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Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

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computers, Materials & Continua 2022年第7期72卷 1157-1172页

作者： Ashit Kumar Dutta Jenyfal Sampson Sultan Ahmad T.Avudaiappan Kanagaraj Narayanasamy Irina V.Pustokhina Denis A.Pustokhin department of computer science and information systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India department of computer science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia department of computer science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India department of computer Applications J.J College of Arts and Science(Autonomous)Affiliated to Bharathidasan UniversityPudukkottai622422India department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia department of Logistics State University of Management109542MoscowRussia

Intelligent Transportation systems(ITS)have become a vital part in improving human lives and modern *** aims at enhancing road safety and environmental *** is a tremendous increase observed in the number of vehicles in recent years,owing to increasing *** vehicle has its own individual emission rate;however,the issue arises when the emission rate crosses a standard *** to the technological advances made in Artificial Intelligence(AI)techniques,it is easy to leverage it to develop prediction approaches so as to monitor and control air *** current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring(OSSOHDLAPM)in ITS *** proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of *** addition,hybridized Convolution Neural Network with Long Short-Term Memory(HCNN-LSTM)model is used to predict pollutant level based on the data attained earlier by the *** HCNN-LSTM model,the hyperparameters are selected and optimized using OSSO *** order to validate the performance of the proposed OSSO-HDLAPM technique,a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

关键词： Deep learning air pollution environment monitoring internet of things intelligent transportation systems oppositional learning LSTM model

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks

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science China(Life sciences) 2014年第11期57卷 1064-1071页

作者： LI Min LI Qi GANEGODA Gamage Upeksha WANG JianXin WU FangXiang PAN Yi School of information science and Engineering Central South University College of Engineering University of Saskatchewan department of computer science Georgia State University

Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease ***,it is still time-consuming and laborious to determine the real disease-causing genes by biological *** the advances of the high-throughput techniques,a large number of protein-protein interactions have been ***,to address this issue,several methods based on protein interaction network have been *** this paper,we propose a shortest path-based algorithm,named SPranker,to prioritize disease-causing genes in protein interaction *** the fact that diseases with similar phenotypes are generally caused by functionally related genes,we further propose an improved algorithm SPGOranker by integrating the semantic similarity of gene ontology(GO)*** not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into *** proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches,ICN,VS and *** experimental results show that SPranker and SPGOranker outperform ICN,VS,and RWR for the prioritization of orphan disease-causing ***,for the case study of severe combined immunodeficiency,SPranker and SPGOranker predict several novel causal genes.

关键词： disease-causing genes prioritization gene ontology protein interaction network shortest path

Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

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computers, Materials & Continua 2022年第11期73卷 2541-2554页

作者： K.Vijaya Kumar E.Laxmi Lydia Ashit Kumar Dutta Velmurugan Subbiah Parvathy Gobi Ramasamy Irina V.Pustokhina Denis A.Pustokhin department of computer science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India department of computer science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India department of computer science and information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia department of computer science Christ UniversityBangalore560029India department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia department of Logistics State University of ManagementMoscow109542Russia

Recently,big data becomes evitable due to massive increase in the generation of data in real time ***,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,*** paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data *** AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT *** AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast *** addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature ***,softmax layer is applied to perform classification *** effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

关键词： Object detection tracking convolutional neural network inception v2 image annotation

Multiple hypothesis tracking based on the Shiryayev sequential probability ratio test

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science China Earth sciences 2016年第12期59卷 86-96页

作者： Jinbin FU Jinping SUN Songtao LU Yingjing ZHANG School of Electronic and information Engineering Beihang University department of Electrical and computer Engineering Iowa State University

To date, Wald sequential probability ratio test(WSPRT) has been widely applied to track management of multiple hypothesis tracking(MHT). But in a real situation, if the false alarm spatial density is much larger than the new target spatial density, the original track score will be very close to the deletion threshold of the WSPRT. Consequently, all tracks, including target tracks, may easily be deleted, which means that the tracking performance is sensitive to the tracking environment. Meanwhile, if a target exists for a long time, its track will have a high score, which will make the track survive for a long time even after the target has disappeared. In this paper, to consider the relationship between the hypotheses of the test, we adopt the Shiryayev SPRT(SSPRT) for track management in MHT. By introducing a hypothesis transition probability, the original track score can increase faster, which solves the first problem. In addition, by setting an independent SSPRT for track deletion, the track score can decrease faster, which solves the second problem. The simulation results show that the proposed SSPRT-based MHT can achieve better tracking performance than MHT based on the WSPRT under a high false alarm spatial density.

关键词： multiple target tracking multiple hypothesis tracking Shiryayev sequential probability ratio test track management track score

Fusion tracking in color and infrared images using joint sparse representation

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science China(information sciences) 2012年第3期55卷 590-599页

作者： LIU HuaPing1,2,3 & SUN FuChun1,2,3 1department of computer science and Technology, Tsinghua university, Beijing 100084, China 2state Key Laboratory of Intelligent Technology and systems, Beijing 100084, China 3Tsinghua National Laboratory for information science and Technology, Beijing 100084, China department of computer science and Technology Tsinghua University Beijing China state Key Laboratory of Intelligent Technology and systems Beijing China Tsinghua National Laboratory for information science and Technology Beijing China

Currently sparse signal reconstruction gains considerable interest and is applied in many fields. In this paper, a similarity induced by joint sparse representation is designed to construct the likelihood function of particle filter tracker so that the color visual spectrum and thermal spectrum images can be fused for object tracking. The proposed fusion scheme performs joint sparse representation calculation on both modalities and the resultant tracking results are fused using min operation on the sparse representation coefficients. In addition, a co-learning approach is proposed to update the reference templates of both modality and enhance the tracking robustness. The proposed fusion scheme outperforms state-of-the-art approaches, and its effectiveness is verified using OTCBVS database.

关键词： joint sparse representation fusion tracking color image infrared image

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

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computers, Materials & Continua 2022年第5期71卷 3785-3801页

作者： Ashit Kumar Dutta S.Srinivasan Bobbili Prasada Rao B.Hemalatha Irina V.Pustokhina Denis A.Pustokhin Gyanendra Prasad Joshi department of computer science and information systems College of Applied SciencesAlmaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India department of Electrical and Electronics Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India department of Electronics and Communication Engineering Thirumalai Engineering CollegeKancheepuram631551India department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia department of Logistics State University of Management109542MoscowRussia department of computer science and Engineering Sejong UniversitySeoul05006Korea

In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking *** and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT *** this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted *** proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and *** proposed model involves two stage processes namely clustering and ***,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct ***,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted *** proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,*** proposed EMOQoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH,PSO-ECHS,E-OEERP,and iCSHS methods have resulted in a lesser NAN of 2,10,42,and 51 *** performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

关键词： Internet of things wireless sensor networks clustering routing metaheuristics cluster head selection QoS parameters

Optimal Policies for Quantum Markov Decision Processes

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International Journal of Automation and computing 2021年第3期18卷 410-421页

作者： Ming-Sheng Ying Yuan Feng Sheng-Gang Ying Centre for Quantum Software and information University of Technology SydneyNSW 2007Australia state Key Laboratory of computer science Institute of SoftwareChinese Academy of SciencesBeijing 100190China department of computer science and Technology Tsinghua UniversityBeijing 100084China

Markov decision process(MDP)offers a general framework for modelling sequential decision making where outcomes are *** particular,it serves as a mathematical framework for reinforcement *** paper introduces an extension of MDP,namely quantum MDP(q MDP),that can serve as a mathematical model of decision making about quantum *** develop dynamic programming algorithms for policy evaluation and finding optimal policies for q MDPs in the case of *** results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.

关键词： Quantum Markov decision processes quantum machine learning reinforcement learning dynamic programming decision making

Local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics

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science China(information sciences) 2014年第10期57卷 158-169页

作者： ZHUO ZiHan ZHAI WeiMing LI Xin LIU LingLing TANG JinTian Key Laboratory Particle & Radiation Imaging Ministry of Education Department of Engineering PhysicsTsinghua University state Key Laboratory of Intelligent Technology and systems National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University department of Medical Imaging Tianjin Medical University

Medical image segmentation is of pivotal importance in computer-aided clinical diagnosis. Many factors, including noises, bias field effect, local volume effect, as well as tissue movement may affect the medical image, thus causing blurring or uneven characteristics when forming a picture. Such quality defects will inevitably impair the gray-scale difference between adjacent tissues and lead to insufficient segmentation or even leakage during tissue or organ segmentation. In the present investigation, a local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics(LARFS) was proposed. By combining segmentation algorithm principles for traditional region growing(RG) and robust feature statistics(RFS), the location and neighborhood image information of input seed point can be comprehensively analyzed by *** show that, for different segmentation objects, under controlling the input parameter of growing factor within certain range, LARFS segmentation algorithm can adapt well to the regional geometric shape. And because the robust feature statistics is applied in the contour evolution process, LARFS algorithm is not sensitive to noises and not easily influenced by image contrast and object topology. Hence, the leakage and excessive segmentation effects are ameliorated with a smooth edge, and the accuracy can be controlled within the effective error range.

关键词： medical image adaptive segmentation region growing robust feature statistics contour evolution

Topology Discovery Sub-Layer for Integrated Terrestrial-Satellite Network Routing Schemes

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China Communications 2018年第6期15卷 42-57页

作者： Zengyin Yang Hewu Li Qian Wu Jianping Wu department of computer science and Technology Tsinghua University Beijing National Research Center for information science and Technology (BNRist) Tsinghua University Institute for Network sciences and Cyberspace Tsinghua University

With the booming development of terrestrial network, scaling terrestrial network over satellite network to build Integrated Terrestrial-Satellite Network(ITSN) and meanwhile to provide the global Internet access, has become ever more attractive. Naturally, the widely and successfully used terrestrial routing protocols are the promising protocols to integrate the terrestrial and satellite networks. However, the terrestrial routing protocols, which rely on propagating routing messages to discover New Network Topology(NNT) in the terrestrial network with rare topology changes, will suffer from overly numerous routing messages in satellite network whose topology frequently changes as satellites move. In this paper, a Topology Discovery Sub-layer for ITSN Routing Schemes(TDS-IRS) is firstly proposed to avoid the propagation of numerous routing messages by taking advantage of the movement predictability of satellite and the requirements of routing schemes to discover NNT in advance of topology change. Secondly, a Weighted Perfect Matching based Topology Discovery(WPM-TD) model is designed to conduct the NNT discovery on the ground. Thirdly, this paper builds a testbed with real network devices and meanwhile interconnect that testbed with real Internet, to validate that RS-TDS can discover NNT immediately with the less on-board overhead compared with optimized routing schemes. Finally, different network scenarios are applied to validate the WPM-TD, i.e., the core module of TDS-IRS. Extensive experiments show WPM-TD can work efficiently, avoiding the invalid NNT discovery and decreasing 20% ~ 57% of potential topology changes, which can also improve up to 47% ~ 105% of network throughput.

关键词： integrated terrestrial-satellite net-work (itsn) routing scheme topology discovery topology discovery sub-hayer