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Article
Automation & Control Systems
Zhongwen Cao et al.
Summary: This paper investigates event-triggered tracking control for switched networked nonlinear systems with asymmetric time-varying output constraints. An output-dependent generic constraint function is constructed to handle the output constraints, and an event-triggering rule is designed to reduce communication frequency. An adaptive control method is proposed based on the common Lyapunov function method and event-triggered control strategy, which guarantees bounded closed-loop signals and avoids Zeno behavior. The proposed scheme relaxes constraint boundaries and addresses both cases with and without output constraints simultaneously. The stability of the system is guaranteed by the small-gain technique. Two simulation examples demonstrate the effectiveness of the proposed scheme.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Mingqiang Wang et al.
Summary: There is a significant gap between academic research and practical application in power distribution system planning (PDSP). Existing PDSP models in academic research mainly focus on cost as the objective function and have constraints such as power flow equality, voltage limits, and capacity limits. However, these models are rarely used in real distribution system companies. This paper proposes a new feeder planning model for urban distribution networks, considering practical requirements such as load moment, block loads, street layout, network configuration, and the crossing requirement of feeders. The model is solved using mixed integer linear programming and is demonstrated on test and real distribution systems.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Computer Science, Information Systems
Fabin Cheng et al.
Summary: This paper focuses on the secure control design problem for nonlinear multi-agent systems under denial-of-service (DoS) attacks. An anti-attack control method is proposed to achieve the secure control goal in an insecure environment. Additionally, a novel distributed adaptive self-triggered control mechanism is introduced to save communication resources and balance system performance under DoS attacks.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Yan Cheng et al.
Summary: This article proposes an event-based adaptive decentralised output feedback control scheme for interconnected systems with Bouc-Wen hysteresis and unmeasured system states. A novel dynamic threshold adjustable event-triggering mechanism is proposed to enhance communication efficiency. Neural networks-based observer and dynamic surface control method are used to address the problems of unmeasured states and complexity explosion. The Nussbaum function is introduced to eliminate the effect of unknown hysteresis, and the effectiveness of the developed control scheme is verified through simulation example.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Oncology
Xingyuan Li et al.
Summary: This paper proposes an ensemble classifier algorithm based on multilayer perceptron neural networks, which adjusts parameters using an evolutionary approach and incorporates a hybrid dimensionality reduction technique. The algorithm shows improved accuracy in breast cancer diagnosis, with an average increase of 17% compared to existing state-of-the-art methods.
JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
(2023)
Article
Automation & Control Systems
Fanghua Tang et al.
Summary: This paper investigates a dynamic event-triggered optimal control problem of discrete-time nonlinear Markov jump systems via policy iteration adaptive dynamic programming algorithms. The performance index function (PIF) defined in each subsystem is updated using an online PI algorithm, and the control policy is derived by solving the optimal PIF. Neural network techniques are used to estimate the iterative PIF and control policy. The designed dynamic event-triggered mechanism (DETM) is employed to avoid wasting additional resources. The developed control scheme guarantees system stability and convergence of all signals, as proven using the Lyapunov difference method. A simulation example is presented to demonstrate the feasibility of the control design scheme.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Amin Rezaeipanah et al.
Summary: Breast cancer is the most common type of cancer among women worldwide. Early detection and treatment are crucial for improving the survival rate. This paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm to optimize the Multi Layer Perceptron neural network. The algorithm achieves high accuracy in classifying breast cancer and compares favorably with other methods in the literature.
Article
Biochemistry & Molecular Biology
Chen Cao et al.
Summary: webTWAS is a new resource that combines comprehensive disease GWAS datasets with information on potential causal genes, allowing researchers to explore gene-disease associations. With data from 1298 high-quality GWAS summary statistics, a total of 235,064 gene-disease associations can be accessed, and custom TWAS analyses can be run on a user-friendly web server.
NUCLEIC ACIDS RESEARCH
(2022)
Article
K. Sangeetha et al.
Journal of Medical Imaging and Health Informatics
(2022)
Article
Biochemical Research Methods
Haoyu Zhang et al.
Summary: This study presents a novel model based on sequence distance matrix and support vector machine (SVM) for predicting DNA 6mA modification. The model achieved high accuracy rates and correlation coefficients on rice and mouse data, showing significant advantages over traditional machine learning methods.
CURRENT BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Saeed Talatian Azad et al.
Summary: The paper presents an intelligent ensemble classification method based on Multi-Layer Perceptron neural network for breast cancer diagnosis. This method goes through two stages of parameter optimization and ensemble classification, successfully improving classification accuracy and reducing misclassification costs.
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Elahe Nasiri et al.
Summary: This article introduces a new link prediction method, Weighted Common Neighbors (WCN), which predicts the formation of new links in multiplex networks based on various types of centrality measures. Experimental results confirm that the proposed method significantly improves the performance of link prediction when using interlayer information.
Article
Pharmacology & Pharmacy
Xueping Lei et al.
Summary: Gli1 promotes the metastasis of non-small cell lung carcinoma and is associated with poor patient survival.
ACTA PHARMACEUTICA SINICA B
(2022)
Article
Madhu Kirola et al.
Biomedical and Pharmacology Journal
(2022)
Article
Computer Science, Artificial Intelligence
Mostafa Ghobaei-Arani et al.
Summary: The rapid development of IoT applications and 5G networks has led to a significant increase in data processing needs. This paper proposes an autonomous solution for IoT service placement using the whale optimization algorithm. By monitoring QoS requirements and fog node capabilities, an efficient service placement plan is determined. Through an evolutionary mechanism, the proposed solution finds the optimal deployment plan while meeting QoS requirements.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Y. CHANG et al.
Summary: This work investigates the finite-time tracking control issue for a class of nonlinear pure-feedback system with prescribed performance and unknown hysteresis. The Nussbaum function and auxiliary virtual control function are used to solve the Bouc-Wen hysteresis with unknown parameters and direction conditions. A finite-time performance function is applied to limit the tracking error within a pre-given boundary in finite time. An adaptive tracking control scheme is designed using backstepping technique to ensure bounded closed-loop signals and convergence of the tracking error to a pro-given boundary. A simulation example is provided to demonstrate the effectiveness of the proposed control scheme.
IMA JOURNAL OF MATHEMATICAL CONTROL AND INFORMATION
(2022)
Article
Engineering, Multidisciplinary
Boyuan Xue et al.
Article
Engineering, Environmental
Jisui Tan et al.
Summary: In this study, a portable nuclear receptor-based biosensor was developed for highly sensitive analysis of a wide range of potential endocrine-disrupting chemicals (EDCs) in environmental water samples. The biosensor showed a detection limit of 5 ng/L E-2-binding activity equivalent (E-2-BAE) and 93 ng/L T3-BAE. The biosensor was used to map the estrogenic binding activities of surface waters from a rural community in the Yellow River basin in China, and a high correlation was observed when compared with the traditional yeast two-hybrid bioassay.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Rahmad Syah et al.
Summary: This paper utilized a Multi-Layer Perceptron Neural Network (MLP-NN) based on Evolutionary Algorithms (EA) to automatically classify breast cancer, and evaluated its performance using stacked generalization technique. Experimental results demonstrated the superior performance of IEC-MLP with ensemble classifiers compared to other algorithms.
INTELIGENCIA ARTIFICIAL-IBEROAMERICAL JOURNAL OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Energy & Fuels
Zhiyuan Si et al.
Summary: This paper proposes a novel method based on satellite images to accurately forecast photovoltaic power by predicting cloud movement and dynamically selecting cloud regions. By utilizing the XGBoost algorithm and considering multiple factors, the accuracy of the forecast can be improved, as demonstrated through testing the effectiveness of the method compared to other benchmarks.
Article
Biology
Mohammad M. Ghiasi et al.
Summary: This study utilizes machine learning algorithms to classify breast cancer, introducing new tools based on Random Forest and Extra Trees algorithms that can accurately distinguish between malignant and benign breast cytology. These algorithms, after going through four main stages, are able to provide the optimal classification results.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Biology
Kamal Berahmand et al.
Summary: The paper introduces a new spectral clustering method named TADWSC for identifying protein complexes in attributed networks. By combining topological structure and node features, the method improves the accuracy of protein complexes through calculating embedding vectors and the affinity matrix. The proposed method shows unexpectedly good performance compared to existing state-of-the-art methods in both real protein network datasets and synthetic networks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Green & Sustainable Science & Technology
Peng Li et al.
Summary: This article introduces a confidence interval based distributionally robust real-time economic dispatch (CI-DRED) approach, which addresses the risk associated with accommodating wind power. By developing a novel ambiguity set based on imprecise probability theory and transforming the original nonlinear dispatch problem into a determined mixed integer linear programming problem, the proposed method effectively balances operational costs and risks. Numerical results on both the IEEE 118-bus system and a real 445-bus system demonstrate the efficiency and effectiveness of the approach.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2021)
Review
Oncology
Alessandro Calabrese et al.
Summary: This review summarizes the existing literature on the use of radiomic approach in predicting axillary lymph node status in breast cancer. The quality of the studies is evaluated to be retrospective in design and there are still inconsistencies in pre-processing methods and standardization of results. The promising results in predicting lymph node status are encouraging, but there is room for improvement in study quality and reproducibility.
JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
(2021)
Article
Medical Informatics
Munder Abdulatef Al-Hashem et al.
Summary: Knowledge extraction in healthcare faces challenges such as noisy and imbalanced datasets, with machine learning algorithms like XGBoost and K-nearest neighbor performing best in diagnosing various diseases in medical datasets, as concluded from comparisons with medical experts and evaluation using performance metrics like accuracy, sensitivity, and specificity.
INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS
(2021)
Article
Pharmacology & Pharmacy
Jianglin Wang et al.
ACTA PHARMACEUTICA SINICA B
(2020)
Article
Computer Science, Artificial Intelligence
Serhat Simsek et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Divyaansh Devarriya et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Radiology, Nuclear Medicine & Medical Imaging
Shallu Sharma et al.
JOURNAL OF DIGITAL IMAGING
(2020)
Article
Computer Science, Artificial Intelligence
Moloud Abdar et al.
PATTERN RECOGNITION LETTERS
(2020)
Article
Computer Science, Information Systems
Hassan Musafer et al.
Article
Engineering, Biomedical
Lingmin Zhang et al.
ADVANCED HEALTHCARE MATERIALS
(2020)
Article
Mechanics
Arvind T. Mohan et al.
JOURNAL OF TURBULENCE
(2020)
Article
Multidisciplinary Sciences
Dina A. Ragab et al.
Article
Computer Science, Information Systems
Yuqian Li et al.
Article
Computer Science, Artificial Intelligence
Bichen Zheng et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2014)
