectiveness of the proposed methodology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Investigating the Impacts of Digital Food Supply Chain Traceability on Sustainability: An Australian Study 44

Investigating the Impacts of Digital Food Supply Chain Trace...

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44th International Conference on information systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

ISBN: (纸本)9781713893622

关键词： Sustainable development

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AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection

arXiv

引用

arXiv 2025年

作者： Qiao, Hezhe Niu, Chaoxi Chen, Ling Pang, Guansong School of Computing and Information Systems Singapore Management University Singapore AAII University of Technology Sydney Sydney Australia

Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years. Existing generalist graph models have achieved remarkable success in different graph tasks but struggle to generalize to the GAD task. This limitation arises from their difficulty in learning generalized knowledge for capturing the inherently infrequent, irregular and heterogeneous abnormality patterns in graphs from different domains. To address this challenge, we propose AnomalyGFM, a GAD-oriented graph foundation model that supports zero-shot inference and few-shot prompt tuning for GAD in diverse graph datasets. One key insight is that graph-agnostic representations for normal and abnormal classes are required to support effective zero/few-shot GAD across different graphs. Motivated by this, AnomalyGFM is pre-trained to align data-independent, learnable normal and abnormal class prototypes with node representation residuals (i.e., representation deviation of a node from its neighbors). The residual features essentially project the node information into a unified feature space where we can effectively measure the abnormality of nodes from different graphs in a consistent way. This provides a driving force for the learning of graph-agnostic, discriminative prototypes for the normal and abnormal classes, which can be used to enable zero-shot GAD on new graphs, including very large-scale graphs. If there are few-shot labeled normal nodes available in the new graphs, AnomalyGFM can further support prompt tuning to leverage these nodes for better adaptation. Comprehensive experiments on 11 widely-used GAD datasets with real anomalies, covering social networks, finance networks, and co-review networks, demonstrate that AnomalyGFM significantly outperforms state-of-the-art competing methods under both zero- and few-shot GAD settings. Our code will be available at https://***/mala-lab/

关键词： Zero-shot learning

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks

Unified Approach Integrating Machine Learning Algorithms for...

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computing, Sciences and Communications (ICCSC), International Conference on

作者： Abeer Mathur Abhinav Sudhakar Dubey Pranav Singh Mahara Usha Devi Gandhi Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Information Technology School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment.

关键词： Support vector machines Radio frequency Phishing Scalability Forestry Natural language processing Vectors Robustness Electronic mail Random forests

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Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning

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Computers in Biology and Medicine 2025年 186卷 109703-109703页

作者： Shaikh, Asadullah Amin, Samina Zeb, Muhammad Ali Sulaiman, Adel Al Reshan, Mana Saleh Alshahrani, Hani Department of Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Institute of Computing Kohat University of Science and Technology Kohat26000 Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

– Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications. BT segmentation with MRI remains challenging despite advancements in image acquisition techniques. Accurate detection and segmentation are essential for proper diagnosis and treatment planning. This study aims to enhance BT detection and segmentation accuracy and effectiveness of categorization through the implementation of an advanced stacking ensemble learning (SEL) approach. This study explores the efficiency of SEL architecture in augmenting the precision of BT segmentation. SEL, a prominent approach within the machine learning paradigm, combines the predictions of base-level models and improves the overall performance of predictions in order to reduce the errors and biases of each model. The proposed approach involves designing a stacked DenseNet201 as the meta-model called SEL-DenseNet201, complemented by six diverse base models such as mobile network version 3 (MobileNet-v3), 3-dimensional convolutional neural network (3D-CNN), visual geometry group network with 16 and 19 layers (VGG-16 and VGG-19), residual network with 50 layers (ResNet50), and Alex network (AlexNet). The strengths of the base models are calculated to capture distinct aspects of the BT MRI, aiming for enhanced segmentation performance. The proposed SEL-DenseNet201 is trained using BT MRI datasets. The augmentation techniques are applied to MRI scans to balance and enhance the model performance through the application of image enhancement and segmentation techniques. The proposed S

关键词： Convolutional neural networks

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TOWARDS FORMAL MODELING AND VERIFICATION OF UML CLASS DIAGRAMS USING FIRST-ORDER LOGIC AND RESOLUTION

TOWARDS FORMAL MODELING AND VERIFICATION OF UML CLASS DIAGRA...

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International Conference on Green Energy, computing and Intelligent technology 2024, GEn-CITy 2024

作者： Saputra, Aditya Bagoes Basuki, Thomas Anung Tirtawangsa, Jimmy Information Systems Department Universitas YPPI Rembang Rembang Indonesia School of Mathematical and Computer Sciences Heriot-Watt University Malaysia Putrajaya Malaysia School of Computing Telkom University Bandung Indonesia Information Technology Department Institut Teknologi Telkom Jakarta South Jakarta Indonesia

ISBN: (纸本)9781837242689

Although Object Management Group (OMG) Unified Modeling Language (UML) introduced Object Constraint Language (OCL) to add formalism as constraints in its models, it still lacks formal semantics and verification. This paper proposes first-order logic for formal modeling UML class diagrams and resolution for verification. We introduce formal definition and semantics for classes, their attributes, and operations. In addition, we provide two case studies of class diagrams in which we prove inconsistencies can be detected in the classes and operations. © The Institution of Engineering & technology 2024.

关键词： Unified Modeling Language

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