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Dopcc: Detecting Overlapping Protein Complexes via Multi-Metrics and Co-Core Attachment Method

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第6期21卷 2000-2010页

作者： Wang, Wenkang Meng, Xiangmao Xiang, Ju Bedru, Hayat Dino Li, Min Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

Identification of protein complex is an important issue in the field of system biology, which is crucial to understanding the cellular organization and inferring protein functions. Recently, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) networks. However, most of these methods only focus on local information of proteins in the PPI network, which are easily affected by the noise in the PPI network. Meanwhile, it's still challenging to detect protein complexes, especially for overlapping cases. To address these issues, we propose a new method, named Dopcc, to detect overlapping protein complexes by constructing a multi-metrics network according to different strategies. First, we adopt the Jaccard coefficient to measure the neighbor similarity between proteins and denoise the PPI network. Then, we propose a new strategy, integrating hierarchical compressing with network embedding, to capture the high-order structural similarity between proteins. Further, a new co-core attachment strategy is proposed to detect overlapping protein complexes from multi-metrics. The experimental results show that our proposed method, Dopcc, outperforms the other eight state-of-the-art methods in terms of F-measure, MMR, and Composite Score on two yeast datasets.

关键词： Proteins Measurement Vectors Noise clustering algorithms Reliability Principal component analysis Protein complex protein-protein interaction network core attachment node embedding

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Handling the Class Imbalance Problem With an Improved Sine Cosine Algorithm for Optimal Instance Selection

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IEEE ACCESS 2024年 12卷 87131-87151页

作者： Moorthy, Rajalakshmi Shenbaga Selvaraj, Arikumar K. Prathiba, Sahaya Beni Yenduri, Gokul Mohanty, Sachi Nandan Ramesh, Janjhyam Venkata Naga Sri Ramachandra Inst Higher Educ & Res Fac Engn & Technol Chennai 600116 India SRM Inst Sci Technol SRMIST Coll Engn & Technol Dept Data Sci & Business Syst Kattankulathur 603203 India Vellore Inst Technol Ctr Cyber Phys Syst Sch Comp Sci & Engn Chennai 600127 India VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravati 522237 Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vijayawada 522302 Andhra Pradesh India

Class imbalance is a significant study problem that is biased, exhibiting excellent performance toward the majority classes in the dataset while showing inferior performance toward minority classes. When dealing with real-world issues, this kind of biased nature affects classification accuracy. The Improved Binary Sine Cosine Algorithm (IBSCA) has been used in this work to identify a subset of the majority class in the best possible way. The proposed IBSCA makes some enhancements over the conventional Binary Sine Cosine Algorithm (BSCA) to address the issue of premature convergence with local optimal solutions. To improve classification accuracy for unbalanced datasets, the proposed IBSCA seeks to identify the optimal collection of instances from the majority class. The advised IBSCA makes use of the alpha agent, beta agent, and random agent's location, which tends to devote considerable time to exploration to find the best possible set of instances. By using the geometric mean (G-mean) and F-score to describe the fitness function, the proposed IBSCA aims to solve the multi-objective optimization issue. On 18 datasets with different imbalance ratios taken from the KEEL repository, experimentation is conducted. Comparisons are made between the suggested IBSCA and the traditional Binary Sine Cosine Algorithm, Binary Particle Swarm Optimization (BPSO), and Binary Grey Wolf Optimization (BGWO). Additionally, the performance of the suggested IBSCA is evaluated against the top outcomes from different research papers. Metrics like sensitivity, F-score, G-mean, and area under curve (AUC) show that the suggested IBSCA outperforms the state-of-the-art algorithms. The statistical findings using the Wilcoxon signed rank test and Friedman test also demonstrate that the suggested IBSCA is more efficient than the other conventional algorithms.

关键词： Classification algorithms Metaheuristics Convergence Power capacitors clustering algorithms Machine learning algorithms Heuristic algorithms Class imbalance instance selection K-nearest neighbor metaheuristic algorithm sine cosine algorithm

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A Novel Algorithm for Efficiently Mining Spatial Multi-Level Co-Location Patterns

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第9期36卷 4361-4374页

作者： Li, Junyi Wang, Lizhen Yang, Peizhong Zhou, Lihua Yunnan Univ Sch Informat Sci & Engn Kunming 650091 Peoples R China Yunnan Univ Sch Informat Sci & Engn Kunming 650091 Peoples R China Yunnan Univ Dianchi Coll Kunming 650091 Peoples R China

The spatial co-location pattern is a collection of spatial features in which instances of features prevalently appear in neighboring spatial regions. Due to the heterogeneity of spatial data distribution, the instances of some patterns appear prevalently in the global region (i.e., Global Prevalent Co-location Patterns, GPCPs), while some patterns are not prevalent globally, and their instances are clustered only in some local regions (i.e., Local Prevalent Co-location Patterns, LPCPs). Multi-level co-location pattern mining aims to mine these two types of patterns simultaneously, but existing methods cannot accurately judge the spatial distribution of patterns in a certain region, leading to unsuitable judgment of both GPCPs and LPCPs. To overcome this problem, this paper firstly proposes the relative distribution coefficient to identify the spatial distribution form of patterns, and provides a more refined way for discovering both GPCPs and LPCPs. Secondly, a novel multi-level co-location pattern mining algorithm is proposed by using the relative distribution coefficient as the interest metrics, and some pruning strategies are suggested to improve the mining efficiency. Finally, extensive experiments are conducted on both real and synthetic datasets to verify the effectiveness and efficiency of the proposed method.

关键词： Data mining Spatial databases Graphical models Distribution functions Particle measurements Atmospheric measurements clustering algorithms Spatial data mining multi-level co-location pattern spatial distribution form relative distribution coefficient

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Management Closed Control Loop Automation for Improved RAN Slice Performance by Subslicing

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IEEE ACCESS 2024年 12卷 103082-103095页

作者： Kulmar, Marika Mahtab Alam, Muhammad Muursepp, Ivo Tallinn Univ Technol Thomas Johann Seebeck Dept Elect Tallinn 19086 Estonia

Network slicing applied to a radio access network (RAN) offers the potential to enhance the satisfaction of service requirements and optimize resource utilization, particularly in scenarios where radio resources are limited. The dependence of RAN slice performance on the allocated bandwidth part (BWP) size has not been investigated. In this paper, we use the acquired knowledge of RAN slice performance dependence on the number of allocated resource blocks (RB) for automatic subslicing to improve slice performance on a fixed BWP size. Subslicing means that the slice user equipments (UE) are clustered, slice BWP is subpartitioned, and slice RBs are allocated to the UE groups. Based on six key performance indicators (KPI), it is decided for each subslice whether it should be split, merged, or not changed for its performance improvement. Performance improvement means that the slice utilization and block error ratio (BLER) are reduced and goodput (application-level throughput) per one resource block (RB) is increased. The monitor-analyze-plan-execute-knowledge (MAPE-K)-type management closed control loop (MCCL) was implemented for slice performance improvement by subslicing. A realistic 5G new radio (NR) band serving a set of UEs of different values of their BLER and requested rates was a setup of the RAN slice simulated using MATLAB R2021b. By automatic subslicing, the slice utilization can be reduced by up to 7%, goodput per one RB by up to 38%, and slice BLER by up to 60%. This effect was greater for the uplink. The best slice performance improvement is achieved by automatic subslicing if the UEs are clustered by their achieved BLER.

关键词： Monitoring Optimization Bandwidth Prediction algorithms Radio access networks Databases clustering algorithms Performance evaluation Telecommunication network performance 5G mobile communication Closed loop systems Simulation telecommunication network performance network slicing closed loop systems systems simulations

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Resource Management for Active RIS Aided Multi-Cluster SWIPT Cooperative NOMA Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第4期21卷 4421-4434页

作者： Zhai, Qi Dong, Limeng Liu, Chenxi Li, Yong Cheng, Wei Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China

Active reconfigurable intelligent surface (RIS) has attracted a lot of attention due to its ability to drastically change the communication environment by adjusting the phase shift and amplifying the amplitude of signals. In this paper, we consider to apply the active RIS to enhance the performance of the multi-cluster cooperative non-orthogonal multiple access (CNOMA) system. Specifically, in terms of the power consumption, we first formulate a transmit power minimization problem by jointly optimizing the beamforming at the base station, power splitting ratio at cluster heads, power allocation in each cluster, and RIS matrices in direct transmission and cooperative transmission phases. Then, to improve the fair energy efficiency (EE), we solve a minimum EE maximization problem. To tackle the coupling of optimization variables, we propose the block coordinates descent (BCD) based algorithms. By applying the successive convex approximation (SCA), semi-definite relaxation (SDR), arithmetic-geometric mean (AGM) inequality, Schur complement, and convex upper bound substitution methods, the developed approaches are guaranteed to converge to local optimal solutions. Simulations results demonstrate that the proposed algorithms outperform the baseline schemes under passive RIS aided case, non-cooperation case, and no-RIS case in terms of power consumption and energy efficiency. It is also revealed that active RIS is not always superior to passive RIS schemes with a large number of RIS elements in terms of the system power consumption.

关键词： Reconfigurable intelligent surfaces Resource management Power demand NOMA Array signal processing Energy efficiency clustering algorithms active RIS cooperative transmission SWIPT

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Sparse Count Data clustering Using an Exponential Approximation to Generalized Dirichlet Multinomial Distributions

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第1期33卷 89-102页

作者： Zamzami, Nuha Bouguila, Nizar Concordia Univ Concordia Inst Informat & Syst Engn CIISE Montreal PQ H3G 1T7 Canada Univ Jeddah Dept Comp Sci & Artificial Intelligence Jeddah 23218 Saudi Arabia

clustering frequency vectors is a challenging task on large data sets considering its high dimensionality and sparsity nature. Generalized Dirichlet multinomial (GDM) distribution is a competitive generative model for count data in terms of accuracy, yet its parameters estimation process is slow. The exponential-family approximation of the multivariate Polya distribution has shown to be efficient to train and cluster data directly, without dimensionality reduction. In this article, we derive an exponential-family approximation to the GDM distributions, and we call it (EGDM). A mixture model is developed based on the new member of the exponential-family of distributions, and its parameters are learned through the deterministic annealing expectation-maximization (DAEM) approach as a new clustering algorithm for count data. Moreover, we propose to estimate the optimal number of EGDM mixture components based on the minimum message length (MML) criterion. We have conducted a set of empirical experiments, concerning text, image, and video clustering, to evaluate the proposed approach performance. Results show that the new model attains a superior performance, and it is considerably faster than the corresponding method for GDM distributions.

关键词： Computational modeling Data models Visualization Approximation algorithms clustering algorithms Mixture models Count data deterministic annealing expectation-maximization (DAEM) exponential family finite mixture models generalized Dirichlet (GD) model selection

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SPCM: Efficient semi-possibilistic c-means clustering algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期43卷 7227-7241页

作者： Mahfouz, Mohamed A. MSA Univ Fac Comp Sci 6th Of October Egypt

The required division and exponentiation operations needed per iteration for the possibilistic c-means (PCM) clustering algorithm complicate its implementation, especially on homomorphically-encrypted data. This paper presents a novel efficient soft clustering algorithm based on the possibilistic paradigm, termed SPCM. It aims at easing future applications of PCM to encrypted data. It reduces the required exponentiation and division operations at each iteration by restricting the membership values to an ordered set of discrete values in [0,1], resulting in a better performance in terms of runtime and several other performance indices. At each iteration, distances to the new clusters' centers are determined, then the distances are compared to the initially computed and dynamically updated range of values, that divide the entire range of distances associated with each cluster center into intervals (bins), to assign appropriate soft memberships to objects. The required number of comparisons is O(log the number of discretization levels). Thus, the computation of centers and memberships is greatly simplified during execution. Also, the use of discrete values for memberships allows soft modification (increment or decrement) of the soft memberships of identified outliers and core objects instead of rough modification (setting to zero or one) in related algorithms. Experimental results on synthetic and standard test data sets verified the efficiency and effectiveness of the proposed algorithm. The average percent of the achieved reduction in runtime is 35% and the average percent of the achieved increase in v-measure, adjusted mutual information, and adjusted rand index is 6% on five datasets compared to PCM. The larger the dataset, the higher the reduction in runtime. Also, SPCM achieved a comparable performance with less computational complexity compared to variants of related algorithms.

关键词： clustering algorithms fuzzy clustering possibilistic c-means hybrid soft clustering homomorphic encryption

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Unleashing Artificial Intelligence: Monitoring and Diagnosing Large Hydrogenerators

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IEEE POWER & ENERGY MAGAZINE 2024年第6期22卷 90-99页

作者： Bechara, Helene Ibrahim, Rony Tahan, Antoine Zemouri, Ryad Merkhouf, Arezki Kedjar, Bachir Al-Haddad, Kamal Ecole Technol Super Montreal PQ H3C 1K3 Canada Ctr Rech Hydroquebec Varennes PQ H3C 1K3 Canada Ctr Rech Hydroquebec Varennes PQ J3X 1S1 Canada

Large hydrogenerators play a critical role in the generation of clean and sustainable electricity from hydropower sources. To ensure optimal performance and availability and to prevent unexpected failures, meticulous monitoring is essential. This article emphasizes the advantages offered by artificial intelligence (AI) techniques in the monitoring and diagnosis of hydrogenerators, benefits that traditional methods lack. Also explored is the application of AI techniques, presenting unprecedented opportunities to enhance the reliability, efficiency, and life span of these generators. Additionally, the article presents two case studies that analyze different types of signals-stray flux and vibration measurements-using an AI-based technique [called the variational autoencoder (VAE)]. It demonstrates the effectiveness of the AI-based algorithm in clustering signals in a 2D space, based on fault severity, while also highlighting the algorithm's superiority over a conventional method.

关键词： Vibrations Time-frequency analysis clustering algorithms Vibration measurement Maintenance engineering Planning Pattern recognition Artificial intelligence Time-domain analysis Monitoring Hydroelectric power generation Sustainable development

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A Region-Based Collaborative Management Scheme for Dynamic clustering in Green VANET

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2022年第3期6卷 1276-1287页

作者： Liu, Bingyi Fang, Zhipeng Wang, Wei Shao, Xun Wei, Wei Jia, Dongyao Wang, Enshu Xiong, Shengwu Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 510275 Peoples R China Kitami Inst Technol Sch Engn Kitami Hokkaido 0908507 Japan Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Univ Queensland Sch Civil Engn Brisbane Qld 4072 Australia Univ Buffalo State Univ New York Buffalo NY 14260 USA

Green Vehicular Ad-hoc Network (VANET) is a newly-emerged research area that focuses on reducing harmful impacts of vehicular communication equipment on the natural environment. Recent studies have shown that grouping vehicles into clusters for green communications in VANETs can significantly improve networking efficiency and reduce infrastructure costs. As a dynamic network system, maintaining the network connectivity and reducing the communication overlap are two critical challenges for green VANET clustering. However, most existing work studies connectivity and overlap separately, lacking a deep understanding of the relationship between them. To address this issue, we present a comprehensive analysis that jointly considers the two critical factors in one model. Specifically, we first design a state resemblance prediction (SRP) model based on the historical trajectory feature relevance between vehicles;Combined with the SRP model, we propose the region-based collaborative management scheme (RCMS) to establish the dynamic clustering;Lastly, we take extensive experiments to verify the region-based collaborative management scheme for dynamic clustering. The results demonstrate that the proposed clustering algorithm can achieve high networking efficiency and better communication stability.

关键词： clustering algorithms Vehicle dynamics Heuristic algorithms Green products Collaboration Prediction algorithms Vehicular ad hoc networks VANETs green communication network connectivity communication overlap dynamic clustering

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An Automatic Cascaded Movement Approach to Solve the Energy Replenishment Problem in WPT-Based Mobile WRSNs

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第3期21卷 2763-2778页

作者： Cheng, Chien-Fu Wang, Chen-Chuan Chang, Hsueh-Yu Natl Taiwan Ocean Univ Dept Comp Sci & Engn Keelung 202301 Taiwan Int Integrated Syst Inc New Taipei 22041 Taiwan

In Wireless Rechargeable Sensor Networks (WRSNs), prolonging the sensors' lifetime is a crucial issue that requires attention. Most studies in the field have focused on path planning for mobile chargers to replenish energy supply for static sensors. Different from the most existing studies, this paper explores the energy replenishment problem in WRSNs with mobile sensors. The paper addresses two issues to extend the network's lifetime: 1) how to balance energy consumption between mobile sensors using their mobility capabilities;and 2) how to dispatch redundant mobile sensors, charged and calibrated, to replace low-energy sensors. For the first issue, an energy balancing algorithm is proposed that uses cascaded movement to better the cascading schedule. For the second issue, a redundant mobile sensor dispatch algorithm is proposed that prioritizes mobile sensors most in need of energy replenishment for replacement by a charged and calibrated redundant mobile sensor. The proposed algorithms are extensively evaluated through simulation and have demonstrated their effectiveness in achieving energy balance and prolonging the network lifetime. Note to Practitioners-Industrial automation applications often involve harsh and hazardous environments with high temperatures, dirt, dust, corrosive materials, and chemical risks. To ensure the longevity of sensors in such conditions, in addition to considering charging, sensors must also be regularly maintained and calibrated for accurate data sensing. However, maintaining and calibrating sensors requires the use of precise instruments that cannot be handled by mobile chargers. To address this issue, we propose studying how mobile sensors can cooperate with each other to complete monitoring tasks and return to the base station for charging, maintenance, and calibration before they run out of energy. The proposed algorithms are applicable to real-life precision monitoring applications in harsh or hazardous environments. This paper

关键词： Robot sensing systems Schedules Wireless sensor networks Charging stations clustering algorithms Wireless communication Maintenance engineering Charging mobile sensors wireless power transfer station wireless rechargeable sensor networks

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