International Journal of Intelligent Networks最新文献

{"title":"Proposed artificial intelligence algorithm and deep learning techniques for development of higher education","authors":"Amin Al Ka'bi","doi":"10.1016/j.ijin.2023.03.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.03.002","url":null,"abstract":"<div><p>Artificial intelligence (AI) has been increasingly impacting various aspects of our daily lives, including education. With the rise of digital technologies, higher education has also been experiencing a transformation, and AI has been playing a crucial role in this transformation. The application of AI in higher education has been rapidly increasing, with a focus on improving student engagement, increasing efficiency, and enhancing the learning experience. The use of AI in higher education is not without its challenges and ethical considerations. One of the biggest challenges is ensuring the accuracy and fairness of AI algorithms, as well as avoiding potential biases. In addition, there are concerns about the privacy of student data, as well as the potential for AI to replace human instructors and support staff. Another challenge is ensuring that AI is used in a way that supports the overall goals of higher education, such as promoting critical thinking and creativity, rather than just being used as a tool for automating tasks and increasing efficiency. In this article, we will discuss the various ways in which AI is being applied in higher education where a proposed model for improving the cognitive capability of students is proposed and compared to other existing algorithms. It will be shown that the proposed model shows better performance compared to other models.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 68-73"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An optimized framework for VANET routing: A multi-objective hybrid model for data synchronization with digital twin","authors":"Madhuri Husan Badole,&nbsp;Anuradha D. Thakare","doi":"10.1016/j.ijin.2023.10.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.10.001","url":null,"abstract":"<div><p>The utilization of Digital Twin technology allows for the simulation of network behavior, anticipating traffic surges, and implementing efficient traffic routing strategies to prevent congestion. This enhances network efficiency and improves overall speed. However, VANETs (Vehicular Ad-Hoc Networks) pose unique challenges due to their dynamic nature and frequent network disconnects. Developing and implementing effective VANET routing protocols becomes complex considering these factors. To address these challenges, a novel hybrid optimization model is proposed in this research. The model comprises optimal Cluster Head (CH) selection for data transmission. The clustering of mobile nodes is initially performed, but ensuring consistency in fast-paced environments remains a significant challenge. Therefore, the selection of the most suitable node as the CH is crucial. This research introduces a novel route selection mechanism that focuses on optimal CH selection. Multiple objectives such as mean routing load, packet delivery ratio, throughput, End-to-End Delay, and Control packet overhead are considered in the CH selection process. To determine the ideal CH from a pool of potential candidates, a new hybrid optimization model called Hunger's Foraging Behavior Customized Honey Badger Optimization (HFCHBO) is introduced. The HFCHBO combines the standard Honey Badger Algorithm (HBA) with Hunger Games Search (HGS). This hybrid model effectively formulates successful routing paths for data transmission between vehicles and the CH to the Base Station (BS). By combining these two approaches, the HFCHBO model aims to overcome the limitations of traditional clustering algorithms in ensuring consistent performance in dynamic environments. The proposed route selection mechanism incorporates multiple objectives to evaluate the performance of potential CHs, including mean routing load, packet delivery ratio, throughput, End-to-End Delay, and Control packet overhead. To facilitate data transmission between vehicles and the CH to the Base Station (BS), the HFCHBO model formulates successful routing paths. By utilizing the simulation capabilities of the Digital Twin technology, the model analyzes the network behavior, predicts traffic patterns, and makes informed decisions on routing strategies.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 272-282"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conversational chat system using attention mechanism for COVID-19 inquiries","authors":"Wang Xin Hui ,&nbsp;Nagender Aneja ,&nbsp;Sandhya Aneja ,&nbsp;Abdul Ghani Naim","doi":"10.1016/j.ijin.2023.05.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.05.003","url":null,"abstract":"<div><p>Conversational artificial intelligence (AI) is a type of artificial intelligence that uses machine learning techniques to understand and respond to user inputs. This paper presents a conversational chat system that uses an attention mechanism to respond to COVID-19 inquiries. The model is based on the Luong Attention Mechanism’s three scoring methodologies the Dot Attention Mechanism, the General Attention Mechanism, and the Concat Attention Mechanism. The results show that the accuracy of the dot attention mechanism is highest and is 87% when the test questions were obtained directly from the database, as determined by an examination of the results, compared to 38% when the attention mechanism is not used. Furthermore, when the questions are asked with natural variations, human verification accuracy is 63% compared to 16% when the attention mechanism is not used. The research suggests that chatbots can be used everywhere due to their accuracy and accessibility around the clock.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 140-144"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Roadside sensor network deployment based on vehicle-infrastructure cooperative intelligent driving","authors":"Xin An ,&nbsp;Baigen Cai","doi":"10.1016/j.ijin.2023.11.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.11.002","url":null,"abstract":"<div><p>The sensor network for intelligent roadways, comprised of devices like cameras, laser radars, millimeter-wave radars, and weather stations, is an integral part of the roadside digital infrastructure. One of the main challenges in building intelligent highway sensor networks is to create a controllable, manageable, and useable sensor network with multi-modal sensors deployed on highways. This network should not only facilitate global and scene sensing but also enable collaborative sensing and control functions. Therefore, this study aims to define the concept, main features, and technical connotation of Vehicle-Infrastructure Cooperative Intelligent Driving (VICID). It also outlines the development of a cloud-native cloud control platform for intelligent roadways and refines the technology requirements and indices. This platform is designed to support open services for innovative applications, such as addressing bottlenecks, managing roadworks zones, and implementing dynamic lane assignments for automated driving. Lastly, we introduce Beijing's highway pilot projects, which can serve as a guide and reference for designing and constructing sensor network equipment for intelligent roadways in China, as well as for key technology research and development.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 283-300"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603023000301/pdfft?md5=098fe9a05d083ab3002d5f2e7e6fabbb&pid=1-s2.0-S2666603023000301-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138423189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Priority based k-coverage hole restoration and m-connectivity using whale optimization scheme for underwater wireless sensor networks","authors":"Sangeeta Kumari ,&nbsp;Pavan Kumar Mishra ,&nbsp;Arun Kumar Sangaiah ,&nbsp;Veena Anand","doi":"10.1016/j.ijin.2023.08.005","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.08.005","url":null,"abstract":"<div><p>Coverage hole restoration and connectivity is a typical problem for underwater wireless sensor networks. In underwater applications like underwater oilfield reservoirs, undersea minerals and monitoring etc., where nodes face many hurdles and are unable to cover the required region during a natural disaster such as tsunami, flood, earthquakes, and environmental interference. It creates a coverage hole and consumes high energy with bad network quality. This problem considered as an <em>N</em>P-complete problem where a set of sensor nodes is required to identify the k-coverage hole and m-connectivity. In the literature, researchers have not focused on <em>k</em>-coverage hole restoration and <em>m</em>-connectivity issues during natural disasters and environmental interference. To mitigate this problem, we proposed priority-based coverage hole restoration and -connection using a whale optimization scheme to restore coverage holes and extract relevant information for the construction of undersea oilfield reservoirs, minerals, and mines. In this scheme, we identified the list of k-coverage holes and addressed autonomous underwater vehicles (AUVs) to place the additional mobile nodes in an appropriate coverage hole. A novel multi-objective function is formulated to obtain the optimal path for AUVs. Furthermore, while restoring coverage holes, we checked the connectivity of nodes. In the network, each node coordinated sleep scheduling with neighbor nodes to maintain energy efficiency. Performance evaluation of the proposed scheme shows better results than the existing schemes under different network scenarios which provide maximum coverage and connectivity, less energy consumption with a high convergence rate.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 240-252"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum regarding previously published articles","authors":"","doi":"10.1016/j.ijin.2023.04.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.04.003","url":null,"abstract":"","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Page 89"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An inception V3 approach for malware classification using machine learning and transfer learning","authors":"Mumtaz Ahmed ,&nbsp;Neda Afreen ,&nbsp;Muneeb Ahmed ,&nbsp;Mustafa Sameer ,&nbsp;Jameel Ahamed","doi":"10.1016/j.ijin.2022.11.005","DOIUrl":"https://doi.org/10.1016/j.ijin.2022.11.005","url":null,"abstract":"<div><p>Malware instances have been extremely used for illegitimate purposes, and new variants of malware are observed every day. Machine learning in network security is one of the prime areas of research today because of its performance and has shown tremendous growth in the last decade. In this paper, we formulate the malware signature as a 2D image representation and leverage deep learning approaches to characterize the signature of malware contained in BIG15 dataset across nine classes. The current research compares the performance of various machine learning and deep learning technologies towards malware classification such as Logistic Regression (LR), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), transfer learning on CNN and Long Short Term Memory (LSTM). The transfer learning approach using InceptionV3 resulted in a good performance with respect to the compared models like LSTM with a classification accuracy of 98.76% on the test dataset and 99.6% on the train dataset.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 11-18"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cache controlled cluster networking protocol","authors":"Priyank Sunhare ,&nbsp;Manju K. Chattopadhyay","doi":"10.1016/j.ijin.2023.07.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.07.003","url":null,"abstract":"<div><p>When technologies such as Wireless Sensor Network, Internet of Things and Cloud Computing are coupled together, real-world issues can be remarkably resolved. Wireless Sensor Network sends enormous data to a cloud server via the internet. Some applications necessitate a huge number of sensors spread across a large area. Limited battery-powered sensors send lots of data to the Base Station. To save energy and extend sensor lifespan, sensors can be networked instead of talking directly with the Base Station. Our present research work proposes an innovative Cache-Controlled Cluster Networking Protocol (CCCNP). It is a cache-supervised dynamic cluster-based sensor networking technique. In CCCNP, as the complete network is under cache control, the number of caches established in the network and their location play a very crucial role. Therefore, we first formulate the equation for the appropriate number of caches and their location. Then the caches create a network cluster and support data transmission. It stabilizes cluster formation process and decreases sensor node overhead to increase network lifetime. We simulate the CCCNP, Low Energy Adaptive Clustering Hierarchy (LEACH), Improved LEACH (I-LEACH), and LEACH with Vice-cluster Head (LEACH-VH) algorithms for two different types of networks and compare them. CCCNP outperforms other algorithms both the configurations. The dead node rate decreased by three times for each round. Even the lifespan of nodes far from the BS improved by 3.51 and 2.87 times for both network configurations, respectively. The average throughput increased by 350%, and the average lifespan increased up to 289% of the rounds.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 182-192"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Localization for V2X communication with noisy distance measurement","authors":"Iram Javed ,&nbsp;Xianlun Tang ,&nbsp;Muhammad Asim Saleem ,&nbsp;Ashir Javed ,&nbsp;Muhammad Azam Zia ,&nbsp;Ijaz Ali Shoukat","doi":"10.1016/j.ijin.2023.11.007","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.11.007","url":null,"abstract":"<div><p>Mobile sensor network localization is a growing research topic after IEEE 802.15.4 specified the procedure of low-rate wireless personal area networks (LR-WPANs), which further helps localize vehicles in the automobile industry. This paper presents a new localization scheme based on flying anchors deployed in vehicular infrastructure. The mobile anchor nodes follow a random C-shaped trajectory. A global positioning system (GPS) is attached to each anchor node, transmitting beacons with ID and location to all other vehicles in a network. Distance calculation is facilitated through link quality induction, employing the centroid method to compute localization error. Mobile anchor localization, particularly when employing a C-shaped trajectory commonly adopted by various topologies, consistently yields optimal positioning outcomes. However, this approach can be susceptible to the impact of noisy measurements, potentially reducing overall localization performance. To overcome this problem, we proposed a framework based on extended Kalman filtering (EKF), which is used to refine the coordinates of the vehicles. To compute the lower bounding of the vehicular node, an analytical framework is also proposed to enhance the localization error accuracy. Simulation results show that the EKF framework provides better positioning accuracy compared to the existing C-shaped solution, irrespective of noise statistics, topology selection, and anchor node density. With the help of the Extended Kalman Filter (EKF) framework, we achieved a comprehensive localization error of 0.99 m, accompanied by a standard deviation of 0.47.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 355-360"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603023000350/pdfft?md5=5129457e0eafe4524036abb863432928&pid=1-s2.0-S2666603023000350-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138570691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel heterogenous ensemble theory for symmetric 5G cells segmentation: Intelligent RAN analytics","authors":"Jean Nestor M. Dahj ,&nbsp;Kingsley A. Ogudo ,&nbsp;Leandro Boonzaaier","doi":"10.1016/j.ijin.2023.11.005","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.11.005","url":null,"abstract":"<div><p>MNOs are investing more in 5G, rolling out sites in urban and specific rural areas. Meanwhile, it remains imperative to consistently maintain the network performance above a certain threshold for optimal user experience. Symmetric network cells characterized by parallel attributes in terms of capacity and coverage are instrumental in planning, optimization, and resource allocation. However, the variation in environmental factors introduces divergence in network cells' behavior, symmetric or not. Therefore, the need arises for intelligent analytic processes within the RAN system to categorize symmetric cells based on their performance and behavior. Intelligent optimization and analytics in 5G rely on the accurate and automated identification of cells exhibiting symmetric behavior, enabling bulk optimization operations. In this paper, we develop and assess a clustering approach using a heterogenous ensemble method to group 5G cells based on their key performance attributes to facilitate network optimization tasks. The approach involves a synergistic integration of K-means and hierarchical clustering algorithms, enabling dynamic segmentation of cells based on their performance behavior. Leveraging the clustering output, we train an XGBoost classifier, paving the way for a comprehensive analytics framework and problematic or poor-performing cells’ detection. We apply the study model to real-world 5G RAN metrics and evaluate the proposed method in terms of clustering accuracy and convergence. The study output showcases the efficacity of the heterogenous ensemble approach compared to individual clustering algorithms, providing a valuable baseline for network performance enhancement. With such a dynamic approach for analyzing 5G new radio (NR) performance, MNOs can move toward intelligent and self-aware networks, making informed decisions regarding resource allocation and coverage optimization.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 310-324"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603023000337/pdfft?md5=9cd8f4c75a1ea9589f7f0a9deac8040e&pid=1-s2.0-S2666603023000337-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138466571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}