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Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

Computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.S.S.Anupama g.jose moses E.Laxmi Lydia Seifedine Kadry Sangsoon Lim Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6g)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6g enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAV networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6g *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6g *** proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct *** QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of *** performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6g unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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Computer Systems Science & Engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani g.jose moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

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Computer Systems Science & Engineering 2023年第11期47卷 2079-2095页

作者： C.S.S.Anupama Saud Yonbawi g.jose moses E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Skin cancer is one of the most dangerous *** of the high melanoma death rate,skin cancer is divided into non-melanoma and *** dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin ***,pathology and biopsy examinations are required for cancer *** studies have formulated computer-based systems for detecting skin cancer from skin lesion *** recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature ***,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)*** major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic ***,Dull razor approach-related hair removal and median filtering-based noise elimination are ***,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic ***,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for ***,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the *** simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion *** comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.

关键词： Deep learning skin cancer dermoscopic images sand cat swarm optimization machine learning

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Assessment of inconclusive left main coronary lesions: lessons from an intravascular ultrasound study

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上海医学 2007年第S1期30卷 109-109页

作者： gary S.Mintz Stéphane g.Carlier jose de Ribamar Costa Jr Koichi Sano Joanna Lui giora Weisz Issam Moussa george D.Dangas Roxana Mehran Edward M.Kreps Michael Collins gregg W.Stone Jeffrey W.moses Junbo ge Martin B.Leon Department of Cardiology Zhongshan HospitalFudan UniversityShanghai 200032China Columbia University Medical Center and Cardiovascular Research Foundation New YorkUSA

Objective Angiographic assessment of a left main coronary artery (LMCA) stenosis is often difficult and unreliable. Intravascular ultrasound (IVUS) assessment of absolute lumen dimensions has been shown to correlate with fractional flow reserve (FFR) and to predict clinical outcome in patients with a LMCA stenosis. Methods During 21 months period (October, 2004 to July, 2006), 153 patients (Ostial lesions, n=47; Non-ostial lesion, n=106) underwent IVUS evaluation specifically to assess the severity of an angiographically inconclusive LMCA narrowing. IVUS analysis included plaque morphology; external elastic membrane (EEM), lumen, plaque cross-sectional areas (CSA), plaque burden (plaque CSA/ EEM) and remodeling index (lesion EEM CSA/reference EEM CSA). Results Overall, minimum lumen area (MLA) and diameter (MLD) and plaque burden measured 8.2 mm2, 2.6 mm, and 59.3 %, respectively. An MLAger MLA and a smaller plaque burden than non-ostial lesions (Table). Conclusions Patients referred for LMCA evaluation commonly have insignificant narrowing. Negative remodeling was prominent at the LMCA ostium. These patients deserve IVUS assessment before revascularization.

关键词： intravascular plaque remodeling stenosis burden severity lumen absolute dimensions correlate

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Intravascular ultrasound assessment of the incidence and predictors of edge dissections and intramural hematomas after drug-eluting stent implantation

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上海医学 2007年第S1期30卷 32-32页

作者： gary S.Mintz Stéphane g.Carlier jose de Ribamar Costa Jr Koichi Sano Joanna Lui giora Weisz Issam Moussa george D.Dangas Roxana Mehran Edward M.Kreps Michael Collins gregg W.Stone Jeffrey W.moses gE Junbo Martin B.Leon Department of Cardiology Zhongshan HospitalFudan UniversityShanghai 200032China Columbia University Medical Center and Cardiovascular Research Foundation New York USA.

Objective We used intravascular ultrasound (IVUS) to assess incidence, predictors, morphology, and angiographic findings of edge dissections and intramural hematomas after drug-eluting stent (DES) implantation. Methods We studied 887 patients with 1 045 non-in-stent restenosis lesions in 977 native arteries undergoing DES implantation with IVUS imaging, and compared the dissected stent end to the non-dissected stent end. Results Eighty-two dissections were detected; 51.2% (42/82) involved the proximal and 48.8% (40/82) the distal stent edge. When compared to the non-dissected stent end, residual plaque area [(8.0±4.3) mm2 vs (5.2±3.0) mm2, Pge symmetry (1.17±0.11 vs 1.14±0.08, P=0.02) were larger; plaque burden≥50% was more frequent (62% vs 17%, Pge dissection were residual plaque eccentricity (OR=1.3, Pge dissections after 9.4% of DES implantations. Residual plaque eccentricity and significant plaque burden predicted coronary stent edge dissections. Dissections in less diseased reference segments with an arc of normal vessel wall (greater plaque eccentricity) more often evolved into an intramural hematoma.

关键词： stent eluting plaque implantation burden restenosis intravascular incidence lumen dissection

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Volumetric intravascular ultrasound comparisons of drug-eluting stent thrombosis and in-stent restenosis

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上海医学 2007年第S1期30卷 207-207页

Objectives We compared intravascular ultrasound (IVUS) findings of drug-eluting stent (DES)-treated lesions that developed stent thrombosis versus in-stent restenosis (ISR) to identify underlying mechanical differences. Methods IVUS findings in 15 post-DES thrombosis patients were compared with 45 matched ISR patients who had no evidence of stent thrombosis. Results Minimum stent area [MSA, (3.7±0.8) mm2 vs (4.9±1.8) mm2, P=0.01], minimum stent diameter [(1.9±0.3) mm vs (2.3±0.4) mm, P=0.005], mean stent area [(5.2±0.8) mm2 vs (7.2±2.1) mm2, Pgnificantly smaller in the stent thrombosis group (vs the ISR group). An MSA group (vs the ISR group). The MSA site occurred more frequently in the proximal stent segment within the stent thrombosis group compared to the ISR group (60% vs 24.4%, P=0.01). There were no differences in edge dissection, stent fracture, or stent-vessel wall malapposition between the two groups. Independent predictors of stent thrombosis were diffuse stent expansion (OR=1.5, P=0.03) and proximal location of the MSA site (OR=12.7, P=0.04). Conclusion DES-treated lesions that develop thrombosis or restenosis are often underexpanded. Underexpansion appears to be more severe in DES-thrombosis lesions. Lesions with diffuse underexpansion and a proximal (vs distal) underexpanded MSA site are more predisposed to thrombus formation than ISR.

关键词： stent restenosis intravascular eluting proximal diffuse lumen thrombus dissection versus

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