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A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT

Computers, Materials & Continua 2023年第3期74卷 6535-6553页

作者： Farah Batool Abdul Rehman Dongsun Kim Assad Abbas raheel nawaz Tahir Mustafa Madni COMSATS University Islamabad IslamabadPakistan School of Computer Science and Engineering Kyungpook National UniversityDaegu41566Korea Department of Operations TechnologyEvents and Hospitality ManagementManchester Metropolitan UniversityUnited Kingdom

The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate ***,the proposed approach minimizes the network size and eliminates undesirable *** that,the proposed approach ranks each of the nodes and prioritizes them to identify an authentic ***,the proposed approach discards the nodes having a rank(α)lesser than 0.5 to reduce the network complexity.αis the variable value represents the rank of each node that varies between 0 to *** with the higher value ofαgets the higher priority and vice *** threshold valueα=0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research ***,the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired *** performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned *** results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original ***,the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original ***,the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops.

关键词： Online social network influencer search query-based approach greedy search social internet of things(siot)

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Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

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Computers, Materials & Continua 2021年第9期68卷 4087-4108页

作者： Sehar Shahzad Farooq Mustansar Fiaz Irfan Mehmood Ali Kashif Bashir raheel nawaz KyungJoong Kim Soon Ki Jung School of Computer Science and Engineering Kyungpook National UniversityDaeguKorea Faculty of Engineering&Informatics School of MediaDesign and TechnologyUniversity of BradfordBradfordBD71DPUK Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Department of Operations TechnologyEvents and Hospitality ManagementManchester Metropolitan UniversityUK Institute of Integrated Technology Gwangju Institute of Science and TechnologyKorea

Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player *** modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the *** behavior focuses on dynamic and static information gathered at the time of *** experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s *** this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral *** Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer *** learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related *** homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’*** not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.

关键词： Game player modeling experience modeling player analytics deep learning LSTM game play data Candy Crush Saga

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Adaptive Multi-Cost Routing Protocol to Enhance Lifetime for Wireless Body Area Network

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Computers, Materials & Continua 2022年第7期72卷 1089-1103页

作者： Muhammad Mateen Yaqoob Waqar Khurshid Leo Liu Syed Zulqarnain Arif Imran Ali Khan Osman Khalid raheel nawaz Department of Computer Science COMSATS University IslamabadAbbottabad Campus22060Pakistan Department of OTEHM Manchester Metropolitan UniversityManchesterUK

Wireless Body Area Network(WBAN)technologies are emerging with extensive applications in several *** is a fascinating domain of WBAN for smart monitoring of a patient’s *** important factor to consider in WBAN is a node’s *** the lifetime of nodes is critical to address many issues,such as utility and *** routing protocols have addressed the energy conservation problem but considered only a few parameters,thus affecting their ***,most of the existing schemes did not consider traffic prioritization which is critical in *** this paper,an adaptive multi-cost routing protocol is proposed with a multi-objective cost function considering minimum distance from sink,temperature of sensor nodes,priority of sensed data,and maximum residual energy on sensor *** performance of the proposed protocol is compared with the existing schemes for the parameters:network lifetime,stability period,throughput,energy consumption,and path *** is evident from the obtained results that the proposed protocol improves network lifetime and stability period by 30%and 15%,respectively,as well as outperforms the existing protocols in terms of throughput,energy consumption,and path loss.

关键词： Multi-cost routing protocol network lifetime wireless body area network adaptive routing residual energy clustering poisson distribution

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Early Detection of Diabetic Retinopathy Using Machine Intelligence throughDeep Transfer and Representational Learning

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Computers, Materials & Continua 2021年第2期66卷 1631-1645页

作者： Fouzia nawaz Muhammad Ramzan Khalid Mehmood Hikmat Ullah Khan Saleem Hayat Khan Muhammad raheel Bhutta Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan and School of Systems and TechnologyUniversity of Management and TechnologyLahore54782Pakistan Department of Computer Science COMSATS University IslamabadWah CampusIslamabadPakistan University Hospital Birmingham BirminghamUK Shifa International Hospital IslamabadPakistan Department of Computer Science and Engineering Sejong UniversitySeoulKorea

Diabetic retinopathy (DR) is a retinal disease that causes irreversible *** occurs due to the high blood sugar level of the patient, and it is clumsy tobe detected at an early stage as no early symptoms appear at the initial level. To preventblindness, early detection and regular treatment are needed. Automated detectionbased on machine intelligence may assist the ophthalmologist in examining thepatients’ condition more accurately and efficiently. The purpose of this study is toproduce an automated screening system for recognition and grading of diabetic retinopathyusing machine learning through deep transfer and representational *** artificial intelligence technique used is transfer learning on the deep neural network,Inception-v4. Two configuration variants of transfer learning are applied onInception-v4: Fine-tune mode and fixed feature extractor mode. Both configurationmodes have achieved decent accuracy values, but the fine-tuning method outperformsthe fixed feature extractor configuration mode. Fine-tune configuration modehas gained 96.6% accuracy in early detection of DR and 97.7% accuracy in gradingthe disease and has outperformed the state of the art methods in the relevant literature.

关键词： Diabetic retinopathy artificial intelligence automated screening system machine learning deep neural network transfer and representational learning

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Genetic Variations and Cultivar Purity within the G. <i>hirsutum</i>Cultivars via RAPD Marker

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American Journal of Plant Sciences 2015年第2期6卷 392-404页

作者： Farah raheel Muhammad Kauser nawaz Shah Muhammad Shahid Iqbal Ijaz Rasool Noorka Zareen Sarfraz Government Postgraduate College (w) Multan Pakistan PMAS Arid Agricultural University Rawalpindi Pakistan Ayub Agriculture Research Institute Faisalabad Pakistan Department of Plant Breeding and Genetics University College of Agriculture University of Sargodha Sargodha Pakistan National Institute of Biotechnology and Genetic Engineering Faisalabad Pakistan

To assess genetic variation within a cultivar, four commercial varieties with five individual G. hirsutum genotypes and their bulk were assayed with selected forty 10-mer RAPDs per cultivar via PCR. CIM-473 cultivar showed amplification of 321 bands in total per primer with an average of 8 bands. A range of similarity i.e.;80% - 100% within the cultivar has been observed for minimum and maximum respectively. CIM-448 showed amplification of 300 bands in total per primer with an average of 7.5 bands. Maximum similarity of 95% and minimum similarity of 84% was observed within the cultivar. In CIM-1100 the total numbers of fragments amplified are 304, with the average of 7.6 bands per primer and are 12% - 20% polymorphic. Similarly, in NIAB-78 the total numbers of fragments amplified were 310, with the average of 7.7 bands per primer. The reliability of RAPD technique for finding genetic resemblances is indicated by the mentioned results. Numerous groups within 5 individual genotypes bulk among 4 cultivars of G. hirsutum L. have been through cluster analysis which was by the unweighted pair group method of arithmetic means (UPGMA). Dendrograms have been used for showing cultivar relationship.

关键词： Polymerase Chain Reaction (PCR) G. hirsutum RAPD Variation within the Cultivars

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