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Attributed Network Clustering through Mutual Affinity Propagated Non-negative Matrix Factorization

SN computer science 2025年第5期6卷 1-17页

作者： Pahari, Saikat Dey, Paramita Department of Computer Science Engineering OmDayal College of Engineering and Architecture West Bengal Howrah 711316 India Department of Information Technology Government College of Engineering and Ceramic Technology West Bengal Kolkata 700010 India

Attributed Network Clustering (ANC) has garnered significant attention in research for identifying communities within a complex network like a social, biological, or information network. Generally, such networks are represented as a graph where the nodes denote entities and edges are relationships between two adjacent nodes. In real-world networks, nodes are associated with attributes to express their properties. In this context, a community is defined as a group of nodes that interact more frequently with each other than with nodes outside their group. ANC involves creating a mapping function that converts nodes and associated attributes into lower-dimensional vector representations. Once transformed into these vectors, various machine learning algorithms are used to cluster the nodes and reveal the underlying communities. However, most ANC methods confront two major challenges. First, they assimilate node attribute information from the local neighborhood, as a result, they can’t explore the mutual affinity between node and attribute. Second, they do not consider the values of the community membership of the nodes at the time of clustering. This leads to poor performance, as the embedding method and clustering methods are independent. To address this, this article proposes a method named Mutual Affinity Propagation-based Nonnegative Matrix Factorization (MAP-NMF). The proposed method works in two phases. In the first phase, an attribute-augmented transition probability matrix is constructed to capture the mutual affinity between the node and attribute and obtain node representation using singular value decomposition of the same. The second phase produces community membership from the obtained latent representation through an NMF-based joint optimization. Moreover, a novel updating rule is proposed for the joint optimization method. Empirical study on both real-world and artificial networks proves the superiority of our proposed algorithm. © The Author(s), under exc

关键词： Attribute augmented graph Attributed network embedding Joint optimization Non-negative matrix factorization

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A new clustering-based semi-supervised method to restrict the users from anomalous electricity consumption: supporting urbanization

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Electrical Engineering 2024年第5期106卷 6431-6448页

作者： Aslam, Zeeshan Javaid, Nadeem Javed, Muhammad Umar Aslam, Muhammad Aldegheishem, Abdulaziz Alrajeh, Nabil Bredesen Center for Interdisciplinary Research and Graduate Education The University of Tennessee TN37996 United States Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Artificial Intelligence Sejong University Seoul05006 Korea Republic of Department of Urban Planning College of Architecture and Planning King Saud University Riyadh11574 Saudi Arabia Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia

One of the crucial issues for power grids in strengthening the urbanization around the world is imbalance between supply and demand, which leads the users to consume electricity in an anomalous manner without paying for it. Electricity theft plays a pivotal role in cutting down on the electricity bills. The existing data-oriented approaches for electricity theft detection (ETD) in the smart cities have limited ability to handle noisy high-dimensional data and features’ associations. These limitations raise the misclassification rate, which makes some of the approaches unacceptable for electric utilities. A new twofold end-to-end methodology is proposed for ETD. In the first fold, it groups the similar electricity consumption (EC) cases through grey wolf optimization (GWO)-based clustering mechanism;clustering by fast search and find of density peaks (CFSFDP), we named it GC. In the second fold, a new relational stacked denoising autoencoder (RSDAE)-based semi-supervised generative adversarial network (GAN), termed as RGAN, is used for ETD. The combined methodology is named as GC-RGAN. In the methodology, RSDAE acts as both feature extraction technique and generator sub-model of the proposed RGAN. The proposed methodology utilizes the advantages of clustering, adversarial learning and semi-supervised EC data. Besides, to validate the effectiveness of the proposed solution, extensive simulations are performed using smart meter data. Simulation results validate the excellent ETD performance of the proposed GC-RGAN against existing ETD schemes, such as random forest and semi-supervised support vector machine. In comparison, GC-RGAN covers the ETD score of 98% that shows its suitability for real-world scenarios. The proposed solution has extraordinary performance for ETD as compared to traditional solutions, which shows its superiority and usefulness for real-world applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Na

关键词： Generative adversarial networks

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A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design

arXiv

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arXiv 2024年

作者： Gharbi, Yossra Mercado, Rocío Department of Computer Science and Engineering Section for Data Science and AI Chalmers University of Technology Chalmersplatsen 4 Gothenburg412 96 Sweden

Targeted protein degradation (TPD) is a rapidly growing field in modern drug discovery that aims to regulate the intracellular levels of proteins by harnessing the cell’s innate degradation pathways to selectively target and degrade disease-related proteins. This strategy creates new opportunities for therapeutic intervention in cases where occupancy-based inhibitors have not been successful. Proteolysis-targeting chimeras (PROTACs) are at the heart of TPD strategies, which leverage the ubiquitin-proteasome system for the selective targeting and proteasomal degradation of pathogenic proteins. This unique mechanism can be particularly useful for dealing with proteins that were once deemed "undruggable" using conventional small-molecule drugs. PROTACs are hetero-bifunctional molecules consisting of two ligands, connected by a chemical linker. As the field evolves, it becomes increasingly apparent that the traditional methodologies for designing such complex molecules have limitations. This has led to the use of machine learning (ML) and generative modeling to improve and accelerate the development process. In this review, we aim to provide a thorough exploration of the impact of ML on de novo PROTAC design — an aspect of molecular design that has not been comprehensively reviewed despite its significance. Initially, we delve into the distinct characteristics of PROTAC linker design, underscoring the complexities required to create effective bifunctional molecules capable of TPD. We then examine how ML in the context of fragment-based drug design (FBDD), honed in the realm of small-molecule drug discovery, is paving the way for PROTAC linker design. Our review provides a critical evaluation of the limitations inherent in applying this method to the complex field of PROTAC development. Moreover, we review existing ML works applied to PROTAC design, highlighting pioneering efforts and, importantly, the limitations these studies face. By offering insights into the current

关键词： Proteins

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A Nonlinear Convolution Network for Image Enhancement and Translation Tasks

SSRN

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SSRN 2024年

作者： Bhattacharya, Jhilik Carini, Alberto Marsi, Stefano Ramponi, Giovanni Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India Department of Architecture and Engineering University of Trieste Italy

In this paper we introduce a special nonlinear convolution block with sine and cosine transformations, channel gating and local linear filter operations, which allows improved nonlinear CNN-based modeling with small network sizes. We motivate and demonstrate significant improvement in the task performances with a major reduction in the network size, thanks to the use of such a nonlinear convolution block. We perform extensive experiments to verify the performance of the network for different image transformation tasks such as deraining, dehazing, deblurring, low to bright light and night to day transformation. For all instances the network either shows acceptable or better performances when compared to State-of-Art (SoA) techniques for each task using benchmark datasets. © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Integrating virtual reality technology into architecture education: the case of architectural history courses

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Open House International 2021年第4期46卷 498-509页

作者： Ibrahim, Anwar Al-Rababah, Amneh Ibrahim Bani Baker, Qanita Department of Architecture Jordan University of Science and Technology Irbid Jordan Department of Computer Science and Software Engineering Jordan University of Science and Technology Irbid Jordan

Purpose: Virtual reality (VR) technology invaded various domains including architecture practice and education. Despite its high applications in architecture design education, VR has a high potential to be used in architectural history courses as well. This paper aims to examine the effect of using VR technology on the students’ learning abilities of history of architecture. Design/methodology/approach: The experimental approach was used. Two experiments were designed by creating virtual environments for two selected architectural examples from the Modern architecture course. The participants who were students of Modern architecture class had to complete two questionnaires, one for each example. The first one was based on Bloom’s taxonomy, and the other was prepared to test the participants’ analytical and critical skills. Besides, participants had to fill out satisfaction and ease-of-use survey on a five-step Likert scale. Findings: Participants in the VR condition achieved better grades in knowledge gain compared to those in the traditional conditions. Their analytical and critical thinking skills were improved in the VR conditions. Gender has a significant impact on analytical and critical thinking skills. Participants recorded a high level of satisfaction;however, male students were more satisfied than female students who reported concerns about the weight of the used tools and nausea symptoms. Research limitations/implications: This study informs architecture education and provides insights into the potentials of using advanced technology in architectural history education. Teaching the various history of architecture courses will be improved, shifted toward a more student-centered curriculum, and may acquire more excitement and conscious curiosity. Originality/value: Using VR in architectural education is rigorous in architectural design courses and students’ design projects’ presentations. This research expands architectural education research by examining ot

关键词： Architectural education Bloom’s taxonomy Built environment tectonics and technologies History of architecture VR technology

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Using clinical entity recognition for curating an interface terminology to aid fast skimming of EHRs

Using clinical entity recognition for curating an interface ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kollapally, Navya Martin Dehkordi, Mahshad Koohi H. Perl, Yehoshua Geller, James Deek, Fadi P. Liu, Hao Keloth, Vipina K. Elhanan, Gai Einstein, Andrew J. Zhou, Shuxin Kean University Department of Computer Science Union United States New Jersey Institue of Technology Department of Computer Science Newark United States New Jersey Institue of Technology Department of DataScience Newark United States New Jeresy Institue of Technology Department of Informatics Newark United States Montclair State University School of Computing Montclair United States Yale University Section of Biomedical Informatics and DataScience New Haven United States University of Nevada School of Medicine Nevada United States Columbia Medical Center Dept. of Medicine New York United States

ISBN: (纸本)9798350386226

Highlighting of Electronic Health Records (EHRs) involves marking essential content of EHR notes, corresponding to concepts of a clinical terminology. However, employing the best clinical terminology (SNOMED CT) for highlighting EHRs, captures only a portion of their crucial content. In this paper, we describe the curation of a Cardiology Interface Terminology (CIT) dedicated to the application of highlighting EHRs of cardiology patients. We utilize a Clinical-Named Entity Recognition (Clinical NER) approach for extracting phrases, of higher granularity than SNOMED CT concepts, from EHRs, for enriching CIT. For this purpose, we train a neural network model with BIOE-tagged (Beginning, Inside, End, and Outside) cardiology entities. Transfer Learning can be used to facilitate the curation of an interface terminology for highlighting EHRs for other specialties e.g. Nephrology. Large-scale highlighting enables overworked physicians and other healthcare providers to fast skim the dense volume of EHRs they regularly read. Secondary research and EHRs interoperability are other applications that can be supported by highlighting. © 2024 IEEE.

关键词： Electronic health record

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Safety Analysis of Riding at Intersection Entrance Using Video Recognition technology

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computers, Materials & Continua 2022年第9期72卷 5135-5148页

作者： Xingjian Xue Linjuan Ge Longxin Zeng Weiran Li Rui Song Neal N.Xiong College of Landscape Architecture Central South University of Forestry and TechnologyChangshaHunan410004China College of Logistics and Transportation Central South University of Forestry and TechnologyChangshaHunan410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequahOK74464United States

To study riding safety at intersection entrance,video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian *** is analyzed the relationship among the width of nonmotorized lanes at the entrance lane of the intersection,the vehicle-bicycle soft isolation form of the entrance lane of intersection,the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles,the speed of right-turning motor vehicles,and straight-going non-motor vehicles,and the conflict between right-turning motor vehicles and straight-going nonmotor *** to the traditional statistical methods,to overcome the discreteness of vehicle-bicycle conflict data and the differences of influencing factors,the Bayesian random effect Poisson-log-normal model and random effect negative binomial regression model are *** results show that the random effect Poisson-log-normal model is better than the negative binomial distribution of random effects;The width of non-motorized lanes,the form of vehicle-bicycle soft isolation,the traffic volume of right-turning motor vehicles,and the coefficients of straight traffic volume obey a normal *** them,the type of vehicle-bicycle soft isolation facilities and the vehicle-bicycle traffic volumes are significantly positively correlated with the number of vehicle-bicycle *** width of non-motorized lanes is significantly negatively correlated with the number of vehicle-bicycle *** periods and flat periods,the average speed of right-turning motor vehicles,and the average speed of straight-going non-motor vehicles have no significant influence on the number of vehicle-bicycle conflicts.

关键词： Video recognition technology vehicle-bicycle conflict intersection entrance random effect poisson-log-normal model random effect negative binomial regression model

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A Machine Learning-Based Model for the Detection of Skin Cancer Using YOLOv10

A Machine Learning-Based Model for the Detection of Skin Can...

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IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

作者： Bushra Shaukat Ali Haidawati Nasir Akbar Khan Muhammad Ashraf Syed Muhammad Akbar Department of Computer Science University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Computer Engineering section Malaysian Institute of Technology Kuala Lumpur Malaysia Department of Computer Engineering University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Melanoma, the most dangerous form of skin cancer originates in pigment cells (melanocytes) and poses a significant threat to patients. Therefore, individuals must have any suspicious lesions or moles promptly evaluated for early detection to improve survival rates. The following study presents a Machine learning-based model for detecting skin cancer using YOLOv10 technology. The process involves preprocessing, augmentation, and training on two datasets: the ISIC 2017 dataset (with classes melanoma, nevus, and seborrheic keratosis) annotated based on YOLOv8 versions via Roboflow; and another dataset from Roboflow containing three classes-malignant tumors, benign growths, and healthy skin. Preprocessing techniques were utilized alongside augmentation methods to enhance the model's performance before fine-tuning the pre-trained YOLOv10 model as per requirements. This approach yielded promising results with achieved precision of 1, recall of 0.88, and $mAP50$ of 0.94 on the ISIC2017 dataset; precision of 1, recall of 0.95, mAP50 of 0.79, and F-score score of 82% on the Skin Detection Dataset respectively.

关键词： Training Industries Image processing Object detection Melanoma Medical services Pigments

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Design of Optimal Service Scheduling based Task Allocation for Improving CRM in Cloud Computing 6

Design of Optimal Service Scheduling based Task Allocation f...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Suvarna Kumar, G. Priyadarshini, R. Parmenas, Naik Henokh Tannady, Hendy Rabbi, Fazle Andiyan, Andiyan Cse Mvgr College of Engg India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kalbis Institute Department of Management Indonesia Australian Computer Society Australia Universitas Faletehan Department of Architecture Indonesia

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Cloud computing is a service level computing that provide various service to the customers in order to establish an effective customer Relationship management (CRM). This offers various services like virtual machine, self-service provisioning, elasticity computing and storage (pay-as-you-go). Cloud computing provides shared services by enhancing resource management scalability, interoperability and prediction resources as a key to realize the resource utilization with high-performance management metrics. However, when the number of users increase, the process of task scheduling and service allocation using a traditional computing environment will degrade the CRM. In order to address this challenge, this research study proposes an Optimal Service Level Scheduling (OSLS) based task allocation design for improving CRM in cloud computing environment. The Adaptive Service Level Scheduling Algorithm (ASLSA) and the Support Level Load Balancer (SLLB) will reduce the workload in cloud computing environment in order to improve the Quality of Service (QoS) of CRM. This process will optimize the resource utilization in cloud platform based on the service requirement. It provides optimal scheduling features to CRM in order to improve the service optimality based on task and enhance the computational processes such as service load management, heterogeneous service delivery, pricing, resource pools and elasticity. The proposed system leverages high performance when compared to the existing models. © 2022 IEEE.

关键词： Quality of service

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Heuristic computing with sequential quadratic programming for solving a nonlinear hepatitis B virus model

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Mathematics and computers in Simulation 2023年第1期212卷 234-248页

作者： Umar, Muhammad Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Baskonus, Haci Mehmet Ali, Mohamed R. Shah, Nehad Ali Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Faculty of Education Harran University Sanliurfa Turkey Department of Mathematics Benha Faculty of Engineering Benha University Egypt Faculty of Engineering and Technology Future University Cairo Egypt Department of Mechanical Engineering Sejong University Seoul05006 Korea Republic of Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

This current study presents a nonlinear system of the hepatitis B virus infection by using the strength of artificial neural network (ANN) together with the competences of global and local search efficiencies of genetic algorithm (GA) and sequential quadratic programming scheme (SQPS), i.e., ANN-GA-SQPS. An error function is designed to use the mathematical form of the hepatitis B virus differential model and its initial conditions. The optimization of the error function is performed by using the hybridization efficiency of the GA-SQPS for the hepatitis B virus infection disease model. For the competence of ANN-GA-SQPS, the matching of obtained and the Adams numerical solutions has been observed. The absolute error is found in good measures for solving a nonlinear hepatitis B virus infection disease model. Moreover, statistical measures using different indices for 50 independent executions and 30 variables have been performed to check constancy, reliability, and effectiveness of the ANN-GA-SQPS. © 2023 International Association for Mathematics and computers in Simulation (IMACS)

关键词： Genetic algorithms

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