Template-Type: ReDIF-Article 1.0 Author-Name: Juan Guo Author-X-Name-First: Juan Author-X-Name-Last: Guo Title: Dynamic grouping method for online learning behaviour based on social network analysis Abstract: In this paper, a dynamic grouping method for online learning behaviour based on social network analysis is proposed. The Louvain community detection algorithm is utilised for social network analysis. Based on the analysis results and distributed web crawler, online learning behaviour information is mined and subjected to standardised processing, including outlier removal, noise filtering, and data segment alignment, to extract relevant features. A grouping objective function based on the XGBoost algorithm is constructed for the dynamic grouping of online learning behaviour. The objective function is solved to obtain the dynamic grouping results. Experimental results demonstrate that the proposed method achieves a minimum relative error rate of 1.3% in feature extraction, a maximum accuracy of 97.9% in grouping, and an average task completion time of 0.77 s. Journal: Int. J. of Networking and Virtual Organisations Pages: 27-43 Issue: 1 Volume: 30 Year: 2024 Keywords: social network analysis; online learning behaviour; dynamic grouping; distributed web crawler; XGBoost algorithm; objective function. File-URL: http://www.inderscience.com/link.php?id=136770 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:1:p:27-43 Template-Type: ReDIF-Article 1.0 Author-Name: Xiyang Li Author-X-Name-First: Xiyang Author-X-Name-Last: Li Author-Name: Quanzhong Yang Author-X-Name-First: Quanzhong Author-X-Name-Last: Yang Title: Evaluation of teaching effectiveness in higher education based on social networks Abstract: The evaluation of educational and teaching effectiveness is beneficial for universities to understand the current teaching situation, formulate reasonable teaching strategies, and improve teaching quality. Therefore, a social network-based evaluation method for educational and teaching effectiveness in universities is proposed. First, we analyse the role of teaching effectiveness evaluation and design the principles for constructing a teaching effectiveness evaluation index system. Then, we calculate the entropy matching degree of evaluation indicators, set consistency criteria for evaluation indicators, and use SVM algorithm to classify evaluation indicators, constructing an evaluation indicator system. Finally, we construct a social network for teaching effectiveness evaluation, determine the subgroup cohesion of evaluation indicators, construct an education and teaching effectiveness evaluation function, and obtain the evaluation results. The experimental results show that the confidence level of the evaluation results of this method is high, and the matching error of the entropy value of the evaluation indicators is low. Journal: Int. J. of Networking and Virtual Organisations Pages: 1-14 Issue: 1 Volume: 30 Year: 2024 Keywords: social network; the effectiveness of higher education and teaching; indicator system; entropy matching degree. File-URL: http://www.inderscience.com/link.php?id=136771 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:1:p:1-14 Template-Type: ReDIF-Article 1.0 Author-Name: Chang Liu Author-X-Name-First: Chang Author-X-Name-Last: Liu Author-Name: Bo An Author-X-Name-First: Bo Author-X-Name-Last: An Title: Research on online free marketing mode based on social network analysis Abstract: In order to control the cost of online free marketing mode, a recommendation model for online free marketing mode based on social network analysis is proposed in this paper. Firstly, a set of cost constraint indicators for online free marketing mode is constructed, and the homomorphic parameters are processed by multi-dimensional parameter estimation fusion. Then, principal component analysis and social network analysis are conducted for block constraints to the dynamic cost characteristics, and the cost correlation analysis of the online free marketing mode is also conducted to realise the optimal cost control solution. The simulation results show that the proposed method can reduce the cost, maximise the data traversal and minimise the computational complexity. Journal: Int. J. of Networking and Virtual Organisations Pages: 15-26 Issue: 1 Volume: 30 Year: 2024 Keywords: social network analysis; online free marketing mode; supply chain; comprehensive coordinated control; homomorphic fusion; principal component analysis. File-URL: http://www.inderscience.com/link.php?id=136772 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:1:p:15-26 Template-Type: ReDIF-Article 1.0 Author-Name: Pankaj Mishra Author-X-Name-First: Pankaj Author-X-Name-Last: Mishra Author-Name: Netra Pal Singh Author-X-Name-First: Netra Pal Author-X-Name-Last: Singh Author-Name: Ayesha Farooq Author-X-Name-First: Ayesha Author-X-Name-Last: Farooq Title: The nexus between allied policies of GST and FDI with dependent telecom policies of licensing and universal service in India Abstract: Telecom operators in India have frequently raised grievances on telecom and allied policies. This paper analyses if this is due to gaps in policy formulation, implementation or are an ex-post rent seeking. The effectiveness of stakeholder participation in foreign direct investment (FDI) and goods and services tax (GST) policy formulation was analysed using the conceptual framework from Leme and Harris-Wai (2015). The causal map of regulation from Coglianese (2012) was used to evaluate gaps in policy implementation. FDI policy formulation has limited stakeholder's participation, whereas GST adopted detailed stakeholder participation. No gap in policy implementation was found. However, contrary to literature FDI policy faced lesser ex-post issues compared to GST policy. Policy with discretionary provisions (FDI) experienced lesser ex-post grievances compared to policy with mandatory provisions (GST). Also, such an outcome was due to policymakers not addressing issues raised during the policy formulation and telecom operators adopting rent seeking behaviour. Journal: Int. J. of Networking and Virtual Organisations Pages: 152-173 Issue: 2 Volume: 30 Year: 2024 Keywords: policy formulation; stakeholder participation; rent seeking; telecommunications; India. File-URL: http://www.inderscience.com/link.php?id=137540 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:2:p:152-173 Template-Type: ReDIF-Article 1.0 Author-Name: Die Meng Author-X-Name-First: Die Author-X-Name-Last: Meng Author-Name: Beibei Ma Author-X-Name-First: Beibei Author-X-Name-Last: Ma Author-Name: Zhanlei Shang Author-X-Name-First: Zhanlei Author-X-Name-Last: Shang Title: A knowledge set recommendation method for online education in universities based on DV-TransE model and social networks Abstract: In order to improve the recommendation accuracy of existing online education knowledge sets in universities and shorten the recommendation response time, a recommendation method for online education knowledge sets in universities based on DV-TransE model and social network is proposed. This method is first based on the principle of knowledge graph, extracting descriptive features of the knowledge set, and introducing the TransE algorithm to construct the DV-TransE model of the online education knowledge set in universities. Then, based on social networks, the similarity between users is calculated, and finally, it is combined with the constructed knowledge set DV-TransE model to achieve recommendation of online education knowledge sets in universities. The experimental results show that after the application of the proposed method, its recommended response time is less than 14.5 ms, and the recommendation accuracy is as high as 95%, which is superior to the comparison method. Journal: Int. J. of Networking and Virtual Organisations Pages: 44-56 Issue: 1 Volume: 30 Year: 2024 Keywords: DV TransE model; social networks; online education; knowledge set; recommended methods. File-URL: http://www.inderscience.com/link.php?id=136773 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:1:p:44-56 Template-Type: ReDIF-Article 1.0 Author-Name: D. Manimegalai Author-X-Name-First: D. Author-X-Name-Last: Manimegalai Author-Name: S. Senthilkumar Author-X-Name-First: S. Author-X-Name-Last: Senthilkumar Title: The triggers on compulsive online shopping of jeans Abstract: This study investigates the antecedents of compulsive online shopping for jeans and identifies the compulsive shoppers of jeans. This descriptive research collects data through an online survey; the sample size is 205 - purposive sampling design used for gathering the data. The study results are 53.2% of males and 51% of females fall into compulsive shopping for jeans. Model fitness analyses through linear regression the p-value is lesser than 0.05. Therefore, the independent variables show a statistical relationship with the dependent variables and predict the dependent variable. This study helps the marketer frame the marketing strategy based on buzz factors and compulsive triggers; it can increase the online jeans sale in the future period. After the lockdown, consumer prefers online mode shopping. Journal: Int. J. of Networking and Virtual Organisations Pages: 206-219 Issue: 2 Volume: 30 Year: 2024 Keywords: compulsive online shopping; internal and external triggers; online usage; buzz. File-URL: http://www.inderscience.com/link.php?id=137541 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:2:p:206-219 Template-Type: ReDIF-Article 1.0 Author-Name: Xiaohuan Ning Author-X-Name-First: Xiaohuan Author-X-Name-Last: Ning Title: Refined push method of marketing data based on social trust network Abstract: In order to reduce the push error of marketing data and improve user satisfaction, a refined push method of marketing databased on social trust networks is proposed. Firstly, crawler technology is used to collect user online browsing data from server logs. Secondly, a social trust network graph is constructed to calculate the cognitive trust strength and interactive trust strength of users. Finally, based on the trust strength calculation results, Pearson correlation coefficient is used to calculate the user's rating similarity, and a marketing data refinement push function is constructed based on the rating similarity to complete the refinement push of marketing data. The experimental results show that compared with existing push methods, the root mean square error and average absolute error of the proposed method are significantly reduced, and user satisfaction is significantly improved, with user satisfaction basically maintained at over 90%. Journal: Int. J. of Networking and Virtual Organisations Pages: 57-69 Issue: 1 Volume: 30 Year: 2024 Keywords: social trust network; marketing data; refined push; Pearson correlation coefficient. File-URL: http://www.inderscience.com/link.php?id=136774 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:1:p:57-69 Template-Type: ReDIF-Article 1.0 Author-Name: Manjula H. Nebagiri Author-X-Name-First: Manjula H. Author-X-Name-Last: Nebagiri Author-Name: P.H. Latha Author-X-Name-First: P.H. Author-X-Name-Last: Latha Title: An efficient optimal load balancing algorithm for distributed file systems in cloud environment Abstract: Efficient operations in distributed environments can be obtained by load balancing (LB). LB has turned out to be a vital and interesting research area with respect to the cloud owing to the swift augmentation of cloud computing, and the more services together with better outcomes demand of the clients. The work has developed a framework named an efficient optimal LB (EOLB) for distributed files system to beat the challenges faced in LB. LB was done by means of the framework centred on node distribution together with task distribution. Centred upon the data aspects as well as cloud servers, say CPU in addition to memory usage, together with disk IO occupancy rate, etc., it renders task distribution. Experimental analysis exhibits that the framework attains a better response rate of 74.68 ms, and a processing time (PT) of 0.43 ms, in addition, remains to be efficient when weighed with the prevailing methods. Journal: Int. J. of Networking and Virtual Organisations Pages: 134-151 Issue: 2 Volume: 30 Year: 2024 Keywords: cloud computing; virtual environment; distributed cloud computing; load balancing; improved K-means clustering; IKMC; modified cockroach swarm optimisation; MCSO. File-URL: http://www.inderscience.com/link.php?id=137542 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:2:p:134-151 Template-Type: ReDIF-Article 1.0 Author-Name: Hao Sun Author-X-Name-First: Hao Author-X-Name-Last: Sun Title: Risk assessment method for cross-border e-commerce products based on social network analysis Abstract: In order to improve the accuracy of product risk assessment and reduce time costs, a cross-border e-commerce product risk assessment method based on social network analysis is proposed. Firstly, based on the structural model of cross-border e-commerce systems, social network analysis is used to construct user product trust and calculate user ratings for the products. Secondly, based on the scoring calculation results, a risk assessment index system is constructed for cross-border e-commerce products. Finally, the entropy weight method is used to calculate the weights of evaluation indicators, and the product risk membership degree is calculated based on the weight results to complete the grading evaluation of product risks. The experimental results indicate that compared to existing risk assessment methods, this method can improve the accuracy of product risk assessment while reducing time costs. Journal: Int. J. of Networking and Virtual Organisations Pages: 100-110 Issue: 1 Volume: 30 Year: 2024 Keywords: social network analysis method; cross border e-commerce; commodity risk assessment; entropy weight method; membership degree. File-URL: http://www.inderscience.com/link.php?id=136775 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:1:p:100-110 Template-Type: ReDIF-Article 1.0 Author-Name: Nimmagadda Srilakshmi Author-X-Name-First: Nimmagadda Author-X-Name-Last: Srilakshmi Author-Name: Naresh Kannan Author-X-Name-First: Naresh Author-X-Name-Last: Kannan Title: Architectural framework for multiplayer cooperative cloud gaming to optimise quality of service Abstract: The gaming industry is getting more attraction from cloud services providing gaming applications for cooperative multiplayer gaming. Real-time services like cloud gaming are possible by performing necessary process-intensive tasks within the cloud. In this paper, an architectural model for supporting cooperative gaming towards multiplayer is proposed to improve the quality of service in terms of bandwidth and latency compared to existing architectural models. According to this study, the time it takes to change a video has grown by 95% compared to how well ad hoc mobile cloudlets and the cloud do at sharing videos. Although P-frames, I-frames, and B-frames account for 23%, 78%, and 94% of the resource consumption, density in intra-stream P-frames is also considered. Similarly, resource and CPU use based on skewness are effective compared to mobile devices' 95% faster video sharing and 77% shorter link delays. Journal: Int. J. of Networking and Virtual Organisations Pages: 174-205 Issue: 2 Volume: 30 Year: 2024 Keywords: cloud gaming; quality of service; cooperative multiplayer gaming; social networking. File-URL: http://www.inderscience.com/link.php?id=137543 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:2:p:174-205 Template-Type: ReDIF-Article 1.0 Author-Name: Xiaodong Chen Author-X-Name-First: Xiaodong Author-X-Name-Last: Chen Title: Consumer preference mining method of online marketing platform based on social network analysis Abstract: In order to address the problems of low text extraction accuracy, mining accuracy, and effectiveness in existing consumer preference mining methods for online marketing platforms, this article uses social network analysis methods to mine consumer preferences on online marketing platforms. First is the analysis of the node density and individual centrality in consumer social networks. Then, LDA model is selected to extract consumer texts from online marketing platforms, and rough set theory is combined for reduction processing. Finally, through the bidirectional association rule mining method, the consumer preferences of online marketing platforms are mined. The experimental results show that the text extraction accuracy of the proposed method is higher than 93%, and the minimum number of association rules can be reduced to below 2 × 10<SUP align="right"><SMALL>3</SMALL></SUP> N; the highest confidence level can reach 28.3%, and the highest support level can reach 97.5%, which can effectively explore consumer preferences on online marketing platforms. Journal: Int. J. of Networking and Virtual Organisations Pages: 82-99 Issue: 1 Volume: 30 Year: 2024 Keywords: social network analysis; online marketing platform; consumers; preference mining; rough set; association rules. File-URL: http://www.inderscience.com/link.php?id=136776 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:1:p:82-99 Template-Type: ReDIF-Article 1.0 Author-Name: Bo Song Author-X-Name-First: Bo Author-X-Name-Last: Song Title: Multimodal interactive classroom teaching strategies based on social network analysis Abstract: In order to improve students' academic performance and satisfaction with teaching strategies, a multimodal interactive classroom teaching strategy based on social network analysis is proposed. Firstly, based on the principles of social network analysis methods, clustering of learners is completed by weighting the similarity of features between learners. Secondly, mining the learning text data of learners. Finally, based on the excavated learner data, a comprehensive understanding of the learner's learning status and needs can be obtained. Starting from multiple aspects such as visual, auditory, hands-on, social, and gamification, a multimodal interactive teaching strategy can be constructed. The example analysis results show that the teaching strategy studied can improve students' academic performance, with a maximum average score of 95 points. Moreover, students have a high degree of satisfaction with the research teaching measurement, with an average satisfaction rate of over 95%. Journal: Int. J. of Networking and Virtual Organisations Pages: 70-81 Issue: 1 Volume: 30 Year: 2024 Keywords: social network analysis; SNA; multimodal interactive classroom; teaching strategies; learning text data. File-URL: http://www.inderscience.com/link.php?id=136777 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:1:p:70-81 Template-Type: ReDIF-Article 1.0 Author-Name: M. Jalasri Author-X-Name-First: M. Author-X-Name-Last: Jalasri Author-Name: L. Lakshmanan Author-X-Name-First: L. Author-X-Name-Last: Lakshmanan Title: An improved data aggregation for fog computing devices in internet of things Abstract: Diverse data that have private information are stored in the cloud by institutions and users. Fog computing has progressed with regard to service latency and has also been comprehensively studied. A proposal has been made in this work to secure IoT data, a clustering algorithm that utilises backup cluster head (CH) for network performance enhancement, and also utilises a multi-route protocol for data transmission towards the fog system. Comparisons are made between the proposed algorithm and the energy efficient heterogeneous clustering algorithm (EEHCA). A novel particle swarm optimisation (PSO) and river formation dynamics (RFDs) algorithm was proposed in this work for effective CH election in wireless sensor network (WSN) to efficiently transmit data towards the base station (BS) with minimised energy. This technique bypasses local optima during the solution search, leading to significantly improved results. RFD-EEHCA outperforms EEHCA by 8.53% and PSO by 8.09% in terms. Journal: Int. J. of Networking and Virtual Organisations Pages: 114-133 Issue: 2 Volume: 30 Year: 2024 Keywords: cloud computing; internet of things; IoT; fog computing; security; wireless sensor network; WSN; routing; clustering; energy efficient heterogeneous clustering algorithm; EEHCA; particle swarm optimisation; PSO; river formation dynamics; RFDs. File-URL: http://www.inderscience.com/link.php?id=137550 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:2:p:114-133 Template-Type: ReDIF-Article 1.0 Author-Name: Vidyapati Jha Author-X-Name-First: Vidyapati Author-X-Name-Last: Jha Author-Name: Priyanka Tripathi Author-X-Name-First: Priyanka Author-X-Name-Last: Tripathi Title: Cognitively-inspired intelligent decision-making framework in cognitive IoT network Abstract: Numerous Internet of Things (IoT) applications require brain-empowered intelligence. This necessity has caused the emergence of a new area called cognitive IoT (CIoT). Reasoning, planning, and selection are typically involved in decision-making within the network bandwidth limit. Consequently, data minimisation is needed. Therefore, this research proposes a novel technique to extract conscious data from a massive dataset. First, it groups the data using k-means clustering, and the entropy is computed for each cluster. The most prominent cluster is then determined by selecting the cluster with the highest entropy. Subsequently, it transforms each cluster element into an informative element. The most informative data is chosen from the most prominent cluster that represents the whole massive data, which is further used for intelligent decision-making. The experimental evaluation is conducted on the 21.25 years of environmental dataset, revealing that the proposed method is efficient over competing approaches. Journal: Int. J. of Networking and Virtual Organisations Pages: 87-105 Issue: 2 Volume: 31 Year: 2024 Keywords: IoT; Internet of Things; CIoT; cognitive IoT; intelligent decision; data reduction. File-URL: http://www.inderscience.com/link.php?id=142239 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:2:p:87-105 Template-Type: ReDIF-Article 1.0 Author-Name: H. Manoranjan Author-X-Name-First: H. Author-X-Name-Last: Manoranjan Author-Name: V. Maheswari Author-X-Name-First: V. Author-X-Name-Last: Maheswari Title: The role of shopping apps and their impact on the online purchasing behaviour patterns of working women in Bangalore Abstract: The study aims to analyse the impact of shopping applications on the shopping behaviour of the working women community in Bangalore, a city known as the IT hub. The research uses a quantitative analysis with SPSS version 23 software and a structured questionnaire survey technique to gather data from the working women community. The study uses descriptive statistics, ANOVA, regression, and Pearson correlation analysis to evaluate the perception of working women regarding the significance of online shopping applications. The results show that digital shopping applications are more prevalent among the working women community in Bangalore. The study also evaluates the socio-economic and psychological factors that influence their purchasing behaviour. The findings suggest that online marketers should enhance their strategies to improve their business on digital platforms. The research provides valuable insights into the shopping habits of the working women community in Bangalore. Journal: Int. J. of Networking and Virtual Organisations Pages: 106-126 Issue: 2 Volume: 31 Year: 2024 Keywords: internet; shopping behaviour; digital platform; Bangalore; working women; socio-economic; psychological factors. File-URL: http://www.inderscience.com/link.php?id=142240 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:2:p:106-126 Template-Type: ReDIF-Article 1.0 Author-Name: Jingya Liu Author-X-Name-First: Jingya Author-X-Name-Last: Liu Author-Name: Zhao Du Author-X-Name-First: Zhao Author-X-Name-Last: Du Author-Name: Qiao Zhong Author-X-Name-First: Qiao Author-X-Name-Last: Zhong Author-Name: Fang Wang Author-X-Name-First: Fang Author-X-Name-Last: Wang Author-Name: Shan Wang Author-X-Name-First: Shan Author-X-Name-Last: Wang Title: Unveiling learner experience in MOOC reviews Abstract: The surge of learner enrolment in massive open online courses (MOOCs) has led to a wealth of learner-generated data, such as online course reviews that document learner experience. To unveil learner experience with MOOCs, this research uses machine learning methods to extract prominent topics from MOOC reviews and assess the sentiments expressed by learners within them. Furthermore, this research investigates the cooccurrence of the topics using association rule mining. The findings reveal six central topics discussed in MOOC reviews, such as "instructor", "design", "material", "assignment", "platform", and "experience". Notably, most learners express positive sentiments in their reviews. The sentiment indicated in reviews of skill-seeking MOOCs is higher than that in reviews of knowledge-seeking MOOCs. Furthermore, the association rule mining identifies four meaningful association rules. The findings offer valuable insights for MOOC instructors to enhance course design and for platform operators to ensure the long-term viability and success of MOOC platforms. Journal: Int. J. of Networking and Virtual Organisations Pages: 147-167 Issue: 2 Volume: 31 Year: 2024 Keywords: online learning; MOOC; massive open online course; course review; learning experience; text mining; sentiment analysis; topic cooccurrence analysis; machine learning; skill-seeking; knowledge-seeking. File-URL: http://www.inderscience.com/link.php?id=142241 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:2:p:147-167 Template-Type: ReDIF-Article 1.0 Author-Name: Chandrakant Athavale Chhaya Author-X-Name-First: Chandrakant Athavale Author-X-Name-Last: Chhaya Author-Name: K.P. Patil Author-X-Name-First: K.P. Author-X-Name-Last: Patil Title: LDSAE: LeNet deep stacked autoencoder for secure systems to mitigate the errors of jamming attacks in cognitive radio networks Abstract: A hybrid network system for mitigating errors due to jamming attacks in cognitive radio networks (CRNs) is named LeNet deep stacked autoencoder (LDSAE) and is developed. In this exploration, the sensing stage and decision-making are considered. The sensing unit is composed of four steps. First, the detected signal is forwarded to filtering progression. Here, BPF is utilised to filter the detected signal. The filtered signal is squared in the second phase. Third, signal samples are combined and jamming attacks occur by including false energy levels. Last, the attack is maliciously affecting the FC decision in the fourth step. On the other hand, FC initiated the decision-making and also recognised jamming attacks that affect the link amidst PU and SN in decision-making stage and it is accomplished by employing LDSAE-based trust model where the proposed module differentiates the malicious and selfish users. The analytic measures of LDSAE gained 79.40%, 79.90%, and 78.40%. Journal: Int. J. of Networking and Virtual Organisations Pages: 127-146 Issue: 2 Volume: 31 Year: 2024 Keywords: CRNs; cognitive radio networks; FC; fusion center; band pass filter; LeNet; DSAE; deep stacked auto encoder. File-URL: http://www.inderscience.com/link.php?id=142242 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:2:p:127-146 Template-Type: ReDIF-Article 1.0 Author-Name: Vidyapati Jha Author-X-Name-First: Vidyapati Author-X-Name-Last: Jha Author-Name: Priyanka Tripathi Author-X-Name-First: Priyanka Author-X-Name-Last: Tripathi Title: Anomalous data detection in cognitive IoT sensor network Abstract: Recent research in the internet of things (IoT) focuses on the insertion of cognition into its system architecture and design, which introduces the new discipline known as cognitive IoT (CIoT). The cognitive internet of things sensor network defines a new paradigm for bridging the gap between the virtual and the real world. Sensors integrated into the CIoT network serve as the primary data collectors. These sensors are used in hazardous or unmanaged a situation, which makes sensor readings prone to errors and abnormalities. Since sensor data are essential to the system's operation, the quality of various data-centric CIoT services will ultimately depend on the accuracy of sensor readings. However, detecting anomalies in sensor data is a complex process because CIoT sensor networks are frequently resource-constrained devices with limited computation, networking, and storage power. To fulfil the objectives, an effective and affordable cognitively-inspired detecting method is required. Therefore, this research proposed a novel technique to identify the anomaly in sensor node data. The experimental evaluation is conducted on the environmental data of 21.25 years, and detection accuracy reveals the efficacy of the proposed method over competing approaches. Journal: Int. J. of Networking and Virtual Organisations Pages: 309-328 Issue: 4 Volume: 30 Year: 2024 Keywords: anomaly; probability; sensor network; cognitive IoT; CIoT. File-URL: http://www.inderscience.com/link.php?id=140208 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:4:p:309-328 Template-Type: ReDIF-Article 1.0 Author-Name: Vasu Thirasak Author-X-Name-First: Vasu Author-X-Name-Last: Thirasak Author-Name: Nopadol Rompho Author-X-Name-First: Nopadol Author-X-Name-Last: Rompho Title: Retaining remote workers: factors that affect virtual and hybrid workers' job retention Abstract: This study examines factors from Herzberg's motivation-hygiene theory, Deci's self-determination theory, and life-course fit theory to understand their effects on virtual and hybrid workers' job retention. Data were collected from 623 respondents in Thailand, and structural equation modelling and data analysis techniques were used to test the relationships between pay, promotion, supervision, fringe benefits, life-course fit, intrinsic motivation, and extrinsic motivation and job retention for virtual and hybrid workers, as well as the moderating effects of job level and virtual intensity. The results indicate that motivator-hygiene factors - pay, promotion, supervision, and fringe benefits - do not significantly contribute to the job retention of virtual and hybrid workers. However, the relationships between life-course fit, intrinsic motivation, and extrinsic motivation and job retention were significant. This is one of the very few studies that applies these theories in the context of virtual and hybrid work, which expands the theories' boundaries of knowledge. Journal: Int. J. of Networking and Virtual Organisations Pages: 329-349 Issue: 4 Volume: 30 Year: 2024 Keywords: remote work; virtual work; hybrid work; job retention; motivators; hygiene factors; intrinsic motivation; extrinsic motivation; work motivation; job satisfaction; life-course fit. File-URL: http://www.inderscience.com/link.php?id=140215 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:4:p:329-349 Template-Type: ReDIF-Article 1.0 Author-Name: Luuk P.A. Simons Author-X-Name-First: Luuk P.A. Author-X-Name-Last: Simons Author-Name: Bas Gerritsen Author-X-Name-First: Bas Author-X-Name-Last: Gerritsen Author-Name: Bas Wielaard Author-X-Name-First: Bas Author-X-Name-Last: Wielaard Author-Name: Mark A. Neerincx Author-X-Name-First: Mark A. Author-X-Name-Last: Neerincx Title: Employee hypertension self-management support with microlearning and social learning Abstract: A majority of employees over the age of 40 have hypertension, impacting their health and performance. A two-week self-management support (SMS) intervention was tested, with daily feedback and microlearning cycles to improve health self-management competences. On average, participants (<i>n</i> = 8) reduced their blood pressure from 145/92 mmHg to 126/86 mmHg. User evaluation confirmed the importance of core SMS aspects: information transfer, daily monitoring, enhancing problem solving/decision making, self-treatment using a tailored action plan, coping skills, and skilful coach follow-up. Several lessons are drawn on microlearning, peer coaching, health results, intrinsic motivation, and social learning, which appear useful for other health improvement initiatives. Journal: Int. J. of Networking and Virtual Organisations Pages: 350-365 Issue: 4 Volume: 30 Year: 2024 Keywords: hypertension; self-management support; SMS; microlearning; social learning; eHealth; employee health. File-URL: http://www.inderscience.com/link.php?id=140218 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:4:p:350-365 Template-Type: ReDIF-Article 1.0 Author-Name: C. Bala Subramanian Author-X-Name-First: C. Bala Author-X-Name-Last: Subramanian Author-Name: R. Ramkumar Author-X-Name-First: R. Author-X-Name-Last: Ramkumar Title: A novel bacterial colony optimisation-based cluster head selection method for the internet of things Abstract: The expanding usage of smart environments has drawn academics' attention to the internet of things (IoT), which is continually evolving. An optimal cluster head (CH) control how much power is used by the cluster's components, extending the battery life of those components and grouping them into manageable clusters that support network expansion. An optimal CH in the IoT network is selected using bacterial colony optimisation (BCO) to optimise energy usage. The suggested BCO has great search effectiveness and dynamic capabilities, extending the lifetime of sensor nodes (SNs). The number of alive nodes, throughput, and residual energy are used to evaluate the ability of the proposed BCO. The suggested BCO method is compared with several cutting-edge approaches and the outcomes demonstrate the suggested novel BCO approach's superiority to already-existing techniques. Journal: Int. J. of Networking and Virtual Organisations Pages: 366-386 Issue: 4 Volume: 30 Year: 2024 Keywords: internet of things; IoT; energy efficient; bacterial colony optimisation; BCO; wireless sensor network; cluster head selection. File-URL: http://www.inderscience.com/link.php?id=140222 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:4:p:366-386 Template-Type: ReDIF-Article 1.0 Author-Name: R. Sundara Rajan Author-X-Name-First: R. Sundara Author-X-Name-Last: Rajan Author-Name: G. Kirithiga Nandini Author-X-Name-First: G. Kirithiga Author-X-Name-Last: Nandini Author-Name: Yuqing Lin Author-X-Name-First: Yuqing Author-X-Name-Last: Lin Author-Name: Remi Mariam Reji Author-X-Name-First: Remi Mariam Author-X-Name-Last: Reji Title: Wide and fault diameter in Kneser graphs for enhanced fault tolerance in parallel computing Abstract: A system's fault tolerance is its capacity to function even if one or more of its components fail. Implementing a fault-tolerant network becomes an important criterion for reliable computing. Reliability measures play a significant part in recognising the role of faulty and non-faulty processors in a parallel computing system. Parallel computing is used primarily for saving time, solving big problems, and doing multiple tasks at once at the same time. Various reliability measures have been introduced to evaluate a network's fault-tolerance capability. We have measured the wide diameter and fault diameter of the Kneser graphs in this study. Also, we have verified the fault diameter obtained using an experimental study. Further, we have described some applications of wide diameter and fault diameter in parallel and distributed computing. Journal: Int. J. of Networking and Virtual Organisations Pages: 169-190 Issue: 3 Volume: 31 Year: 2024 Keywords: Kneser graph; connectivity; fault tolerance; wide diameter; fault diameter. File-URL: http://www.inderscience.com/link.php?id=143320 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:3:p:169-190 Template-Type: ReDIF-Article 1.0 Author-Name: Xiameng Zhong Author-X-Name-First: Xiameng Author-X-Name-Last: Zhong Title: Enabling green supply chain practices and its impact on financial performance with moderation of i4.0 industrial technologies Abstract: This study examines the nexus of green supply chain practices (G-SCP) toward financial performance (FnP) from the perspective of China. Second, the study explores the moderating impact of i4.0 model of technologies between the connection of G-SCP and FnP. The outcomes confirmed a positive correlation between G-SCP and FnP. Furthermore, a moderation of i4.0 technologies was confirmed between the association of G-SCP and FnP. This study assures that G-SCPs are essential for organisations such as optimising FnP. This study additionally provided insights into i4.0 model of technologies which is found to be a valid factor that affects relational strength. However, this study provides interesting and fruitful applications for the relevant management and future directions for the scholars to consider more studies in future to validate the current outcomes. Journal: Int. J. of Networking and Virtual Organisations Pages: 224-238 Issue: 3 Volume: 31 Year: 2024 Keywords: G-SCP; green supply chain practices; FnP; financial performance; Industrial 4.0 technologies; SEM; structure equation modelling. File-URL: http://www.inderscience.com/link.php?id=143321 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:3:p:224-238 Template-Type: ReDIF-Article 1.0 Author-Name: Cuiping Liu Author-X-Name-First: Cuiping Author-X-Name-Last: Liu Author-Name: Jing Li Author-X-Name-First: Jing Author-X-Name-Last: Li Title: Embedding artificial intelligence into business operations: an examination of AI on corporate social and economic development Abstract: AI optimises decision-making processes, streamlines business operations, and fosters innovation which ultimately drives competitiveness and growth of the corporates. AI's adaptive capabilities empower businesses to adapt to dynamic market conditions and stay at the forefront of technological advancements. The study presently explores the correlation of AI-embedded technologies toward social development (SOD) and economic development (ECOD). An insight examination of each AI technology such as predictive analytics, big data analytics, Internet of Things (IoT), virtual reality, blockchain, and neuromorphic computing was additionally validated by affirming the nexus of social and ECOD. It was deeply observed that each dimension of AI has a direct relationship with SOD. Likewise, the study assured a positive nexus between AI dimensions and ECOD. Journal: Int. J. of Networking and Virtual Organisations Pages: 239-253 Issue: 3 Volume: 31 Year: 2024 Keywords: artificial intelligence; SOD; social development; ECOD; economic development; SEM; structural equation modelling; AI-enabled tools. File-URL: http://www.inderscience.com/link.php?id=143323 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:3:p:239-253 Template-Type: ReDIF-Article 1.0 Author-Name: Prashant Kumar Pandey Author-X-Name-First: Prashant Kumar Author-X-Name-Last: Pandey Author-Name: Praveen Kumar Pandey Author-X-Name-First: Praveen Kumar Author-X-Name-Last: Pandey Title: Examining the potential effects of augmented reality on the retail customer experience: a systematic literature analysis Abstract: This study aimed to investigate the impact of Augmented Reality (AR) on consumer behaviour and decision-making. A conceptual framework was developed to understand how AR's unique characteristics and features can create experiential values for users and influence cognitive and affective responses, which in turn impact consumer decision-making and behavioural outcomes. A delve into the literature unveiled that AR boasts two one-of-a-kind traits, interactivity and vividness, which can elevate consumers' enjoyment. Other factors such as body image perception, narcissism, information processing preference, privacy concerns, and situational surroundings were also pinpointed as potential influencers on how consumers process AR experiences. The findings of this study provide a deeper understanding of the mechanisms through which AR can influence consumer behaviour and decision-making. This knowledge can be used by businesses and marketers to design and implement AR in a way that effectively enhances the customer experience and increases engagement. The study also highlights the need for further research to explore the moderating effect of various consumer traits and situational contexts on the outcomes of AR experiences. Journal: Int. J. of Networking and Virtual Organisations Pages: 191-223 Issue: 3 Volume: 31 Year: 2024 Keywords: AR; augmented reality; consumer behaviour; decision-making; information processing style; word-of-mouth; purchase intention. File-URL: http://www.inderscience.com/link.php?id=143324 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:3:p:191-223 Template-Type: ReDIF-Article 1.0 Author-Name: Shaheen Parveen Author-X-Name-First: Shaheen Author-X-Name-Last: Parveen Author-Name: Naila Zia Author-X-Name-First: Naila Author-X-Name-Last: Zia Title: Establishing strategic management framework to implement CSR in the food industry: a sustainable livelihood approach Abstract: Corporate social responsibility has become prevalent sparked the attention of academics, government, private enterprises, and social organisations due to its many advantages. As businesses grow increasingly conscientious of their ethical business conduct, we anticipate that they will address their obligation to the society and individuals they aspire to serve with great care. Corporate social responsibility (CSR) in the agribusiness sector has gained traction in developing countries, including India because CSR has become compulsory. This study documents the real social challenges of the food processing industry in the areas and discovers that companies in the food sector are viable systematically to uplift affected communities, which are the organisation's foundation for long-term sustainability has a positive effect on CSR. This paper gathers evidence from various literature reviews: CSR, strategic intent, strategic orientation, livelihood generation, and capacity building in Agribusiness. We cover the research and provide a platform for work generating livelihoods through capacity building with CSR funding. Journal: Int. J. of Networking and Virtual Organisations Pages: 254-279 Issue: 3 Volume: 31 Year: 2024 Keywords: CSR; corporate social responsibility; the agribusiness sector; capacity building; strategic management; livelihood generation; and community development. File-URL: http://www.inderscience.com/link.php?id=143326 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:3:p:254-279 Template-Type: ReDIF-Article 1.0 Author-Name: Yan Zhou Author-X-Name-First: Yan Author-X-Name-Last: Zhou Author-Name: Yong Xia Author-X-Name-First: Yong Author-X-Name-Last: Xia Title: The hybrid augmented intelligence open innovation platform's architecture and scheme, which combine interconnected virtual and actual systems Abstract: The goal of this project is to address the main issues that exist today by creating an open innovation platform for hybrid augmented intelligence (HAI). We describe the fundamental structure of the platform, including the module methods and duties at each level, by putting forth a plan for the platform architecture. We have created protection and reward systems for open innovation to encourage greater participation to foster innovation. Furthermore, we have investigated in detail how blockchain technology is applied to the HAI open innovation platform's data security, which offers a workable way to guarantee information security. This research offers a comprehensive structure and strategy for building the platform. In light of this, the HAI open innovation platform's background welcomes the new era of AI technology development and highlights its importance in fostering innovation, collaboration, and the resolution of challenging issues, as well as the acceleration of technological application. Journal: Int. J. of Networking and Virtual Organisations Pages: 1-21 Issue: 1 Volume: 31 Year: 2024 Keywords: HAI; hybrid augmented intelligence; open innovation platform; virtual reality systems; human-computer interaction; digital virtual industrial technology. File-URL: http://www.inderscience.com/link.php?id=141551 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:1:p:1-21 Template-Type: ReDIF-Article 1.0 Author-Name: Sha Sha Author-X-Name-First: Sha Author-X-Name-Last: Sha Title: Topic text detection by clustering algorithm for social network media Abstract: The advent of the internet era has promoted the development of social network media, making the number of people active in these social network platforms greatly increase, and the resulting large amount of data and information makes the fast location retrieval of topics of interest a problem. This paper detected topic texts of social network media by the modified particle swarm optimisation-based K-means (MPSO-means) clustering algorithm to achieve topic text clustering effect and alleviate the problem of inconvenience caused by information overload. The results of the study showed that the clustering results of short texts showed a trend of outperforming long texts; and the MPSO-means algorithm was closer to 1 than the other two algorithms in the values of silhouette coefficient, clustering purity, and homogeneity, with better clustering effect, and also consumed the shortest time in detection, only 1,196 s. Journal: Int. J. of Networking and Virtual Organisations Pages: 246-256 Issue: 3 Volume: 30 Year: 2024 Keywords: text clustering; social network media; topic text; modified particle swarm optimisation-based K-means. File-URL: http://www.inderscience.com/link.php?id=138480 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:3:p:246-256 Template-Type: ReDIF-Article 1.0 Author-Name: Oleh Vysochan Author-X-Name-First: Oleh Author-X-Name-Last: Vysochan Author-Name: Vasyl Hyk Author-X-Name-First: Vasyl Author-X-Name-Last: Hyk Author-Name: Olha Vysochan Author-X-Name-First: Olha Author-X-Name-Last: Vysochan Title: Towards the creation of a cluster theory-based accounting system Abstract: To ensure the integration of the economic interests of the participants of network formations through the improvement of the coordination and controllability of interactions, an important place is given to the accounting system as the main source of information. The purpose of the paper is to research and develop the theoretical and conceptual provisions of the cluster economy paradigm formation in the accounting system. The methodological basis is the fundamental provisions of modern economic institutional theory and the scientific works of scientists. As a result of the research, it was possible to analyse the historical aspects of the development of accounting support for managing cluster structures. The areas of development of accounting based on the provisions of economic theory and considering the specific features of cluster structures are identified and substantiated. Journal: Int. J. of Networking and Virtual Organisations Pages: 43-62 Issue: 1 Volume: 31 Year: 2024 Keywords: cluster; network; economic theory; institutional theory; inter-organisational management; accounting. File-URL: http://www.inderscience.com/link.php?id=141553 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:1:p:43-62 Template-Type: ReDIF-Article 1.0 Author-Name: Shrikant P. Sanas Author-X-Name-First: Shrikant P. Author-X-Name-Last: Sanas Author-Name: Tanuja Sarode Author-X-Name-First: Tanuja Author-X-Name-Last: Sarode Title: Hybridised pre-trained deep network with Aspen-Lupus bidirectional long short-term memory classifier for image-based event classification Abstract: The proposed Aspen-Lupus optimisation-based BiLSTM classifier (ALO opt BiLSTM) is employed in this research to develop an event classification model that accurately identifies the events. The pre-trained hybridised model, which is proposed for feature extraction, is developed via a conventional hybridisation of the VGG-16 and ResNet-101 models. The deep BiLSTM classifier gathers the collected features and utilises them to effectively increase prediction accuracy. The development of the proposed ALO algorithm resulted from the typical hybridisation of the Aspen and Lupus optimisation. Based on the achievements, at training percentage 90, the accuracy of 95.65%, sensitivity of 94.27%, specificity of 96.63% in database-1 respectively is attained and for database-2, achievements of 94.22% in accuracy, 92.86% insensitivity and 95.18% in specificity is acquired. Journal: Int. J. of Networking and Virtual Organisations Pages: 282-307 Issue: 3 Volume: 30 Year: 2024 Keywords: event classification model; Aspen-Lupus optimisation; BiLSTM classifier; border collie; grey wolf; hybrid pre-trained model. File-URL: http://www.inderscience.com/link.php?id=138482 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:3:p:282-307 Template-Type: ReDIF-Article 1.0 Author-Name: R. Venketesh Author-X-Name-First: R. Author-X-Name-Last: Venketesh Author-Name: K. Sasikala Author-X-Name-First: K. Author-X-Name-Last: Sasikala Title: The design of an intrusion detection system in MANET using the IGWO-ANN classification algorithm Abstract: Presently, attacks on the internet are maximised with the internet's enhancement. Intrusion detection system (IDS) is one of the compassionate layers relevant to information protection. Though researchers have found enormous techniques, there are still issues in detecting new intrusions. So, this framework proposes an effective IDS using IQDFA-based feature selection and the IGWO-ANN classification algorithm. Initially, data conversion occurs, where the input data in the form of characters is replaced by the number. Then, to avoid the similar data's training, redundant data is removed. Then, the normalisation occurs, where the feature values are normalised using an average of min and max attribute values. Next, by utilising the IQDFA, the extra features are extracted after the best feature selection. Data classification is conducted using IGWO-ANN. For determining whether the sensor data was attacked or not, the testing of classified data is done. The proposed model's performance analysis exhibited enhanced performance than the prevailing methodologies. Journal: Int. J. of Networking and Virtual Organisations Pages: 22-42 Issue: 1 Volume: 31 Year: 2024 Keywords: IDS; intrusion detection system; numeralisation; GWO; grey-wolf optimisation; feature extraction; ANN; artificial neural network; DFA; dragon fly algorithm; classification; MANET; mobile adhoc network. File-URL: http://www.inderscience.com/link.php?id=141554 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:1:p:22-42 Template-Type: ReDIF-Article 1.0 Author-Name: Bingzhou Li Author-X-Name-First: Bingzhou Author-X-Name-Last: Li Author-Name: Wei Yu Author-X-Name-First: Wei Author-X-Name-Last: Yu Title: Influence of sense of virtual brand community on value co-creation Abstract: The research objective is to formulate and verify a theoretical model about the influence of sense of virtual brand community on value co-creation of enterprises with self-efficacy and psychological contract as moderating variables. Four hypotheses are presented based on theoretical deduction. This article uses the empirical research design and survey methodology. The data analysis approaches include reliability test, validity analysis, descriptive statistics, variance analysis, correlation analysis, regression analysis, path analysis of structural equation model and hierarchical regression analysis. By collecting data with 275 valid questionnaires in many virtual brand communities, this research empirically confirms that sense of virtual brand community has a positive impact on value co-creation. Moreover, customer self-efficacy and psychological contract with an enterprise respectively positively moderate the relationship between sense of virtual brand community and value co-creation. However, the model does not give the specific mediating influence mechanism of sense of virtual brand community on value co-creation. An enterprise should enhance the cultivation of sense of virtual brand community and improve a customer's self-efficacy and psychological contract. Theoretically, this article enriches the human sense and communication analysis in the marketing context and explores new antecedents and moderating factors of customer value co-creation for the whole enterprise. Journal: Int. J. of Networking and Virtual Organisations Pages: 221-245 Issue: 3 Volume: 30 Year: 2024 Keywords: sense of virtual brand community; value co-creation; self-efficacy; psychological contract; communication platform. File-URL: http://www.inderscience.com/link.php?id=138483 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:3:p:221-245 Template-Type: ReDIF-Article 1.0 Author-Name: Alireza Mighaninejad Author-X-Name-First: Alireza Author-X-Name-Last: Mighaninejad Author-Name: Ismail Jafarpanah Author-X-Name-First: Ismail Author-X-Name-Last: Jafarpanah Title: Boundary-spanning in an inter-organisational project: a systematic review of the literature and directions for future research Abstract: The development and implementation of inter-organisational projects (IOPs) cause many inter-organisational challenges and tensions. Dealing appropriately with these challenges requires adapting the project to the environment through the boundary-spanning mechanism. Despite the significant expansion of boundary-spanning research, studies show that few review studies were published decades after forming this stream of literature. This study identified the critical elements of boundary-spanning (boundary concept, boundary-spanning definitions, activities, levels, structure, and boundary objects), and a theoretical framework was presented. The literature gaps and future research directions were also explained. Findings showed that boundary-spanning research was moving towards innovation management, collaboration management, and knowledge management. Journal: Int. J. of Networking and Virtual Organisations Pages: 63-85 Issue: 1 Volume: 31 Year: 2024 Keywords: boundary; boundary-spanning; IOPs; inter-organisational projects; systematic review; bibliometric study; boundary-spanning process; boundary-spanning levels; boundary concept; boundary object. File-URL: http://www.inderscience.com/link.php?id=141555 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:31:y:2024:i:1:p:63-85 Template-Type: ReDIF-Article 1.0 Author-Name: M. Nagalingayya Author-X-Name-First: M. Author-X-Name-Last: Nagalingayya Author-Name: Basavaraj S. Mathpati Author-X-Name-First: Basavaraj S. Author-X-Name-Last: Mathpati Title: Deep learning-based decision-making system for cooperative routing in wireless multimedia sensor network Abstract: This research aims to present a deep belief network (DBN) based a technique for choosing the most suitable cooperative nodes. Additionally, constraints such as: 1) tri-level energy consumption of nodes (text at level 1 has a lower energy level than information; at level 2, which has a medium energy level for text, image and multimedia data; level 3: higher energy levels for high-definition pictures); 2) reliability; 3) delay are examined while sending multimedia data across the network. Making the right decision, a novel hybrid dragon integrated firefly (DIFF) schemes that integrate the ideas of firefly optimisation (FF) and dragonfly optimisation (DA) method is intended to adjust the optimal weight of DBN. The chosen scheme's efficiency is then compared to other traditional methods in terms of alive nodes, delay, residual energy, network lifespan and analysis method. Journal: Int. J. of Networking and Virtual Organisations Pages: 257-281 Issue: 3 Volume: 30 Year: 2024 Keywords: routing protocol; optimisation; deep belief network; DBN; multimedia system; reliability. File-URL: http://www.inderscience.com/link.php?id=138491 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijnvor:v:30:y:2024:i:3:p:257-281