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Spotted Hyena Optimizer Driven Deep Learning-Based Drug-Drug Interaction Prediction in Big Data Environment

computer Systems science & engineering 2023年第9期46卷 3831-3845页

作者： Mohammed Jasim Mohammed Jasim Shakir Fattah Kak Zainab Salih Ageed Subhi R.M.Zeebaree Engineering College Al-Kitab UniversityKirkukIraq Information Technology Department Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Engineering Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Nowadays,smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature,computational approaches,and discoveries,owing to which a massive quantity of experimental datasets was published and generated(Big Data)for describing and validating such ***-drug interaction(DDI)significantly contributed to drug administration and *** continues as the main obstacle in offering inexpensive and safe *** normally happens for patients with extensive medication,leading them to take many drugs *** may cause side effects,either mild or severe health *** reduced victims’quality of life and increased hospital healthcare expenses by increasing their recovery *** efforts were made to formulate new methods for DDI prediction to overcome this *** this aspect,this study designs a new Spotted Hyena Optimizer Driven Deep Learning based Drug-Drug Interaction Prediction(SHODL-DDIP)model in a big data *** the presented SHODL-DDIP technique,the relativity and characteristics of the drugs can be identified from different sources for *** input data is preprocessed at the primary level to improve its ***,the salp swarm optimization algorithm(SSO)is used to select *** this study,the deep belief network(DBN)model is exploited to predict the DDI *** SHO algorithm is involved in improvising the DBN model’s predictive outcomes,showing the novelty of the *** experimental result analysis of the SHODL-DDIP technique is tested using drug databases,and the results signified the improvements of the SHODLDDIP technique over other recent models in terms of different performance measures.

关键词： Drug-drug interaction deep learning spotted hyena optimization feature selection classification

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Target Detection and Tracking of Survey Robots Using Multimodal Internet of Things Sensor data Fusion Using YOLOv5

Target Detection and Tracking of Survey Robots Using Multimo...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Chengjing Ye Miaoyong Wu Yefen Yang Ke Zhang Xinhua Bu Miaomiao Huang Faculty of Robotics Guangdong Polytechnic of Science and Technology Zhuhai Guangdong China Guangdong Polytechnic of Science and Technology and & Computer Engineering Technical College Zhuhai Guangdong China Guangdong Polytechnic of Science and Technology Faculty of Architectural Engineering Zhuhai Guangdong China

In recent years, due to the rapid development of computer vision technology, real-time detection and tracking of objects using computer vision has become a hot topic. Object detection mainly consists of two parts: object segmentation and object recognition, that is, object acquisition. Accurate detection of targets is the key to achieving high-precision and high-efficiency tracking. Based on the above discussion, this article explored the target detection and tracking of survey robots using the multimodal Internet of Things (IoT) sensor data fusion of YOLOv5, and compared and analyzed the performance of direct frame difference method, traditional ORB method, and the algorithm in this paper through experiments. The results indicated that the accuracy and recall of the robot image object detection algorithm proposed in this paper were both above 90%, and higher than other algorithms, indicating that the algorithm has extremely high accuracy in object detection.

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Vibration analysis of elastically supported sandwich beam with damaged core and functionally graded face sheets

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Steel and Composite Structures 2025年第1期54卷 71-83页

作者： Wang, Lulu Mohsin, Hasan F. Bains, Pardeep Singh Sharma, Rohit Bohlooli, Nasrin College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan523083 China Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad10011 Iraq Computer Technical Engineering Department College of Technical Engineering The Islamic University Najaf54001 Iraq University Karnataka Bengaluru560069 India Department of Mechanical Engineering Vivekananda Global University Rajasthan Jaipur303012 India School of Engineering and Technology Shobhit University Uttar Pradesh Gangoh247341 India Department of Mechanical Engineering Arka Jain University Jharkhand Jamshedpur831001 India Nabi Data Science & Computational Intelligence Research Co. Tehran Iran

This paper presents the influence of carbon nanotubes (CNTs) waviness, aspect ratio and damaged core on the vibrational behavior of functionally graded nanocomposite sandwich beams resting on two-parameter elastic foundations. The distributions of CNTs are considered functionally graded (FG) or uniform along the thickness of upper and bottom layers of the sandwich beam and their mechanical properties are estimated by an extended rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes’ effective aspect ratio as the filler content increases. A damage model is introduced to provide an analytical description of an irreversible rheological process that causes the decay of the mechanical properties, in terms of engineering constants. An isotropic damage is considered for the core of the sandwich beams. The equations of motion are derived based on Timoshenko beam theory and employing Hamilton’s principle. The problem is modeled using a semi-analytical approach composed of generalized differential quadrature method (GDQM) and series solution adopted to solve the equations of motion. Detailed parametric studies are carried out to investigate the mechanical behavior of these multi-layered structures depending on the damage features, through-the-thickness distribution and boundary conditions. Also, the effects of carbon nanotubes (CNTs) waviness, CNT aspect ratio, Winkler foundation modulus, shear elastic foundation modulus and geometrical conditions on the vibrational behavior of the sandwich structure are studied. © 2025 Techno-Press, Ltd.

关键词： Aspect ratio

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Analysis of E-commerce Review Text Based on Capsule Network 4

Analysis of E-commerce Review Text Based on Capsule Network

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4th International Conference on Frontiers technology of Information and computer, ICFTIC 2022

作者： Shao, Hui Computer Engineering Technology College Artificial Intelligence College Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (纸本)9798350321951

For traditional models such as CNN, BIGRU, lacks the representation of sentence system features and lacks the ability to extract deep features and other problems. This paper took the e-commerce Chinese reviews as the research objects, in the BERT word vector and attention mechanism, an sentiment analysis is proposed based on the capsule network model. First, the model uses BERT to obtain a vector, then uses CNN to extract deep-level phrase features, then uses BIGRU to extract global features deep-learning phrase system features to obtain the features of the sentence system;and then uses the increase of the attention layer to filter the effective features. Finally, the model uses a richer expression of text sentiment information to improve the overall abstraction ability of the model. After comparing experiments on the dataset, this method is better than the control group, indicating that the proposed model can effectively improve the accuracy of text classification. © 2022 IEEE.

关键词： Sentiment analysis

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A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

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computer Modeling in engineering & sciences 2023年第5期135卷 1209-1228页

作者： Chuan Wang Ruoyu Zhu Yi Jiang Weili Liu Sang-Woon Jeon Lin Sun Hua Wang College of Software Henan Normal UniversityXinxiang453007China College of Computer and Information Engineering Henan Normal UniversityXinxiang453007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou510665China Department of Military Information Engineering Hanyang UniversityAnsan15588South Korea College of Engineering and Science Institute for Sustainable Industries and Liveable CitiesVictoria UniversityMelbourneVIC 8001Australia

The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to *** paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP *** work is novel and contributes to three aspects:problemmodel,optimization framework,and ***,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over ***,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic *** framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous *** way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme *** the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational ***,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search *** evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic *** experimental results show that ACO with 2-opt can solve the DTSPs effectively.

关键词： Dynamic traveling salesman problem(DTSP) offline optimization and online application ant colony optimization(ACO) two-optimization(2-opt)strategy

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A Visualization Data Analysis of Second-hand Houses in Zhuhai Based on Python and ECharts and Bootstrap 22

A Visualization Data Analysis of Second-hand Houses in Zhuha...

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Proceedings of the 2022 International Conference on Big Data, IoT, and Cloud Computing

作者： Qiao Lu Fan Xiang Ban Duan Jie Gao Guangdong Polytechnic of Science and Technology Computer Engineering and Technical College China

ISBN: (纸本)9781450399548

Abstract. In the era of huge data, because of the information explosion, it becomes extremely difficult for users to completely understand the source data information of second-hand houses. It is particularly necessary to use large data analysis technology and data visualization technology to meet the needs of buyers and renters. In view of the complicated information system of second-hand housing, buyers and renters need to obtain objective and comprehensive second-hand housing data in a short time, a visual analysis system for second-hand housing data is developed. Selenium+XPath crawler is used to climb the second-hand house data of Zhuhai in Shell second-hand housing network. Then, the data is stored into corresponding CSV files. The CSV files are converted into JSON files through the conversion website. Finally, the visual interface is built by Bootstrap and is displayed with the visual analysis chart of ECharts. Users can directly click the menu on the web-page, a detailed understanding of the second-hand housing source intuitive and appropriate visualization chart data, so as to better make the right decision to buy or rent. The system has a relatively powerful function, to meet the real needs of buyers and renters.

关键词： ECharts, Bootstrap, Selenium, second-hand house Python data visualization, big data analysis, crawler

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Research on named entity identification method based on medical field 3

Research on named entity identification method based on medi...

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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Zhou, Runnan Li, Mingxuan Ruan, Peiguang Tong, Wang Gong, Bing Lv, Wei Zhuhai College of Science and Technology Alibaba Cloud Big Data Application College Guangdong Zhuhai519000 China Beijing LongChuangYueDong Network Technology Co. Ltd Beijing100000 China Guangdong Polytechnic of Science and Technology Computer Engineering Technology College Guangdong Zhuhai519000 China

ISBN: (纸本)9781510663473

With the development of Internet technology, more and more scholars are applying computer technology to research in the medical field. In this paper, we will investigate the named entity recognition method. Under the study of BERT-CRF model, we propose a named entity recognition model based on multi-task pre-training model with adversarial learning and network sharing and apply it to entity recognition in medical field with the aim of improving the accuracy of entity recognition in medical field. The model introduces multi-task joint learning and adversarial learning modules to improve the entity boundary effect and solve the noise problem of word boundary information, while achieving the purpose of information enhancement. On the CMeEE (Chinese Medical Entity Extraction) dataset, the model showed a significant improvement in accuracy, recall, and F1 score. © 2023 SPIE.

关键词： Learning systems

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Intelligent Line Loss Analysis of New Energy Connected to the Power Grid 5

Intelligent Line Loss Analysis of New Energy Connected to th...

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5th International Academic Exchange Conference on science and technology Innovation, IAECST 2023

作者： Tang, Yifang Hou, Ruiyun Zhong, Dafu Xu, Yingbo Guangdong Polytechinic of Science and Technology Computer Engineering Technical College Zhuhai China Zhuhai Yineng Electronic Technology Co. Ltd Zhuhai China

ISBN: (纸本)9798350357738

Under the background of rapid development of new energy, it is imperative to study its impact on power grid operation. This study analyzed the mechanism of line loss changes caused by the access of distributed photovoltaic new energy through case studies, collected operational data using IoT devices, and established an equivalent resistance power line loss prediction model. The model achieved high-precision predictions. Based on the prediction results, a line loss analysis optimization strategy was proposed to ensure the reliability and economic rationality of power supply. Case studies showed that this strategy can effectively reduce line loss. This research provides predictive and control means for large-scale integration of new energy into the power grid, with significant engineering significance. © 2023 IEEE.

关键词： Reliability analysis

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An adaptive and Sparse Decomposition for Analytic Signals in Hardy-2 Space

An adaptive and Sparse Decomposition for Analytic Signals in...

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Electronic Information engineering and computer science (EIECS), 2021 International Conference on

作者： Shuang Li Jianhong Xu Ying Li Kang Wu Computer Engineering Technical College Guangdong Polytechnic of Science and Technology Zhuhai China

In this paper, an adaptive algorithm is proposed to decompose analytic signals of Hardy-2 space in a sparse way. The limitation of previous related studies such as AFD and Matching Pursuit always find local optimal solutions by greedy-flavor iteration. These solutions also rely on initial values. Our new algorithm utilizes l-1 minimization or Basis Pursuit strategy with a redundant dictionary consists of reproducing kernels. We prove that the design matrix has singular values which tend to zero rapidly given that the parameters are selected in some special meaner. These theoretical results lay the foundation for sparse representations of analytic signals. Two numerical examples are discussed to illustrate the algorithm.

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